KR102342105B1 - Method of grid pattern-based disease screening using multi-capillary column-ion mobility spectrometry - Google Patents

Abstract

The present invention relates to a method for diagnosing diseases using MCC-IMS. The expiration data of a specific patient group measured using MCC-IMS is divided into grid cells, grouped, and only meaningful data is stored and compared with the patient's expiration data for disease. By diagnosing this, it is possible to analyze various components of expiration, unlike expiration analysis that relied on a specific sensor, and thus has the effect of reducing the sensor dependence as well as the false diagnosis rate.

Description

Grid pattern-based disease diagnosis method using multi-capillary column ion mobility analyzer {METHOD OF GRID PATTERN-BASED DISEASE SCREENING USING MULTI-CAPILLARY COLUMN-ION MOBILITY SPECTROMETRY}

The present invention relates to a method for testing a disease, and more particularly, to a method for diagnosing a disease by analyzing a person's respiration.

With the development of medical technology, human life expectancy is increasing. As life expectancy increases, interest in a healthy life is also increasing. For a healthy life, studies on early diagnosis of diseases as well as treatment of diseases are being actively conducted.

As one of the test methods for early diagnosis of disease, analysis of exhaled breath is used. Conventionally, for diagnosing a disease, professional equipment such as visiting a hospital to collect blood and taking X-rays is required, and an examination method that harms the human body has been used. However, breath analysis has the advantage of not harming the human body because it is a non-invasive method of examining the breath during daily breathing of a person.

Sensors have been conventionally used for breath analysis. It was a method of detecting a desired substance with a sensor capable of detecting a specific component in order to detect a specific component, that is, a biomarker in exhaled air. Since it is difficult to detect a very small amount of material with one sensor, a method of analyzing electrical signal changes by arranging several sensors in an array method is mainly used.

However, this sensor array method has several problems. First of all, in order to analyze the pattern of a biomarker, the reactivity to the biomarker must be at the level of parts per billion (ppb), but the process of manufacturing such a sensor is not easy. If necessary, there is a difficulty in manufacturing a new sensor for the material. In addition, since it may take a lot of time for the electrical signal generated when the biomarker is detected by the sensor to be stabilized back to the initial state, there is a problem that the test time may be prolonged.

Even if a sensor array is manufactured with a specific biomarker as a target, its characteristics change when various gases are adsorbed to the sensor. Therefore, it is difficult to accurately detect a biomarker and determine its concentration because an electrical signal may be changed due to the interaction of substances other than biomarkers. It is also a problem that sensitivity and reproducibility are deteriorated when used for a long time due to the gas adsorbed to the sensor.

Since the sensor array method has a limitation in accurately detecting biomarkers, the sensitivity and specificity of disease diagnosis using the sensor array are also lowered, thereby increasing the false diagnosis rate.

The inventors of the present invention have been researching and trying to solve the problems of the method of exhalation analysis using the sensor of the prior art. After much effort to complete a disease diagnosis method based on multi-capillary column-ion mobility spectrometry (MCC-IMS) that can compensate for the disadvantages of the sensor method, the present invention has been completed.

It is an object of the present invention to provide a method for diagnosing respiratory diseases based on MCC-IMS to supplement the problems of the conventional sensor array method for analyzing breath.

It is another object of the present invention to reduce the false diagnosis rate by diagnosing diseases by analyzing all various volatile organic compounds contained in the exhaled air by using MCC-IMS rather than using a sensor that can detect only a specific substance.

On the other hand, other objects not specified in the present invention will be additionally considered within the range that can be easily inferred from the following detailed description and effects thereof.

The present invention is a grid pattern-based disease screening method using MCC-IMS,

collecting expiration information of a plurality of users having the same characteristics using multi-capillary column-ion mobility spectrometry (MCC-IMS); For the expiratory information data of a plurality of users output from the MCC-IMS, the x-axis is the drift time by IMS, the y-axis is the retention time by the MCC, and the z-axis is the output signal according to the concentration of the detection component. Normalizing each intensity to three-dimensional grid data (Grid Data); calculating average grid data by summing the z-axis output signal strengths of grid data having the same x-axis and y-axis among the grid data of the users; and comparing the average grid data with the average grid data of a normal user group without disease, and storing only average grid data larger than the average grid data of the normal user group and having a difference by more than a predetermined threshold as the first group, and storing only average grid data smaller than the average grid data of the normal user group and having a difference by more than a predetermined threshold value as a second group.

In addition, after the step of storing the average grid data, collecting the exhalation information of the subject using MCC-IMS; normalizing the exhalation information data of the examinee outputted from the MCC-IMS into three-dimensional grid data of the x-axis, y-axis, and z-axis; comparing the z-axis value of the grid data of the examinee with the stored average grid data in which the x-axis and the y-axis of the grid data of the examinee are at the same position; and determining whether to diagnose a disease of the examinee according to the comparison result, the number of grid data of the examinee that is greater than the average grid data of the first group and the number of grid data of the examinee that is smaller than the average grid data of the second group It may include further steps.

A grid pattern-based disease screening method using MCC-IMS according to another embodiment of the present invention,

collecting expiration information of a plurality of users having the same characteristics using multi-capillary column-ion mobility spectrometry (MCC-IMS); For the expiratory information data output from the MCC-IMS, the x-axis represents the drift time by IMS, the y-axis represents the retention time by the MCC, and the z-axis represents the output signal intensity according to the concentration of the detection component, respectively. Normalizing to three-dimensional grid data (Grid Data); calculating average grid data by summing the z-axis output signal strengths of grid data having the same x-axis and y-axis among the grid data of the users; Grouping the average grid data into grid cells having regular intervals on the x-axis and y-axis, summing the average grid data included in the grid cells, and calculating the average value as a representative value of each grid cell step; and comparing the representative value of each grid cell with a corresponding representative value of a grid cell of a normal user group without disease. Storing the representative value as a third group, and storing the representative value of the grid cell that is smaller than the representative value of the grid cell of the normal user group and has a difference by more than a predetermined threshold as a fourth group do.

Preferably, after the step of storing the representative value of the grid cell, the step of collecting the exhalation information of the subject using MCC-IMS; normalizing the exhalation information data of the examinee outputted from the MCC-IMS into three-dimensional grid data of the x-axis, y-axis, and z-axis; The grid data of the examinee is grouped into grid cells having regular intervals on the x-axis and the y-axis, and an average value is obtained by summing the z-axis signal intensities of the grid data of the examinee included in the grid cells. storing the representative values of the grid cells of the examinee; comparing the stored representative values of the stored grid cells in the same range as the x-axis and the y-axis with the representative values of the grid cells of the examinee; and as a result of the comparison, the number of representative values of the grid cells of the examinee that is greater than the representative value of the grid cells of the third group and the number of representative values of the grid cells of the examinee that are smaller than the representative value of the grid cells of the fourth group Accordingly, it is preferable to further include the step of determining whether the subject is diagnosed with a disease.

By means of the above-described problem solving means of the present invention, the present invention enables analysis of various components by one MCC-IMS device, so there is no need to install an additional sensor to detect a specific substance, and it has excellent reproducibility even in long-term operation. have.

In addition, since it is possible to analyze a concentration of several tens of ppb or less, it has an effect of increasing the sensitivity and specificity of disease diagnosis by providing excellent sensitivity.

On the other hand, even if it is an effect not explicitly mentioned herein, it is added that the effects described in the following specification expected by the technical features of the present invention and their potential effects are treated as described in the specification of the present invention.

1 is a flowchart of a method for collecting disease diagnostic index data according to a preferred embodiment of the present invention.
2 shows an example of a graph analyzing data collected according to a preferred embodiment of the present invention.
3 is a flowchart of a method for diagnosing a disease according to a preferred embodiment of the present invention.
4 is a flowchart of a method for collecting disease diagnostic index data according to another preferred embodiment of the present invention.
5 is an example showing grouping of disease diagnostic index data according to another preferred embodiment of the present invention.
6 shows an example of a disease diagnostic index data pattern grouped according to another preferred embodiment of the present invention.
7 is a flowchart of a disease diagnosis method according to another preferred embodiment of the present invention.
8 shows an example of an apparatus for collecting disease diagnostic index data for carrying out the present invention.
※ It is revealed that the accompanying drawings are exemplified as a reference for understanding the technical idea of the present invention, and the scope of the present invention is not limited thereby.

Hereinafter, the configuration of the present invention guided by various embodiments of the present invention and effects resulting from the configuration will be described with reference to the drawings. In the description of the present invention, if it is determined that related known functions are obvious to those skilled in the art and may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

8 is a view showing an example of a disease diagnosis device through expiration analysis used in the present invention.

The apparatus for diagnosing diseases through breath analysis includes an expiratory inlet 100 and a detection unit.

The exhalation inlet 100 is a place where the examinee blows the exhalation by exhalation, and may include a mouthpiece. The subject holds the mouthpiece in his mouth and exhales for a long time to deliver exhalation. It is recommended that the mouthpiece be made of a material with excellent heat resistance and chemical resistance because it is used while being held in the mouth.

The detection unit functions to detect the volatile organic compounds (VOCs) mixed gas in the exhaled air that has passed through the exhalation inlet 100 . The detection unit includes a multi capillary column (MCC: Multi Capilary Column, 400) and an ion mobility analyzer (IMS: Ion Mobility Spectrometry, 200).

The IMS 200 may detect a biomarker by identifying a component through a difference in ion mobility according to the size, shape, mass, charge, etc. of ionized molecules or ions in the exhalation of a subject.

The MCC 400 may separate the various mixed gas components in the expiratory air before analysis by moving the introduced expiratory air to the IMS 200 . That is, the MCC 400 is used as a pre-seperation device before the exhalation is separated by the IMS 200 . The MCC 400 is composed of about a thousand thin parallel capillaries and has a very high capacity, a wide range of flow rates, and a short total length of the column in centimeters (cm), so it has excellent resolution in a short time. Mixtures can be separated.

1 is a flowchart of a method for collecting disease diagnostic index data according to a preferred embodiment of the present invention.

In order to collect disease diagnostic indexes, it is necessary to collect and database the expiration data of patients with specific diseases. For example, data from lung cancer or chronic obstructive pulmonary disease (COPD) patients are collected and compared with data from healthy people and compared to data from healthy people.

For this purpose, in the present invention, the expiration of a specific group of patients is collected using the MCC-IMS of FIG. 8 (S110).

Unlike the conventional sensor array that detects only a specific target biomarker, the MCC-IMS used in the present invention can detect various volatile organic compounds (VOCs) in the air. It can be used as an index for diagnosing diseases by checking changes in the body.

The expiratory data collected using MCC-IMS is normalized to three-dimensional grid data in which the x-axis represents the drift time by IMS, the y-axis represents the retention time by the MCC, and the z-axis represents the signal strength (S120) .

FIG. 2 is an example of normalizing expiratory air data to three-dimensional grid data and showing it in a topographic plot format.

Fig. 2 (a) is an example of expressing grid data as a three-dimensional graph, and Fig. 2 (b) is an example of expressing grid data using a topographic plot that expresses signal strength in different colors and shows it like a contour line.

On the y-axis of the topographic plot, a total of 400 IMS spectra are accumulated and displayed for each retention time at 1.5 second intervals for 10 minutes.

The x-axis of the topographic plot displays a total of 1,250 positions with an IMS spectrum drift time of 0 ms to 25 ms at 0.02 ms intervals for each retention time.

In the positions of retention time and drift time of the topographic plot, the signal strength (Amplitude, mV) is displayed in different colors according to the signal strength.

Therefore, the data displayed by one topographic plot includes 1250x400=500000 data.

After data normalization, the average grid data is obtained by averaging the grid data at the same position on the x-axis and y-axis (S130).

Although the expiratory data of patients obtained by using MCC-IMS is 500,000 per person, not all data provide disease diagnosis information. Therefore, it is necessary to select (screen) the data to be stored in order to be used as a diagnostic index (S140).

In order to select the diagnostic index data, the average grid data of the patient group for each position is compared with the average grid data of the normal group without disease.

As a result of comparison, only average grid data having a significant difference is selected and stored as diagnostic index data in the database (S150). At this time, in order to diagnose a disease, it is necessary to store the diagnosis of disease when the value is larger than the average grid data or the disease is diagnosed when the value is smaller than the average grid data. For example, average grid data of a patient group larger than the average grid data of a normal group is stored as a first group, and average grid data of a patient group smaller than the average grid data of a normal group is stored as a second group.

A significant difference may be determined by a predetermined threshold value. That is, when the difference between the average grid data of the patient group and the average grid data of the normal group is equal to or greater than a threshold value, the data is stored as diagnostic index data.

However, this threshold may appear differently depending on the type of VOCs in the exhalation. There are VOCs that are significant even with a very small difference, and there can be VOCs that become meaningful only when there is a relatively large difference. Therefore, the threshold value can be changed according to the type of VOCs, that is, the position of the topographic plot.

3 is a schematic flowchart of a method for diagnosing a disease using the diagnostic index data stored in the database.

First, the process of collecting the exhaled breath of the examinee for diagnosing the disease (S210) and standardizing the data (S220) is as described above.

Among the collected grid data of the examinee, the grid data of the location serving as the diagnostic index is compared with the stored average grid data (S230). Using the stored average grid data as a reference value, it is compared whether it is larger or smaller than the average grid data.

As a result of comparison, if it is larger or smaller than the average grid data according to the characteristics of the stored average grid data, the diagnosis count is increased (S250). In the previous example, if it is greater than the average grid data belonging to the first group, the diagnostic count is increased, and if it is smaller than the average grid data belonging to the second group, the diagnostic count is increased.

When a diagnosis count is determined by comparing all of the grid data to be compared as a diagnostic index among the grid data of the subject, whether or not to diagnose a disease is determined according to the ratio of the diagnostic count to the total number of diagnostic index grid data (S260).

A method of using a diagnosis score instead of a diagnosis count to determine whether a disease is diagnosed is also possible as another embodiment. Weights are different according to the importance of each grid data location, weights are accumulated in the diagnosis score according to whether it is larger or smaller than the stored average grid data, and whether or not a disease is diagnosed is determined based on whether the diagnosis score exceeds a certain threshold. will decide A disease can be diagnosed based on several important grid data with high weight. Alternatively, a diagnosis score may be calculated by accumulating a value multiplied by a threshold value and a difference value of each grid data, and disease may be determined.

4 is a flowchart of a method for collecting disease diagnostic index data according to another preferred embodiment of the present invention.

Grid data collected by MCC-IMS is a total of 500,000 per person, and it is inefficient to store and compare all of them to diagnose diseases. Accordingly, in another embodiment of the present invention, a method for diagnosing diseases with little difference in accuracy while reducing the amount of computation by effectively grouping them is presented.

First, the process of collecting expiratory information of a specific disease patient group (S110) and standardizing the data (S120) is the same as in the previous embodiment.

Since the collected grid data is 500,000 per patient, it is not easy to select them to set diagnostic indicators. Therefore, in another embodiment of the present invention, a method of storing diagnostic index data by dividing and grouping them in cell units was used.

Grid data are grouped in a cell unit in a meaningful area among 1250 x-axis and 400 y-axis patterns of the topographic plot (S430).

5 shows an example of grouping grid data in units of cells.

In (a) of FIG. 5, of all 400x1250 data, x?? The grid cells are formed with 470~610 positions of drift time at 20 position intervals and 0~400 positions at 10 position intervals on the y-axis. That is, 17 cells on the x-axis and 40 cells on the y-axis are grouped into a total of 680 cells.

5 (b) is an enlarged view of only grouped cells. 680 grouped grid cells can be managed by assigning a number to each. Grid cells include drift time and retention time.

When the grid data is grouped into grid cells, the signal strengths of the grid data in the corresponding grid cell are all summed, and the average of values of the same grid cells of the patient group thus obtained may be set as a representative value of the grid cell. Alternatively, the same representative value may be obtained by first obtaining an average value of grid data in a grid cell and then summing the average values.

By comparing the representative values of the grid cells of the patient group with the representative values of the grid cells of the normal group, a grid cell pattern can be obtained (S440).

If the difference between the grid cell representative value of the patient group and the grid cell representative value of the normal group is greater than or equal to the threshold, it is considered that there is a significant difference, and the representative grid cell value of the patient group is set as the reference value of the corresponding grid cell. A threshold value for determining a significant difference may be determined for each specific disease or may be determined for each grid cell.

6 shows an example of a grid pattern in which only meaningful cells are displayed among grid cells grouped in cell units.

A meaningful grid cell can be displayed by setting the representative value of the grid cell as a reference value and displaying a different color from other cells. For example, when the representative value of the grid cell of the examinee is greater than or equal to the reference value, a cell increasing the diagnostic count may be displayed in red, and when the representative value of the grid cell of the subject is equal to or greater than the reference value, a cell increasing the diagnostic count may be displayed in blue. 6 shows a state in which 108 meaningful grid cells are displayed together with their reference values.

When the grid pattern is determined, it is stored as a database of a specific patient group together with the reference value of each grid cell of the grid pattern (S450). At this time, in order to diagnose a disease, it is necessary to store the diagnosis of disease when the value is greater than the reference value or the diagnosis of the disease when the value is less than the reference value. For example, the representative value of the grid cell of the patient group that is larger than the representative value of the grid cell of the normal group is stored as the third group, and the representative value of the grid cell of the patient group that is smaller than the representative value of the grid cell of the normal group is stored in the fourth group to save as a group.

7 is a flowchart illustrating a method for diagnosing a disease using a grid pattern stored in a database.

First, exhalation data of a subject is collected using MCC-IMS (S710), and grid data is standardized (S720).

The standardized grid data are grouped in units of cells (S730). The method of grouping grid data into meaningful cell units is the same as the method used when creating the database.

By summing all the signal strengths of grid data belonging to the grouped grid cells, a representative value of the corresponding grid cell can be obtained. It is determined whether the representative value of the exhalation data grid cell of the subject obtained in this way is larger or smaller than the representative value of the grid cell stored in the database of the target disease (S740).

If the difference as a result of the determination is greater than the threshold, the diagnosis count is increased ( S750 ). Comparing the representative value of the grid cell to be inspected with the representative value of the corresponding grid cell in the database varies depending on the characteristics of the grid cell. In the previous example, compared with the representative values of the grid cells of the specific patient group stored as the third group and the fourth group, if the representative value of the grid cell of the subject is greater than the representative value of the grid cells belonging to the third group, the diagnosis count is increased and, if it is less than the representative value of the grid cells belonging to the fourth group, the diagnostic count is increased.

When the comparison of the reference values for all cells of the grid pattern is completed, a diagnosis count is calculated to calculate a determination result (S760).

The determination result may be determined as a ratio of the number of diagnostic counts among the number of cells of the grid pattern. Alternatively, a method of calculating a score by varying the weight for each cell may be used. If the calculated ratio or score exceeds a certain standard, it is determined that the subject has a possibility of having the target disease, and if it is lower than the standard, it is determined that there is no possibility of having the target disease.

If the standard of the judgment result is set to 100%, the false diagnosis rate will be lowered, but there is a possibility that an examinee with an actual disease will judge that there is no disease. Conversely, if the standard is lowered to 80-90%, the disease detection rate will increase, but the false diagnosis rate for judging normal people as having a disease may increase. Therefore, it is important to set a standard that can reduce errors by collecting more data.

The grid pattern-based disease diagnosis method using MCC-IMS as described above has the advantage of overcoming the limitations of the prior art in which detection materials are limited according to the number of sensor arrays, and detecting various substances and utilizing them for disease diagnosis. In addition, since the grid pattern is used, it is possible to obtain accurate disease diagnosis results while reducing the amount of data and computation.

For reference, the grid pattern-based disease diagnosis method using MCC-IMS according to an embodiment of the present invention may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software.

Examples of computer-readable media include hard disks, magnetic media such as floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and ROM, RAM, A hardware device specifically configured to store and execute program instructions, such as flash memory, may be included. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The protection scope of the present invention is not limited to the description and expression of the embodiments explicitly described above. In addition, it is added once again that the protection scope of the present invention cannot be limited due to obvious changes or substitutions in the technical field to which the present invention pertains.

Claims (6)

delete delete collecting expiratory information of a plurality of users having the same characteristics using a multi-capillary column-ion mobility spectrometry (MCC-IMS);
The x-axis is the drift time by IMS, the y-axis is the retention time by MCC, and the z-axis is the output according to the concentration of the detection component for the expiratory information data output from the multi-capillary column ion mobility analyzer. Normalizing the three-dimensional grid data (Grid Data) representing the signal strength, respectively;
calculating the average grid data by summing the z-axis output signal strengths of the grid data having the same x-axis and y-axis among the grid data of the users;
Grouping the average grid data into grid cells having regular intervals on the x-axis and y-axis, summing the average grid data included in the grid cells, and calculating the average value as a representative value of each grid cell step; and
By comparing the representative value of each grid cell with the corresponding grid cell representative value of the pre-registered normal user group, the grid cell that is larger than the representative value of the grid cell of the normal user group and has a difference by more than a predetermined threshold storing the representative values as a third group, and storing the representative values of grid cells that are smaller than the representative values of the grid cells of the normal user group and have a difference by more than a predetermined threshold as a fourth group; including,
The grouping of the x-axis and y-axis into grid cells having a constant interval is to target only grid data within a predetermined range of the x-axis and the y-axis,
The threshold value is determined differently for each grid cell,
collecting, after storing the representative value of the grid cell, the exhalation information of the subject using a multi-capillary column ion mobility analyzer;
normalizing the exhalation information data of the subject output from the multi-capillary column ion mobility analyzer to the three-dimensional grid data of the x-axis, y-axis, and z-axis;
The grid data of the examinee is grouped into grid cells having regular intervals on the x-axis and the y-axis, and the average value is obtained by summing the z-axis signal intensities of the grid data of the examinee included in the grid cells. Saving as a representative value of the grid cell of the subject
comparing the stored representative values of the stored grid cells in the same range as the x-axis and the y-axis with the representative values of the grid cells of the subject; and
According to the comparison result, the number of representative values of the grid cells of the subject that are greater than the representative values of the grid cells of the third group and the number of representative values of the grid cells of the subject that are smaller than the representative values of the grid cells of the fourth group determining the presence or absence of a disease to be compared in the subject;
Grid pattern-based expiratory information inspection method using a multi-capillary column ion mobility analyzer, characterized in that it further comprises. delete delete delete

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