EP3888094A1 - Method for detecting and monitoring the formation of biofilms - Google Patents

Abstract

The present invention relates to a method for detecting and/or monitoring and/or characterising the formation of a biofilm. The present invention also relates to a device for detecting and/or characterising the formation of a biofilm which is capable of implementing the method. The present invention is particularly used in the fields of analysis, biological and enzymological research, and in the pharmaceutical and/or medical fields.

Description

TITLE: DETECTION PROCESS AND MONITORING OF THE FORMATION OF

BIOFILMS

DESCRIPTION

Technical area

The present invention relates to a method for detecting and / or monitoring and / or characterizing the formation of a biofilm.

The present invention also relates to a device for detecting and / or characterizing the formation of a biofilm capable of implementing the above method.

The present invention finds an application in particular in the analytical fields, biological, enzymological research, in the pharmaceutical field and / or in the medical field.

In the description below, the references in square brackets ([]) refer to the list of references presented at the end of the text.

State of the art

Biofilm is the main way of life for bacteria since it represents the preferred form of life for 90% of bacteria in the wild. Unlike the planktonic way of life in which bacteria live in suspension, biofilm forms a heterogeneous and complex community of bacteria which attach to a surface and which bind to each other through the secretion of an extracellular matrix composed of proteins. , sugars and water. Biofilm formation is a common feature of many bacteria. Also, it has been accepted by the community of microbiologists that the biofilm lifestyle is a fully-fledged stage in the life of the bacteria. The planktonic form would only be a transition to biofilm. As with many living species, community is necessary for their survival. This form of community life gives bacteria significant metabolic benefits. The adhesion capacity of biofilms on natural or artificial surfaces is very high and their resistance to all kinds of stress makes them difficult to remove. Biofilms are indeed very resistant to chemical stress (antibiotics for example) and mechanical stress (fluid for example). Resistance increased biofilms makes them ubiquitous. Although they can be used for innovative processes of bioremediation or synthesis of chemical compounds for example, biofilms can prove to be a real nuisance in various fields where bacterial contamination is to be banished as in the medical environment, food industry and in some human environments. In France, biofilms are responsible for 50 to 60% of nosocomial infections (Bryers, 2008) and 40% of food poisoning.

Studies show that in 2050, the number of deaths linked to a recalcitrant infection could exceed the figures for deaths caused by cancer (O’Neill, 2014). In addition, many industries also suffer from the recalcitrance of biofilms. The presence of a thick layer of biofilms on the hulls of boats increases friction on the water and leads to an overconsumption of 35 to 50% of fuel (Schultz et al., 201 1). These few figures prove that the study of biofilms represents a considerable socio-economic issue.

In particular, the study of biofilms is crucial in the hospital environment, for which the difficulties of elimination of biofilm are a public health problem. The recalcitrance of biofilms comes largely from the mechanical integrity provided by the extracellular matrix. This material ensures cohesion between bacteria and adhesion to surfaces. Bacteria protected by extracellular polymeric substances (EPS) in biofilms can survive even after a decontamination procedure and represent a source of infection for humans and animals. In addition, the viscoelastic nature of the matrix allows the biofilm to adapt to shear stresses which the biofilm's natural environment can induce. The matrix serves as a means of protection for bacteria and allows them to acquire a great resistance to their environment.

Also, studies on biofilms help determine how biofilms grow, change, and detach. Thus, it is necessary to study their structure and the phenomenon of detachment in order to control their development. Knowledge of the structures of biofilms makes it possible to predict their influences on certain systems. One of the benefits of determining the mechanical properties of biofilms is to be able to predict and control the formation, accumulation and spread of bacteria that spread infections. The first observations of biofilms date back to the 17th century but investigations around biofilms remain relatively recent (35 years) with their definition proposed by Costerton et al. (1978). Bacteria have long been studied in their free, isolated and planktonic form. However, in a survival principle, the bacteria attach themselves to a solid support and secrete viscous substances to form a biofilm. Biofilms consist of bacterial communities adhering to each other and to a surface through the secretion of a polymer matrix. At the end of the 1990s, there was an exponential explosion in the number of publications dealing with biofilms. The biofilm behavior of organisms is increasingly taken into consideration in particular in the health field where biofilms develop on very many surfaces such as catheters, valves, prostheses, teeth, skin or mucous membranes. ...

The formation of a biofilm is a dynamic and unfixed process which takes place in several stages detailed below:

• reversible adhesion: suspended in a fluid, bacteria approach a surface and adhere to it in a reversible manner by means of physicochemical attraction forces such as Van der Waals forces. This proximity to a surface is favored by Brownian agitation, gravity or agitation of the fluid. Certain microorganisms are provided with their own propulsion means which ensure their motility. These means of propulsion consist of protein structures (flagellum, pili) located at the level of the bacteria's envelopes and allow the bacteria to move autonomously;

· Irreversible adhesion: bacteria multiply by cell division and continue to bond to the surface. Furthermore, the modification of the expression profile of the genes of adhered bacteria leads to the synthesis of structures on the surface of the bacteria and the secretion of extracellular polysaccharides irreversibly binding the bacteria to each other and to the surface;

• formation of microcolonies: the growth of microorganisms in biofilm and the adhesion of new bacteria to the biofilm formed create microcolonies; • maturation: the microcolonies develop and the biofilm is structured in a three-dimensional way according to environmental conditions. Channels allowing nutrient-laden fluids to flow through the microcolonies;

· Detachment: release of specialized free cells or biofilm aggregates that allow colonization of another surface, starting a new cycle (Davies and Marques, 2009). Dispersed bacteria have predispositions to form microcolonies (Kragh et al., 2016).

A biofilm is mainly made up of microorganisms embedded in an EPS matrix secreted by microorganisms. Microorganisms represent 10% of the dry mass of the biofilm (Flemming and Wingender, 2010). By analogy, Flemming et al. (2007) see biofilms as cities of microbes living in houses, represented by the extracellular matrix, sheltering them from their physical and chemical environment. Biofilms are highly hydrated since 90 to 99% of the biofilm is made up of water. The rest of the biofilm is made up of biological macromolecules. Among these compounds are polysaccharides, proteins, extracellular DNA (eDNA) and RNA. The matrix or the EPS compounds represent 90% of the dry mass. The matrix acts as a binder for bacteria and EPS play an important role in the development and life of the biofilm (Das et al., 2013). Depending on their strain of origin and the growth conditions in terms of nutrients and physical and chemical stress, the rate of production of extracellular matrix and its composition are different. Also, the proportion of each of the elements contained in a biofilm is determined by a number of factors, among which the type of nutrient, the hydrodynamic conditions and the temperature.

Observations of sections of biofilms under the microscope show that the composition of the biofilm is highly heterogeneous (Lawrence et al., 1991). The bacteria colonies are traversed by channels through which fluids provide the nutrient supply. The behavior of the biofilm is controlled by a very complex and dynamic micro-structure.

Biofilm bacteria develop properties that planktonic bacteria do not have. Quorum sensing allows bacteria to communicate and organize themselves to collaborate. This collaboration is also characterized by the horizontal transfer of genes (Madsen et al., 2012). Bacteria in biofilms are more resistant to biocides than their planktonic analogs. While in the 1990s tolerance to antibiofilm agents was attributed to diffusion problems, it was later found that other phenomena were responsible for tolerance to anti-infective agents. For example, the ability of bacteria to adapt and differentiate allows them to evolve in the direction of their preservation. In general, biofilms are more resistant to cleaning, whether chemical or mechanical. In addition, frequent and repeated exposure to antibiotics can lead to a phenomenon of multidrug-resistant bacteria, that is to say that antibiotic treatments no longer have an effect against it. All these characteristics mean that biofilms are present in all environments, that they are resistant and that they become reservoirs of infections by the release of germs. In the food industry, they are present in bioreactors where they are used for the production of alcohol, acids or polysaccharides. In the textile industry, biofilms are studied to make nylon. On the other hand, they serve as an alternative to mechanical finishes on certain denim garments (sanding ...). Also, studies on biofilms are becoming more numerous and often at the interface of several disciplines. The study of biofilms from a mechanical point of view is complex because of its multi-scale and multi- physical nature. However, it represents a real scientific issue. The results of mechanical studies on biofilms reported in the literature remain ambiguous. Few protocols are standardized and the multiple test models developed by research teams make comparison of results difficult.

There is therefore a real need to find a means and / or process remedying these defects, drawbacks and obstacles of the prior art, in particular a process allowing a reproducible and comparable study of the mechanics of biofilms.

The search for elements that exclusively characterize biofilms is also of great interest. Among the near or future research subjects, we can cite the following problems: locating bacteria within biofilms, quantifying fluxes, visualizing the interior of biofilms and understanding interactions within multi-species biofilms. The ability of bacteria to form a biofilm is of interest to develop strategies to combat biofilms or to use biofilms positively, in bioremediation applications for example. The prevention of biofilm formation is a major issue in the health field. Currently, the usual doses of biocidal substances effective in the case of bacterial infections very often prove to be ineffective once the biofilm is installed. Therefore, the detection of biofilm formation in vitro is crucial information, especially for the development of prophylactic treatments. Many protocols have been proposed for the analysis of biofilm formation (Azeredo et al., 2017). However their variability and their number does not allow, in particular, a comparison of the results obtained and / or a uniformity of results.

There is therefore a real need to find a means and / or process remedying these defects, drawbacks and obstacles of the prior art, in particular a standardized process allowing rapid and reliable formation of biofilm.

There are methods used to try to characterize biofilms. For example, the following processes can be mentioned: microbiological enumeration on solid medium: The enumeration of colony-forming units (English acronym CFU - “Colony-Forming Units”) allows a count of the microorganisms of a microbiological culture on a solid medium. This protocol is limited to bacteria that grow on an agar plate. This method is also very time consuming. Microtitre plate (Tissue Culture Plates): In this method, no flow circulates and growth takes place in a closed environment. The biomass present in the wells is made visible using dyes. The importance of the coloration, linked to the amount of biofilm, is read from the optical density of the well. The results are classified into three categories: inoculum which does not form a biofilm, moderate inoculum, or inoculum which strongly forms biofilm. This method is indirect. However, the lack of standardized protocol and the lack of reproducibility are the notable disadvantages of this method. MBECTM system (Calgary device): The MBECTM system (Minimal Biofilm Eradication Concentration) is part of a category of system allowing the production of reproducible biofilms for the simple and simultaneous evaluation of several biocidal agents at different concentrations. Using microtiter plate-based methods, the Calgary model was developed to avoid bias due to bacteria sedimentation (Ceri et al., 1999; Harrison et al., 2010). The device Calgary consists of a two-part container. The lower part is composed of a 96-well micro-plate. The upper part is a cover made up of pins which coincide with the center of the wells and which are inserted into each of the wells of the plate. The biofilm is formed on the pins. The device is used to characterize the formation of the biofilm and to assess the susceptibility to biocides (Melchior et al., 2007; Arias-Moliz et al., 2010). The dynamic models under flow are methods where the culture medium is open, that is to say that there is a continuous supply of nutrients. Hydrodynamic flows influence the formation of the biofilm. The type of flow as well as the shear stress generated by the flow have an effect on the adhesion strength of the biofilm. These elements are therefore constraints to the reproducibility of the results and also to a reproducibility of biofilms as they are in nature. Robbins apparatus: It consists of a series of coupons inserted in a chamber in which the bacterial medium circulates (Kharazmi et al., 1999) (Fig. 1.3). The coupons can be removed (and replaced) to be studied without disturbing the flow and in a sterile manner (Coenye et al., 2008). This model was used in particular for the study of catheter material (Nickel et al., 1985). However, this process involves destruction of the biofilm and does not allow its mechanistic study. Infusion Reactor: This device simulates an environment with low shear stresses. It is used for applications in the medical environment (catheter, pulmonary disease, oral-dental biofilm). It only allows the formation and study of biofilms at the air-liquid interface. Microfermenters: Several types of microfermenters are available and sold commercially:

· CDC (Center for Disease Control) biofilm reactor (CBR): this reactor consists of a cylindrical container constantly replenished with nutrients, in which are placed plastic tubes. These tubes have locations dedicated to the placement of coupons which will be colonized by the biofilm. The glass container is placed on a magnetic stirrer to create circular agitation of the fluid. The flow generated by the rotation is adjustable so that it can be laminar or turbulent. This agitation makes it possible to generate a constant shear stress of moderate to high intensity. The coupons are removable once the surface has been colonized with a biofilm. This reactor allows the repeatable formation of biofilm samples from various strains (Goeres et al., 2005);

• Rotating disk biofilm reactor: in this system, the coupons are placed on the bottom of the cylindrical glass container. The platform on which the coupons are placed is rotated while the bacterial medium is regenerated by circulation within the container. The shear stress generated by the rotation of the platform is of moderate to high intensity. However, it remains less important than for the CDC;

• Rotating annular reactors: in this configuration, the reactor is made up of two concentric cylinders, one of which is rotating. The samples are blades attached to the rotating cylinder.

Flow-cell:. The main drawback of the dynamic models presented above is the difficulty of monitoring the formation of the biofilm live. The flow chambers are transparent cells in which the bacterial medium circulates. The material used for the chamber is suitable for visualization by microscopy and allows observation of the development of the biofilm. However, its implementation requires great technicality. The nucleation of bubbles formed by the fluid constitutes an important problem. Furthermore, observation in a flow chamber is not very suitable for broadband because relatively few samples are analyzed simultaneously. Microfluidic platform: These highly integrated or even automated devices have been developed to be able to observe the behavior of bacteria in a controlled environment, both hydrologically and chemically. Small channel systems distribute the growth fluids to the biofilm formation area. The quantities to be analyzed involved in this type of model are very limited.

These methods remain little practiced because they are expensive, not very reusable and require great technicality. Consequently, few publications report the use of microfluidic platforms and there is therefore little feedback.

There is therefore a real need to find a means and / or process remedying these defects, drawbacks and obstacles of these processes.

There is also a real need to find a means and / or process remedying these faults, drawbacks and obstacles of the prior art, in particular a process which can be used whatever the conditions of culture of the bacteria and rapid in particular. The BioFilm Ring Test®, based on microplate devices, the BioFilm Ring Test® (BRT) developed by the company BioFilm Control makes it possible to evaluate with great efficiency the ability of microorganisms to form a biofilm. It does not require staining of the sample, nor any enema, thus limiting the alteration of the biofilm. BRT applications are found in microbiology and in the pharmaceutical industry. The test is available in several derivatives corresponding to different applications: high throughput screening of antibacterial agents and preventive and curative antibiotics, characterization of anti biofilm coating agents, study of the impact of enzymatic activity on the formation of biofilm and degradation. The application of BRT for the screening of antibiotic molecules is called Antibiofilmogramme®. This test leads to the determination of the MIC (minimum inhibitory concentration for biofilm) allowing to select among antibiotics effective on planktonic bacteria (MIC) those having in addition a preventive activity on the formation of biofilm. However, this method does not make it possible to determine the kinetics of biofilm formation, for example continuously, without time bias. This process also does not allow specific characterization of the mechanics of biofilms. There are inherent links between the metabolism of bacteria living in the form of biofilm and the mechanical properties of the material consisting of the community of bacteria and of the matrix. The development of effective strategies to control the presence of biofilms or the development of biotechnological processes based on the use of biofilms require the understanding of the mechanisms inherent in the biofilm lifestyle and the knowledge of the mechanical parameters describing the behavior of biofilm in its environment. In this context, many test methods have emerged to assess these parameters. They are adapted from test methods and identification of parameters of conventional engineering materials or inert fluids. However, the lack of standardization both in the field of mechanical tests and in the methods of identifying mechanical parameters for the characterization of living materials remains a scientific obstacle. The quest for normalization is problematic since biofilms are living structures. This characteristic leads to a complexity in the study of mechanical properties. These are spatially and temporally heterogeneous. Indeed, by their composition, biofilms are composites. In addition, bacteria have the ability to modulate dynamically mechanical properties in response to stress they feel. Currently, the mechanical properties of biofilms are equivocal. The parameters reported in the literature are difficult to interpret and compare given the lack of standardization in the various ways in which biofilms are tested. The definition of standardized protocols appears essential to grant an interdisciplinary community. The spatio-temporal heterogeneity of biofilms represents a real scientific challenge, but the problems linked to the living nature of this type of material are real problems. Living materials represent fragile samples that should be handled with care. In vitro testing of live samples can result in reworking that skews the results. On the other hand, we must expect a greater variability in the results compared to common engineering materials which are inert. In the case of biofilms, physiological conditions often require non-invasive imaging methods, but precise characterization of the geometry of the sample can be difficult to achieve. Also, the flatness of the surfaces often required for field measurements and the control of out-of-plane movements represent constraints specific to these materials.

There is therefore a real need to find a means and / or process remedying these defects, drawbacks and obstacles of the prior art, in particular a process making it possible to obtain reliable, reproducible, comparable results and also to reduce costs and the time to obtain said results

Description of the invention

The present invention makes it possible to solve the problems and drawbacks of the methods of the prior art by providing a method for detecting and / or monitoring and / or characterizing the formation of a biofilm comprising the following steps

a) carrying out a temporal succession of observations of a solution comprising at least one microorganism and a plurality of particles while the solution is maintained under conditions allowing the development of a biofilm by said at least one micro- organism, b) detection of the presence of the biofilm and / or characterization of the kinetics of biofilm formation by a comparative statistical analysis movements of the particles observed during the different observations.

The inventors have surprisingly demonstrated that the process of the invention makes it possible to observe biofilm formation in real time, without altering the biofilm and advantageously makes it possible to determine the kinetics of biofilm formation, continuously, without time bias.

The formation of the biofilm is accompanied by the development of a material whose three-dimensional structure can be composed by entanglements of bacteria in a polymer matrix whose mechanical behavior turns out to be viscoelastic.

Advantageously, the method of the invention is a non-destructive method advantageously making it possible to determine the evolution of the state of the biofilm over time, in particular by analysis of its material properties.

Advantageously, the method of the invention makes it possible to study and determine the mechanical properties of the extracellular matrix of biofilms.

Advantageously, the inventors have demonstrated that the process of the invention advantageously makes it possible in particular by incorporating particles and / or microbeads into the culture medium, in particular the bacterial medium, and in particular by recording their movement, for example with a optical device, for example viewed under a microscope, to study and determine the mechanical properties of biofilms, in particular the mechanical properties of the extracellular matrix of biofilms.

Advantageously, the inventors have demonstrated that the results of such a process are dynamic and local, for example by means of the kinematics of the movement of each of the microbeads / particles observed and / or filmed.

Advantageously, the inventors have taken advantage of the displacement of the microbeads / particles in the biofilm in order to characterize the development of the latter.

In the present, the vectors or displacements are independently noted in the form n or n, n being an example of designation of a vector.

According to the invention, by temporal succession of observations is understood to mean at least two observations over time. In particular, we mean at least two observation steps while the solution is maintained under conditions allowing the development of a biofilm by said at least one microorganism. For example, the method can comprise from 1 to 1000 observation steps, for example from 1 to 100 observation steps that the solution is maintained under conditions allowing the development of a biofilm by said at least one microorganism.

According to the invention, the observation steps can be carried out at regular time intervals. For example, the observation steps can be carried out according to a time interval of 1 to 10 minutes, 2 to 8 minutes, 3 to 6 minutes, equal to 5 minutes.

In other words, the temporal succession of observations can be carried out at regular time intervals. For example, the observation steps can be carried out according to a time interval of 1 to 10 minutes, 2 to 8 minutes, 3 to 6 minutes, equal to 5 minutes.

According to the invention, the observation can be carried out by any means known to the person skilled in the art. It can be for example an optical device, for example a microscope, for example an Olympus CKX53 inverted light microscope equipped with a 40x lens, a camera, a scanner, a camera, for example PCO Edge or a Basler Ace camera.

According to the invention, the observation can include at least two images of the solution. It can be for example an observation comprising a taking of at least two images. It can be, for example, an observation comprising a series of images taken, for example from 2 to 100,000 images, from 2 to 50,000 images, for example from 2 to 300 images.

According to the invention, the time interval between two successive images during an observation can be noted Dί. In the present, the time interval At must be> 0.

According to the invention, each observation can comprise, for each particle of a set of particles observed during said observation, a determination of a trajectory corresponding to successive displacements carried out by said particle during said observation.

In other words According to the invention, an observation 0 * can, for example, be obtained by determining the trajectories of n particles / beads, along a series of N + 1 images, for example N is greater than or equal to 1, for example ranging from 1 to 3000, for example ranging from 1000 to 3000. For example N can be such that 1 <N, preferably 1000 <N + 1 <3000. According to the invention, the observation of the solution can be carried out for a given time. For example, the observation can be carried out for a time and / or a duration ranging from 0.01 ms to 15 s, for example from 0.1 ms to 10 seconds.

According to the invention, the observation can be carried out by taking images at any suitable frequency known to those skilled in the art. It can be taking pictures with a frequency of 1 to 1000 images per second, for example 100 to 300 images per second, for example 200 images per second.

According to the invention, the image can be a digital image.

In the present digital image means any digital image known to those skilled in the art. This could be a digital image, for example, made up of a two-dimensional orthogonal grid. The smallest constituent entity of this grid is called pixel ("picture element" in English). The digital image can therefore be a matrix of pixels quantified by their coordinates and their gray level. The magnitude of the pixel values may depend on the number of bits on which the image is encoded. If the image is coded on

N bits, a pixel can be quantified by 2N different shades of gray. These can for example be images coded in 16 bits, which represents a range of 65536 different gray level values. This sampling of the information in space and in the depth of the gray levels defines the dynamics of the image. The linear transposition of a continuous signal of the material observed in a sampled signal can be constituted by a matrix of pixels can require some precautions. Preferably, in order to be able to apply the Cl N, the surface observed can be and remain plane during the observation in order advantageously not to introduce differences in scales in the images. Preferably, the lighting can be constant and the dynamic range of the image adapted to the desired measurement. The objective of Cl N is to find the measured displacement ü corresponding to the true displacement ü _t of a surface element between an initial state (t = 0) and a deformed state (t). The displacement ü can be calculated in each pixel over a small portion of the surface of the material. This portion is commonly referred to by its acronym King of English “Region of Interest”. A selection of an area of the image where the calculation is made can advantageously allow on the one hand to get rid of the image edge pixels which are liable to disappear during the mechanical test and on the other hand, set aside areas where mechanical information is not relevant. According to the invention, the trajectory can be determined by any process and / or method known to those skilled in the art. It may for example be a particle trajectory, for example the set of successive displacements carried out by a particle, for example during an observation.

According to the invention by trajectory, for example trajectory of a ball, we can also hear all of the successive positions occupied, for example by the center of the ball during a series of images, for example successive. According to the invention, the trajectory can be in two dimensions, the positions can be given by the Cartesian coordinates, x (t) and y (t), in an orthogonal coordinate system centered on the ball, for example at time t = 0.

In other words, according to the invention by trajectory, for example trajectory of a particle, one can hear the set of successive positions occupied, for example by the center of the particle, during a series of images. , for example successive. According to the invention, the trajectory can be in at least two dimensions, for example in two dimensions or three dimensions. For example when the trajectory is in two dimensions the positions can be given by the Cartesian coordinates, x (t) and y (t), in an orthogonal coordinate system, for example centered on the particle.

According to the invention by observation, for example by observation O one can observe and / or hear a set of trajectories of n particles. For example, n particle can be observed, with n greater than 1, for example ranging from 1 to 350, for example ranging from 150 to 350. For example n may be such that 1 <n, preferably 150 <n <350 particles, for example present in the sample and / or the solution.

In other words, each observation can comprise a set of n trajectories of n particles, for example greater than 1, for example comprised from 1 to 350, for example comprised from 150 to 350. For example n can be such that 1 <n , preferably 150 <n <350 particles present in the sample and / or the solution.

According to the invention by observation O _i; we can hear a set of trajectories at a time T _{jC corresponding} to the age of the sample (T _£ > 0), for example of n particle, for example n is greater than or equal to 1, for example ranging from 1 to 350, for example from 150 to 350. According to the invention, by displacement vector is meant, for example, a vector of components according to the formula [(x (t + At) - x (t), (y (t + At) - y (t))], Vt, 0 <t £ NAt

According to the invention, the position of the particles, for example during observation, can be determined by any method known to a person skilled in the art. For example, the position of the particles can be determined by identification of any point on the particle, for example by identification of the circumference and / or periphery of said particles or of the center of said particles.

According to the invention, the method for detecting and / or monitoring and / or characterizing the formation of a biofilm can for example comprise the following steps:

at. introduction into a solution of at least one microorganism, b. introduction into the solution obtained in (a) of at least two particles,

vs. observation 01 of the solution obtained in (b) at a time (t1), d. maintaining the solution obtained in (c) under conditions allowing the development of a biofilm by said at least one microorganism, e. observation 02 of the solution obtained in (d), at a time (t2), f. comparison of observations 01 and 02,

g. determination of the presence and / or characterization of a biofilm by comparison of the trajectories of the particles observed during observations 01 and 02.

According to the invention, the method for detecting and / or monitoring and / or characterizing the formation of a biofilm can for example comprise the following steps:

at. introduction into a solution of at least one microorganism, b. introduction into the solution obtained in (a) of at least two particles,

vs. observation 01 of the solution obtained in (b) at a time (T- _L ), d. maintaining the solution obtained in (c) under conditions allowing the development of a biofilm by said at least one microorganism, e. observation 02 of the solution obtained in (d), at a time (T ₂ ), f. determination of the presence and / or characterization of a biofilm by comparison of observations 01 and 02,

According to the invention, the detection of particles, in particular on an image, for example a digital image, can be carried out by any suitable method known to those skilled in the art. It can for example be a method of comparing images with at least one reference image (imagev). According to the invention, the reference image (imagev) can be determined by any method known to those skilled in the art. It can for example be a process comprising the following steps:

identification of the center of a particle on an image, for example in an area of interest,

selection of a particle image area (imageb) corresponding to a square centered on said particle, and

obtaining a reference image (imagev) according to the following formula:

in which imagette _b is the raw thumbnail of the area around a ball, image _b ^T is the transpose of imageb, image _b ^Rh is the horizontal symmetry of imagette _b , imageb ^Rv is vertical symmetry of the imagetteb.

According to the invention, the transposition of the imagetteb can be carried out by any suitable method known to those skilled in the art. For example for A = (<¾ ·) a matrix of M _np , the transpose of A, noted s is the matrix with p rows and n columns of general term b _kl defined by: k, l £ k £ p, vl, l £ l £ nb _kl = al, k

According to the invention, horizontal symmetry of the imagetteb can be achieved by any suitable method known to those skilled in the art. For example for A = (a _ί ·) a matrix of M _np , the horizontal symmetry of A, noted A ^Rh is the matrix with n rows and p columns of general term b _kl defined by: vk, l £ k £ n, v Z, l <l £ P b _{k: i} = a _{k: P} _i ₊₁ According to the invention, vertical symmetry of the imagetteb can be achieved by any suitable method known to those skilled in the art. For example for A = a matrix of M _np , the vertical symmetry of A, noted A ^Rv is the matrix with n rows and p columns of general term b _kl defined by: vk, l £ k £ n, v Z, l <l £ pb _ki = a _{n-fe + 1 i}

According to the invention, the comparative statistical analysis of the displacements of the particles can be carried out by any suitable method known to those skilled in the art.

According to the invention, the comparative statistical analysis of the displacements of the particles may include in particular a step of comparing the observations.

According to the invention, the comparison step can be carried out by any image comparison method known to a person skilled in the art. It can be, for example, a method of comparing images, for example for a grayscale image, comprising an aggregation of gray levels, a distribution of gray levels, or for example for a gray image colors, a method of comparing images comprising an aggregation of color levels. It may, for example, be a method of comparing images, for example a method of comparing a digital image, for example a digital image in grayscale. For example, the image comparison method, in particular digital (CIN) can for example be based on the principle of conservation of the gray levels between two images of the same surface at successive instants. For example, the images used for CIN may correspond to the intact and distorted surfaces of the object of study. They can be represented by 2D functions f and g. The comparison can be based on a comparison of the amplitude of the signal between the reference image and the image of the deformed state. The functions f and g can represent the gray levels interpolated at the pixel whose position is identified by its coordinates (x, y). The problem to translate consists in determining ü, the field of displacements sought in the center of a King surface element. During an X transformation, a point x in the reference image f moves to X (x) in the distorted image g. For any point with coordinates x belonging to the King, the conservation of the gray levels between the two moments can be written:

f l r! = g x -f u)

Advantageously, for example when there are external disturbances of the gray levels, for example caused by differences in lighting or by sensor noise, between the two images f and g, a distance between f and g can be introduced. The minimization of this distance can then be implemented. For example, the amplitude of the signals over an area, here in 1 D, can be compared between the reference image and the image of the deformed state. As it stands, determining displacement ü is an ill-posed problem. It can be regularized for example by adding a priori information on the solution of the inverse problem. It can be for example a kinematic regularization. For example, a kinematic base to which the displacement belongs is defined. In practical terms, the displacement can be approached by a set of families of simple transformations. The displacement field ü can be decomposed, a priori, on the basis of N arbitrarily chosen form functions denoted F ,, where / = 1, .., N. The _i are the functions of forms forming a base describing the field of displacement.

where, F _ί , is the degree of freedom associated with the function of form F ,,

A spatial regularization, by the choice of the size of the zones, support of the kinematics can also be used. Determining the optimal coefficients of the approximate transformation may require minimizing the distance between f and g. This distance can be formalized in the form of a cost function. Various functions can be used as described in Pan et al., 2010. It can be for example the standard S ² in which the conservation of the gray levels is sought by minimizing the square of the difference in gray levels: This function is non-linear. Assuming they are small displacements, it can be linearized by performing a first order Taylor expansion. The previous equation then becomes:

By injecting ü in its linear combination form into the approximation subspace, this system can be put in matrix form:

[- «.,] '¾] - [¾]

In which the terms M and b are known data which depend on the gradient of the reference image, and on the residue between the initial image and the “back-deformed” image.

corresponds to the Euclidean dot product between two vectors.

The regularization can be carried out iteratively by updating the displacement increment by a Gauss-Newton algorithm. The displacement increment can be obtained by solving the linearized system equation

The resolution of the system makes it possible Dί “to find and thus, to calculate the field of displacement on the element King when the d is found. +! - Sa 4- D½ + i The Gauss-Newton algorithm can operate until the stopping criterion is reached, for example when AS _it <10 ^-5 .

Advantageously, the images can be discretized and therefore known to the nearest pixel. As it stands, the calculated displacement takes a value in N2. To achieve sub-pixel precision, an interpolation of gray levels at non-integer coordinates can be carried out, for example a bi-cubic or “spline” interpolation. According to the invention, the method can comprise, during the comparison of the observations, the determination of the statistical distribution of the possible trajectory of particles and / or of the geometry of the possible trajectory of particles.

According to the invention, the method can comprise, during the comparison of the observations, the determination independently by particle of the statistical distribution of the possible trajectory of each of the particles and of the geometry of the possible trajectory of each of the particles present.

According to the invention, the method can also comprise an overall statistical analysis of the displacements made by the particles observed during each observation and / or a calculation of times characteristic of the formation of the biofilm on the basis of the results of the statistical analysis. overall.

In the present, by global statistical analysis is meant any statistical analysis known to a person skilled in the art suitable for a displacement analysis, for example of particles. It can be for example an analysis of the distribution, for example based on the variance, for example the sobol index, an analysis of the variance on the distribution, hypothesis tests on the type of statistical law representative of the motion / displacement distribution.

According to the invention, the global statistical analysis can comprise the calculation of a value of at least one statistical parameter of a distribution of the displacements carried out respectively by the particles of the plurality of particles and an analysis of the variations as a function of the time of the values of said at least one statistical parameter obtained for the succession of observations.

According to the invention, the at least one statistical parameter of a displacement distribution can be any statistical parameter known to those skilled in the art. he for example, it may be the determination of the standard deviation of the distribution of displacements and / or trajectories, of statistical moments. For example the determination of the standard deviation of the distribution of displacements can be established according to the following formula:

in which P corresponds to the particle displacement vector, m = length (P), and P = mean (P).

In the present, the particle displacement vector can correspond to the vector connecting an old position to a new position of the particle, therefore the final position vector minus the initial position vector. In other words, the vector can also be designated here by trajectory.

In the present, the displacement vector and / or trajectory of each of the particles can be determined during each of the observations, for example by determining the position of the particles on each of the images obtained during said observation.

According to the invention, the global statistical analysis can include the calculation of the mean square displacement (MSD) according to the following formula: MSD (At) = Ar ² in which Ar is the radial displacement of the particles and At the time interval . In the present, the time interval At may correspond to the time interval between two images.

In the present, when the observation includes several digital images, the coordinates of the vector can be expressed in pixels.

In other words, according to the invention, the method can include determining the statistical distribution of all the particles.

In addition, according to the invention, the method can include determining the geometry of the trajectory of all the particles. According to the invention, the determination of the geometry of the trajectory can be carried out by any method known to a person skilled in the art. Advantageously, the variation of the value of the standard deviation of the displacements and / or trajectories, preferably as a function of time, for example of the observation time T *, can make it possible to characterize the possible biofilm.

Advantageously, the inventors have demonstrated that during the production of a biofilm, the value of the standard deviation of the distributions of the displacements carried out by the particles varies over time. In particular, the inventors have surprisingly demonstrated that when a biofilm is produced, the value of the standard deviation of the distribution of displacements increases over time to a maximum value before decreasing, while in the absence of biofilm production, once the maximum value of the standard deviation of the displacement distribution reached, this does not vary over time.

Advantageously, the inventors have also surprisingly demonstrated that the variation in the value of the standard deviation of the distributions of the displacements made by the particles over time during the formation of biofilm can be modeled, making it possible advantageously to determine the time or times increase and / or decrease in the value of the standard deviation of the distributions and / or the slope (s) of the curve represented by the variations in the value of the standard deviation of the distributions of displacements as a function of time. The inventors also demonstrated that the variations in the slope of the curve for the evolution of the standard deviation of the distribution of displacements as a function of time corresponded to variations in the "behavior" of the particles in the medium and / or in the solution.

According to the invention, the method can further comprise a statistical analysis of the individual contributions of the particles observed from the displacements made by each particle observed during each observation and an identification of at least a percentage of particles performing the same type of movement based on the results of the statistical analysis of individual contributions.

According to the invention, the statistical analysis of the contributions of each of the particles and / or of the individual contributions of the particles observed can be carried out by any suitable statistical analysis method known to those skilled in the art. It may for example be a method of statistical analysis as mentioned above. According to the invention, the statistical analysis of the contributions of each of the particles and / or of the individual contributions of the particles observed can be carried out for each observation.

According to the invention, the statistical analysis of the individual contributions of the particles can include, for each observation:

a calculation, for each trajectory followed by a particle observed during said observation, of a vector composed of parameter values characteristic of the displacements defining the trajectory concerned; a constitution of a matrix from the vectors calculated for the particles observed during said observation;

a decomposition of said matrix by an analysis into principal components;

an identification of at least one predominant main component among the main components resulting from the decomposition;

a generation of a diagram in at least one dimension of the projections of the different vectors corresponding to the trajectories of the particles observed on said at least one main component identified.

According to the invention, the characteristic parameters of the displacements can be at least one characteristic parameter is chosen from the group comprising

length c1 of the diagonal of the rectangle comprising said trajectory according to the following formula:

cl

= (m ax (cumsum (A-)) - m \ n (cumsum (X))) ² + (ma

where max (21) returns the largest component of vector A, min (21) returns the smallest component of vector 21 and cumsum (B) returns the cumulative sum of vector B.

- the average speed c2 on the trajectory:

where mean (21) returns the mean of the components of vector 21 - the standard deviation c3 of the velocity distribution of each particle at each point of the trajectory

where std (A) returns the standard deviation of the components of the vector!

- the standard deviation of the distribution of the trajectories along the transverse axis c4 or vertical c5:

c4 = std. {X)

c5 = std (Y)

where std (A) returns the standard deviation of the components of vector A

- the asymmetric distribution of the trajectories along the transverse axis c6 or vertical c7:

c 6 = skw (X)

cl = skw (Y)

where skw (A) returns the asymmetry coefficient of the components of the vector A In the present, max corresponds to the largest component, std corresponds to the standard deviation, mean corresponds to the mean, and skw corresponds to the asymmetry coefficient , cumsum corresponds to the cumulative sum, min corresponds to the smallest component of the vector.

According to the invention, the statistical analysis of the contributions of each of the particles and / or of the individual contributions of the particles observed can comprise the determination of the mean displacement squared (MSD) according to the following formula: <Ar ² (At)> in which Ar is the radial displacement of the particle and At the time interval.

According to the invention, the vector composed of parameter values characteristic of the displacements defining the trajectory concerned can be calculated by any method known to those skilled in the art. It can for example be a process comprising a concatenation of all the values of characteristic parameters.

According to the invention, the constitution of a matrix from calculated vectors can be achieved by any suitable method known to those skilled in the art. This may for example be a principal component analysis. According to the invention, the principal component analysis can be any principal component analysis known to those skilled in the art. For example, it can be an analysis allowing the selection of the components of the values which express the maximum variance.

According to the invention, the identification of at least one predominant main component among the main components resulting from the decomposition can be carried out by any process and / or means known to those skilled in the art.

According to the invention, the predominant main component or components can come to correspond to the components preferably corresponding to at least 40% of the sets of values, preferably to 60% of the sets of values.

The person skilled in the art, by virtue of his general knowledge, will be able to choose and / or determine the main components, for example the predominant main components.

According to the invention, the generation of a diagram in at least one dimension of the projections of the different vectors corresponding to the trajectories of the particles observed on said at least one predominant main component identified can be produced by any process known to those skilled in the art.

According to the invention, the diagram can be a one-dimensional, two-dimensional, three-dimensional or four-dimensional diagram, preferably one-dimensional, two-dimensional or three-dimensional.

According to the invention, the number of dimensions of the diagram can be a function of the number of predominant main components identified. For example, when only three main components are identified, the diagram can be a two-dimensional diagram, each of the dimensions of the diagram independently corresponding to a main component, the third main component being reported in the diagram.

According to the invention, the method can comprise from the diagram generated a step of identifying a cluster of points in said diagram. According to the invention, the identification of a point cluster in said diagram can be carried out by any method and / or method of analysis known to those skilled in the art. It may, for example, be any suitable clustering method known to those skilled in the art. It can for example be a visual analysis of the distribution of points, a K-mean, k-Medoides analysis. According to the invention, the method can comprise a calculation of the percentage of particles from a percentage of points belonging to a given cluster. According to the invention, the calculation of the percentage of particles from a percentage of points belonging to a given cluster can be carried out by any suitable method known to those skilled in the art. The person skilled in the art, by virtue of his general knowledge, will know how to choose the method according to the diagram.

Advantageously, the percentage of particles calculated from a percentage of points belonging to a given cluster makes it possible to classify and / or characterize the biofilm formed.

According to the invention, the method can further comprise a step of analyzing a change over time in said percentage of particles for the different observations.

Advantageously, the analysis of the evolution over time of said percentage of particles for the various observations makes it possible to determine the dynamics of formation of the biofilm, from a quantitative and qualitative point of view.

According to the invention, the microorganism can be any microorganism known to those skilled in the art. It can for example be a bacteria, a fungus. They can also be prokaryotic cells, for example any bacterium known to a person skilled in the art, for example bacteria included in the group, without being limited to this group, consisting of Acetobacter aurantius, Actinobacillus actinomycetemcomitans, Agrobacterium tumefaciens, Azorhizobium caulinodans, Azotobacter vinelandii, Bacillus anthracis, Bacillus brevis, Bacillus cereus, Bacillus fusiformis, Bacillus licheniformis, Bacillus megaterium, Bacillus stearothermophilus, Bacillus subtilis, Bacteroides gingivalis, Bordonetella bronchiella, Bacteroides melaninellaellae, Bartonidesella bronchiella, , Branhamella catarrhalis, Brucella abortus, Brucella melitensis, Brucella suis, Burkholderia mallei, Burkholderia pseudomallei Calymmatobacterium granulomatis, Campylobacter coli, Campylobacter jejuni, Campylobacter pylori, Chlamydia pneumoniae, Chlamydia psittaci Chlamydia pneumonia sittaci, Clostridium botulinum, Clostridium difficile, Clostridium perfringens, Clostridium tetani, Clostridium welchii, Corynebacterium diphtheriae, Corynebacterium fusiforme, Coxiella burnetii Ehrlichia chaffeensis, Enterococcus avium, Enterococcus durans, Enterococcus faecalis, Enterococcus faecium, Enterococcus galllinarum, Enterococcus maloratus, Escherichia coli Francisella tularensis, Fusobacterium nucleatum Gardnerella vaginalis Haemophilus ducreyi, Haemophilus influenzae, Haemophilus parainfluenzae, Haemophilus pertussis, Haemophilus vaginalis, Helicobacter pylori, Klebsiella pneumoniae, Klebseilla rhinoscleromatis, Klebsiella oxytoca Lactobacillus acidophilus, Lactobacillus casei, Lactococcus lactis, Legionella pneumophila, Methanobacterium extroquens, Microbacterium multiforme, Micrococcus luteus, Mycobacterium avium, Mycobacterium bovis, Mycobacterium diphtheriae, Mycobacterium intracellulare mycobacteriumcumcumcumcumcumcumcumcumcumcumcum Mycoplasma hominis, Mycoplasma pneumoniae Neisseria gonorrhoeae, Neisseria meningitidis, Nocardia asteroides Pasteurella multocida, Pasteurella tularensis, Porphyromonas g ingivalis, Pseudomonas aeruginosa, Pseudomonas maltophilia, Rhizobium radiobacter, Rickettsia prowazekii, Rickettsia mooseri, Rickettsia psittaci, Rickettsia quintana, Rickettsia rickettsii, Rickettsia trachomae, Rochalimaea henselae, Rochalimaea quintanaid tothiocellmonium, Rothiaummonia, quintanaid rothiaocythia, Shigella dysenteriae, Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus agalactiae, Streptococcus avium, Streptococcus bovis, Streptococcus cricetus, Streptococcus faceium, Streptococcus faecalis, Streptococcus savvy, Streptococcus gallinarum, Streptococcus lactis, Streptococcus mitior, Streptococcus mitis, Streptococcus mutans, Streptococcus oralis, Streptococcus pneumoniae , Streptococcus pyogenes, Streptococcus rattus, Streptococcus salivarius, Streptococcus sanguis, Streptococcus sobrinus, Treponema pallidum, Vibrio cholerae, Vibrio comma, Vibrio parahemolyticus, Vibrio vulnificus, Xanthomonas maltophilia Yers inia enterocolitica, Yersinia pestis and Yersinia pseudotuberculosis, etc.

It can for example from any fungus known to a person skilled in the art, for example pathogenic or non-pathogenic fungi, for example fungi responsible for pathologies, for example in human health or not, environmental fungi, fungi chosen from the group comprising yeasts, for example Candida and / or Cryptococcus, for example yeasts responsible for pathologies, for example in human health, for example candidiasis and / or cryptococcosis, and / or the group comprising molds, for example Aspergillus, for example mold responsible for pathology, for example in human health, for example of aspergillosis and / or pulmonary mycosis.

According to the invention, the microorganism can be introduced into the solution at a concentration of from 1.10 ⁶ to 2.10 ⁸ CFU / ml (CFU: Colony-Forming Unit; Colony-forming Unit; Colony-forming Unit), for example 1.10 ⁷ at 8.10 ⁷ CFU / ml. For example, in a volume of 200 ml the amount of microorganism can be from 2.10 ⁵ to 4.10 ⁷ CFU, for example from 2.10 ⁶ to 1, 6.10 ⁷ CFU.

According to the invention, the solution can be contained in a container and / or present on a surface. It can for example be any container known to the person skilled in the art. It can be for example a culture container.

According to the invention by "the surface" is meant any culture surface known to the skilled person. It can for example be a glass slide, Plexiglas or other suitable material. It can be, for example, a glass slide to which a drop of medium and bacteria can be added.

According to the invention, "culture container" means any culture container known to a person skilled in the art. It can be for example a culture reactor, wells, tubes or wells for example microdilution plates.

According to the invention, the culture container can for example be an enclosure with a closed end, of the tube, well, etc. type. or an enclosure with two openings. It can also be a container comprising a closed end so as to form a flat bottom, a container with a closed end so as to form a hemispherical bottom, a container comprising two open ends. When the container has two open ends, said container can be configured to allow a flow of the culture medium in constant flow or in discontinuous flow.

It can for example be a microdilution plate, the type of plate defined for example by the American National Standards Institute and the Society for Biomolecular Screening (“microplates”) carrying 96 wells, 384 and even 1536, or conversely 48 or 24 wells or any other number of wells.

It may for example be a culture container constituted by any material known to a person skilled in the art. It can for example be plastic, for example polycarbonate, polypropylene, polystyrene, etc., glass, metal.

These may for example be microplates made of polycarbonate or polypropylene with a flat, conical or round bottom.

It can for example be a polystyrene container, for example any polystyrene known to a person skilled in the art.

According to the invention, the solution can also be on a surface. It can be any surface known to a person skilled in the art on which a microorganism can be incubated and / or can grow and / or on which a biofilm can form. It can be, for example, a biotic or abiotic surface. It may, for example, be a surface of a medical device and / or supports, for example dental implants, catheter, prostheses and / or any medical device known to those skilled in the art on which microorganisms can develop.

In the present biotic surface is understood to mean any biotic surface known to a person skilled in the art.

In the present case, abiotic surface means any abiotic surface known to a person skilled in the art. It can also be any device surface known to a person skilled in the art on which a bacteria is capable of forming a biofilm. It can be for example the surface of a medical device, for example a catheter, a needle, a catheter with an implantable chamber (or Porth-a-Cath), a valve, for example a heart valve, a prosthesis, for example a joint prosthesis, a ligament prosthesis, a dental implant, a urinary tract prosthesis, the peritoneal membrane, peritoneal dialysis catheters, synthetic vascular grafts, stents, internal fixation devices, percutaneous sutures and / or tracheal, ventilation tubes.

According to the invention, the solution can be any solution and / or medium known to those skilled in the art suitable for the cultivation of microorganisms.

According to the invention, the solution can be any solution known to a person skilled in the art in which a microorganism can be incubated and / or can grow and / or on which a biofilm can form. It may, for example, be a culture medium, for example any culture medium known to a person skilled in the art and / or commercially available in which at least one microorganism is liable to develop. It can be for example a natural environment or synthetic. It can be, for example, a culture medium for bacterial growth, for example the BHI medium (“Brain Heart Infusion”), the LB medium (“Lysogeny broth” also called “Luria Bertani”) , MH medium (“Mueller-Hinton medium”), glucose broth, yeast culture medium, for example Sabouraud medium. Those skilled in the art on the basis of this general knowledge will be able to choose the culture medium suited to the microorganism. .

According to the invention, by plurality of particles means at least two particles. According to the invention, said at least two particles can be any particle making it possible to implement the present invention. Advantageously, said particle can be a particle of any shape suitable for the implementation of the present invention, for example in the form of a ball, of a puck, of asymmetrical geometric shape, for example with a flat face, etc.

According to the invention, the shape of the particles can be identical or different.

Advantageously, the shape of the particles is identical.

According to the invention, the particle can be of any suitable material known to a person skilled in the art. It can be for example a metal particle, a plastic particle.

Any suitable particle size can be used. The size can be chosen for example according to the size of the container of the solution and / or of the microorganism. For example, the particle size may be less than a tenth of the size of the container, preferably less than a hundredth, even more preferably less than a thousandth of the size of the container. For example, the particle may have a size of, for example, 10 nm to 100 nm, from 0.1 to 100 µm.

According to the invention, the method of the invention can be implemented with at least 2 particles, with for example from 2 to 10,000,000, from 1,000 to 1,000,000, from 10,000 to 1,000,000, from 100 000 to 1,000,000, 10,000 to 100,000.

The plurality of particles advantageously makes it possible to observe a plurality of trajectories of said particles. According to the invention, when the method is implemented with a plurality of particles, said particles can be of identical or different size. Advantageously, the particle size is identical.

When the particles are of different sizes, the small particles can have a size, for example, of 10 nm to 1 pm, for example of 100 at 500nm, and the large particles can have a size, for example, from 1 pm to 100pm, for example from 1 pm to 10 pm, for example from 1 pm to 5 pm.

According to the invention, it is also possible to use a plurality of particles of identical sizes.

According to the invention, the particles can generate a detectable signal. The detection of this signal will depend on the properties of the particle. For example, said at least one particle may be fluorescent, phosphorescent, chemiluminescent, reflective or colored.

For example in the case where said at least one particle is fluorescent, the fluorescence emitted by the particle can be detected for example visually, and / or by any optical means known to those skilled in the art. Said at least one particle can be, for example, illuminated to follow its movement by means of a light source, for example by a laser beam.

For example, in the case where the at least one particle is phosphorescent, this particle can be viewed, for example visually, by any optical means known to a person skilled in the art.

For example, in the case where at least one particle is chemiluminescent, the detection of the particle can be formed by adding chemical reagents to the medium, allowing the emission of light energy by the particles.

Advantageously, said at least one particle can, for example, be lit, to follow its movement by means of a light source, for example by a laser beam.

Those skilled in the art will readily understand that for the implementation of the present invention, the choice of the visual properties of said at least two particles can also be made according to the solution.

According to the invention, the method can comprise beforehand and / or during the step of maintaining the solution obtained in (c) under conditions allowing the development of a biofilm by said at least one microorganism, the introduction of at least one compound chosen from an antibiotic, an antifungal and / or mixture thereof.

According to the invention, by compound is meant any compound, for example natural or obtained by synthesis, chemical hemi-synthesis, or obtained by extraction, known those skilled in the art capable of modifying the growth / development of microorganisms and / or capable of killing a microorganism. It may for example be a biocide, for example a disinfectant, a protection product, a product for combating so-called harmful species and the like and / or any biocide included in the list according to Directive 98/8 / EC or Regulation (EU) No 528/2012, an antibiotic, an anti-fungal.

It may, for example, be an antibiotic chosen from the group comprising fusidic acid, aminoglycosides, beta-lactams, penicillins, beta-lactamase inhibitors, cephalosporins, carbapenems, monobactams, cycloserine , daptomycin, fosfomycin, lincosamides, macrolides and ketolides, mupirocin, nitroimidazoles, nitrofurans, oligosaccharides, oxazolidinones, polypeptides: bacitracin, glycopeptides, polymyxins and colistin, quinolones, quinolones sulfonamides and diaminopyridines, synergistins, tetracyclines, antifungals, or any mixture thereof.

It may for example be an anti-fungal selected from the group comprising Miconazole, Ketoconazole, Clotrimazole, Econazole, Bifonazole, Butoconazole, Fenticonazole, Isoconazole, Oxiconazole, Sertaconazole , Sulconazole, Thiabendazole, Tioconazole, Fluconazole, Itraconazole, Isavuconazole, Ravuconazole, Posaconazole, Voriconazole, Terbinafine, Amorolfine, Naftifine, Butenafine, Mycosubtiline or any these.

It may also be any new compound for which it is sought to demonstrate a possible antibiotic and / or antifungal activity, alone or in combination with one or more other compounds.

According to the invention, the antibiotic and / or the anti-fungal may be in solution in the solution, for example in the culture medium, or in dehydrated or lyophilized form, and / or fixed on the surface immersed in said middle.

The fixing means which can be used for the antibiotic and / or the anti-fungal can be any means known to a person skilled in the art for fixing an antibiotic and / or an anti-fungal on a surface. It can be for example means or device available on the market, for example supports of the microplate type, for example means or device marketed by the company NUNC (Denmark), Corning (United States), and Greiner Bio -One (Austria). According to the invention, one or more compounds, for example one or more antibiotics, can be introduced into the culture medium.

Advantageously, when the method according to the invention makes it possible to test different antibiotics and / or anti-fungal alone and / or to search for a combination or combination effect of antibiotics and / or anti-fungal in a commonly used format called "chessboard" (or checkerboard in English). The implementation of the method of the invention with a mixture of antibiotics and / or anti-fungal (combination, combination) can advantageously make it possible to detect possible additive, synergistic and / or antagonistic effects.

By "additive effect" is meant the addition of the actions of at least two antibiotics making it possible, for example, to find a combination of antibiotics having the same antibiotic effect as an antibiotic alone, but with lower concentrations.

By "synergistic effect" is meant an improvement in the effectiveness of an antibiotic and / or anti-fungal by adding at least a second antibiotic and / or anti-fungal, this mixture of antibiotics and / or anti-fungal having an effect greater than the addition of the effect of each antibiotic and / or anti-fungal alone.

Finally, "antagonistic effect" means the inhibition of the action of an antibiotic and / or an anti-fungal on the microorganism by the addition of at least one second antibiotic and / or anti-fungal. This embodiment can make it possible to select combinations, combinations of antibiotics and / or anti-fungals ("co-drugs") relevant for, for example, inhibiting the development of the microorganism.

According to the invention, antibiotics and / or anti-fungals can also be introduced successively into the medium in order to identify additive, synergistic or antagonistic effects as described above.

Advantageously, the method according to the invention makes it possible to determine / detect and / or identify the sensitivity of microorganisms to compounds. Also, the method according to the invention advantageously makes it possible to identify a new biocide, for example a new antibiotic and / or antifungal. In addition, the method according to the invention can advantageously make it possible to determine the specificity of a compound, for example if it is an antibiotic and / or an antifungal. The present invention also relates to a device for detecting and / or characterizing the formation of a biofilm capable of implementing the above method.

It can be any suitable device known to those skilled in the art. It may for example be a device comprising detection means, for example optical detection means, for example a camera, an electron microscope, analysis means, for example a calculation module, means of graphical representation, for example a visual representation module.

Other advantages may still appear to a person skilled in the art on reading the examples below, illustrated by the appended figures, given by way of illustration.

Brief description of the figures

Figure 1 shows an example of digital image comparison. On the diagrams the abscissa represents the pixels and the ordinate the gray level.

FIG. 2 represents an experimental diagram of a study of the formation of biofilm by means of microbeads trapped in the bacterial medium

FIG. 3 represents diagrams representing the relative BioFilm indices (rBFI) (ordered) as a function of the incubation time (abscissa) for P. aeruginosa (cross or vertical lines), S. aureus (cross or circles) and without bacteria ( cross or triangle). The shaded areas on the left graph represent the standard deviation for the two replicas.

FIG. 4 represents diagrams illustrating the different velocities of the balls for four different types of movement encountered during the growth of the biofilm, namely passive movement (a), an ejected / dredged movement (b), an idle movement (c) or movement of linked balls (d).

FIG. 5 represents the mean square displacement (MSD: “Mean Square Displacement) (ordered) as a function of time in seconds (abscissa) for beads taken in a suspension of P. aeruginosa for different incubation times: 0.45, 75 or 105 minutes.

FIG. 6 represents the average square displacement (ordered) as a function of time in seconds (abscissa) for beads taken in a suspension of S aureus for different incubation times: 0, 40, 120 or 160 minutes. FIG. 7 represents a diagram representing the evolution of the standard deviation of displacements of all the particles (ordinate) as a function of the incubation time in minutes (abscissa) of S aureus or not.

FIG. 8 represents two diagrams representing two models of evolution of the standard deviation of the displacements of the particles as a function of the incubation time. In the figure, a and b respectively represent the slope of the first linear part and the vertical interception, t1 a first break in the slope and t2 a second break in the slope. On the diagrams, the ordinate corresponds to the standard deviation of the displacements of the particles and the abscissa to the incubation time.

FIG. 9 represents the results obtained after analysis in principal components after incubation with bacteria, FIG. 9 (a) represents a bar diagram representing the result of analysis in principal component of the displacement parameters or trajectories of the particles, FIG. 9 (b) represents a graph as a function of the first (PCA1) (abscissa) and second principal components (PCA2) (ordinate). FIG. 9 (c) represents the proportion of particles / beads as a function of the distribution of the points as a function of time in FIG. 9 (b). The light gray dots correspond to the particles / beads having blocked movements, the ordinate corresponds to the percentage of beads and the abscissa the incubation time in minutes.

FIG. 10 represents the results obtained after analysis in principal components after incubation without bacteria, FIG. 10 (a) represents a histogram representing the result of analysis in principal component of the displacement parameters or trajectories of the particles, FIG. 10 (b) represents a graph according to the first and second main components. FIG. 10 (c) represents the proportion of particles / beads as a function of the distribution of the points as a function of time in FIG. 10 (b). The light gray dots correspond to the particles / beads having blocked movements, the ordinate corresponds to the percentage of beads and the abscissa the incubation time in minutes.

FIG. 11 represents the results obtained after analysis in principal components after incubation in the presence of S. aureus:, FIG. 11 (a) represents a bar diagram representing the result of analysis in principal component of the displacement parameters or trajectories of the particles , Figure 1 1 (b) represents a graph according to the first and second components main. Figure 11 (c) shows the proportion of particles / beads as a function of the distribution of points as a function of time in Figure 1 1 (b). The light gray dots correspond to the particles / balls having blocked movements.

Figure 12 corresponds to a diagram representing the evolution of the (standard) standard deviation as a function of the incubation time for P. aeruginosa. Slope change occurs at 34 minutes

Figure 13 represents the results obtained after analysis in principal components after incubation with P. aeruginosa, figure 13 (a) represents a histogram representing the result of analysis in principal component of the parameters of displacements or trajectories of the particles, the ordinate represents the percentage of variance, the abscissa the components, figure 13 (b) represents a graph according to the first (abscissa) and second principal components (ordinate). Figure 13 (c) shows the proportion of particles / beads as a function of the distribution of points as a function of time in Figure 13 (b). The light gray dots correspond to the particles / balls having blocked movements.

FIG. 14 represents two diagrams (A and B) representing the evolution of the standard deviation of displacements of all the particles (ordinate) as a function of the incubation time in minutes (abscissa) of the medium alone (white) , S aureus alone, S aureus with vancomycin, or S aureus and fusidic acid. The indicators on the diagrams indicate the corresponding curves.

FIG. 15 represents the results obtained after analysis in principal components after incubation of S aureus with vancomycin, FIG. 15 (a) represents a bar diagram representing the result of analysis in principal component of the parameters of displacements or trajectories of the particles , the ordinate represents the percentage of variance, the abscissa the components, FIG. 15 (b) represents a graph as a function of the first (abscissa) and second principal components (ordinate). FIG. 15 (c) represents the proportion of particles / beads as a function of the distribution of the points as a function of time in FIG. 15 (b). The light gray dots correspond to the particles / balls having blocked movements

FIG. 16 represents the results obtained after analysis in principal components after incubation of S aureus with vancomycin, FIG. 16 (a) represents a bar diagram representing the analysis result in principal component of the displacement parameters or trajectories of the particles, the ordinate represents the percentage of variance, the abscissa the components, figure 16 (b) represents a graph according to the first (abscissa) and second principal components (ordinate) . Figure 16 (c) shows the proportion of particles / beads as a function of the distribution of points as a function of time in Figure 16 (b). The light gray dots correspond to the particles / balls having blocked movements.

FIG. 17 represents diagrams representing the relative BioFilm indices (rBFI) (ordinate) as a function of the incubation time (abscissa) for S. aureus alone, with vancomycin, with fusidic acid and without bacteria (white) . The indicators in the figure indicate the corresponding curves.

FIG. 18 represents diagrams representing the number of colonies formed per well (CFU / well) (ordered) as a function of the incubation time (abscissa) for S. aureus alone, with vancomycin, with fusidic acid. The indicators in the figure indicate the corresponding curves.

FIG. 19 represents diagrams of the mean square displacement (ordinate) as a function of time in seconds (abscissa).

FIG. 20 represents two images of a particle, the image on the left corresponds to the raw image and the image on the right to the image after transformation.

FIG. 21 represents an image of a well comprising a medium with bacteria and particles.

FIG. 22 is a representation of an example of an area of interest (particle) used to track and / or calculate the movements of the particles, in particular by image correlation.

Figure 23 is an image of a grayscale well illustrating the background noise of the sensor.

FIG. 24 is a diagram representing a point cloud representing the variance as a function of the average of the gray level distribution of a pixel over 1000 images.

FIG. 25 represents diagrams of the mean square displacement (ordinate) as a function of time in seconds (abscissa) for the trajectories of 19 beads diffusing in a 68% glycerol solution. The slopes of the curves in the right graph were approximately equal to 1 (1.5 ± 0.1) FIG. 26 represents diagrams of the mean square displacement (ordinate) as a function of time in seconds (abscissa) the trajectories of 39 balls diffusing in a BHI solution. The slopes of the curves in the right graph are approximately equal to 1 (0.98 ± 0.11)

FIG. 27 represents a photograph (FIG. 27 a)) of a well of a trajectory of a ball diffusing in the liquid N350 with the presence of a bias due to a flow of the medium and a diagram of the mean square displacement ( ordinate) as a function of time in seconds (abscissa) (Figure 27b)) of seven ball trajectories present in the same medium. The polynomial trend of the second degree translating a flow in the medium.

FIG. 28 represents diagrams representing the displacement of a particle (ordered) as a function of the frequency of image taking per second (fps) during an observation (abscissa)

Figure 29 represents histograms representing the displacement of a particle (ordered) according to the frequency of image taking (abscissa), namely for 1000 fps (line 1), 500 fps (line 2) and 200 fps ( line 3), during the same observation

EXAMPLES

Example 1. Monitoring of microbeads in situ for the detection of biofilm formation

In the example below, the particles are independently designated beads, particles, microparticles.

The microbead tracking method presented here is applied to the study of biofilms of P. aeruginosa and S. aureus. For its strains, biofilm growth kinetics were performed. Series of microscopic images were extracted at successive times during the growth of the biofilm. The trials therefore focused on the first stages of formation of the biofilms studied, from the characterization of the BHI medium containing the planktonic bacteria and the microbeads, to the determination of the first signs of biofilm formation via the movements of the microbeads. In the example below, the dynamics of microbeads have been studied rather than calculating mechanical parameters based on the theory of pure Brownian motion. Indeed, it is advantageous to be able to discriminate the different movements of the balls on a different criterion than that of the comparison of MSD (mean square displacement or "Mean Square Displacement") which is valid in homogeneous and inert media. It is therefore a question of studying the formation of biofilms from a mechanical point of view, by determining a parameter revealing the genesis of a biofilm. By taking advantage of the observation of microbead movements introduced into the bacterial medium, changes in trajectory profiles have been determined and are linked to the activity of sessile bacteria (adhesion or formation of extracellular material). The experimental laboratory was in containment class P2 (“Concerns non-genetically modified microorganisms of class 2 which can cause diseases in humans but whose dissemination in the environment is unlikely, which are without risk for community and against whom effective prophylaxis or treatments are known ”). An Olympus CX53 reference optical microscope was used and the manipulation was carried out under a microbiological safety cabinet. The dynamics of microbeads within the bacterial fluid over time have been observed. The control consisted of a solution of microbeads in the BHI used for the tests. The free movement of the microbeads in the bacterial medium was visualized at room temperature (20 ° C) in transmitted light. From the recordings by series of images of the observations of the beads moving freely (without external solicitation) in the bacterial medium, the objective of the analyzes was to detect changes in dynamics in the trajectories of the beads over time. For this, series of images at a frequency of 200 images per second were taken at successive times of the biofilm growth of P. aeruginosa and S. aureus in BHI and in the presence of microbeads, preferably paramagnetic, with a diameter of 1 pm. The portion of the bottom of the well observed varies between 600 pm ² and 1000 pm ² . FIG. 2 represents an experimental diagram of the steps implemented. A few tens to a few hundred microbeads could be viewed and followed in the same series of images. Series of images from the bottom of the wells were recorded until all the beads were immobilized within the biofilm, or adhered bacteria. The blocking / immobilization time for most of the beads was dependent on the strain. A BRT test, in the same culture medium, was carried out in parallel for the strain studied, thus corresponding to a reference in terms of biofilm training. The results of the analyzes of the movements of the beads extracted by Cl N are set out below.

1. Materials and Methods

The materials and methods used were as follows:

a) The kinetics protocol for biofilm formation without antibiotics was as follows and was based on observation, until the beads were immobilized (a few hours), every 5 or 10 minutes, the bottom of a test well and a control well.

The material used was as follows: 1 box of bacteria transplanted twice; TON4 (paramagnetic microbeads); BHI (Brain Heart Infusion) medium; as many 96-well microplates as incubation times read.

The protocol included the following steps:

1. Prepare 2 mixes:

• Mixture for BRT test: a bacterial solution with OD (600nm) 0.004 and I OmI / ml of TON4;

• Mixture for control BRT: a mixture of BHI + TON 4 at 10 / jl / ml.

2. Fill 4 wells of each of the mixtures in each of the plates (200jivl / well) according to a plate plan established beforehand;

3. Put all the plates, except one, in the oven.

4. With the remaining plate, take a series of images for the test well and for the control well;

5. Add contrast liquid to the wells and perform a BRT (image + BFI analysis);

6. Repeat the protocol from step 4 for all incubation times by removing a plate from the oven at the desired time.

Analysis: The trajectories of all the beads present on the series of images for the test well and the control well were followed, for each incubation time. The evolution of the tendency of the free movements of the balls over time is sought. The results obtained with the BRT served as a reference. b) The biofilm formation kinetics protocol in the presence of antibiotics was as follows and was based on observation, until the beads were immobilized (a few hours), every 5 or 10 minutes, the background of a test well and a well witness. The material used was as follows: 1 box of bacteria transplanted twice; 1 µm bead solution (TON4); BHI medium; antibiotics in solution: Vancomycin at 4 pg / mL and Fusidic Acid at 2pg / mL (final concentrations in the wells); as many 96-well microplates as the incubation time observed. The protocol included the following steps:

Preparation of the antibiotic plates: in the plates, the antibiotics were supplied by a deposit of 20 pL. As this deposit was diluted by adding the bacterial suspension (180 pL / well), the solutions deposited had to be 10 times more concentrated than the desired value, ie 40 pg / mL for Vancomycin and 20pg / mL for Fusidic Acid . The deposition solutions were prepared from existing solutions prepared according to ISO 20776-1: 2006. The solutions were then deposited in the microplates according to an established plan / distribution of plate, at a rate of 20 μL / well. Water for injection (EPPI) is placed in the control wells.

Inoculation of the plates:

1. Prepare a “Control” mixture with BHI and TON4 at a rate of 10 muhhu,

2. Prepare a “strain” mixture with BHI and TON4 at a rate of 10 muhhu,

3. From the second transplanting of the strain, take a few colonies with the oese and resuspend them in a tube with 3ml of BHI;

4. Measure the OD at 600 nm (OD 600 nm) of the suspension and calculate the volume of inoculum to be added to the “strain” mixture in order to have an OD600nm = 1/225 = 0.004444 (the OD600 nm in a well will be 0.004). Inoculum calculation = 0.004444 VmixtureStrain / DOQOOnm

5. Inoculate the “strain” mixture with the strain.

6. After homogenizing the “Control” and “Strain” mixes, distribute them in the antibiotic microplates according to an established plate plan, at the rate of 180 mZ. / well;

7. The plates are immediately incubated at 37 ° C.

At each time, a plate is taken out of the oven and is observed under the microscope. A series of images of a well for each condition tested is recorded. The plate is then read at BRT after adding the contrast liquid and the magnetization of the plate for 1 minute. c) Bacterial strains and growth conditions P. aeruginosa CIP 104 116 and S. aureus CIP 76 25 were analyzed. The bacterial cells were resuspended in 100 mL of sterile BHI (Brain Heart Infusion medium) perfusion medium to an optical density at 600 nm OD600 = 0.004. This corresponded to the Initial Bacterial Suspension (IBS). The same IBS was used for the BioFilm Ring Test® and for microscopic tests. The Toner was added to IBS to obtain a final concentration of 10mI / ml. The mixture was homogenized by vortex and 96-well polystyrene microplates were inoculated with 200 mI / well of IBS. A plate was prepared for each reading incubation time. Cultures grow at 37 ° C without agitation. The lower part of the wells has a diameter of 6 mm and is uncoated. The plates were not touched during growth until they were used for the experiments. For S. aureus, the effect of two antibiotics was studied in order to detect the capacities of the antibiotics. The two antibiotics tested were vancomycin and fusidic acid at a concentration of 4pg / ml and 2pg / ml respectively. In these cases, the wells were inoculated after being loaded with 20 μl of antibiotics at the abovementioned concentrations. These two antibiotics are known to have different mechanisms of action. Vancomycin acts on the bacterial wall while fusidic acid modifies protein synthesis. d) Microscopy and image processing

The adhesion kinetics were analyzed every 5 minutes for P. aeruginosa and every 10 minutes for S. aureus until the beads were immobilized. For each reading incubation time, a plate was removed from the incubator and placed on an Olympus CKX53 inverted light microscope equipped with a 40x objective. This magnification was a good compromise between the number of particles observed, plotted and the resolution of the particles. The devices used, in particular for image capture, were a PCO Edge camera sold by the company PCO or a Basler Ace camera, the camera being connected to the video output of the microscope. The first is equipped with a 4.2 million pixel CCD sensor and the other with a CMOS sensor with 1.3 million pixels. The difference between the two sensors had no impact on the study. The image series were captured at a rate of 200 images per second with 0.5 ms of lighting time per image to respect the economy capacity of computer and frequency of measurements. Several thousand images per series were taken for each well, at a different location, near the center of the wells. The surface area of the bottom of the well taken by the image represented between 600 pm ² and 1000 pm ² depending on the size of the image. The movement of the particles was confined vertically by observing the lower part of the well. However, the particles were allowed to leave the field of vision. The first microplate was read immediately, without being placed in the incubator. The tracking of the movement of the particles was determined according to the method described below making it possible to determine the displacement of the centers of beads between two consecutive images. Tracking the particles over a series of several images made it possible to determine and obtain the trajectory of each of them. The resolution of the optical tracking and tracking was evaluated at ± 0.045 pixel. e) Verification of the formation of the biofilm with the BioFilm Ring Test (BRT) The verification of the formation of the biofilm was carried out after each microscopic capture using the so-called Biofilm Ring Test® (BRT). This is the process described in Chavant et al "A new device for rapid evaluation of biofilm formation potential by bacteria. J Microbiol Methods. 2007 Mar; 68 (3): 605-12 [29] based on the immobilization of magnetic beads inoculated in bacterial suspension thanks to adherent cells. The wells of the strip were covered with a contrast liquid. Then, the plate was placed for one minute on a test block consisting of permanent magnets placed under each center of the wells. After magnetization, the plate was scanned to obtain an image of the bottom of the plate. The adhesion capacity of each strain was expressed in the form of the biofilm index (BFI) calculated by the BioFilm Element software. f) Enumeration of adhered bacteria

A count of the adherent bacteria was carried out in order to link the formation of the biofilm to the microbiological activity of the strain of S. aureus. The effect of the antibiotics was evaluated by the number of colonies which adhered during the growth of the biofilm. g) Analysis of trajectories - Global analysis: a first analysis on the trajectories of balls was the study of the evolution of the standard deviation of the distribution of the displacements of all the balls followed for each incubation time. The trajectory pi observed consisted of Nt, i successive displacements traversed by the particles or balls during a constant time interval corresponding to the time interval between two images as pi = (xi, yi), i = 1,. .., Nt, i. Nt, i is the length of the trajectory i. The values of the displacements traversed by each ball were concatenated in a vector P like P = {p1, p2, pNb}. Nb is the number of beads for a given incubation time. P has been reshaped so that it is a vector of one-dimensional size

For each incubation period, an evaluation of the standard deviation of the displacement distribution (vector P) was carried out and calculated according to the following formula: with m = length (P) and P = average (P). For each incubation time, an aggregated value representing the overall state of biofilm growth was available. h) Individual contributions of each particle / ball

As each trajectory provides information on its environment, the ball movements have also been individually assessed. The displacement vector provided by tracking the movements of all of the beads / particles constitutes a set of data for the observation of hundreds of beads / particles throughout the duration of the observation of the growth of the biofilm. To describe these trajectories with a small number of values, the tracking vectors have been replaced by characteristic criteria describing the distribution of displacements along the movement. Each criterion brings together the information given by each movement of the balls / particles measured according to the aforementioned comparison method, in particular by comparison of image observations digital. The movement could be analyzed more quantitatively using these different criteria. For each track, discriminating parameters were calculated.

These parameters are based on the statistical distribution of displacements and displacements and the geometry of all the trajectory. From the displacement as a function of time [X (t), Y (t)]. of each particle / ball, each track / trajectory has been associated with a n-tuples of seven parameters:

- length c1 of the diagonal of the rectangle comprising said trajectory according to the following formula: ci = (max (eum tm (X)) - min (cumsum (X))) ² + (max (cumsum (Y)) - min {cumsum (Y))) ^

The average speed c2 along / on the trajectory

standard deviation of the velocity distribution of the particle / ball at each point of the trajectory

the standard deviation of the distribution of paths along the transverse and vertical directions / axes (one parameter for each component)

C4 = st <IiX) c¾ = std (Y)

the asymmetry of the distribution of displacement along the transverse and vertical axes (one parameter for each component).

f, = skm (X)

Cy = skw (Y) These 7 parameters made it possible to evaluate the way in which the movement and / or the trajectory of the beads inside a bacterial suspension. They have made it possible to demonstrate that it is not a question of a normal movement and / or trajectory. Then, they allowed and can be used to classify trajectories in clusters, also called cluster, in order to clearly differentiate active movements, random trajectories and blocked particles. In order to keep the most discriminating information among these criteria, a principal component analysis (PCA) was performed on all of the data. PCA is a well-known method of analysis. It advantageously makes it possible to conserve the maximum of information for describing the trajectories while conserving only a limited number of components, namely those which most influence the observed phenomenon. From seven parameters, three main components were constructed. The choice of three main components was made in order to keep the components that express the maximum variance in the information. In other words, from a base of vectors defined by the physical criteria c _£ , the problem is translated into a base of orthogonal vectors PC _£ . The latter are constructed from linear combinations of the criteria c _£ . The coefficients of the linear decomposition give information on the contribution of the criteria c _£ in the description of the trajectories. However, the dimensions of the vectors PC _£ represent abstract information, which is not relevant for the analysis. In addition, the units of the components of the vectors PC _£ may vary from one analysis to another.

Each trajectory was then represented in the coordinate system of the three main components (PC1, PC2, PC3). Of the three main components above, a spatial grouping of applications based on the density of points with the DBSCAN algorithm ("Density-Based Spatial Clustering of Applications with Noise") (Ester et al., 1996) was used to detect two unsupervised clusters in the graph from the first 3 main components. These main elements were subsequently linked to the shape of the trajectories and therefore to the behavior of the balls. i) Reproducibility test Each experiment for the S. aureus strain was carried out twice. In what follows, the two replicates of the tests are called replicate 1 and replicate 2. For the strain of P. aeruginosa, the growth kinetics were carried out only once. The sensitivity of biofilm growth is very dependent on environmental conditions.

2. Results

S. aureus and P. aeruginosa were cultured in BHI medium in microplates at 37 ° C. in the presence of microbeads / particles. The movement of microbeads / particles moving directly above the bottom of the well was observed. These microbeads / inert particles have a spherical geometry, of homogeneous size and constitute indeformable probes of the behavior of the medium surrounding the bacteria.

2.1. Kinetics of biofilm growth

The two selected strains have different capacities and specificities for biofilm formation as shown in Figure 3. The kinetics of biofilm growth deduced with BRT for S. aureus (round dots curve) shows that the immobilization of the beads occurs at 120 minutes. In addition, the blocking begins at 40 minutes. For the P. aeruginosa strain (curve with vertical lines), blocking of the beads begins at the start of incubation. The total disappearance of the spot, which means a total blockage of the beads, occurred in about 80 minutes. Qualitatively, P. aeruginosa produces a very mucous matrix in a short time compared to S. aureus which forms a biofilm over time. The rBFI for P. aeruginosa increased from the start unlike S. aureus.

2.2. Qualitative analysis of ball movement

The movement of the particles was monitored by taking images of the particles / beads incorporated in the bacterial medium with a high magnification, just above the bottom of the wells, individually, at short time scales, for each time. incubation. Different movements of the beads could be observed in the bacterial media from the different strains. The first bacterial suspension was made with a strain of P. aeruginosa while the second was made with a strain of S. aureus. Experimental observations have shown that the movement of the beads in a suspension bacterial at 37 ° C was a combination of active and thermal movements. In addition, the movement of the beads differed according to the type of bacteria in the growth medium. The trajectory of the beads which results from the medium comprising P. aeruginosa differed from the purely Brownian movement. Visual observations on the trajectory of the beads inside the bacterial suspension during the growth of the biofilm showed that the beads tended to move in a preferred direction. While the beads present in the control solution (composed of beads in the same nutrient medium) underwent a characteristic Brownian movement, the beads in a solution / bacterial medium showed an increase in diffusion in their movements, especially at the start of incubation . For long periods of time, the "increased" diffusion phenomena could be erased. For S. aureus, the beads / particles seemed less mobile. They experienced random movement, possibly disturbed by interactions with other particles. As the biofilm developed, the beads slowed down to a complete blockage. Four ball movement scenarios were observed during the growth of the biofilm:

- purely passive movement: balls in a quasi-homogeneous medium;

- dragged or ejected: balls with homogeneous or partially propelled activity;

- idle: balls in a viscous material;

- delimited passive: balls in a more viscous material, where the thermal energy kBT is not sufficient.

These types of movement were observed in different ways in the two deformation suspensions. Table 1 and Figure 4 illustrate the speeds of the balls with different types of movement.

Table 1: Types of movements observed for two strains

Dragged and ejected movements appear to be induced by bacteria in no time. Microorganisms like P. aeruginosa swim and sweep the surface. While inert particles undergo Brownian motion when they are drowned in a liquid medium, the active bacteria were able to propel themselves with a directed movement, and therefore to go out of equilibrium, causing the beads / particles. The movement of the beads / particles embedded in a solution of BHI medium and of S. aureus bacteria corresponded to a random diffusion at uniform speed. There was no erratic superdiffusive movement. As the biofilm developed, the range of motion decreased while retaining random characteristics. Without the action of bacteria, the passive beads had a Brownian motion. The Brownian motion is characterized by the mean displacement squared (MSD) (Ar ² (At)) proportional to the time interval At between the positions. Ar is the radial displacement during a time interval At. The theory predicts that, for a ball with a purely two-dimensional Brownian motion, (Ar ² (At)) = 4DAt, where D is the diffusion coefficient. For a superdiffusive movement, (Ar ² A (t)) = 4Dt ^a with a> 1. In this experiment, the mean values of the displacement squared of the microbeads incorporated in the bacterial solutions were calculated during the growth of the biofilm. For each trajectory, the mean displacement squared was calculated and the curves of MSD as a function of the time interval were plotted. Figures 5 and 6 show the curves of the mean of the squared displacement as a function of the time interval for the two strains during the formation of the biofilm. For P. aeruginosa (Figure 5), some beads show superdiffusive movements, while for S. aureus, these behaviors have hardly been observed. As shown in the figures above, it appears that the movements of the beads can provide indications on the microbiological activity of bacteria. While the movement of the ball taken in a biofilm of S. aureus is relatively simple, the balls in the biofilm of P. aeruginosa exhibit different characteristics / behavior. This agrees with the visual observations of Figures 5 and 6 which show that the particle tracks consisted of straight segments of different lengths (ballistic type movement) more or less important depending on the interactions with bacteria and surrounding environments. The random movement due to thermal agitation is biased by the active motility of the bacteria. Observing ball trajectories is a step in the study of ball movement. A quantitative analysis by defining quantitative criteria to describe the movement of the balls was carried out. 2.3 S. aureus: quantitative analysis of biofilm formation

Analysis of the distribution of the overall displacements of all the beads monitored for each reading incubation time was carried out as described above. FIG. 7 represents the standard deviation of the distribution of the movements / trajectories of the beads as a function of the incubation time for two replicates of suspension of S. aureus and for the two replicas of control wells (composed of the same microbeads in BHI medium ). As shown in this figure, the standard deviation increases at the start of biofilm growth. This increasing period was also observed in the control blank. Then, at 20 minutes, the standard deviation decreases for the beads in the bacterial medium while it stabilizes for the control blank. After about 140 minutes, the standard deviation of travel drops. The curves were approximated by piecewise linear functions. Bilinear or trilinear functions were used to approximate the curves depending on the condition tested. A first approximation of the curves with a bilinear function was carried out. If the coefficient R ² is too low, a tri-linear function has been chosen to adapt to the curves. The equations of the two types of adjustment curves are:

f a.t + b for t <ri

Bi-linear regression: <

1 a> i (t ri) + a.ti + b for T <t <

The regression identified the analytical parameters and described the evolution of the standard deviation with few indicators. The models and their parameters are described schematically in Figure 8. The slopes of the second and third parts of the multilinear approximations cd and a2 as well as the break times of the slopes t1 and x2 have been reported. a1 and a2 represent a change in the behavior of the balls; t1 and x2 are the times of these changes. Their numerical values for the two repetitions are shown in Table 2 below. Table 2: Parameters deduced from the standard deviation curves in Figure 8

By modeling the change in standard deviation as a function of the incubation time by various regular curves, two sets of conditions were distinguished.

An analysis of the individual contributions of the balls was carried out in order to take into account the differences in the behavior of the balls. For each trajectory, seven criteria based on the distribution of displacements were measured as described above. A principal component analysis (PCA) was implemented in order to reduce the size of the data set. The first main component (PC1) represented around 60% of the total information, while PC2 and PC3 each represented 14% of the information (Figure 9). All the trajectories followed for all incubation times have been represented in the new coordinate system consisting of (PC1, PC2, PC3). All the trajectories could be divided into two groups, as shown in Figure 9. All the beads behaved in the same way during the growth of the biofilm. The proportion of beads in each cluster as a function of the incubation time was calculated and plotted on a graph (Figure 9 (c) below). The variation in the proportion of logs in each cluster was very gentle and could be divided into three stages. The times of these changes are shown in Table 3 below. For the control well, only one group was distinguished, as illustrated in Figure 10. Analysis of the second replica (Figure 11) demonstrated similar results.

Table 3: Parameters deduced from the proportion curves

2.4. P. aeruginosa: quantitative analysis of biofilm formation An analysis of the distribution of mass displacements of all the beads followed for each incubation time of P. aeruginosa in suspension was carried out. FIG. 12 represents the standard deviation of the distribution of the movements of the balls as a function of the incubation time. As for the suspension of S. aureus, the variation of the standard deviation was modeled by a linear function. The bi-linear model made it possible to detect a change in trend at 34 minutes, where the standard deviation stops increasing. After 34 minutes, the values of the standard deviation seem to decrease overall, with great variability in these values for the last incubation times. This variability may be due to the heterogeneity of the biofilm formation of P. aeruginosa. An analysis of the movement of the individual balls was also carried out. The seven parameters, previously defined, were calculated for each particle. After a PCA on all the data of the seven parameters of each trajectory, all the trajectories reported during the biofilm formation were in a new final coordinate system built according to three main components (PC1, PC2, PC3). As before, clusters were made to gather the balls corresponding to the same movement characteristics. The number of trajectories in each cluster as a function of the incubation time was counted. Like what was done for S. aureus, Figure 13 (b) below shows the distribution of point groups (cluster). The figure below 13 (c) shows the evolution of the number of beads belonging to a group during the growth of the biofilm for P. aeruginosa. As the biofilm develops, the general trend shows an increase in the number of blocked beads. However, the decrease is not regular. The variability of the proportion of blocked beads over the growth time shows that the number of blocked beads can vary significantly. The results on P. aeruginosa may explain that the formation of the biofilm is more heterogeneous. We note that the analysis of the individual behavior of the beads shows that at the start a particular activity in the bacterial suspension is not observed and appears after the first minutes of growth.

2.5. S. aureus: effect of antibiotics on biofilm formation

The characterization of the effect of antibiotics is an important question in the pharmaceutical field. We tested here two antibiotics for detect their ability to delay or stop the formation of biofilm. The same processing of the experimental data is carried out. The figure below shows the evolution of the standard deviation as a function of the incubation time for a suspension of S. aureus, including antibiotics. As the biofilm developed in the presence of Vancomycin or Fusidic Acid, the standard deviation increased during the first periods of biofilm growth and the standard deviation increased, then decreased more slowly than without antibiotic. The bi-linear functions adapt to the curves of the standard deviation equation for the bacterial suspension with antibiotics. Parameters a1 and t1 of the bi-linear approximation of the bacterial suspension with antibiotics were reported and compared with the control (white) and the suspension of S. aureus without antibiotic in Table 4 below. The change in the standard deviation of the displacement distribution makes it possible to discriminate the presence of antibiotics in the bacterial suspension / medium. In the presence of antibiotics, the standard deviation curves resemble those of the blank control.

Table 4: Parameters deduced from the standard deviation curves

The analysis of the movement of the individual balls after analysis in principal components of the 7 parameters as mentioned above (PC1, PC2, PC3) has shown that the DBSCAN algorithm makes it possible to detect a main cluster and another smaller one (Figures 15 ). The scatter plot of the points (Figures 15 (b)) is more displaced in the direction of the first main component than the resulting scatter plot for the control (BHI + beads only). As demonstrated by the two antibiotics, the immobilization of the beads is avoided, and this is clearly illustrated and demonstrated in the figures obtained, in particular after analysis of the main components. A BRT was carried out in order to compare the results of the process with the evolution of IΊAB as a function of the incubation time. For the suspension of S. aureus in the presence of antibiotics, the rBFI curves in as a function of the incubation time (Figure 17) show that the antibiotics limit the adhesion of the beads. The enumeration of adherent bacteria in the wells of S. aureus with or without antibiotics (Figure 18) demonstrated the action of the two antibiotics on the activity of bacterial adhesion.

3. Discussion

Qualitative observations of the movement of the beads during the growth of the biofilm of P. aeruginosa and S. aureus reveal different types of displacement of the beads. For both strains, the formation of a biofilm induces the formation of trapped beads. For the strain of S. aureus, the amplitude of movement decreases with the growth of the biofilm while retaining random characteristics while the beads incorporated in the suspension of P. aeruginosa often undergo erratic ejection. The method of the invention advantageously makes it possible to detect, on a microscopic scale, biological events which may be linked to bacterial adhesion or to the growth of the biofilm. To this end, the movement of microscopic beads incorporated into bacterial suspensions of S. aureus or P. aeruginosa was followed thanks, for example, to an optical device coupled to a digital image correlation algorithm. Due to the large number of data generated by the measurement of the trajectories, these have been advantageously characterized by specific characteristics / parameters. On the one hand, all the displacements during the elementary time intervals of all the trajectories followed have been mixed for each reading incubation time. On the other hand, the balls were taken into account individually and the parameters / criteria determined make it possible to describe the behavior of the ball. Thanks to this monitoring and analysis methodology, quantitative characteristic times during biofilm growth have been determined. For the analysis of the global displacements as well as the analysis of the individual displacements of the beads, we detect a biological event at around 140 minutes of incubation for S. aureus. In addition, the classification of the balls into two groups in a well-chosen framework allows fine detection of the start of blocking of the balls. For S. aureus, the start of the blockage appears between approximately 71 minutes and 97 minutes depending on the response in question. A second event appears between approximately 143 and 160 minutes. These trends are consistent with the BRT results. No adhesion occurs at first (rBFI = 0), then the blocking of the balls appears gradually (increase in rBFI) until a significant part of the balls is blocked (maximum rBFI). The study of the action of two antibiotics on the biofilm of S. aureus has shown that the analysis of microscopic movements of inert beads makes it possible to discriminate the effectiveness of antibiotics compared to a bacterial suspension without antibiotics. Indeed, in the presence of antibiotics (indifferently for one or the other antibiotic), the variation of the standard deviation of the movements as a function of the incubation time follows the same trend as the control. As demonstrated above, the results obtained comply with the BioFilm Ring Test® test and the count, on an agar plate, of adhered bacteria. In addition, it clearly appears that the process of the invention advantageously makes it possible, unlike the known processes, to obtain a result continuously over time thanks to its non-destructive nature. In particular, the method of the invention allows obtaining a result without any action and / or addition of compounds, for example contrast agents, during bacterial culture. In addition, the method of the invention can be implemented without direct contact with the culture and thus is advantageously a non-invasive and non-destructive test while allowing the obtaining of numerous parameters which may be useful in order to characterize the biofilm in real time. In addition, the method of the invention advantageously makes it possible at the end of the culture incubation time to obtain the results, to dispense with the use of products / consumables, which advantageously makes it possible to reduce the time of analysis, as well as costs in terms of consumables and labor. Advantageously, the method of the invention makes it possible, by monitoring the movement of the particles inserted within a biofilm, to bring a temporal dimension to the measurement.

Advantageously, the method of the invention allows non-destructive detection of the formation of biofilm. In addition, the method of the invention advantageously makes it possible to detect biological events occurring during the formation of biofilm and / or discriminating phenomena by means of the characteristics of the microscopic trajectories of inert beads. The process of the invention advantageously allows an almost continuous temporal monitoring of the kinetics of biofilm formation.

Advantageously, the method of the invention allows control of the kinetics of biofilm growth, non-invasive and non-destructive. The process of the invention advantageously allows multiple observation of the bacterial culture during its incubation, for example it advantageously allows to multiply the observations at incubation times as close as desired as opposed to known methods. Also, the method according to the invention advantageously makes it possible to obtain reliable and comparable results.

Advantageously, the method of the invention by the use of inert microbeads within a bacterial medium and the observation of the passive movements of said inert microbeads within a bacterial medium makes it possible to discriminate characteristic phenomena and times during the growth kinetics of biofilms of bacteria, for example of P. aeruginosa and S. aureus.

Tables 5 to 13 below summarize the parameters of the experiments and the corresponding image series.

Example 2: example of a particle tracking method

The CIN requires the use of digital images which can translate information on the placement of material points on the surface of the material studied. This information can be materialized by a grayscale marking. For many engineering materials and hard bone-like living materials, the application of a speckle to the surface of the test samples is required. Living materials sometimes have a natural grain due to their texture, which makes the use of digital image correlation suitable. This is the case, for example, for biofilm images taken via a microscope (Mathias and Stoodley, 2009) or for the soft tissue of the leg (Bouten, 2009).

However, the use of the proper texture of living material to obtain quantitative information on its mechanical behavior can be biased. Due to its living nature, this type of material is capable of reacting actively to the mechanical stress induced by the stress during the test. In the present passive microbeads are arranged randomly within the material. The CIN can advantageously be used for the purpose of monitoring particles incorporated in a heterogeneous and dynamic medium. The medium studied was placed in a small volume container which allows observation under an inverted optical microscope without solicitation. The free movements of the beads located directly at the bottom of the container are observed. Series of images were recorded via the video output of the inverted microscope

The Brownian movement consists of a perpetual movement (no damping due to friction phenomena), without privileged orientation of micrometric particles in a fluid. It is the incessant collisions of the particle with the molecules of the fluid which cause the displacement of the colloid particle. In this case, the driving force is thermal, with an energy of the order of kBT, kB being the Boltzmann constant and 71a temperature, expressed in Kelvin (Wirtz, 2009). Brownian motion is related to the probability of impact with the molecules of the solvent. On average, the particle remains stationary since it goes as often in one direction as in the other.

For a pure Brownian motion, the arithmetic mean of the displacements is zero (Qian et al., 1991). What characterizes a Brownian motion is the quadratic mean of displacements, in English “Mean Square Displacement” (MSD). It has been demonstrated in the literature that this quantity is dependent on the characteristics of the medium containing the microbeads. For a pure Brownian scattering, the mean square displacement of the particles of radius varied linearly as a function of the measurement time interval At of the displacement. The linearity coefficient depends on mechanical parameters of the fluid in which the particles diffuse. The relation between the MSD and the time interval At for a two-dimensional trajectory r (t) = (x (t), y (f)) is given by:

D is Einstein's diffusion coefficient. It is related to the dynamic viscosity h of the solvent.

¼ T

ïDa

These equations are for an unbiased bias Brownian motion. Furthermore, the shape of the MSD curve of the particles as a function of the travel time interval can be characteristic of the medium in which the microbeads are immersed. For a movement qualified as under-diffusive, the MSD is proportional

For a so-called super-diffusive movement, the MSD is proportional to

The higher the time interval, the larger the error bar can be. In fact, the larger the time interval between two positions, the fewer terms we have to calculate the average.

Figure 19 represents three diagrams of variation of the MSD as a function of time and movement: Brownian, super-diffusive or under-diffusive

Example 3: example of a method for monitoring particles in an example of material, locating region of interest, reducing the region of interest and validating the metrology of particle tracking

. Particle tracking

The beads / particles were observed under an optical microscope using a x40 objective (x400 magnification). These were beads / particles identical to those used in Example 1. The x400 magnification made it possible to have a good compromise between the resolution of the information on the balls (the balls were then coded on 10 pixels (px) in diameter) and the number of visible balls in an image. Series of a few thousand images have been recorded. The beads were tracked individually using a Digital Image Comparison (CIN) algorithm. The formulation of the CIN in its local version was used: we are interested in a portion of the image reduced to a square of 51x51 px ² around a ball and the area is calculated in its entirety. The kinematics of the displacement remains simple and has been defined on this portion of the image. It is composed of a vertical displacement component and a horizontal displacement component. For each ball, the image correlation algorithm measures the displacement in pixels between a reference image and a subsequent image in the series, this sequentially. This calculation was carried out along the series of images in order to reconstruct the trajectory of the ball. This tracing by CIN allows a fast follow-up, suitable and whose error is known. If the algorithm shows difficulties in converging, different explanations are possible:

• the ball is out of the frame of the image;

• the ball is out of the depth of field, the distorted image is then too different from the initial image;

• the ball has moved too far between the initial image and

the distorted image,

• the algorithm would then need a non-zero initialization.

For the first case, the coordinates of the ball make it possible to consider that the trajectory leaves the image. For the other two cases, the calculation is initialized with a previous image in the series then the calculation resumes between this new initial image and the images that follow it.

2. Location of a region of interest

As mentioned above, the tracking of the beads / particles was also carried out individually for each particle. The objective of this part was to detect the presence of balls in the image in order to restrict the region of interest around a ball. In each series of images, a number of beads was observed ranging from a few tens to a few hundred. It was agreed to be able to detect the beads systematically. For this, an automatic process based on image processing techniques has been implemented. For each sequence images, the first image is open. The center of a ball among the image has been manually selected by the user. A 20x20 px ² Imagetteb thumbnail of this ball and the surrounding area has been created. In order to remove the intrinsic specificity due to the choice of a particular ball, a new image lmagette _v was created from the selected image. This new virtual image was composed of a linear combination of simple transformations of the basic thumbnail. The new virtual image is thus created according to the following combination:

where Imagette _b is the gross imagette of the area around a ball, lmagette ^T _b is the transpose of the gross imagette, lmagette ^Rh _b corresponds to the horizontal symmetry of the gross imagette and lmagette ^Rv _b is the vertical symmetry of the rough thumbnail. These transformations have corrected the symmetry of the motif coding the ball. FIG. 20 represents two images of a particle, the image on the left corresponds to the raw image and the image on the right to the image after transformation. Based on the OpenCV library, the algorithm of localization of the areas of interest proceeds to the research of the maximum of correlation between the whole image and the imagette lmagettev which represents an independent ball. Correlation maximums can be present on several pixels around a ball. The center of gravity of each of the clusters formed by the set of correlation maximums around a ball was chosen to designate the center of the ball, by means of a translation of half the width and the height of the thumbnail . This process makes it possible to detect the centers of the beads present in the image. Figure 21 corresponds to an image on which particles (black circles) are detected. 3. Reduction of the area of interest

The bottom of the well images represent a 2D view of the three-dimensional beads. Given the sphericity of the balls, they are not coded on homogeneous gray levels. In fact, the center of the balls has been coded on lighter pixels so that the image of a ball is seen as a dark crown. In order to get rid of the clearest pixels located in the center, the area of interest taken into account in the image correlation algorithm has been reduced to the crown formed by the dark pixels around the bead. The ball was coded on 10 pixels (px) in diameter. On the 51x51 size region of interest px ² , the clear pixels within a 3 px radius around the center of the ball have been hidden. Likewise, the pixels coding the bottom of the well beyond 13 px around the center of the ball were masked. FIG. 22 is a representation of an area of interest (particle) used to track the movements of the particles, in particular by image correlation. The clear pixels in the center of the ball have been hidden because they do not provide relevant information for the CIN calculation. Likewise, the bottom pixels of the well beyond a small periphery around the ball did not take into account in the calculation. 4. Validation of the metrology of particle tracking

In the case where the sensor is perfect (and the light source is constant), the images of a stationary scene are identical. Actually, the images taken by the optical measurement system can be altered by a sensor noise inherent in the electronic operation of image taking. The noise generated by the sensor can propagate in the measurement of the displacement calculated by CIN and directly impact the measurement resolution. To calculate the sensor noise, 1000 images of a sample of dried beads on a petri dish were acquired. The focus on the aggregates of dried beads has been shifted slightly in depth to deliberately create a blur that smooths the grayscale gradient on the images. For each pixel p, of position (xi, yi), the average of the gray levels Imi as well as the variance Ivi over the 1000 images was calculated. For a series of images of size 300x200 px ^2, 60,000 couples (Imi, Ivi) were calculated. They have been plotted on a variance graph as a function of the average of the gray level of the pixel in question. The dispersion of the measurement points in the coordinate system (Im, Iv) showed that, as expected, the brightest pixels showed the most variability. This graph profile (Im, Iv) was expected for the cameras used for field measurement. The point cloud forms a beam oriented along an affine line. On the other hand, the bulge in the center of the beam was due to movements of rigid solid (Grediac and Sur). To be conservative, the standard deviation of the gray levels on the image was arbitrarily chosen as being the maximum of variance observed in this example. So we have : The images were coded in 16 bits. The measurement resolution in displacement was then determined by the following equation (Réthoré et al., 2008; Blaysat et al., 2016):

In practice, the values of the correlation matrix A / fyfont so that on average, the measurement resolution of the calculation by CIN for the two components of the displacement was:

for the horizontal component of the displacement

¾ = 0.0464 by

for the vertical component of the displacement

FIG. 23 corresponds to an image illustrating the background noise of the sensor. As shown, the focus has been intentionally shifted to smooth the grayscale gradients of the image and remove / reduce background noise. FIG. 24 is a diagram representing a point cloud representing the variance as a function of the average of the gray level distribution of a pixel over 1000 “identical” images.

As demonstrated, in this example the CIN has been used for the appropriate monitoring of balls / particles for which variations and / or errors and / or inaccuracies, for example related to background noise, in terms of displacement measurement has been mastered. In addition, the consideration of material heterogeneity was ensured by monitoring several microbeads dispersed within the medium. . Validation in different types of environments

Validation tests were carried out with standard liquids which were intended to simulate the biofilm material. Several preliminary tests of calculations of free movement of balls moving in a homogeneous medium of known viscosity were carried out. In this example, the movement of 1 µm microbeads in standard liquids of known viscosity was studied. A first solution of glycerol at 68% (mass percentage) was studied. For this solution, the viscosity values are tabulated (Takamura et al., 2012). Between 20 ° C and 25 ° C, the viscosity of such a solution was approximately 16.4 mPa.s ± 4.8 mPa.s. The experimental viscosity value for this medium was calculated by calculating at beforehand the diffusion coefficient D, obtained thanks to the curve MSD = f (At). These curves were plotted for 19 beads diffusing in the glycerol solution. In the log-log scale, the slopes of the lines are approximately equal to 1 (1.05 ± 0.1). The MSD values were therefore proportional to At and the beads diffused well according to a random trend, without flow or confinement. From these curves, the viscosity values calculated for these 19 balls were on average 16.6 mPa.s ± 5 mPa.s. The same process was carried out to find the viscosity of the BHI (Brain Heart Infusion) medium used for the initial bacterial suspensions. Although the theoretical value of a BHI solution was not known, the order of magnitude of this value was 10 ³ Pa.s. The BHI being made from the dissolution of a powder in a large volume of water (37g / L), the resulting liquid has a texture close to that of water. Similarly to the approach implemented for the glycerol solution, the MSD curves as a function of the At were plotted for 39 beads diffusing in a BHI solution. The movements of the beads in this solution are of the Brownian movement type.

The MSD curves as a function of the time interval confirmed the qualitative observations of the trajectories (Figures 24 and 25). Indeed, the MSD is dependent on At. The experimental viscosity measured by this test was 3.2 mPa.s ± 1, 3 mPa.s. These first tests made it possible to confirm the measurement of a mechanical parameter under experimental conditions, for inert materials of low viscosity.

Measurements were made in higher viscosity media, two other standard liquids one hundred times more viscous were used. The viscosities of these two samples range from about 500 to 700 mPa.s. The liquids used have for reference D500 and N350 (Sigma-Aldrich) of respective viscosity 498.4 mPa.s and 712.9 mPa.s (values at 25 ° C). Ball trajectories were extracted from series of images. Drying of the beads so as not to modify the viscosity of the liquid with the solution of beads was carried out in order to avoid additional physico-chemical interactions between the beads and the liquid. Observations of beads diffusing into the N350 liquid have shown trajectories of beads which seem to be carried along by an involuntary fluid flow (Figure 26). The MSD curves, as a function of the time interval considered, associated with the trajectories of six beads in this medium were typical of viscous media with presence of flow (figure 27). On the other hand, for these viscosities, the average displacements of a ball of 1 mhi were smaller than for the liquids studied previously. The trajectories of balls in the D500 liquid were movements of very small amplitudes. The frequency of image acquisition was therefore increased in order to capture changes in the direction of the balls, in a more detailed manner (Figure 29). Figure 28 shows a component of the displacement of a ball trapped in the D500 liquid for different acquisition frequencies. The horizontal component of the ball displacement was increased when the image acquisition frequency was higher

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Claims

Claims:

[Claim 1] Method for detecting and / or monitoring and / or characterizing the formation of a biofilm comprising the following steps

a) carrying out a temporal succession of observations of a solution comprising at least one microorganism and a plurality of particles while the solution is maintained under conditions allowing the development of a biofilm by said at least one micro- organism, b) detection of the presence of the biofilm and / or characterization of the

kinetics of biofilm formation on the basis of a comparative statistical analysis of the displacements of the particles observed during the different observations.

[Claim 2] Method according to claim 1, in which each observation comprises, for each particle of a set of particles observed during said observation, a determination of a trajectory corresponding to successive displacements carried out by said particle during said observation .

[Claim 3] Method according to claim 1 or 2 comprising a global statistical analysis of the displacements made by the particles observed during each observation and a calculation of times characteristic of the formation of the biofilm on the basis of the results of the global statistical analysis .

[Claim 4] Method according to claim 3, in which the global statistical analysis comprises a calculation for each observation of a value of at least one statistical parameter of a distribution of the displacements carried out respectively by the particles of the plurality of particles and an analysis of the variations as a function of time of the values of said at least one statistical parameter obtained for the succession of observations.

[Claim 5] Method according to claim 4, in which the statistical parameter is the standard deviation of the distribution of the displacements made by the particles observed during said observation

[Claim 6] The method of claim 1 or 2 further comprising a statistical analysis of the individual contributions of the particles observed from the displacements made by each particle observed during each observation and an identification of at least a percentage of particles performing the same type of movement on the basis of the results of the statistical analysis of the individual contributions.

[Claim 7] Method according to claim 6, in which the statistical analysis of the individual contributions of the particles comprises for each observation:

a calculation, for each trajectory followed by a particle observed during said observation, of a vector composed of parameter values characteristic of the displacements defining the trajectory concerned; a constitution of a matrix from the vectors calculated for the particles observed during said observation;

a decomposition of said matrix by an analysis into principal components;

an identification of at least one predominant main component among the main components resulting from the decomposition;

a generation of a diagram in at least one dimension of the projections of the different vectors corresponding to the trajectories of the particles observed on said at least one predominant main component identified.

[Claim 8] Method according to claim 7 wherein said at least one characteristic parameter is chosen from the group comprising

length c1 of the diagonal of the rectangle comprising said trajectory according to the following formula:

cl

= (m ax (cumsum (A-)) - m \ n (cumsum (X))) ² + (ma

where max (71) returns the largest component of vector A, min (71) returns the smallest component of vector 71 and cumsum (B) returns the cumulative sum of vector B.

- the average speed c2 on the trajectory:

where mean (71) returns the mean of the components of vector 21 - the standard deviation c3 of the velocity distribution of each particle at each point of the trajectory

where std (74) returns the standard deviation of the components of vector 4

- the standard deviation of the distribution of the trajectories along the transverse axis c4 or vertical c5:

c4 = std (X)

c5 = std (Y)

where std (74) returns the standard deviation of the components of the vector!

- the asymmetric distribution of the trajectories along the transverse axis c6 or vertical c7:

c 6 = skw (X)

cl = skw (Y)

where skw (74) returns the asymmetry coefficient of the components of vector 4

[Claim 9] The method of claim 7 or 8, further comprising identifying at least one cluster of points in said diagram and calculating said percentage of particles from a percentage of points belonging to a given cluster.

[Claim 10] Method according to claim 9, comprising an analysis of a change over time of said percentage of particles for the different observations.

[Claim 1 1] Method according to claim 5, comprising the calculation of the standard deviation of the distribution of the displacements of the particles according to the following formula:

in which P corresponds to the particle displacement vector over all the observations, m = length (P), and P = mean (P).]