WO2024050035A1 - Diagnostic d'espèces bactériennes de la parodontite canine par réaction en chaîne par polymérase quantitative - Google Patents

Diagnostic d'espèces bactériennes de la parodontite canine par réaction en chaîne par polymérase quantitative Download PDF

Info

Publication number
WO2024050035A1
WO2024050035A1 PCT/US2023/031742 US2023031742W WO2024050035A1 WO 2024050035 A1 WO2024050035 A1 WO 2024050035A1 US 2023031742 W US2023031742 W US 2023031742W WO 2024050035 A1 WO2024050035 A1 WO 2024050035A1
Authority
WO
WIPO (PCT)
Prior art keywords
cot
animal
sample
oral health
microbial taxa
Prior art date
Application number
PCT/US2023/031742
Other languages
English (en)
Inventor
Lucy Jane HOLCOMBE
Corryn Victoria WALLIS
Avika Kishorlal RUPARELL
Steve Harris
Alison OAKDEN
Matthew Ronald GIBBS
Original Assignee
Mars, Incorporated
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mars, Incorporated filed Critical Mars, Incorporated
Publication of WO2024050035A1 publication Critical patent/WO2024050035A1/fr

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • the presently disclosed subject matter relates to the compositional analysis of the canine oral microbiome.
  • the presently disclosed subject matter further relates to the compositional analysis of the canine oral microbiome as a diagnostic for canine periodontal disease.
  • Gum disease such as periodontal disease
  • veterinary treatment with prevalence of such disease estimated between 9% and 20% [1, 2]
  • These data imply that the disease is underdiagnosed in veterinary practice, where the majority of examinations are performed on conscious dogs.
  • Bacterial species in dental plaque that are associated with periodontal health and disease are known in the art [7-12], For example, International Publication No. WO 2008/137506, incorporated herein by reference in its entirety, describes presence or absence of at least one microorganism, from a sample from the mouth of a dog, wherein the microorganism which is associated with periodontal disease in a dog is one or more selected from: Peptostreptococcus sp., Synergistes sp., Clostridiales sp., Eubacterium nodatum, Selenomonas sp., Bacteroidetes sp., Odoribacter denticanis, Desulfomicrobium ovale, Moraxella sp., Bacteroides denlicanoris, Fillifactor viUosus, Porphyromonas canoris, Porphyromonas gulae, Treponema demicola, and Porphyromonas salivosa.
  • a number of techniques have been developed to assess oral disease and disorders, such as periodontal disease. Such techniques include, but are not limited to, probing of the gums and acquiring intra-oral dental radiographs. However, such methods and techniques require animals to be given a general anesthetic, require highly trained individuals, and are costly. Additionally, such methods can be stressful for both the subject animal and the owner of the subject animal. The development of a method for the diagnosis of an oral disease or disorder, such as periodontal disease, in conscious animals would therefore be appealing to both veterinarians and owners and would improve animal welfare.
  • the present disclosure provides a method for determining oral health status of an animal.
  • the method for determining oral health status of an animal comprises quantifying one or more microbial taxa (e.g., microorganism species) from a sample to determine abundance or relative abundance of the one or more microbial taxa, comparing the abundance or relative abundance of said one or more microbial taxa in the sample to abundance or relative abundance of the microbial taxa in a control data set, and determining the oral health status of the animal.
  • the one or more microbial taxa is quantified using quantitative polymerase chain reaction (qPCR) or DNA sequencing methods.
  • the one or more microbial taxa is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa are bacterial species. In certain embodiments, the one or more microbial taxa is selected from the group consisting of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp, Fusobacterium sp, Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptost
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp.
  • COT-018 Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT- 044, Peptostreptococcaceae XI [G-l] sp. COT-004, Peptostreptococcaceae XI [G-l] sp. COT-006, Peptostreptococcaceae XI [G-3] sp. COT-104, Peptostreptococcaceae XI [G-4] sp. COT-019, Peptostreptococcaceae XI [G-6] sp.
  • the one or more microbial taxa is selected from the group consisting of Clostridiales sp, COT-028, Peptostreptococcaceae XI [G-4] sp. COT-019, or Capnocytophaga sp. COT-339, and combinations thereof.
  • the abundance or relative abundance of the one or more microbial taxa is determined by amplifying and/or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA.
  • the bacterial taxa are determined by amplifying and/or sequencing one or more of the V1-V3, V3-V4, or V4 regions of the 16S rDNA, such as by using 454- pyrosequencing or Illumina sequencing.
  • the one or more microbial taxa is a fungal species, an archaea species, or a protozoan species.
  • the animal is a domestic animal.
  • the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat.
  • the animal is a wild animal.
  • the wild animal is a wolf, bison, elk, deer, lion, or tiger.
  • the presently disclosed methods further comprise extracting DNA from the sample.
  • the sample is a gingival area sample, e.g., gingival margin, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, or a buccal sample.
  • the sample is obtained from a conscious animal or from an unconscious animal.
  • the animal has or is suspected to have gingivitis and/or periodontitis.
  • the oral health status comprises periodontal disease.
  • the present disclosure provides methods implemented by a computer system for determining the animal oral health state.
  • the methods comprise receiving input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount or relative abundance of a type of one or more microbial taxa; determining, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtaining the animal oral health state from the machine learning model; and outputting the animal oral health state.
  • the input data comprises quantified one or more microbial taxa associated with periodontal health or periodontal disease from the sample.
  • the one or more microbial taxa is quantified using qPCR or DNA sequencing methods.
  • the bacterial taxa are determined by amplifying and/or sequencing one or more of the V1-V3, V3-V4, or V4 regions of the 16S rDNA, such as by using 454-pyrosequencing or Illumina sequencing.
  • the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease.
  • the methods further comprise: obtaining training data for a plurality of animals, wherein the training data indicates an amount or relative abundance of a type of microorganism that is present within a sample for each animal from among the plurality of animals; associating the training data with animal oral health state classifications, wherein associating the training data with the animal oral health state classifications comprises associating each animal from among the second plurality of animals with an animal oral health state classification; and training the machine learning model using the training data that is associated with the animal oral health state classifications.
  • the methods further comprise: identifying, by the machine learning model, particular microbial taxa in canine plaque that are significantly associated with health, gingivitis, and/or periodontitis for determining the oral health state of the animal.
  • the input data further comprises one or more of an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, a geographical location information, a sample location, or a combination thereof.
  • the input data comprise abundance or relative abundance of the one or more microbial taxa.
  • the one or more microbial taxa are bacterial species.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp, Fusobacterium sp, Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp. COT- 083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT- 005, Clostridiales sp.
  • COT-017 Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI [G-l] sp. COT-004, Peptostreptococcaceae XI [G-l] sp. COT-006, Peptostreptococcaceae XI [G-3] sp. COT- 104, Peptostreptococcaceae XI [G-4] sp.
  • the one or more microbial taxa is selected from the group consisting of Clostridiales sp, COT-028, Peptostreptococcaceae XI [G-4] sp. COT-019, Capnocytophaga sp. COT-339, and combinations thereof.
  • the one or more microbial taxa is a fungal species, an archaea species, or a protozoan species.
  • the abundance or relative abundance of the one or more microbial taxa is determined by amplifying 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA
  • the animal is a domestic animal.
  • the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat.
  • the animal is a wild animal.
  • the wild animal is a wolf, bison, elk, deer, lion, or tiger.
  • the one or more microbial taxa is associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease. In certain embodiments, the one or more microbial taxa is present in a sample. In certain embodiments, the sample is a gingival area sample, a gingival margin sample, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, or a buccal sample. In certain embodiments, the sample is obtained from a conscious animal or from an unconscious animal. In certain embodiments, the animal has or is suspected to have gingivitis and/or periodontitis.
  • the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount of a type of one or more microbial taxa; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.
  • the present disclosure provides a system comprising one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute instructions to perform any of the methods disclosed herein.
  • the present disclosure provides a non-transitory computer - readable medium comprising: instructions that, when executed by one or more processors of a computing system, cause the one or more processors to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount of a type of one or more microbial taxa; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.
  • the present disclosure provides a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to perform any of the methods disclosed herein.
  • Figure 1 provides principal component scores with ellipses representing 95% confidence regions from analysis performed on the counts and proportions identified via high-throughput sequencing (HTS) and quantitative polymerase chain reaction (qPCR) respectively. Discriminated by health state: health (green), gingivitis (orange) and periodontitis (red); and analytical method: qPCR (empty data points; dotted ellipse) and HTS (filled data points; solid ellipse).
  • Figures 2A - 2C provide correlations between high-throughput sequencing (HTS) and quantitative polymerase chain reaction (qPCR) datasets acquired from analysis of the same sample cohort for the following bacteria: 2A) Capnocytophaga sp.
  • Figure 3 provides Sensitivity against 1 - Specificity estimations using 5 machine learning classification models, for Peptostreptococcaceae XI [G-4] sp. COT-019, Clostridiales sp. COT-028 and Capnocytophaga sp. COT-339. Methods employed were logistic regression (LR, red), weighted k-nearest neighbour (KKNN, dark blue), kernel support vector machines (KSVM, yellow), linear discriminant analysis (LDA, purple) and random forest (RF, aqua). Average estimates +/- standard deviation are presented from the bootstrap testing of the optimised trained models.
  • LR logistic regression
  • KKNN weighted k-nearest neighbour
  • KSVM kernel support vector machines
  • LDA linear discriminant analysis
  • RF random forest
  • Figure 4 is a schematic diagram of an embodiment of a diagnostic system 100 that is configured to perform animal diagnostics using machine learning.
  • Figure 5 is a flowchart of an embodiment of an oral health state determination process 200 for the diagnostic system 100.
  • Figure 6 is an embodiment of a network device 102 for the diagnostic system 100.
  • the presently disclosed subject matter relates to methods for sampling the oral microbiome and monitoring oral health in animals.
  • the presently disclosed subject matter is particularly suited for sampling the oral microbiome of a companion animal, e.g., a domestic dog.
  • references to a percentage sequence identity between two nucleotide sequences mean that, when aligned, that percentage of nucleotides are the same in comparing the two sequences.
  • This alignment and the percent homology or sequence identity can be determined using any suitable software programs. For example, those described in section 7.7.18 of reference [13], In one embodiment, an alignment is determined using the BLAST algorithm or the Smith-Waterman homology search algorithm using an affine gap search with a gap open penalty of 12 and a gap extension penalty of 2, BLOSUM matrix of 62.
  • the Smith-Waterman homology search algorithm is disclosed in reference [14], The alignment can be over the entire reference sequence, i.e., it can be over 100% length of the sequences disclosed herein.
  • the use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification can mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” Still further, the terms “having,” “including,” “containing” and “comprising” are interchangeable and one of skill in the art is cognizant that these terms are open ended terms. Further, the term “comprising” encompasses “including” as well as “consisting,” e.g., a composition “comprising” X can consist exclusively of X or can include something additional, e.g., X + Y.
  • the term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2- fold, of a value.
  • taxa refers to taxonomical groups, for example, kingdom, phylum, class, order, family, genus, and species.
  • the term “abundance” can refer to an absolute amount (including presence or absence) of given bacterial taxa present within a sample. For example, an abundance can refer to the count of bacterial sequences of bacterial taxa after appropriate amplification of 16S rDNA.
  • the term “relative abundance” can refer to a percentage composition of particular bacterial taxa (e.g., species) relative to the total number of bacteria in the sample. It can be calculated by determining the number of sequences of given bacterial taxa divided by the total number of all bacterial sequences which is then multiplied by 100.
  • the relative abundance can refer to the relative amounts of nucleic acid present in a sample after appropriate amplification or sequencing of 16S rDNA.
  • the relative abundance can refer to a binary classification of bacteria taxa.
  • binary classification can include detected versus undetected taxa or presence versus absence of taxa.
  • the relative abundance is calculated as odds ratio.
  • odds ratio can be a fold change, i.e., it is a measure of how much higher or lower the abundance or relative abundance is when comparing one group to another group.
  • the 16S rRNA comprises or consists of one of the sequences provided in Table 1. Table 1. 16S rRNA sequences.
  • animal refers to a wide variety of animals, such as quadrupeds, primates, and other mammals.
  • the term “animal” can refer to domestic animals including, but not limited to, dogs, cats, horses, cows, ferrets, rabbits, pigs, rats, mice, gerbils, hamsters, goats, and the like.
  • the term “animal” can also refer to wild animals including, but not limited to, wolf, bison, elk, deer, lion, tiger, and the like.
  • the animal is a companion animal. In certain instances, the animal is a dog or a cat.
  • nucleic acid molecule and “nucleotide sequence,” as used herein, refers to a single or double stranded covalently-linked sequence of nucleotides in which the 3’ and 5’ ends on each nucleotide are joined by phosphodiester bonds.
  • the nucleic acid molecule can include deoxyribonucleotide bases or ribonucleotide bases, and can be manufactured synthetically in vitro or isolated from natural sources.
  • oral disease or disorder refers to a disease or disorder that occurs in an oral cavity of a subject (e.g., an animal) and that is caused by or is associated with one or more bacteria.
  • the disease or disorder can affect the teeth, structures that support the teeth such as periodontal ligament, alveolar bone, or the gums of the subject.
  • Exemplary oral diseases or disorders of the present disclosure include, but are not limited to, periodontal disease, gingival stomatitis, odontoclastic resorptive lesions, and oral malodor.
  • Periodontal disease also known as gum disease, refers to an inflammation or infection that affect the tissues surrounding the teeth. Periodontal disease can range in severity, e.g., from gingivitis (e.g., dental plaque-induced gingivitis) to periodontitis. An example of the range is found on https://avdc.org/avdc-nomenclature/.
  • isolated refers to a nucleic acid, a polypeptide, or other biological moiety that is removed from components with which it is naturally associated.
  • isolated can refer to a polypeptide that is separate and discrete from the whole organism with which the molecule is found in nature or is present in the substantial absence of other biological macromolecules of the same type.
  • isolated with respect to a polynucleotide can refer to a nucleic acid molecule devoid, in whole or part, of sequences normally associated with it in nature; or a sequence, as it exists in nature, but having heterologous sequences in association therewith; or a molecule disassociated from the chromosome.
  • biomarker can refer to a characteristic that is objectively measured and evaluated as an indicator of physiological biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
  • biomarker can refer to any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease.
  • treatment is an approach for obtaining beneficial or desired results, including clinical results.
  • beneficial or desired clinical results include, but are not limited to, alleviation or amelioration of one or more symptoms, diminishment of extent of a disorder, stabilized (i.e., not worsening) state of a disorder, prevention of a disorder, delay or slowing of the progression of a disorder, and/or amelioration or palliation of a state of a disorder.
  • the decrease can be an about 0.01%, about 0.1%, about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98% or about 99% decrease in severity of complications or symptoms. “Treatment” can also mean prolonging survival as compared to expected survival if not receiving treatment.
  • an “effective treatment” or “effective amount” of a substance means the treatment or the amount of a substance that is sufficient to effect beneficial or desired results, including clinical results, and, as such, an “effective treatment” or an “effective amount” depends upon the context in which it is being applied.
  • a composition e.g., a dietary change, a functional food, a supplement, a nutraceutical composition, or a pharmaceutical composition
  • the effective amount is an amount sufficient to bring the health status of the microbiome back to a healthy state, which is determined according to one of the methods disclosed herein.
  • an effective treatment can also include administering a treatment in an amount sufficient to decrease any symptoms associated with an unhealthy microbiome.
  • the decrease can be an about 0.01%, about 0.1%, about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98% or about 99% decrease in severity of symptoms of an unhealthy microbiome.
  • An effective amount can be administered in one or more administrations.
  • a likelihood of an effective treatment described herein is a probability of a treatment being effective, i.e., sufficient to alter the microbiome, or treat or ameliorate a disorder and/or inflammation, as well as decrease the symptoms.
  • kits and related methods for detecting one or more microbial taxa e.g., bacteria
  • the one or more microbial taxa e.g., bacteria
  • the animal can be a companion animal, such as a dog or a cat.
  • the one or more microbial taxa are one or more bacteria.
  • the one or more bacteria associated with periodontal disease is selected from the group consisting of Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp.
  • COT-018 Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI [G-l] sp. COT-004, Peptostreptococcaceae XI [G-l] sp. COT- 006, Peptostreptococcaceae XI [G-3] sp. COT-104, Peptostreptococcaceae XI [G-4] sp. COT-019, Peptostreptococcaceae XI [G-6] sp.
  • Bacterial community profiles within an oral microbiome of an animal can vary depending on the source of a sample taken from the animal.
  • three discrete oral niches can include soft tissue surfaces, such as the lip, cheek, and tongue; hard tissue surfaces, such as the teeth; and saliva.
  • the oral niche is from a hard tissue surface, such as one or more teeth.
  • the oral niche includes the gingival margin, subgingival margin, or supragingival surface.
  • the methods and kits of the disclosed subject matter can be used to detect bacteria in the oral microbiome of a wide variety of animals, such as quadrupeds, primates, and other mammals. The methods and kits of the disclosed subject matter are particularly well suited for use with companion animals, such as dogs, cats, and other domesticated animals.
  • the companion animal is a domestic dog.
  • the present disclosure relates to, inter alia, methods for assessing health and wellbeing of animals.
  • Characteristics of companion animals can vary, including by size, sex, breed, and species. However, for the most common member within this category, dogs, can in general provide an indication of the efficacy of a method when applied to other animals.
  • size category refers to the definition of the animal (e.g., dogs, cats, etc.) in terms of the average weight of the particular animal breed. Animals (e.g., dogs, cats, etc.) of the same breed can have relatively uniform physical characteristics, such as size, coat color, physiology, and behavior, as compared to animals of a different breed. It is noted that the discussion below is focused on dogs, however, other companion animals and wild animals are intended to be covered by the scope of this disclosure and the present disclosure is not intended to be limited to dogs.
  • the dog can be any breed of dog, including toy/extra-small, small, medium-small, medium, medium-large, large or extra-1 arge/gi ant breeds.
  • toy/extra-small breeds include Affenpinscher, Australian Silky Terrier, Bichon Frise, B perfumese, Cavalier King Charles Dogl, Chihuahua, Chinese Crested, Coton De Tulear, English Toy Terrier, Griffon Bruxellois, Havanese, Italian Greyhound, Japanese Chin, King Charles Dog Lowchen (Little Lion Dog), Maltese, Miniature Pinscher, Papillon, Pekingese, Pomeranian, Pug, Russian Toy, and England Terrier.
  • Examples of small breeds include, but are not limited to, French Bulldog, Beagle, Dachshund, Pembroke Welsh Corgi, Miniature Schnauzer, Cavalier King Charles Dogl, Shih Tzu, and Boston Terrier.
  • Examples of medium dog breeds include, but are not limited to, Bulldog, Cocker Dogl, Shetland Sheepdog, Border Collie, Basset Hound, Siberian Husky, and Dalmatian.
  • Examples of large breed dogs include, but are not limited to, Great Dane, Neapolitan mastiff, Scottish Deerhound, Dogue de Bordeaux, Newfoundland, English mastiff, Saint Bernard, Leonberger, and Irish Wolfhound.
  • Other non-limiting examples of breeds include those listed in Wallis et al. (2021).
  • Cross-breeds can generally be categorized as toy/extra- small, small, medium-small, medium, medium-large, large, and extra-large/giant dogs depending on their body weight.
  • the dog is a toy/extra-small breed.
  • the dog is a small, medium-small, medium, medium-large, large or extra-large/giant breed.
  • the dog is a mix of two or more breeds. In such instances, the mixed-breed dog can still be categorized by size depending on their body weight and can exhibit traits (e.g., behavioral traits, genetic traits, etc.) associated with each of the two or more breeds found in the dog.
  • a pedigree dog is the offspring of two dogs of the same breed, which is eligible for registration with a recognized club or society that maintain a register for dogs of that description.
  • Table 2 A list of dog size categories.
  • the dog size categories are selected according to Salt et al., 2017 (Table 2). In other embodiments, the dog size categories are selected according to alternative designations.
  • a small breed can correspond with animals that have an average body weight of from about 6.5 kilograms to about 9 kilograms.
  • a medium breed can correspond with an animal that has an average body weight between about 9 kilograms and about 30 kilograms.
  • a large breed can correspond with an animal that has an average body weight of between about 30 kilograms and about 40 kilograms.
  • a giant breed can correspond with an animal that has an average body weight of between over about 40 kilograms.
  • the disclosed methods of using the methods of the disclosed subject matter can include performing an assay on the sample to measure an amount of a microbial nucleic acid.
  • the microbial nucleic acid can be a microbial DNA or RNA, e.g., a 16S ribosomal DNA (rDNA) or 16S ribosomal RNA (rRNA).
  • rDNA 16S ribosomal DNA
  • rRNA 16S ribosomal RNA
  • rDNA 16S ribosomal DNA
  • rRNA 16S ribosomal RNA
  • rDNA 16S ribosomal DNA
  • rRNA 16S ribosomal RNA
  • qPCR quantitative polymerase chain reaction
  • any of the disclosed methods can include performing an assay for testing for the presence and/or relative amounts of any bacteria disclosed herein.
  • the bacteria are Peptostreptococcaeceae sp. and/or volatile organic compound producing bacteria, or any other periodontal bacterium, such as but not limited to Peptostreptococcus sp., Synergistes sp., Clostridiales sp., Eubacterium nodatum, Selenomonas sp., Bacteriodetes sp., Odoribacter denticanis, Desulfomicrobium ovale, Moraxella sp.
  • any of the disclosed methods can include performing a universal qPCR assay which detects the presence of bacterial DNA in the dog oral microbiome.
  • Universal primers are known to skilled people in the art and examples include those described in Ott et al., J. Clin. Microbiol. 2004 Jun; 42(6): 2566 -2572. Doi:
  • the methods can also include a step of extracting a nucleic acid, e.g., performing a DNA extraction, according to methods known in the art prior to performing the qPCR assay.
  • a DNA extraction can be performed by lysing the cells containing the DNA and precipitating and purifying the DNA.
  • any of the disclosed methods can include detecting bacteria by testing the sample for the presence of bacteria.
  • testing the sample can include for presence and/or relative abundance of one or more of the bacteria disclosed herein, e.g., bacteria associated with an oral disease or disorder (e.g., periodontal disease), bacteria associated with good oral health, or both.
  • testing the sample can include detecting the abundance or increased relative abundance compared to a training data set (e.g., bacteria associated with good oral health, bacteria associated with an oral disease or disorder, bacteria not associated with good oral health or oral disease or disorder, and combinations thereof). Detecting a presence or relative increased abundance of one or more of the bacteria disclosed herein can, for instance, indicate that the animal has or is susceptible to developing an oral disease or disorder.
  • a training data set e.g., bacteria associated with good oral health, bacteria associated with an oral disease or disorder, bacteria not associated with good oral health or oral disease or disorder, and combinations thereof.
  • any of the disclosed methods can include detecting bacteria by testing the sample for an absence or relatively low abundance of bacteria.
  • testing the sample can include for the absence or relatively low abundance of one or more of the bacteria disclosed herein, e.g., bacteria associated with an oral disease or disorder (e.g., periodontal disease).
  • testing the sample can include detecting the decreased abundance or decreased relative abundance compared to a training data set. Detecting the absence or relatively low abundance of the one or more of the bacteria associated with the oral disease can, for instance, indicate that the animal does not have an oral disease or disorder or is less likely to develop an oral disease or disorder.
  • the disclosed methods of using the kits of the disclosed subject matter can include testing the sample for the presence and/or relative amounts of microbes associated with oral health.
  • the testing includes for the presence and/or relative amounts of a bacterial nucleic acid (e.g., DNA or RNA).
  • the disclosed methods can include determining oral health status of an animal.
  • the method for determining oral health status of an animal comprises quantifying one or more microbial taxa from a sample to determine abundance or relative abundance of the one or more microbial taxa, comparing the abundance or relative abundance of said one or more microbial taxa in the sample to abundance or relative abundance of the microbial taxa in a control data set, and determining the oral health status of the animal.
  • the one or more microbial taxa are bacterial species.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp, Fusobacterium sp, Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp. COT-083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT-005, Clostridiales sp. COT-028, Desulfomicrobium orale COT-008, Filifactor sp.
  • COT-018 Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT- 044, Peptostreptococcaceae XI [G-l] sp. COT-004, Peptostreptococcaceae XI [G-l] sp. COT-006, Peptostreptococcaceae XI [G-3] sp. COT-104, Peptostreptococcaceae XI [G-4] sp. COT-019, Peptostreptococcaceae XI [G-6] sp.
  • the one or more microbial taxa is selected from the group consisting of Clostridiales sp, COT-028, Peptostreptococcaceae XI [G-4] sp. COT-019, or Capnocytophaga sp. COT-339, and combinations thereof.
  • the one or more microbial taxa is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa is a fungal species, an archaea species, or a protozoan species.
  • the one or more microbial taxa is present in a sample
  • the animal is a domestic animal.
  • the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat.
  • the animal is a wild animal.
  • the wild animal is a wolf, bison, elk, deer, lion, or tiger.
  • the presently disclosed methods further comprise extracting DNA from the sample.
  • the sample is a gingival area sample, e.g., gingival margin, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, or a buccal sample.
  • the sample is obtained from a conscious animal or from an unconscious animal.
  • the animal has or is suspected to have gingivitis and/or periodontitis.
  • the oral health status comprises periodontal disease.
  • the one or more microbial taxa is quantified using quantitative polymerase chain reaction (qPCR) or DNA sequencing methods.
  • the abundance or relative abundance of the one or more microbial taxa is determined by amplifying and/or sequencing 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA.
  • the bacterial taxa are determined by amplifying and/or sequencing one or more of the V1-V3, V3-V4, or V4 regions of the 16S rDNA, such as by using 454-pyrosequencing or Illumina sequencing.
  • the detection of the presence of bacteria or other markers can include measuring the amounts of bacteria or other markers, and the amounts can be compared to a scale that correlates the amount of bacteria or other markers to the likelihood that the animal has oral disease or disorder (e.g., periodontal disease) or poor oral health.
  • the likelihood can be indicated as a percentage.
  • the Cq (cycle quantitation) score of a qPCR test that detects the nucleic acid (e.g. DNA or RNA) of bacteria associated with oral disease or disorder (e.g., periodontal disease) can be used to create the scale for calculating the likelihood that the animal has oral disease or disorder (e.g., periodontal disease).
  • a lower Cq score can indicate the presence of higher levels of the bacteria associated with oral disease or disorder (e.g., periodontal disease) and therefore the likelihood that the animal has oral disease or disorder (e.g., periodontal disease) can be higher than the animal with a higher Cq score.
  • oral disease or disorder e.g., periodontal disease
  • all qPCR data can be normalized to the level of a universal assay for each sample; this adjusts the data for differences in the overall amount of total bacterial DNA in each sample, i.e., yield the abundance relative to the total bacterial population.
  • the data can be then linearised, such that the final qPCR data outputs are relative proportions (2' ( Cq Test ' Cq Total) ).
  • Cq.Test refers to the Cq score associated with a microbial species.
  • Cq.Test refers to the Cq score associated with a canine oral taxon (COT).
  • a report can be generated summarizing the results of sample testing.
  • electronic communications can be used to communicate the report.
  • a personalized report can be generated and sent to communicate the animal’s oral health status.
  • the report can be provided as a hard copy.
  • the personalized report can, for example, include an indicator system such as a traffic light system, e.g., green, yellow, red, to communicate the oral health status of the animal.
  • the personalized report can also include a representation of the scale as reference above and an indication of where the animal’s oral health falls on the scale, e.g., 0% is indicative of no disease and 100% is indicative of severe disease.
  • the present disclosure also provides machine learning models for detecting one or more microbial taxa (e.g., bacteria) in an oral microbiome of an animal.
  • the one or more microbial taxa e.g., bacteria
  • the animal can be a companion animal, such as a dog or a cat.
  • the one or more microbial taxa are one or more bacteria.
  • the disclosed methods can include methods implemented by a computer system for determining the animal oral health state.
  • the methods comprise receiving input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount or relative abundance of a type of one or more microbial taxa; determining, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtaining the animal oral health state from the machine learning model; and outputting the animal oral health state.
  • the input data comprises quantified one or more microbial taxa associated with periodontal health or periodontal disease from the sample.
  • the one or more microbial taxa is quantified using qPCR or DNA sequencing methods.
  • the bacterial taxa are determined by amplifying and/or sequencing one or more of the V1-V3, V3-V4, or V4 regions of the 16S rDNA, such as by using 454- pyrosequencing or Illumina sequencing.
  • the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease.
  • the methods further comprise: obtaining training data for a plurality of animals, wherein the training data indicates an amount or relative abundance of a type of microorganism that is present within a sample for each animal from among the plurality of animals; associating the training data with animal oral health state classifications, wherein associating the training data with the animal oral health state classifications comprises associating each animal from among the second plurality of animals with an animal oral health state classification; and training the machine learning model using the training data that is associated with the animal oral health state classifications.
  • the methods further comprise: identifying, by the machine learning model, particular microbial taxa in canine plaque that are significantly associated with health, gingivitis, and/or periodontitis for determining the oral health state of the animal.
  • the input data further comprises one or more of an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, a geographical location information, a sample location, or a combination thereof.
  • the input data comprise abundance or relative abundance of the one or more microbial taxa.
  • the one or more microbial taxa are bacterial species.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp., Anaerovorax sp., Bacteroides sp., Bergeyella sp., Capnocytophaga sp., Clostridiales sp., Desulfomicrobium sp., Filifactor sp., Frigovirgula sp, Fusobacterium sp, Gemella sp., Granulicatella sp., Helcococcus sp., Lachnospiraceae sp., Leptotrichia sp., Moraxella sp., Neisseria sp., Odoribacter sp., Pasteurellaceae sp., Peptococcus sp., Peptostreptococcaceae sp., Peptostreptococcus sp.
  • the one or more microbial taxa is selected from the group consisting of Actinomyces sp. COT- 083, Actinomyces sp. COT-252, Anaerovorax sp. COT-125, Bacteroides sp. COT-040, Bergeyella zoohelcum COT-186, Capnocytophaga sp. COT-339, Clostridiales sp. COT- 005, Clostridiales sp.
  • COT-017 Moraxella sp. COT-018, Neisseria animaloris COT-016, Odoribacter denticanis COT-084, Pasteurellaceae sp. COT-271, Peptococcus sp. COT-044, Peptostreptococcaceae XI [G-l] sp. COT-004, Peptostreptococcaceae XI [G-l] sp. COT-006, Peptostreptococcaceae XI [G-3] sp. COT- 104, Peptostreptococcaceae XI [G-4] sp.
  • the one or more microbial taxa is selected from the group consisting of Clostridiales sp, COT-028, Peptostreptococcaceae XI [G-4] sp.
  • the one or more microbial taxa is a fungal species, an archaea species, or a protozoan species.
  • the abundance or relative abundance of the one or more microbial taxa is determined by amplifying 16S rRNA, 16S rDNA, ITS, 18S rRNA, or 18S rDNA.
  • the one or more microbial taxa is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa in the control data set is associated with periodontal health or periodontal disease.
  • the one or more microbial taxa is present in a sample.
  • the animal is a domestic animal.
  • the domestic animal is a dog, cat, horse, cow, ferret, rabbit, pig, rat, mouse, gerbil, hamster, or goat.
  • the animal is a wild animal.
  • the wild animal is a wolf, bison, elk, deer, lion, or tiger.
  • the sample is a gingival area sample, a gingival margin sample, a subgingival area sample, a supragingival area sample, a saliva sample, a tongue sample, or a buccal sample.
  • the sample is obtained from a conscious animal or from an unconscious animal.
  • the animal has or is suspected to have gingivitis and/or periodontitis.
  • the present disclosure provides a system comprising: one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute the instructions to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount of a type of one or more microbial taxa; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.
  • the present disclosure provides a system comprising one or more non-transitory computer-readable storage media including instructions; and one or more processors coupled to the storage media, the one or more processors configured to execute instructions to perform any of the methods disclosed herein.
  • the present disclosure provides a non-transitory computer - readable medium comprising: instructions that, when executed by one or more processors of a computing system, cause the one or more processors to: receive input data for an animal, wherein the input data comprises at least a first array comprising a first plurality of entries, and each entry comprises a numerical value that indicates an amount of a type of one or more microbial taxa; determine, by a machine learning model, an animal oral health state based on the input data for the animal, wherein the animal oral health state identifies a predicted oral health state classification for the animal; obtain the animal oral health state from the machine learning model; and output the animal oral health state.
  • the present disclosure provides a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the one or more processors to perform any of the methods disclosed herein.
  • Figure 4 is a schematic diagram of an embodiment of a diagnostic system 100 that is configured to perform animal diagnostics using machine learning.
  • the diagnostic system 100 is generally configured to input various types of information that are associated with the health and attributes of an animal into a machine learning model 112.
  • the machine learning model 112 is configured to predict an oral health state of the animal based on the provided inputs. This process allows the diagnostic system 100 to determine the oral health state of an animal based on the physical attributes of the animal.
  • the diagnostic system 100 comprises one or more user devices 104 and a network device 102 that are in signal communication with each other over a network 106.
  • the network 106 can be any suitable type of wireless and/or wired network including, but not limited to, all or a portion of the Internet, an Intranet, a private network, a public network, a peer-to-peer network, the public switched telephone network, a cellular network, a local area network (LAN), a metropolitan area network (MAN), a personal area network (PAN), a wide area network (WAN), and a satellite network.
  • the network 106 can be configured to support any suitable type of communication protocol as would be appreciated by one of ordinary skill in the art.
  • Examples of user devices 104 include, but are not limited to, a computer, a laptop, a tablet, a smartphone, a smart device, an Intemet-of-Things (loT) device, a data storage device (e.g., a Universal Serial Bus (USB) drive or flash drive), or any other suitable type of device.
  • a user device 104 is configured to provide input data 118 for an animal to the network device 102.
  • the input data 118 can comprise information associated with bacterial, fungal, or archaea information such as taxa or genetic information, an animal breed identifier, an animal size, an animal weight, an animal age, animal health information (e.g., veterinary records), animal diet, geographical location information, or any other suitable type of information that is associated with an animal.
  • the user device 104 In response to providing the input data 118 for an animal to the network device 102, the user device 104 is configured to receive an animal oral health state classification from the network device 102 and to display the animal oral health state classification to a user.
  • the user device 104 can comprise a graphical user interface (e.g., a display or a touchscreen) that allows a user to view the animal oral health state classification.
  • the user device 104 can further comprise a touchscreen, a touchpad, keys, buttons, a mouse, or any other suitable type of hardware that allows a user to provide inputs into the user device 104.
  • the oral health state classification can include health (H or PD0), gingivitis (G or PD1), and periodontitis (periodontal disease stages 2, 3, or 4; PD2, PD3, or PD4),e.g., as described by AVDC.
  • Examples of the network device 102 include, but are not limited to, a server (e.g., a cloud server), a computer, a laptop, or any other suitable type of network device.
  • the network device 102 comprises a diagnostics engine 108 and a memory 110. Additional details about the hardware configuration of the network device 102 are described in Figure 6.
  • the memory 110 is configured to store machine learning models 112, training data 114, test data 116, health information 122, a control data set 124, and/or any other suitable type of data.
  • the diagnostics engine 108 is generally configured to employ a machine learning model 112 to determine an animal’s oral health state based on information that is associated with an animal.
  • a machine learning model 112 to determine an animal’s oral health state based on information that is associated with an animal.
  • An example of the diagnostics engine 108 in operation is described in more detail below in Figure 5.
  • machine learning model types include, but are not limited to, a multilayer perceptron, a recurrent neural network (RNN), an RNN long short-term memory (LSTM), a convolutional neural network (CNN), deep learning algorithms, probabilistic models, a linear regression, a non-linear regression, or any other suitable type of algorithm or model, e.g., logistic regression (LR), weighted k-nearest neighbour (KKNN), kernel support vector machines (KSVM), linear discriminant analysis (LDA), and random forest (RF).
  • the machine learning model 112 can be configured with any suitable type of hyperparameters or settings.
  • hyperparameters and settings include, but are not limited to, a sensitivity level, a tolerance level, an epoch value, a number of layers (e.g., hidden layers), a number of inputs, a number of outputs, an output type, an output format, or any other suitable type or combination of settings.
  • the machine learning model 112 can be configured with hyperparameters such as a learning rate of 0.15, a max depth of 2, and a maximum number of rounds set to 22.
  • the machine learning model 112 can be configured with hyperparameters such as a learning rate of 0.15, a max depth of 5, and a maximum number of rounds set to 32.
  • the machine learning model 112 can be configured with any other suitable hyperparameters.
  • the machine learning model 112 is generally configured to receive input data 118 for an animal as an input and to output an animal oral health state classification 120 based on the provided input data 118.
  • the animal oral health state classification 120 is a classification (e.g., health (H or PD0), gingivitis (G or PD1), and periodontitis (stage 2; PD2, stage 3; PD3 or stage 4; PD4)) that corresponds with a predicted oral health state for the animal based on the provided input data 118. Additional information on the health state classification can be found in the American Veterinary Dental College (AVDC) website (www.avdc.org).
  • AVDC American Veterinary Dental College
  • the machine learning model 112 is trained using supervised learning with training data 114 that comprises information associated with different animals with their corresponding labels (e.g., animal oral health state classification 120). During the training process, the machine learning model 112 determines weights and bias values that allow the machine learning model 112 to map information associated with different animals to different animal oral health state classification 120. Through this process, the machine learning model 112 is able to identify an animal oral health state classification 120 based on the provided input data 118.
  • the diagnostics engine 108 can be configured to train the machine learning model 112 using any suitable technique as would be appreciated by one of ordinary skill in the art. For example, the machine learning model 112 can be trained using an XGBoost algorithm. In some embodiments, the machine learning model 112 can be stored and/or trained by a device that is external from the network device 102.
  • the network device 102 can be configured to use statistical models, regression models (e.g., non-linear regression models), parametric models, or any other suitable type of model with or in place of the machine learning model 112.
  • regression models e.g., non-linear regression models
  • parametric models or any other suitable type of model with or in place of the machine learning model 112.
  • the control data set 124 can comprise information associated with microbial taxa, an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, geographical location information, or any other suitable type of information that is associated with a plurality of animals.
  • the control data set 124 can comprise information about the oral microbiome of canids at different oral health status.
  • the training data 114 can comprise information associated with microbial taxa, an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supragingival, or subgingival), or any other suitable type of information that is associated with an animal that can be input into a machine learning model 112.
  • the training data 114 can comprise at least a portion of the control data set 124 that is collected for a plurality of animals.
  • the test data 116 is the same data type as the training data 114. In some embodiments, the test data 116 is a subset or a portion of the training data 114. For example, twenty percent of the training data 114 can be used as test data 116. In other examples, any other suitable percent of the training data 114 can be used as test data 116.
  • the health information 122 comprises information that is associated with one or more animals.
  • Examples of health information include, but are not limited to, contact information for an owner of an animal, an animal name or identifier, information associated with microbial taxa, an animal breed identifier, DNA information, an animal size, an animal weight, an animal age, animal health information, animal diet, geographical location information, gingivitis information, periodontitis information, or any other suitable type of information that is associated with an animal.
  • Figure 5 is a flowchart of an embodiment of an oral health state determination process 200 for the diagnostic system 100.
  • the diagnostic system 100 can employ process 200 to predict the oral health state of an animal using machine learning.
  • Process 200 allows the diagnostic system 100 to input various types of information that are associated with the health and physical attributes of an animal into a machine learning model 112 that is configured to predict the oral health state of the animal. This process allows the diagnostic system 100 to determine the oral health state of an animal based on the physical attributes of the animal.
  • the network device 102 before employing the machine learning model 112 to determine an animal oral health state classification 120 for an animal, the network device 102 first trains the machine learning model 112 for determining an animal oral health state classification 120. During the training process, the machine learning model 112 determines weights and bias values that allow the machine learning model 112 to map certain types of training data 114 to different types of animal oral health state classifications 120. In one embodiment, the machine learning model 112 is trained using a supervised learning training process using labelled training data 114.
  • the supervised learning training process can comprise obtaining training data 114 for a plurality of animals, associating the training data 114 for each animal with an animal oral health state classification 120, and then training the machine learning model 112 using the training data 114 that is associated with the animal oral health state classification 120. Associating the training data 114 with the animal oral health state classification 120 links the metadata for each animal with its corresponding animal oral health state classification 120.
  • each machine learning model 112 is configured to receive microbial taxa, an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supragingival, or subgingival), or any other suitable type of information that is associated with an animal as an input and to output an animal oral health state classification 120 based on the input data 118.
  • each machine learning model 112 is trained to predict an animal’s oral health state (i.e., an animal oral health state classification 120) based on the input data 118.
  • the network device 102 can be configured to train the machine learning model 112 using any suitable technique.
  • the machine learning model 112 can be trained by a third-party device (e.g., a cloud server) that is external from the network device 102. After training the machine learning model 112, the machine learning model 112 is stored in memory (e.g. memory 110). This concludes the training process for the machine learning model 112.
  • a third-party device e.g., a cloud server
  • memory e.g. memory 110
  • the network device 102 obtains input data 118 for an animal.
  • the network device 102 can obtain the input data 118 from a user device 104.
  • the user device 104 can send or transfer the input data 118 to the network device 102 as a message or a data file.
  • the user device 104 can send or transfer the input data 118 to the network device 102 using any suitable messaging or data transfer technique.
  • a user can directly provide the input data 118 to the network device 102.
  • the user can enter (e.g., type) the input data 118 into the network device 102 using a user interface (e.g., keyboard, mouse, and/or touch screen) on the network device 102.
  • a user interface e.g., keyboard, mouse, and/or touch screen
  • the input data 118 can comprise any suitable combination of microbial taxa, an animal breed identifier, an animal size, an animal weight, an animal age, animal health information, animal diet, geographical location information, sample location (e.g., gingival margin, supragingival, or subgingival), or any other information associated with the animal.
  • the input data 118 comprises an array of microbial taxa values.
  • the bacterial taxa can be determined using qPCR amplification or sequencing of 16S rDNA, bacterial genomes, or archaeal genomes. These methods identify the type and/or amount of bacteria that are present within a sample that is collected from the mouth of the animal.
  • the sample can comprise bacteria from a gingival area (e.g., near the gums), subgingival area (e.g., below the gum line), supragingival (e.g., above the gum) in the mouth of the animal, saliva, tongue, and buccal samples. Additional details for the process of collecting a sample and identifying bacterial taxa from within the sample are also provided below.
  • the input data 118 can comprise an array that is associated with different types of bacteria that can be present within the mouth of the animal.
  • the array comprises a plurality of entries that are each associated with a particular type of bacteria.
  • a value of zero for an entry indicates that the type of bacteria that is associated with the entry was not present or detected within the mouth of the animal.
  • a value of one for an entry indicates that the type of bacteria that is associated with the entry was present or detected within the mouth of the animal.
  • an entry can comprise a numeric value that indicates the amount of the bacteria for each bacteria type that was present or detected within the mouth of the animal.
  • a bacterial taxa amount such as a Cq value can be a numeric value or code that uniquely identifies a bacteria type that is present in the mouth of the animal.
  • each bacteria type can be linked with a unique numerical value or code.
  • the bacterial taxa can use any other suitable type of format or data structure to identify a bacteria type that is present in the mouth of the animal.
  • the bacterial taxa can comprise one or more, preferably two, bacterial taxa.
  • the bacterial taxa are preferably collected from a subgingival portion of the mouth.
  • the input data 118 comprises an array of eukaryotic taxa values.
  • the eukaryotic taxa can be determined using qPCR amplification or sequencing of eukaryotic microorganism genomes, e.g., fungal genomes, or protozoan genomes.
  • the microbial taxa can be collected from an animal while it is conscious. In this case, the microbial taxa can be collected from a gingival margin, a subgingival area, a supragingival area, saliva, tongue, or a buccal sample from the mouth of the animal. In some embodiments, the microbial taxa can be collected from an animal while it is unconscious. In this case, the microbial taxa can be collected from a gingival margin, a subgingival area, a supragingival area, saliva, tongue, or a buccal sample from the mouth of the animal.
  • the input data 118 can further comprise an animal breed identifier that identifies a breed of the animal.
  • animal breeds include, but are not limited to, Affenpinscher, Australian Silky Terrier, Bichon Frise, B perfumese, Cavalier King Charles Dogl, Chihuahua, Chinese Crested, Coton De Tulear, English Toy Terrier, Griffon Bruxellois, Havanese, Italian Greyhound, Japanese Chin, King Charles Dogl, Lowchen (Little Lion Dog), Maltese, Miniature Pinscher, Papillon, Pekingese, Pomeranian, Pug, Russian Toy, England Terrier, French Bulldog, Beagle, Dachshund, Pembroke Welsh Corgi, Miniature Schnauzer, Cavalier King Charles Dogl, Shih Tzu, Boston Terrier, Bulldog, Cocker Dogl, Shetland Sheepdog, Border Collie, Basset Hound, Siberian Husky, Dalmatian, Great Dan
  • the animal breed type can be identified using one-hot encoding.
  • the input data 118 can comprise an array that is associated with different breeds of the animal.
  • the array comprises a plurality of entries that are each associated with a particular breed type.
  • a value of zero for an entry indicates that the animal is not a member of the breed type that is associated with the entry.
  • a value of one for an entry indicates that the animal is a member of the breed type that is associated with the entry.
  • the animal breed identifier can be a numeric value or code that uniquely identifies a breed type.
  • each breed type can be linked with a unique numerical value.
  • the animal breed identifier can use any other suitable type of format or data structure to identify a breed type for the animal.
  • the input data 118 can further comprise an animal size classification value.
  • the animal size classification value identifies the size of the animal based on the physical size and/or weight of the animal.
  • animal sizes include, but are not limited to, toy/extra-small breeds, extra-small breeds, small breeds, mediumsmall breeds, medium breeds, medium-large breeds, large breeds, and giant/extra-large breeds.
  • a toy/extra-small breed can correspond with animals that are physically smaller than small breed animals.
  • a small breed can correspond with animals that have an average body weight of from about 6.5 kilograms to about 9 kilograms.
  • a medium breed can correspond with an animal that has an average body weight between about 9 kilograms and about 30 kilograms.
  • the animal size classification value can be identified using one-hot encoding.
  • the input data 118 can comprise an array that is associated with different animal size classifications.
  • the array comprises a plurality of entries that are each associated with a particular animal size.
  • a value of zero for an entry indicates that the animal is not a member of the animal size classification (e.g., toy/extra-small breed, small breed, medium breed, or large breed) that is associated with the entry.
  • a value of one for an entry indicates that the animal is a member of the animal size classification that is associated with the entry.
  • the animal size classification value can be a numeric value or code that uniquely identifies an animal size classification.
  • each animal size classification can be linked with a unique numerical value.
  • the animal size classification value can use any other suitable type of format or data structure to identify an animal size classification for the animal.
  • the input data 118 can comprise the animal size classification value and one or more, preferably two, microbial taxa.
  • the microbial taxa are preferably collected from a gingival margin, subgingival, or supragingival portion of the mouth.
  • the input data 118 can further comprise a sample location value that identifies a location in the mouth where a sample was collected for the animal.
  • the sample location value can comprise a numeric value that corresponds with a gingival margin location, a subgingival location, a supragingival location, saliva, tongue, a buccal sample, or a combination thereof.
  • the input data 118 can further comprise a weight value that identifies a weight for the animal.
  • the weight value can comprise a numeric value that corresponds with the weight of the animal in pounds or kilograms. In other examples, the weight value can be in any other suitable of units.
  • the input data 118 can further comprise a gingivitis value for the animal.
  • the gingivitis value is a numeric value that is associated with a time to bleeding in the gums of the animal when probing the mouth of the animal.
  • the gingivitis value can be an average value that is associated with a plurality of teeth in the mouth of the animal.
  • the gingivitis value can be based on a visual inspection for inflamed gums.
  • the input data 118 can further comprise a periodontitis value for the animal.
  • the periodontitis value is a numeric value that is associated with the amount of periodontitis that is present in the mouth of the animal.
  • the periodontitis value can correspond with a periodontitis stage as defined by the American Veterinary Dental College (AVDC) or the number/proportion of teeth in the mouth with periodontitis.
  • the periodontitis value can be based on a visual inspection for gingival recession (e.g., receding gums), furcation exposure (e.g., root exposure), or mobile or missing teeth.
  • the input data 118 can further comprise geographic location information that identifies a physical location that is associated with the animal.
  • the geographic location information can identify a country or region where the animal is physically located.
  • the geographic location information can identify a country such as China, Thailand, the United Kingdom, the United States of America, etc.
  • the input data 118 can further comprise any other suitable type or combination of information that is associated with the animal.
  • the geographic location information can be identified using one-hot encoding.
  • the input data 118 can comprise an array that is associated with the geographic location information. The array comprises a plurality of entries that are each associated with a particular country or region.
  • a value of zero for an entry indicates that the animal is not located within a country or region that is associated with the entry.
  • a value of one for an entry indicates that the animal is located within a country or region that is associated with the entry.
  • the geographic location information can be a numeric value or code that uniquely identifies a particular country or region. For example, each country and region can be linked with a unique numerical value. In other embodiments, the geographic location information can use any other suitable type of format or data structure to identify a physical location for the animal.
  • the network device 102 inputs the input data 118 for the animal into the machine learning model 112.
  • the network device 102 inputs any suitable combination of information from the input data 118 that was obtained in step 204 into the machine learning model 112.
  • the network device 102 can input the input data 118 as a sequential or parallel combination of arrays or values into the machine learning model 112.
  • the network device 102 receives an animal oral health state classification 120 for the animal from the machine learning model 112.
  • the machine learning model 112 is configured to predict an animal oral health state classification for the animal based on the microbial taxa values, the breed of the animal, the size of the animal, the weight of the animal, the health of the animal, the gingivitis value associated with the animal, the periodontitis value associated with the animal, the geographic location information associated with the animal, or any other suitable type of information, or combination of information, thereof.
  • the network device 102 receives an animal oral health state classification 120 as an output from the machine learning model 112.
  • the network device 102 outputs the animal oral health state classification 120.
  • the network device 102 outputs the animal oral health state classification 120 for a user to view.
  • the network device 102 can output the animal oral health state classification 120 by displaying the animal oral health state classification 120 on a graphical user interface (e.g., a display).
  • the network device 102 can output the animal oral health state classification 120 by writing and saving the animal oral health state classification 120 within a document of file.
  • the network device 102 can output the animal oral health state classification 120 by sending the animal oral health state classification to a user device 104.
  • the network device 102 can send the animal oral health state classification 120 to the user device 104 as a message, an email, a text document, a file, a link, or in any other suitable format.
  • the user device 104 can then display the animal oral health state classification 120 to a user using a graphical user interface (e.g., a display).
  • a graphical user interface e.g., a display
  • the network device 102 can use any other suitable technique for outputting the animal oral health state classification 120.
  • the network device 102 determines whether to process additional animal information.
  • the network device 102 determines whether there is any more animal information to process for other animals.
  • a user can provide samples to the network device 102 for one or more other animals to process to determine their oral health state classifications.
  • the network device 102 determines to process additional animal information when there are one or more samples remaining to process.
  • the network device 102 returns to step 204 in response to determining to process additional animal information. In this case, the network device 102 returns to step 204 to obtain input data 118 for another animal and to repeat the process of using the machine learning model 112 to determine the oral health state classification of the animal based on the new input data 118. Otherwise, the network device 102 terminates process 200. In this case, the network device 102 determines that there are no more animals to process and terminates process 200.
  • the machine learning model can identify particular microbial species in canine plaque that are significantly associated with health, gingivitis, and/or periodontitis for determining the oral health state of the animal.
  • Figure 6 is an embodiment of a network device 102 for the diagnostic system 100.
  • the network device 102 can be a server or a computer.
  • the network device 102 comprises a processor 302, a memory 110, and a network interface 304.
  • the network device 102 can be configured as shown or in any other suitable configuration.
  • the processor 302 is a hardware device that comprises one or more processors operably coupled to the memory 110.
  • the processor 302 is any electronic circuitry including, but not limited to, state machines, one or more CPU chips, logic units, cores (e.g., a multi-core processor), field-programmable gate array (FPGAs), application-specific integrated circuits (ASICs), or digital signal processors (DSPs).
  • the processor 302 can be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination of the preceding.
  • the processor 302 is communicatively coupled to and in signal communication with the memory 110 and the network interface 304.
  • the one or more processors are configured to process data and can be implemented in hardware or software.
  • the processor 302 can be 8-bit, 16-bit, 32-bit, 64-bit, or of any other suitable architecture.
  • the processor 302 can include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components.
  • ALU arithmetic logic unit
  • the one or more processors are configured to implement various instructions.
  • the one or more processors are configured to execute diagnostics instructions 306 to implement the diagnostics engine 108.
  • processor 302 can be a specialpurpose computer designed to implement the functions disclosed herein.
  • the diagnostics engine 108 is implemented using logic units, FPGAs, ASICs, DSPs, or any other suitable hardware.
  • the diagnostics engine 108 is configured to operate as described in FIGS. 1-2.
  • the diagnostics engine 108 can be configured to perform the steps of process 200 as described in Figure 5.
  • the memory 110 is a hardware device that is operable to store any of the information described above with respect to Figure 1-6 along with any other data, instructions, logic, rules, or code operable to implement the function(s) described herein when executed by the processor 302.
  • the memory 110 comprises one or more disks, tape drives, or solid-state drives, and can be used as an over-flow data storage device, to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution.
  • the memory 110 can be volatile or non-volatile and can comprise a read-only memory (ROM), random-access memory (RAM), ternary content-addressable memory (TCAM), dynamic random-access memory (DRAM), and static random-access memory (SRAM).
  • ROM read-only memory
  • RAM random-access memory
  • TCAM ternary content-addressable memory
  • DRAM dynamic random-access memory
  • SRAM static random-access memory
  • the memory 110 is operable to store diagnostics instructions 306, machine learning models 112, training data 114, test data 116, health information 122, a control data set 124, and/or any other data or instructions.
  • the diagnostics instructions 306 can comprise any suitable set of instructions, logic, rules, or code operable to execute the diagnostics engine 108.
  • the machine learning models 112, the training data 114, the test data 116, the health information 122, and the control data set 124 are configured similar to the machine learning models 112, the training data 114, the test data 116, the health information 122, and the control data set 124 described in Figures. 4-5, respectively.
  • the network interface 304 is a hardware device that is configured to enable wired and/or wireless communications.
  • the network interface 304 is configured to communicate data between user devices 104 and other devices, systems, or domains.
  • the network interface 304 can comprise an NFC interface, a Bluetooth interface, a Zigbee interface, a Z-wave interface, a radio-frequency identification (RFID) interface, a WIFI interface, a LAN interface, a WAN interface, a PAN interface, a modem, a switch, or a router.
  • the processor 302 is configured to send and receive data using the network interface 304.
  • the network interface 304 can be configured to use any suitable type of communication protocol as would be appreciated by one of ordinary skill in the art.
  • EXAMPLE 1 Diagnostic value of species associated with periodontal disease of a companion animal
  • the present example evaluated bacterial species of the canine oral microbiome that were associated with periodontal health or disease. These were determined using qPCR measurements relating the abundance of individual species to the total sample, and then applying machine learning models to enable their diagnostic potential to be determined.
  • subgingival plaque samples were harvested from a cohort of 223 dogs, which consisted of 72 with healthy gingiva (clinically normal; periodontitis stage 0, PD0), 77 with gingivitis (gingivitis only without attachment loss; periodontitis stage 1, PD1) and 74 with early periodontitis (attachment loss up to 25%; periodontitis stage 2, PD2), with the stage of periodontitis defined according to the American Veterinary Dental College (AVDC) nomenclature.
  • the subgingival plaque samples were pooled from multiple teeth of the same health state from the same dog. Specific details on these collections including inclusion/exclusion criteria, assignment of clinical health status and associated metadata can be found in Davis et al. (2013).
  • Taxonomy was assigned, and the number of sequence reads assigned to a particular taxonomic classification at both species and genus levels determined, as previously described by Davis et aL (2013).
  • qPCR assay development and validation qPCR assays were designed based on 16S rRNA sequence information for individual taxa. Taxa selected for assay development were prioritised based on associations with periodontal health or disease, and rankings of presence (% of samples containing the species) and relative abundance (% of species within total bacterial population).
  • probes were designed with a fluorescein based (FAM) reporter dye, and TaqMan minor groove binder (MGB) or black hole quencher (BHQ), respectively.
  • FAM fluorescein based reporter dye
  • MGB TaqMan minor groove binder
  • BHQ black hole quencher
  • assays were designed using full length consensus 16S sequences from a clone library, developed for a previous study to characterise canine oral microbiota (Dewhirst et aL 2012). Sequences were aligned in Vector NTI with the AlignX tool (InvitrogenTM, Thermo Fisher Scientific Inc.) to enable regions of greatest variation to be identified around the 16S rRNA variable regions VI and V2. Subsequently, Primer Express 3 (Applied BiosystemsTM, Thermo Fisher Scientific Inc.) was used to generate candidate primer and probe selections.
  • the qPCR assays were performed on a subset of the same DNA samples, extracted from 205 of the subgingival plaque samples (70 with healthy gingiva, 69 with gingivitis and 66 with early periodontitis) used for HTS (Davis et al., 2013). Briefly, qPCR reactions were conducted using the Biomark system with 48x48 assay chips (Standard BioTools Inc., previously Fluidigm Corporation Ltd). Due to the high throughput nature of the platform, a pre-amplification, enrichment step was conducted. A pre-amplification mixture was prepared, consisting of 25 pl TaqManTM PreAmp Master Mix (Thermo Fisher Scientific Inc.), 12.5 pl pooled assay mix and 12.5 pl DNA.
  • the pre-amplification conditions were an initial denaturation at 95°C for 10 min, then 14 cycles with denaturation at 95°C for 15 s and annealing at 60°C for 4 min.
  • the main qPCR amplification was then performed on the Biomark instrument, according to the manufacturer’s guidelines. Briefly, pre-amplified sample DNA, TaqManTM Gene Expression Master Mix (Applied BiosystemsTM, Thermo Fisher Scientific Inc.) and sample loading reagent (Standard BioTools Inc.), mixed with individual qPCR assays were combined for a total volume of 6pL. Chips were primed, loaded and run according to the manufacturer’s instructions. qPCR data analysis
  • Cq.Test refers to the Cq score associated with a microbial species. In certain embodiments, Cq.Test refers to the Cq score associated with a canine oral taxon (COT). Samples with Cq.Test values outside of the reliable range of the assay (where Cq>21) were assumed to have undetectable amounts of DNA and therefore those relative proportions were imputed as 0.
  • the qPCR data for each microbial taxa was modelled to evaluate single species prediction of periodontitis. Modelling was defined to classify between ‘periodontitis’ (PD2) or ‘not-periodontitis’ (health, PD0 and gingivitis, PD1) samples.
  • Five classification machine learning methods were employed to estimate the diagnostic ability of each species: logistic regression (LR), linear discriminant analysis (LDA), random forest (RF), kernel support vector machines (KSVM) and weighted k-nearest neighbour (KKNN).
  • the models were optimised to maximise the accuracy parameter (sum of correctly classified samples/total number of samples) using 5-fold cross-validation with hyper parameter tuning using a grid search.
  • the samples Prior to modelling, the samples were split into a training and a test subset with stratification applied to the health state to reduce over specifying the ‘not-periodontitis’ state. Specifically, 72 samples (34 PD2: 18 PD1 : 20 PD0) were used for training the models, and the remaining samples were used for bootstrap sampling the test subset, with 1000 repetitions in the ratio of 35 PD2: 20 PD1 : 20 PD0 samples. The average accuracy, sensitivity (true positive rate: % of periodontitis samples correctly classified as periodontitis) and specificity (true negative rate: % of not-periodontitis samples correctly classified as not-periodontitis) and their standard deviations from those 1000 bootstrap repetitions were then used to estimate the performance of the models.
  • the machine learning models were performed in R v4.2.1 statistical software, using the mlr (Bischl et al. 2016) library.
  • Canine periodontal disease associated species differ in their diagnostic potential
  • the plaque sample qPCR outputs from assays developed against Capnocytophaga sp. COT-339, Peptostreptococcaceae XI [G-4] sp. COT-019 and Clostridiales sp. COT-028 were modelled to assess their sensitivity (correct classification of periodontitis samples) and specificity (correct classification of non-periodontitis samples) (Figure 3).
  • three models gave outputs with estimated 82.9-88.6% sensitivity and 25.0-27.5% specificity.
  • the performance of the presently disclosed portfolio of single-species targeted qPCR assays could be assessed by comparing their bacterial detection capability against the equivalent findings delivered via a HTS approach. This indicated moderate to strong overall alignment between the targeted, qPCR approach and the broad-spectrum HTS technology.
  • HTS targets ubiquitous 16S rDNA sequence, thereby promoting amplification of all the members of the microbial community within a given sample.
  • the approach undertaken for qPCR assay design exploits novelty in regions of the 16S rRNA sequence to enable targeting of individual bacterial species.
  • the qPCR assays were able to discriminate subgingival plaque samples from dogs with healthy gingiva and periodontitis. This result was similar to the observations from the HTS cross-sectional study which employed the same clinical plaque samples (Davis et al. 2013).
  • qPCR assay design was based on a consensus sequence derived for each bacterial target, in this instance a similarity level of 99%, HTS amplification can be less specific and detect a broader range of related 16S targets. Optimization work with qPCR assay design could result in improved performance and increase the potential of these assays for utilization as diagnostic tools.
  • Prominent examples include zoonotic leptospirosis and leishmaniosis, caused by bacterial Leptospira spp., and protozoa Leishmania infantum and L. donovani. respectively.
  • the literature on qPCR for canine veterinary application is substantial and covers a broad spectrum of epidemiological areas (Flores et al. 2017, Scorza et al. 2021, Smith et al. 2021, Griebsch et al. 2022), as well as discussion of the selection of appropriate sample types (Cavalera et al. 2022, Peris et al. 2021) and the development of detection methods (Fink et al. 2015, Miotto et al. 2018).
  • the present example reports an approach which could be developed towards a qPCR-based diagnostic tool to detect microbial biomarkers of canine periodontitis in supragingival plaque. Based on strong correlations with HTS data, qPCR assays designed to target specific bacterial species offer an accurate, cost and time efficient strategy with promise for improving diagnosis of this prevalent yet under-reported condition.

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Organic Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Engineering & Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Biotechnology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Biochemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

La présente invention concerne des procédés pour déterminer l'état de santé bucco-dentaire d'un animal grâce à l'analyse de la composition du microbiome bucco-dentaire. L'abondance ou l'abondance relative d'un ou plusieurs taxons microbiens est dosée par qPCR et comparée aux données de modélisation produites à partir de canins présentant un état sain ou une maladie parodontale.
PCT/US2023/031742 2022-09-02 2023-08-31 Diagnostic d'espèces bactériennes de la parodontite canine par réaction en chaîne par polymérase quantitative WO2024050035A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263403542P 2022-09-02 2022-09-02
US63/403,542 2022-09-02

Publications (1)

Publication Number Publication Date
WO2024050035A1 true WO2024050035A1 (fr) 2024-03-07

Family

ID=88192330

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/031742 WO2024050035A1 (fr) 2022-09-02 2023-08-31 Diagnostic d'espèces bactériennes de la parodontite canine par réaction en chaîne par polymérase quantitative

Country Status (1)

Country Link
WO (1) WO2024050035A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008137506A2 (fr) 2007-05-01 2008-11-13 Mars Incorporated Procédés et trousses de lutte contre la parodontopathie chez les chiens
WO2008137541A2 (fr) * 2007-05-01 2008-11-13 Mars Incorporated Santé périodontique chez le chien
WO2014199115A1 (fr) 2013-06-14 2014-12-18 Mars, Incorporated Analyse et procédé
WO2017044902A1 (fr) * 2015-09-09 2017-03-16 uBiome, Inc. Méthode et système de diagnostics dérivés du microbiome et traitements thérapeutiques pour la santé bucco-dentaire
WO2022072935A1 (fr) * 2020-10-02 2022-04-07 Mars, Incorporated Diagnostic d'animal à l'aide d'un apprentissage machine

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008137506A2 (fr) 2007-05-01 2008-11-13 Mars Incorporated Procédés et trousses de lutte contre la parodontopathie chez les chiens
WO2008137541A2 (fr) * 2007-05-01 2008-11-13 Mars Incorporated Santé périodontique chez le chien
WO2014199115A1 (fr) 2013-06-14 2014-12-18 Mars, Incorporated Analyse et procédé
WO2017044902A1 (fr) * 2015-09-09 2017-03-16 uBiome, Inc. Méthode et système de diagnostics dérivés du microbiome et traitements thérapeutiques pour la santé bucco-dentaire
WO2022072935A1 (fr) * 2020-10-02 2022-04-07 Mars, Incorporated Diagnostic d'animal à l'aide d'un apprentissage machine

Non-Patent Citations (56)

* Cited by examiner, † Cited by third party
Title
AMERICAN VETERINARY DENTAL COLLEGE, AVDC, Retrieved from the Internet <URL:https://www.avdc.org>
ANONYMOUS: "Basepaws Feline Dental Health Test", BASEPAWS, INC., 21 March 2021 (2021-03-21), pages 1 - 17, XP093000892, Retrieved from the Internet <URL:Https://cdn.shopify.com/s/files/1/0087/3798/0475/files/Basepaws_Cat_Dental_Health_White_Paper.pdf?v=1616969122.> [retrieved on 20221122] *
BISCHL, B.LANG, M.KOTTHOFF, L. ET AL.: "Machine Learning in R", JOURNAL OF MACHINE LEARNING RESEARCH, vol. 17, 2016, pages 1 - 5, Retrieved from the Internet <URL:http://jmlr.org;papers;v17/15-066,html>
CAVALERA, M. A., ZATELLI, A., DONGHIA, R.: "Conjunctival Swab Real Time-PCR in Leishmania infantum Seropositive Dogs: Diagnostic and Prognostic Values", BIOLOGY, vol. 11, 2022, pages 184, Retrieved from the Internet <URL:https://doi.org/10.3390/bioloay11020184>
DANESI, P.PETINI, M.FALCARO. C. ET AL.: "Pneumocystis Colonization in Dogs Is as in Humans", INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESESEARCH AND PUBLIC HEALTH, vol. 19, 2022, pages 3192, Retrieved from the Internet <URL:https://doi.org/10.3390/ijerphl9063192>
DANESI, P.RAVAGNAN, S.JOHNSON, L.R. ET AL.: "Molecular diagnosis of Pneumocystis pneumonia in dogs", MEDICAL MYCOLOGY, vol. 55, 2017, pages 828 - 842, Retrieved from the Internet <URL:https://doi.org/10.1093/mmv/myx007.>
DAVIS, I. J.WALLIS, C.DEUSCH, O. ET AL.: "A cross-sectional survey of bacterial species in plaque from client owned dogs with healthy gingiva, gingivitis or mild periodontitis", PLOS ONE, vol. 8, 2013, pages e83158, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0083158>
DEBOWES, L.J., MOSIER, D.LOGAN, E. ET AL.: "Association of periodontal disease and histologic lesions in multiple organs from 45 dogs", JOURNAL OF VETERINARY DENTISTRY, vol. 13, 1996, pages 57 - 60, Retrieved from the Internet <URL:https://doi.org/10.1177%2F089875649601300201.>
DEWHIRST, F. E.KLEIN, E. A.THOMPSON, E. C. ET AL.: "The canine oral microbiome.", PLOS ONE 7, 2012, pages e36067, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0036067.>
DREIER, M.MEOLA, M.BERTHOUD, B. ET AL.: "High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota.", BMCMICROBIOLOLOGY, vol. 22, 2022, pages 48, Retrieved from the Internet <URL:https://doi.org/10.1186/s12866-022-02451-y.>
FINK, J. M.MOORE, G.E.LANDAU, R. ET AL.: "Evaluation of three 5' exonuclease-based real-time polymerase chain reaction assays for detection of pathogenic Leptospira species in canine urine.", JOURNAL OF VETERINARY DIAGNOSTIC INVESTIGATION, vol. 27, 2015, pages 159 - 66, Retrieved from the Internet <URL:https://doi.org/10.1177%2F1040638715571360.>
FLORES, B.J.PEREZ-SANCHEZ, T.FUERTES, H. ET AL.: "A cross-sectional epidemiological study of domestic animals related to human leptospirosis cases in Nicaragua.", ACTA TROPICA, vol. 170, 2017, pages 79 - 84, XP029988360, Retrieved from the Internet <URL:https://doi.org/10.1016/j.actatropica.2017.02.031> DOI: 10.1016/j.actatropica.2017.02.031
GAD, T.: "Periodontal disease in dogs. 1. Clinical investigations", JOURNAL OF, vol. 3, 1968, pages 268 - 272, Retrieved from the Internet <URL:https://doi.org/10.1111/j.1600-0765.1968.tb01929.x.>
GLICKMAN, L. T.GLICKMAN, N. W.MOORE, G. E. ET AL.: "Evaluation of the risk of endocarditis and other cardiovascular events on the basis of the severity of periodontal disease in dogs.", JOURNAL OF THE AMERICAN VETERINARY MEDICAL ASSOCIATION, vol. 234, 2009, pages 486 - 94, Retrieved from the Internet <URL:https://doi.org/10.2460/javma.234.4.486.>
GORREL, C.: "Veterinary Dentistry for the General Practitioner", 2013, ELSEVIER HEALTH SCIENCES
GRIEBSCH, C.KIRKWOOD, N.WARD, M.P. ET AL.: "Emerging leptospirosis in urban Sydney dogs: a case series", AUSTRALIAN VETETERINARY JOURNAL, vol. 100, 2017, pages 190 - 200, Retrieved from the Internet <URL:https://doi.org/10.1111/avj.13148.>
GRUBBS, F.: "Procedures for Detecting Outlying Observations in Samples", TECHNOMETRICS, vol. 11, 1969, pages 1 - 21
HALL, J. A.FORMAN, F. J.BOBE, G. ET AL.: "The impact of periodontal disease and dental cleaning procedures on serum and urine kidney biomarkers in dogs and cats.", PLOS ONE, vol. 16, 2021, pages e0255310, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0255310.>
HARVEY, C. E.: "Periodontal disease in dogs. Etiopathogenesis, prevalence, and significance.", VETERINARY CLINICS OF NORTH AMERICA: SMALL ANIMAL PRACTICE, vol. 28, 1998, pages 1111 - 1128, Retrieved from the Internet <URL:https://doi.org/10.1016/S0195-5616(98)50105-2.>
HARVEY, C. E.SHOFER, F. S.LASTER, L.: "Association of age and body weight with periodontal disease in North American dogs.", JOURNAL OF VETERINARY DENTISTRY, vol. 11, 1994, pages 94 - 105, Retrieved from the Internet <URL:https://doi.org/10.1177%2F089875649401100301>
HAWKINS, S. F. C.GUEST, P. C.: "Multiplex Quantitative Polymerase Chain Reaction Diagnostic Test for SARS-CoV-2 and Influenza A/B Viruses", METHODS IN, vol. 2511, 2022, pages 53 - 65, Retrieved from the Internet <URL:https://doi.org/10.1007/978-1-0716-2395-44>
HOFFMANN, T.GAENGLER, P.: "Epidemiology of periodontal disease in poodles", JOURNAL OF SMALL ANIMAL PRACTICE, vol. 37, 1996, pages 309 - 316, Retrieved from the Internet <URL:https://di.rg/l0.1111/j.1748-5827.1996.tb02396.x>
HOLCOMBE, L. J., PATEL, N., COLYER, A.: "Early canine plaque biofilms:characterization of key bacterial interactions involved in initial colonization of enamel.", PLOS ONE, vol. 9, 2014, pages e1 13744, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0113744>
KORTEGAARD, H. E.ERIKSEN, T.BAELUM, V.: "Periodontal disease in research beagle dogs--an epidemiological study.", JOURNAL OF SMALL ANIMAL PRACTICE, vol. 49, 2008, pages 610 - 616, Retrieved from the Internet <URL:https://doi.org/10.1111/j.1748-5827.2008.00609.x>
KWON, D.BAE, K.KIM, H. ET AL.: "Treponema denticola as a prognostic biomarker for periodontitis in dogs.", PLOS ONE, vol. 17, 2022, Retrieved from the Internet <URL:e0262859.https://doi.org/10.1371/journal.pone.0262859.>
KYLLAR, M.WITTER, K: "Prevalence of dental disorders in pet dogs", VETERINARY, vol. 50, 2005, pages 496 - 505, XP055668086, Retrieved from the Internet <URL:https://doi.org/10.17221/5654-VETMED> DOI: 10.17221/5654-VETMED
LINDHE, J.HAMP, S.LOE, H.: "Experimental periodontitis in the beagle dog.", JOURNAL OF PERIODONTALRESEARCH, vol. 8, 1973, pages 1 - 10, Retrieved from the Internet <URL:https://doi.org/l0.1111/j.1600-0765.1973.tb00735.x>
LUND, E. M., ARMSTRONG, P. J., KIRK, C. A.: "Health status and population States.", JOURNAL OF THE AMERICAN VETERINARY MEDICALASSOCIATION, vol. 214, 1999, pages 1336 - 1341, XP055523743
MANFRA MARRETTA, S., LEESMAN, M., BURGESS-CASSLER, A.: "Pilot evaluation of a novel test strip for the assements of dissolved thiol levels, as an indicator of canine gingival health and periodontal status.", THE CANADIAN VETERINARY JOURNAL, vol. 53, 2012, pages 1260 - 5
MIOTTO, B.A.DA HORA, A. S.TANIWAKI, S.A. ET AL.: "Development and validation of a modified TaqMan based real-time PCR assay targeting the lipl32 gene for detection of pathogenic Leptospira in canine urine samples.", BRAZILIAN JOURNAL OF MICROBIOLOGY, vol. 49, 2018, pages 584 - 590, Retrieved from the Internet <URL:https://doi.org/10.1016/j.bjm.2017.09.004.>
MURRAY, D. C.BUNCE, M.CANNELL, B. L. ET AL.: "DNA-based faecal dietary analysis: a comparison of qPCR and high throughput sequencing approaches.", PLOS, 2011, pages 25776, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0025776>
NIEMIEC, B. A.GAWOR, J.TANG, S. ET AL.: "The bacteriome of the oral cavity in healthy dogs and dogs with periodontal disease.", AM J VET RES, vol. 83, 2021, pages 50 - 58, Retrieved from the Internet <URL:https://doi.org/10.2460/ajvr.21.02.0027>
OKSANEN, J., SIMPSON, G., BLANCHET, F.: "vegan: Community Ecology Package", R PACKAGE VERSION 2.6-2, 2022, Retrieved from the Internet <URL:https://CRAN.R-project.org/package:=vegan>
O'NEILL, D. G.CHURCH, D. B.P. D. MCGREEVY, P. D. ET AL.: "Prevalence of disorders recorded in dogs attending primary-care veterinary practices in England.", PLOS ONE, vol. 9, 2014, pages e90501, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0090501>
OTT ET AL., J. CLIN. MICROBIOL., vol. 42, no. 6, June 2004 (2004-06-01), pages 2566 - 2572
PAVLICA, Z.PETELIN, M.JUNTES, P. ET AL.: "Periodontal disease burden and pathological changes in organs of dogs.", JOURNAL OF VETERINARY DENTISTRY, vol. 25, 2008, pages 97 - 105, Retrieved from the Internet <URL:https://doi.org/10.1177%2F089875640802500210>
PEREIRA DOS SANTOS, J.D.CUNHA, E.NUNES, T. ET AL.: "Relation between periodontal disease and systemic diseases in dogs. Research in Veterinary", SCIENCE, vol. 125, 2019, pages 136 - 140, Retrieved from the Internet <URL:https://doi.org/10.1016/j.rvsc.2019.06.007.>
PERIS, M. P.ESTEBAN-GIL, A.ORTEGA-HERNANDEZ. P. ET AL.: "Comparative Study of Real-Time PCR (TaqMan Probe and Sybr Green), Serological Techniques (ELISA, IFA and DAT) and Clinical Signs Evaluation, for the Diagnosis of Canine Leishmaniasis in Experimentally Infected Dogs.", MICROORGANISMS, vol. 9, 2021, pages 2627, Retrieved from the Internet <URL:https://doi.org/10.3390/microorganisms9122627>
POPPL A. G.DE CARVALHO, G. L. C.VIVIAN, I. F. ET AL.: "Canine diabetes mellitus risk factors: A matched case-control study.", RESEARCH IN VETERINARY SCIENCE, vol. 114, 2017, pages 469 - 473, XP085223410, Retrieved from the Internet <URL:https://doi.org/10.1016/j.rvsc.2017.08.003.> DOI: 10.1016/j.rvsc.2017.08.003
QUECK, K. E.CHAPMAN, A.HERZOG, L. J. ET AL.: "Oral-Fluid Thiol-Detection Test Identifies Underlying Active Periodontal Disease Not Detected by the Visual Awake Examination.", JOURNAL OF AMERICAN ANIMAL HOSPITAL ASSOCIATION, vol. 54, 2018, pages 132 - 137, Retrieved from the Internet <URL:https://doi.org/10.5326/JAAHA-MS-6607.>
QUROLLO, B.A.RIGGINS. D.COMYN, A. ET AL.: "Development and validation of a sensitive and specific sodB-based quantitative PCR assay for molecular detection of Ehrlichia species.", JOURNAL OF CLINICAL MICROBIOLOGY, vol. 52, 2014, pages 4030 - 2, XP093042881, Retrieved from the Internet <URL:https://doi.org/10.1128/JCM.02340-14> DOI: 10.1128/JCM.02340-14
RUPARELL AVIKA ET AL: "Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models", BMC VETERINARY RESEARCH, vol. 19, no. 1, 18 September 2023 (2023-09-18), GB, XP093108494, ISSN: 1746-6148, Retrieved from the Internet <URL:https://bmcvetres.biomedcentral.com/counter/pdf/10.1186/s12917-023-03668-3.pdf> DOI: 10.1186/s12917-023-03668-3 *
RUPARELL, A.WALLIS, C.HAYDOCK, R.: "Comparison of subgingival and gingival margin plaque microbiota from dogs with healthy gingiva and early periodontal disease.", RESEARCH IN VETERINARY SCIENCE, vol. 136, 2021, pages 396 - 407, Retrieved from the Internet <URL:https://doi.org/10.1016/j.rvsc.2021.01.011>
SALT, C.MORRIS, P. J.GERMAN, A. J. ET AL.: "Growth standard charts for monitoring bodyweight in dogs of different sizes.", PLOS ONE, vol. 12, no. 9, 2017, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pone.0182064.>
SANTIBANEZ, R., RODRIGUEZ-SALAS, C., FLORES-YANEZ, C., VETERINARY SCIENCES, vol. 8, pages 291, Retrieved from the Internet <URL:https://doi.ora/10.3390/vctsci8120291>
SCORZA B. M.MAHACHI, K. G.COX, A. C. ET AL.: "Leishmania infantum xenodiagnosis from vertically infected dogs reveals significant skin tropism", PLOS NEGLECTED TROPICAL DISEASES, vol. 15, 2021, pages e0009366, Retrieved from the Internet <URL:https://doi.org/10.1371/journal.pntd.0009366>
SMIDT I.R. KIIKER, R.OOPKAUP, H. ET AL.: "Comparison of detection methods for vaginal lactobacilli", BENEFICIAL MICROBES, vol. 6, 2015, pages 747 - 51, Retrieved from the Internet <URL:https://doi.org/10.3920/BM2014.0154.>
SMITH, A. M.STULL, J. W.EVASON, M. D. ET AL.: "Investigation of spatio-temporal clusters of positive leptospirosis polymerase chain reaction test results in dogs in the United States", JOURNAL OF VETERINARY INTERNAL MEDICINE, vol. 35, 2009, pages 1355 - 1360, Retrieved from the Internet <URL:https://doi.org/10.1111/jvim.16060>
SORENSEN, W. P.H. LOE, H.RAMF ORD, S. P.: "Periodontal disease in the beagle dog. A cross sectional clinical study", JOURNAL OF PERIODONTAL RESEARCH, vol. 15, 1980, pages 380 - 389, Retrieved from the Internet <URL:https://doi.org/10.1111/j.1600-0765.1980.tb00295.x>
SZYMON P SZAFRANSKI ET AL: "High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis", APPLIED AND ENVIRONMENTAL MICROBIOLOGY, vol. 81, no. 3, 1 February 2015 (2015-02-01), US, pages 1047 - 1058, XP055538161, ISSN: 0099-2240, DOI: 10.1128/AEM.03534-14 *
THOMSON, K.YAARAN, T.BELSHAW, A. ET AL.: "A new TaqMan method for the reliable diagnosis of Ehrlichia spp. in canine whole blood", PARASITES VECTORS, vol. 11, 2018, Retrieved from the Internet <URL:https://doi.org/10.1186/s13071-018-2914-5>
WALLIS ET AL., CROSS-BREEDS, 2021
WALLIS, C.HOLCOMBE, L. J.: "A review of the frequency and impact of periodontal disease in dogs", JOURNAL OF SMALL ANIMAL PRACTICE, vol. 61, 2020, pages 529 - 540, Retrieved from the Internet <URL:https://doi.org/10.1111/jsap.13218>
WALLIS, C.MARSHALL, M.COLYER, A. ET AL.: "A longitudinal assessment of changes in bacterial community composition associated with the development of periodontal disease in dogs", VETERINARY MICROBIOLOGY, vol. 181, 2015, pages 271 - 282, XP029340119, Retrieved from the Internet <URL:https://doi.org/l0,1016/j.vetmic.2015.09.003> DOI: 10.1016/j.vetmic.2015.09.003
WALLIS, C.MILELLA, L.COLYER, A. ET AL.: "Subgingival microbiota of dogs with healthy gingiva or early periodontal disease from different geographical locations", BMC, vol. 275, 2021, pages 105717, Retrieved from the Internet <URL:https://doi.org/10.1186/s12917-020-02660-5>
WALLIS, C.SAITO, E. K.SALT, C. ET AL.: "Association of periodontal disease with breed size, breed, weight, and age in pure-bred client-owned dogs in the United States", VETERINARY JOURNAL, vol. 275, 2021, pages 105715, Retrieved from the Internet <URL:https://doi.org/10.1016/j.tvjl.2021.105717.>

Similar Documents

Publication Publication Date Title
Pilotte et al. Improved PCR-based detection of soil transmitted helminth infections using a next-generation sequencing approach to assay design
Nichols et al. Browsed twig environmental DNA: diagnostic PCR to identify ungulate species
Yang et al. Development of a touch-down multiplex PCR method for simultaneously rapidly detecting three novel insertion/deletions (indels) within one gene: an example for goat GHR gene
Guernier et al. Leptospira diversity in animals and humans in Tahiti, French Polynesia
Kostoulas et al. Bayesian estimation of sensitivity and specificity of serum ELISA and faecal culture for diagnosis of paratuberculosis in Greek dairy sheep and goats
US20180107783A1 (en) Validating biomarker measurement
Gürsoy et al. Cumulative use of salivary markers with an adaptive design improves detection of periodontal disease over fixed biomarker thresholds
Beaumelle et al. Metabarcoding in two isolated populations of wild roe deer (Capreolus capreolus) reveals variation in gastrointestinal nematode community composition between regions and among age classes
Wattanaphansak et al. Development of a polymerase chain reaction assay for quantification of Lawsonia intracellularis
Perera et al. Comparison of the performance of three PCR assays for the detection and differentiation of Theileria orientalis genotypes
Silbermayr et al. Phylogenetic relationships and new genetic tools for the detection and discrimination of the three feline Demodex mites
US20210324473A1 (en) Indices of Microbial Diversity Relating To Health
Silbermayr et al. A novel qPCR assay for the detection of African animal trypanosomosis in trypanotolerant and trypanosusceptible cattle breeds
Hearps et al. Evaluation of the portable Cepheid SmartCycler real‐time PCR machine for the rapid diagnosis of foot‐and‐mouth disease
US20200357485A1 (en) System and method for nucleotide analysis
Puig et al. Quantification of Malassezia pachydermatis by real-time PCR in swabs from the external ear canal of dogs
Sargison et al. Faecal egg counts and nemabiome metabarcoding highlight the genomic complexity of equine cyathostomin communities and provide insight into their dynamics in a Scottish native pony herd
Oakes et al. Coinfection of cattle in Virginia with Theileria orientalis Ikeda genotype and Anaplasma marginale
Paltrinieri et al. Serum α1-acid glycoprotein (AGP) concentration in non-symptomatic cats with feline coronavirus (FCoV) infection
Gray et al. Wild deer in the United Kingdom are a potential reservoir for the livestock parasite Babesia divergens
Matějková et al. Oral and vaginal microbiota in selected field mice of the genus Apodemus: a wild population study
US20230386661A1 (en) Animal diagnostics using machine learning
WO2024050035A1 (fr) Diagnostic d&#39;espèces bactériennes de la parodontite canine par réaction en chaîne par polymérase quantitative
More et al. A molecular survey of canine respiratory viruses in New Zealand
Oney et al. Evaluation of a commercial microbial enrichment kit used prior DNA extraction to improve the molecular detection of vector-borne pathogens from naturally infected dogs

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23776768

Country of ref document: EP

Kind code of ref document: A1