Oral microbiome inspection system and inspection method for early diagnosis of pet periodontal disease

Abstract

According to some embodiments of the present invention, an oral microbiome inspection system for early diagnosis of pet periodontal disease comprises: a genome analysis device configured to generate microbiome data on the basis of a specimen collected from the oral cavity of a pet to be inspected; and a processing device configured to generate abundance data, occurrence frequency data, and correlation data of the microbiome data on the basis of a list of microorganisms of interest in a pet standard database, generate health state data of the specimen by comparing the abundance data, the occurrence frequency data, and the correlation data with standard samples of the pet standard database, and generate an oral health report for the pet to be inspected, on the basis of the health state data.

Description

Oral microbiome testing system and testing method for early diagnosis of periodontal disease in companion animals

[0001] The present invention relates to a system and method for analyzing the oral microbiome of a pet to diagnose oral health conditions, such as periodontal disease, at an early stage.

[0002] Previously, the oral health of pets was primarily assessed through visual inspection, dental X-rays, and direct oral examinations by veterinarians, with the status determined by external signs such as tooth condition, gum inflammation, and bad breath. Mechanical methods, such as tartar removal, were the main approach to managing oral biofilm; veterinarians physically removed tartar using dental instruments or reduced biofilm through tooth rinsing. While some methods were used to inhibit the growth of oral bacteria using antimicrobial agents or mouthwashes, these were limited to superficial bacterial removal, making it difficult to regulate the balance of the microbial community.

[0003] Existing technologies had problems and room for improvement in various aspects. While visual inspection and physical methods are effective for detecting advanced diseases, they had limitations in detecting changes in the microbial community or subtle imbalances in the early stages of disease. Existing microbial analysis techniques were useful for confirming the presence of microorganisms, but it was difficult to apply them directly to predicting health status or preventive management. Mechanical removal methods for managing biofilms were inefficient because they caused pain and stress during tartar removal, posed risks due to the need for general anesthesia, and were costly and cumbersome as they required regular procedures. The use of antimicrobial agents or detergents was effective for short-term bacterial reduction, but it was difficult to regulate the long-term balance of the oral microbial community or protect beneficial bacteria through them.

[0004] Research on the oral microbiome of companion animals is relatively limited compared to humans, and there has been a lack of predictive models that reflect species-specific differences. Existing technologies have made it difficult to precisely assess the oral health status of individual companion animals. In particular, when applying oral microbiome data to health prediction and personalized preventive care, there has been a lack of standardized methods for clinically interpreting analysis results.

[0005] (Prior Art Literature)

[0006] Patent documents

[0007] Patent Document 1: Published Patent Application No. 10-2023-0173676 (December 27, 2023)

[0008] Patent Document 2: Published Patent Application No. 10-2024-0033007 (March 12, 2024)

[0009] One of the objectives of the present invention is to provide a testing system and a testing method capable of diagnosing the oral health status of a pet subject to testing by generating microbiome data from an oral sample and analyzing it, in order to improve upon the limitations of existing pet oral care methods. The technical objectives of the present invention are not limited to the technical problems mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below.

[0010] According to some embodiments of the present invention, an oral microbiome testing system for the early diagnosis of periodontal disease in pets comprises: a genome analysis device configured to generate microbiome data based on a sample collected from the oral cavity of a pet subject to testing; and a processing device configured to generate abundance data, prevalence data, and correlation data of the microbiome data based on a list of microorganisms of interest in a pet standard database, generate health status data of the sample by comparing the abundance data, prevalence data, and correlation data with standard samples in the pet standard database, and generate an oral health report for the pet subject to testing based on the health status data.

[0011] According to some embodiments of the present invention, the genome analysis device is configured to generate microbiome data by performing 16S rRNA sequencing based on next-generation sequencing (NGS) to identify the types of microorganisms present in the sample.

[0012] According to some embodiments of the present invention, the list of microorganisms of interest defines n phylums and m genera referenced for the early diagnosis of periodontal disease, and the processing device is configured to generate the abundance data, the frequency data, and the correlation data based on the n phylums and m genera.

[0013] According to some embodiments of the present invention, the processing device is configured to generate abundance data by calculating a first composition ratio for n phyla and a second composition ratio for m genera of microorganisms present in the sample, and to generate health status data of the sample by comparing the first composition ratio and the second composition ratio with standard samples.

[0014] According to some embodiments of the present invention, the processing device is configured to generate occurrence frequency data by generating a first heatmap representing the probabilities that each of the n gates exceeds detection thresholds of relative abundance and a second heatmap representing the probabilities that each of the m attributes exceeds detection thresholds of relative abundance, and to generate health status data of the sample by comparing the first heatmap and the second heatmap with the standard samples.

[0015] According to some embodiments of the present invention, the processing device is configured to generate correlation data by calculating m correlation values ​​between m microbial communities present in the sample and a representative microbial community corresponding to the m genera, and to generate health status data of the sample by comparing the m correlation values ​​with the standard samples.

[0016] According to some embodiments of the present invention, the processing device is configured to determine the standard sample closest to the abundance data, the frequency data, and the correlation data for the specimen using a data classification model trained to output the standard sample closest to the input data among the standard samples, and to generate the health status data based on the standard sample determined to be closest to the specimen.

[0017] According to some embodiments of the present invention, the processing device is configured to generate the oral health report by considering metadata including the species, age, gender, weight, oral care status, and specific diseases of the pet subject to examination.

[0018] According to some embodiments of the present invention, the standard samples of the pet standard database are constructed based on five disease stages consisting of bad breath, plaque, tartar, gingivitis, and periodontal disease stages, and five progression stages consisting of 1 point, 2 points, 3 points, 4 points, and 5 points for each of the five disease stages.

[0019] According to some embodiments of the present invention, the processing device is configured to determine an evaluation indicator corresponding to the microbiome data based on the PCoA cluster of the pet standard database and to generate the oral health report by further considering the evaluation indicator.

[0020] According to some embodiments of the present invention, a method for testing an oral microbiome for the early diagnosis of periodontal disease in pets comprises: generating microbiome data based on a sample collected from the oral cavity of a pet subject to testing through a genome analysis device; generating abundance data, frequency data, and correlation data of the microbiome data based on a list of microorganisms of interest in a pet standard database through a processing device; generating health status data of the sample by comparing the abundance data, frequency data, and correlation data with standard samples in the pet standard database through the processing device; and generating an oral health report for the pet subject to testing based on the health status data through the processing device.

[0021] According to embodiments of the present invention, in order to improve the limitations of existing oral care methods for pets, a testing system and a testing method may be provided that generate microbiome data from an oral sample and analyze it to diagnose the oral health status of a pet subject to testing.

[0022] The technical effects according to the embodiments of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art in accordance with the disclosure of this document.

[0023] FIG. 1 illustrates an environment in which an oral microbiome testing system for early diagnosis of periodontal disease in companion animals, according to some embodiments, operates.

[0024] FIG. 2 illustrates elements constituting an oral microbiome testing system for the early diagnosis of periodontal disease in companion animals according to some embodiments.

[0025] FIG. 3 illustrates the process of an oral microbiome testing system according to some embodiments generating an oral health report based on an oral sample.

[0026] FIG. 4 illustrates a method of collecting a sample from the oral cavity of a pet subject to examination according to some embodiments.

[0027] FIG. 5 illustrates a method for designing and optimizing primers and probes for biomarkers according to some embodiments.

[0028] FIG. 6 illustrates abundance data according to some embodiments.

[0029] FIG. 7 illustrates prevalence data according to some embodiments.

[0030] FIGS. 8 and 9 illustrate correlation data according to some embodiments.

[0031] FIG. 10 illustrates a method for classifying the condition of a sample based on a PCoA cluster regarding a representative microorganism according to some embodiments.

[0032] FIG. 11 illustrates metadata additionally considered to generate health status data of a pet subject to examination according to some embodiments.

[0033] FIG. 12 illustrates steps constituting an oral microbiome test method for early diagnosis of periodontal disease in companion animals according to some embodiments.

[0034] An oral microbiome testing system for the early diagnosis of periodontal disease in companion animals comprises: a genomic analysis device configured to generate microbiome data based on a sample collected from the oral cavity of a companion animal to be tested; A processing device configured to generate abundance data, prevalence data, and correlation data of the microbiome data based on a list of microorganisms of interest in a pet standard database, generate health status data of the specimen by comparing the abundance data, prevalence data, and correlation data with standard samples of the pet standard database, and generate an oral health report for the pet subject to examination based on the health status data; wherein the standard samples of the pet standard database are constructed of primary standard samples including abundance data, prevalence data, and correlation data for a plurality of diseases including bad breath, plaque, tartar, gingivitis, and periodontal disease stages, and additional secondary standard samples in which the disease progression stage is subdivided for each of the primary standard samples to include abundance data, prevalence data, and correlation data for disease progression, and wherein the prevalence data indicates the appearance of each microorganism by displaying the probability that a specific microorganism in the specimen exceeds a preset threshold in the primary standard samples and the secondary standard samples in different colors. It is characterized by being provided as a heatmap that visually displays frequency in two dimensions.

[0035] Embodiments of the present invention will be described in detail below with reference to the drawings. The description below is intended only to illustrate the embodiments and is not intended to limit or restrict the scope of the rights according to the present invention. Anything that can be easily inferred by a person skilled in the art from the detailed description and embodiments of the invention should be interpreted as falling within the scope of the rights according to the present invention. Detailed descriptions of matters widely known to those skilled in the art regarding the present invention are omitted.

[0036] The terms used in this invention are described as general terms widely used in the technical field relating to this invention; however, the meaning of the terms used in this invention may vary depending on the intent of those skilled in the field, the emergence of new technologies, examination standards, or case law. Some terms may be selected at the discretion of the applicant, and in such cases, the meaning of the arbitrarily selected terms will be explained in detail. The terms used in this invention should be interpreted not merely in their dictionary meanings, but in a sense that reflects the overall context of the specification.

[0037] FIG. 1 illustrates an environment in which an oral microbiome testing system for early diagnosis of periodontal disease in companion animals, according to some embodiments, operates.

[0038] Referring to FIG. 1, the oral microbiome testing system (200) for early diagnosis of periodontal disease in pets can generate an oral health report for a pet subject to testing based on a sample collected by a sample collection kit (100) and provide it to a user terminal (300).

[0039] The user can collect a sample from the oral cavity of the pet being tested using a sample collection kit (100). The oral microbiome testing system (200) can perform genomic analysis on the sample and process the resulting data to generate health status data of the pet being tested, and can generate an oral health report based on this. The user can view the oral health report through a user terminal (300). The environment in which the oral microbiome testing system (200) operates can be implemented through a computer program and / or a mobile application, and the user terminal (300) can be the user's mobile phone and / or PC.

[0040] The oral microbiome testing system (200) may aim to overcome the limitations of existing technology and contribute to the overall health improvement of pets by precisely analyzing oral microbiome data of pets and utilizing it for preventive health care. By analyzing correlation, prevalence, and abundance data of microorganisms collected from the oral cavity, it may be possible to detect disease signals at an early stage and respond early.

[0041] Through the learning of microbial patterns, the balance of the microbiome can be maintained by protecting beneficial bacteria while effectively managing pathogenic bacteria. Efficiency can be enhanced by utilizing data-driven predictive models to suggest prevention and management strategies tailored to the specific characteristics of individual pets. By systematically analyzing oral microbial data and implementing preventive measures, benefits such as disease prevention, health promotion, cost reduction, and improved quality of life through personalized care can be expected.

[0042] The oral microbiome testing system (200) can analyze the microbiome in the oral cavity of a pet using Next Generation Sequencing (NGS) techniques, and can detect disease signals at an early stage and respond early based on the analysis results of the correlation, prevalence, and abundance data of the microorganisms.

[0043] Through cooperation with veterinary clinics, more than 500 oral samples can be obtained, and 16S rRNA sequencing can be performed using genomic sequencing (e.g., Illumina MiSeq) on these samples.

[0044] In an exemplary embodiment, correlation, prevalence, and abundance data and pattern changes of microorganisms present in the oral cavity can be identified by classifying them into bad breath, plaque, gingivitis, tartar, and periodontal disease stages. A domestic standard database for companion animals can be constructed using oral samples, and through this, the microbiome (the entire microbial community) can be analyzed.

[0045] 16S rRNA sequencing refers to a technique used to identify the types of bacteria or microorganisms present and to distinguish between different bacteria by analyzing specific genes (16S rRNA) of bacteria or microorganisms. Since genes vary by bacterial species, 16S rRNA sequencing can enable the rapid and accurate identification of bacterial species. 16S rRNA exists only in bacteria and archaea, and the types and quantities of all bacteria present in a sample can be identified.

[0046] Microbiome data can be generated by analyzing bacteria and microorganisms present in specimens collected from the oral cavity of pets such as dogs, from which correlation, prevalence, and abundance data can be derived. Based on such data, the oral health status of the pet can be assessed.

[0047] The oral microbiome testing system (200) can acquire data according to the progression stages, such as bad breath (early disease), plaque, gum inflammation, tartar, and periodontal disease. Such data can be obtained from an animal hospital through oral samples of pets with diseases according to each progression stage. Animal hospital data for building a standard DB may further include metadata such as species, age, gender including neutering (F, M, CM, IF), whether teeth have been extracted, whether scaling has been performed, and specific diseases.

[0048] The oral microbiome testing system (200) can interpret the interactions between all microorganisms in a sample using NGS data. Beyond the interactions of one or two types of microorganisms, it may be possible to determine the types and trends of approximately 10^7 or more types of bacteria / microorganisms present in the oral cavity, and these can be classified at the Phylum and Genus levels. At the Phylum and Genus levels, the top m / n types of bacteria / microorganisms that have a high correlation with the oral condition of the pet can be selected, and the oral health of the pet can be diagnosed using this.

[0049] FIG. 2 illustrates elements constituting an oral microbiome testing system for the early diagnosis of periodontal disease in companion animals according to some embodiments.

[0050] Referring to FIG. 2, the oral microbiome testing system (200) for early diagnosis of periodontal disease in pets may include a genome analysis device (210) and a processing device (220). However, it is not limited thereto, and some components may be omitted from the oral microbiome testing system (200), or other general-purpose components may be further included in the oral microbiome testing system (200).

[0051] The genome analysis device (210) may include a PCR device capable of performing genome analysis on a sample collected through a sample collection kit (100). For example, the genome analysis device (210) may be a third-generation PCR device and / or a next-generation sequencing (NGS) device.

[0052] The genomic analysis device (210) can identify the types of bacteria / microorganisms present in the sample to generate oral microbiome data or oral microbiome data. Based on the oral microbiome data, the types and quantities of bacteria / microorganisms present in the oral cavity of a pet can be analyzed.

[0053] The processing unit (220) can perform processing operations on oral microbiome data analyzed by the genome analysis unit (210) to generate health status data and oral health reports. The processing unit (220) may include memory and a processor. The memory may store various data, instructions, computer programs, software, mobile applications, etc. The processor may execute control operations by executing instructions, etc. stored in the memory. For example, the memory may include non-volatile memory, volatile memory, storage devices, etc., and the processor may include a microprocessor, CPU, GPU, AP, etc.

[0054] The genomic analysis device (210) may be configured to generate microbiome data based on a sample taken from the oral cavity of a pet subject to testing. The genomic analysis device (210) may generate microbiome data by identifying all bacteria / microorganisms present in the sample through third-generation PCR and / or NGS sequencing.

[0055] The processing device (220) may be configured to generate abundance data, prevalence data, and correlation data of microbiome data based on the list of microorganisms of interest in the pet standard database. The pet standard database may be constructed based on oral condition data provided through animal hospitals, etc.

[0056] The oral microbiome testing system (200) may include an output device. In an exemplary embodiment, the output device may include a printer. As described below in FIG. 12, the processing device may generate an oral health report for a pet subject to testing based on health status data and transmit an output signal to the printer. The printer may output the health report after receiving the output signal.

[0057] That is, the processing device can generate microbiome data based on an oral sample, generate abundance data, frequency data, and correlation data based on the microbiome data, generate health status data of a companion animal based on these, and generate an oral health report based on the oral status data. Additionally, when the generation of the oral health report is completed, the processing device can automatically transmit an output signal to a printer, and the printer receiving the output signal can automatically print the oral health report.

[0058] The standard pet database may contain a list of bacteria / microorganisms used to diagnose the oral condition of pets. The detection levels of the microorganisms of interest in the list may vary depending on the oral condition of the pet, and the top n / m species of bacteria / microorganisms most suitable for diagnosing the oral condition may be registered in the list of microorganisms of interest.

[0059] Abundance data can represent the detection rate of n / m species of bacteria / microorganisms. Prevalence data can represent the frequency / probability of detection of n / m species of bacteria / microorganisms in the form of a two-dimensional heatmap. Correlation data can represent the correlation coefficient values ​​between the microorganisms in the list of microorganisms of interest and representative microorganisms.

[0060] The processing device (220) may be configured to generate health status data of a specimen by comparing it with standard samples of a pet standard database based on at least one of abundance data, occurrence frequency data, and correlation data. For example, the processing device (220) may be configured to generate health status data of a specimen by comparing it with standard samples of a pet standard database using all of the abundance data, occurrence frequency data, and correlation data.

[0061] A standard database for companion animals may contain standard samples generated based on data collected by oral condition from veterinary clinics, etc. Standard samples may classify oral diseases into stages such as bad breath, plaque, and tartar, and each stage may be further divided into five stages according to severity.

[0062] For example, standard samples may include abundance data, incidence data, and correlation data regarding specific oral disease conditions and specific severity levels. By comparing the specimen data with the standard samples, the standard sample closest to the specimen data may be treated as representing the condition of the pet being tested.

[0063] The selection of the closest data can be performed through machine learning-based artificial intelligence models and / or statistical techniques. Once the standard sample closest to the specimen data is selected, the specimen's health status data can be generated based on this.

[0064] The processing device (220) may be configured to generate an oral health report for the pet being examined based on health status data. The health status data may indicate which of the stages—such as bad breath, plaque, or tartar—the current oral condition of the pet being examined corresponds to, and the severity of the corresponding stage. It may also indicate whether the overall condition of the pet being examined corresponds to good, average, or bad.

[0065] For example, health status data may include causes, symptoms, signs, prevention methods, management methods, etc. The oral health report may include solutions regarding the oral health status. For example, if the pet is a dog, the oral health report may provide customized solutions based on metadata such as breed, age, gender, and underlying diseases. The oral health report may be provided to the user terminal (300) through an app / program.

[0066] According to an embodiment, a genome analysis device (210) may be configured to generate microbiome data by performing 16S rRNA sequencing based on next-generation sequencing (NGS) to identify the types of microorganisms present in a sample. Next-generation sequencing (NGS) is a genome analysis technique capable of rapidly sequencing large amounts of DNA or RNA, and unlike conventional sequencing technology, it can simultaneously sequence millions of small DNA fragments. For example, next-generation sequencing (NGS) may be performed based on third-generation PCR techniques. The types of microorganisms present in a sample can be identified through 16S rRNA sequencing.

[0067] According to the embodiment, the list of microorganisms of interest may define n phylums and m genera referenced for early diagnosis of periodontal disease, and the processing device (220) may be configured to generate abundance data, occurrence frequency data and correlation data based on the n phylums and m genera.

[0068] Generally, while a vast number of microorganisms exist in the oral specimens of companion animals, the number of these utilized for diagnosing oral conditions may be limited. Meanwhile, the classification of microorganisms can be performed based on phyla or, alternatively, on genus. By considering both phyla and genus together, it may be possible to analyze microorganisms at different levels.

[0069] According to an embodiment, the processing device (220) may be configured to generate abundance data by calculating a first composition ratio for n phyla and a second composition ratio for m genera of microorganisms present in the sample, and to generate health status data of the sample by comparing the first composition ratio and the second composition ratio with standard samples.

[0070] For the first composition ratio (610) and the second composition ratio (620), reference may be made to FIG. 6, which will be described later. The oral condition of the pet can be diagnosed based on which standard sample the composition ratio generated for the specimen is closest to.

[0071] According to an embodiment, the processing device (220) may be configured to generate occurrence frequency data by generating a first heatmap representing the probabilities that each of n gates exceeds detection thresholds of relative abundance and a second heatmap representing the probabilities that each of m genera exceeds detection thresholds of relative abundance, and to generate health status data of a sample by comparing the first heatmap and the second heatmap with standard samples. Reference may be made to FIG. 7, which will be described later, for the first heatmap and the second heatmap. The heatmap can visually display the frequency of appearance of each microorganism by displaying the probability that a specific microorganism in the sample exceeds a specific threshold in different colors. Standard samples for each of the first heatmap and the second heatmap may be generated for each degree of progression / severity on a 1 to 5 point scale.

[0072] According to an embodiment, the processing device (220) may be configured to generate correlation data by calculating m correlation values ​​between m microbial communities present in the sample and a representative microbial community corresponding to m genera, and to generate health status data of the sample by comparing the m correlation values ​​with standard samples. The representative microorganism may be selected as a type of microorganism that best represents the oral condition of the pet. Based on this, the top m types of microorganisms with high correlation may be registered in the pet standard database, and m correlation values ​​between m genera and the representative microorganism may be generated for the sample to be diagnosed. The m correlation values ​​may be used as indicators to diagnose the oral condition of the pet to be diagnosed. For example, the m correlation values ​​may be used as input data for a model trained to diagnose the oral condition. Reference may be made to FIGS. 8 and FIGS. 9, which will be described later, regarding the correlation data.

[0073] According to an embodiment, the processing device (220) may be configured to determine the standard sample closest to the abundance data, frequency data, and correlation data for the sample using a data classification model trained to output the standard sample closest to the input data among standard samples, and to generate health status data based on the standard sample determined to be closest to the sample.

[0074] For example, three standard samples closest to the abundance data, frequency data, and correlation data can be selected, and health status data can be generated based on the three standard samples. Each standard sample may indicate which disease stage the disease belongs to among bad breath, plaque, tartar, gingivitis, and periodontal disease, and what score on a scale of 1 to 5 corresponds to the severity of each disease stage.

[0075] According to an embodiment, the processing device (220) may be configured to generate an oral health report by considering metadata including the species, age, gender, weight, oral care status, and specific diseases of the pet being examined. The oral health report may be generated based on health status data and metadata. The metadata may be referenced to provide an optimized solution for each pet being examined.

[0076] According to the embodiment, standard samples of the pet standard database can be constructed based on 5 disease stages consisting of bad breath, plaque, tartar, gingivitis, and periodontal disease stages, and 5 progression stages consisting of 1 point, 2 points, 3 points, 4 points, and 5 points for each of the 5 disease stages.

[0077] For example, standard samples for abundance data can be prepared in advance for 25 cases corresponding to combinations of disease stage 5 and progression stage 5, as described later in Figure 6, and frequency data and correlation data can also be prepared in a similar manner.

[0078] According to an embodiment, the processing device (220) may be configured to determine an evaluation indicator corresponding to microbiome data based on a PCoA cluster of a pet standard database and to generate an oral health report by further considering the evaluation indicator.

[0079] For example, as shown in Figure 10 to be described later, PCoA clusters can be classified into poor / average / good states, and this classification can be performed based on representative microorganisms that serve as criteria for generating correlation data. That is, based on the amount of representative microorganisms detected in the specimen of the pet being tested, evaluation indicators corresponding to microbiome data can be classified into one of poor / average / good, and information regarding this can be included in an oral health report.

[0080] FIG. 3 illustrates the process of an oral microbiome testing system according to some embodiments generating an oral health report based on an oral sample.

[0081] Referring to FIG. 3, the process (310–350) of an oral microbiome testing system generating an oral health report based on an oral sample can be illustrated.

[0082] In the first process (310) and the second process (320), the genomic analysis device (210) can perform genomic analysis on a sample taken from the oral cavity of a pet subject to examination to extract DNA and generate microbiome data. For example, microbiome data may be generated using next-generation sequencing (NGS) and / or third-generation PCR-based 16S rRNA sequencing.

[0083] In the third process (330), microbiome data generated from the sample can be compared with a standard pet database. The standard pet database may have multiple standard samples, and the standard sample closest to the sample data can be selected. For example, the abundance, frequency, and correlation of the sample data can be compared with the standard samples, and the standard sample closest to the sample data can be selected using statistical techniques or an artificial intelligence model trained based on machine learning.

[0084] In the fourth process (340), health status data can be generated based on the results of comparison with standard samples, and a step-by-step solution can be generated based on this. In the fifth process (350), an oral health report for the pet subject to examination can be generated based on the step-by-step solution, and this can be provided to the user through the user terminal (300).

[0085] FIG. 4 illustrates a method of collecting a sample from the oral cavity of a pet subject to examination according to some embodiments.

[0086] Referring to FIG. 4, a manual (400) illustrating a method of collecting a sample from the oral cavity of a pet subject to examination may be illustrated.

[0087] According to the manual (400), the usage of the sample collection kit (100) may consist of five steps, and the five steps may include the step of opening the test swab, the step of collecting the sample, the step of shaking the extract, the step of collecting it in a collection bag, and the step of filling out the test request form. When a sample of the pet subject to test is collected according to the manual (400), the sample may be transferred to an oral microbiome testing system (200) for genomic analysis.

[0088] FIG. 5 illustrates a method for designing and optimizing primers and probes for biomarkers according to some embodiments.

[0089] Referring to FIG. 5, an image (500) illustrating a method for designing and optimizing primers and probes for biomarkers may be shown.

[0090] Image (500) may show a screen of a tool for designing and optimizing primers and / or probes for detecting selected n / m microorganisms of interest. Each of the selected n / m microorganisms of interest may be a biomarker. Two primers and two probes may be designed for each biomarker. Once the design and optimization of the primers / probes for the microorganisms of interest are complete, microbiome data from the sample can be analyzed using them.

[0091] Various factors can be considered for primer design and optimization. For example, primer Tm values ​​should be similar to ±2°C, generally around 60-62°C for 5' nucleic acid qPCR analysis, target 18-30 bases, not contain more than 4 consecutive Gs, and the GC content range should be 35-65%. Similarly, for probe design and optimization, Tm values ​​should be 4-10°C higher than primers, Gs should not be consecutive, the G+C content should be 30-80%, Gs cannot be present at the 5' end, the probe length should not exceed 30 bases, and the probe can be designed as a sense or antisense strand.

[0092] FIG. 6 illustrates abundance data according to some embodiments.

[0093]

[0094] * Referring to FIG. 6, a first composition ratio (610) and a second composition ratio (620) corresponding to abundance data may be illustrated. The first composition ratio (610) and the second composition ratio (620) may be registered in a pet standard database.

[0095] Abundance data, which represents the relative proportion of microorganisms as a percentage, can be obtained. Abundance data can be collected for each of the five conditions: bad breath, plaque, tartar, gingivitis, and periodontal disease. Through this data, the abundance of microorganisms (proportion by type) according to oral conditions can be obtained. As shown in this graph, data can be obtained by setting data categories differently, such as at the phylum level and the genus level.

[0096] The first composition ratio (610) and the second composition ratio (620) may be standard samples for the periodontal disease stage, and in addition, standard samples of the same form as the first composition ratio (610) and the second composition ratio (620) may be provided in the DB for bad breath stage, plaque stage, tartar stage, gum inflammation stage, etc.

[0097] The first composition ratio (610) represents the proportion of microorganisms of interest classified based on phylum, whereas the second composition ratio (620) may represent the proportion of microorganisms of interest classified based on genus. Since analysis of microorganisms of interest is possible based on different scales, microbiome data can be analyzed more comprehensively.

[0098] The first composition ratio (610) and the second composition ratio (620) may represent standard samples for five severity levels ranging from 1 to 5 points. The severity level of a sample may be determined based on which sample the distribution of the abundance data of microorganisms present in the sample is closest to.

[0099] FIG. 7 illustrates prevalence data according to some embodiments.

[0100] Referring to FIG. 7, heatmap data (710–750) corresponding to prevalence data can be illustrated. The heatmap data (710–750) can correspond to five severity levels ranging from 1 to 5 points. The heatmap data (710–750) can be generated for any one of five stages of bad breath, plaque, tartar, gingivitis, and periodontal disease, and can be generated based on phylum or genus.

[0101] The vertical axis of the heatmap data (710–750) may represent microorganisms of interest classified at the phylum level or microorganisms of interest classified at the genus level, and the horizontal axis of the heatmap data (710–750) may represent a detection threshold for relative abundance. The colors of the heatmap data (710–750) may represent the probability that a corresponding microorganism will be detected in a sample for a corresponding detection threshold. For example, if the color for a detection threshold of 0.010 for a specific microorganism is the darkest color, it can be interpreted that the probability of that microorganism being detected for an abundance of 0.010 is 100%.

[0102] Prevalence data, expressed in the form of heatmap data (710–750), can serve as an indicator to explain the characteristics of the microbiome data of the specimen. Heatmap data representing a combination of 5 disease levels and a severity scale of 1–5 can be registered in the database as standard samples, and the oral health status of the specimen can be analyzed by comparing this with the specimen data. The comparison between the specimen data and the standard samples can be performed based on machine learning-based artificial intelligence models and / or statistical techniques.

[0103] FIGS. 8 and 9 illustrate correlation data according to some embodiments.

[0104] Referring to FIG. 8, a graph (800) illustrating correlation data may be shown. Each node of the graph (800) may be a microorganism of interest or a biomarker.

[0105] In this correlation, the connection lines between nodes can be classified into lines indicating positive correlation and lines indicating negative correlation, which can be distinguished by the color of the lines. The size of each node may indicate the frequency or importance of the corresponding microorganism, and this correlation data can represent the relationships between microorganisms.

[0106] For example, correlation data may include correlation coefficient values ​​between m microorganisms corresponding to m genera of the list of microorganisms of interest and representative microorganisms. Alternatively, correlation data may be generated based on n phyla.

[0107] Graphs (800) can be generated for each combination of 5 levels of disease severity and a 1-5 point scale of severity, and these can be registered as standard samples in the DB. Correlation data can be an indicator to explain the characteristics of the microbiome data of the specimen, and the oral health status of the specimen data can be estimated by comparing the specimen data of the pet being tested with standard samples.

[0108] Referring to FIG. 9, a graph (900) showing correlation coefficient values ​​between a representative microorganism and 25 genus-level microorganisms of interest can be illustrated. Since the 25 genus-level microorganisms of interest have a high correlation with the representative microorganism, the amount detected may vary significantly depending on the oral condition of the pet being tested. Considering this, the top 25 microorganisms based on the correlation coefficient values ​​can be selected as biomarkers or microorganisms of interest and registered in a DB to be used for comparison with sample data.

[0109] FIG. 10 illustrates a method for classifying the condition of a sample based on a PCoA cluster regarding a representative microorganism according to some embodiments.

[0110] Referring to FIG. 10, a graph (1000) illustrating a method of classifying the condition of a sample based on a PCoA cluster regarding a representative microorganism can be shown.

[0111] The graph (1000) can represent PCoA clusters for a specific type of representative microorganism. PCoA clusters can be classified into good / average / bad through a data clustering technique, and such classification criteria can be registered in a DB. When new data regarding representative microorganisms is collected from a specimen of a pet subject to testing, it can be compared with the PCoA cluster in the DB to determine whether the new data belongs to good / average / bad, and a health status report can be generated based on this.

[0112] FIG. 11 illustrates metadata additionally considered to generate health status data of a pet subject to examination according to some embodiments.

[0113] Referring to FIG. 11, a table (1100) illustrating metadata that is additionally considered to generate health status data of a pet subject to examination may be illustrated.

[0114] The metadata exemplified in the table (1100) may include classification of the type of pet, the breed of the pet, age, gender considering neutering, weight, whether teeth have been extracted, whether scaling has been performed, the date of scaling, whether probiotics have been consumed, and whether specific diseases have occurred. For example, the data displayed in the table (1100) may be associated with standard samples registered in the DB. For the standard samples, a disease level of 5 and a severity level of 1-5 points may be labeled.

[0115] FIG. 12 illustrates steps constituting an oral microbiome test method for early diagnosis of periodontal disease in companion animals according to some embodiments.

[0116] Referring to FIG. 12, the oral microbiome testing method (1200) for early diagnosis of periodontal disease in pets may include steps (1210) to (1240). However, it is not limited thereto, some steps may be omitted or other general steps may be added, and the steps of the oral microbiome testing method (1200) may be executed in a different order than the illustrated order.

[0117] The oral microbiome testing method (1200) may consist of steps processed sequentially in the oral microbiome testing system (200). Therefore, even if the content is omitted below, the description of the oral microbiome testing system (200) above may be equally applicable to the oral microbiome testing method (1200).

[0118] Steps (1210) to (1240) of the oral microbiome testing method (1200) can be performed by the genome analysis device (210) and processing device (220) of the oral microbiome testing system (200).

[0119] In step (1210), the oral microbiome testing system (200) can perform the step of generating microbiome data based on a sample collected from the oral cavity of a pet subject to testing through a genome analysis device.

[0120] In step (1220), the oral microbiome testing system (200) can perform the step of generating abundance data, occurrence frequency data, and correlation data of microbiome data based on the list of microorganisms of interest in the pet standard database through a processing device.

[0121] In step (1230), the oral microbiome testing system (200) can perform the step of generating health status data of a specimen by comparing abundance data, occurrence frequency data, and correlation data with standard samples of a pet standard database through a processing device.

[0122] In step (1240), the oral microbiome testing system (200) can perform the step of generating an oral health report for the pet being tested based on health status data through a processing device.

[0123] According to an embodiment, the oral microbiome testing method (1200) may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the oral microbiome testing method (1200), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0124] According to an embodiment, a computer-readable storage medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a hardware device specifically configured to store and execute computer program instructions such as ROM, RAM, and flash memory. Computer program instructions may include machine code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0125] Although embodiments of the present invention have been described in detail above, the scope of rights according to the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as described in the following claims should also be interpreted as being included within the scope of rights according to the present invention.

Claims

1. In an oral microbiome testing system for the early diagnosis of periodontal disease in companion animals, A genomic analysis device configured to generate microbiome data based on a sample collected from the oral cavity of a pet subject to examination; and Generate abundance data, prevalence data, and correlation data of the above microbiome data based on the list of microorganisms of interest in the pet standard database, and Generate health status data of the specimen by comparing the above abundance data, the above frequency of occurrence data, and the above correlation data with standard samples of the above pet standard database, and A processing device configured to generate an oral health report for the pet subject to examination based on the above health status data; comprising The standard samples of the above pet standard database are, Primary standard samples including multiple disease-specific abundance data, incidence data, and correlation data including bad breath stage, plaque stage, tartar stage, gingivitis stage, and periodontal disease stage, and For each of the above primary standard samples, the disease progression stage is subdivided and constructed into additional secondary standard samples including abundance data, incidence data, and correlation data by disease progression level, and An oral microbiome testing system for the early diagnosis of periodontal disease in companion animals, characterized in that the above occurrence frequency data is provided as a heatmap that visually displays the frequency of appearance of each microorganism in two dimensions by indicating the probability that a specific microorganism in the specimens of the above primary standard samples and the above secondary standard samples exceeds a preset threshold in different colors.

2. In Paragraph 1, An oral microbiome testing system for the early diagnosis of periodontal disease in companion animals, wherein the above-described genome analysis device is configured to generate microbiome data by performing 16S rRNA sequencing based on next-generation sequencing (NGS) to identify the types of microorganisms present in the sample.

3. In Paragraph 1, The above list of microorganisms of interest specifies n phylums and m genera referenced for the early diagnosis of periodontal disease, and An oral microbiome testing system for the early diagnosis of periodontal disease in companion animals, configured such that the processing device generates the abundance data, the frequency data, and the correlation data based on the n phyla and the m genera.

4. In Paragraph 3, The processing device generates the abundance data by calculating the first composition ratio of the n phyla and the second composition ratio of the m genera of microorganisms present in the sample. An oral microbiome testing system for early diagnosis of periodontal disease in pets, configured to generate health status data of the specimen by comparing the first composition ratio and the second composition ratio with the standard samples.

5. In Paragraph 3, The processing device generates the occurrence frequency data by generating a first heatmap representing the probabilities that each of the n doors exceeds detection thresholds of relative abundance and a second heatmap representing the probabilities that each of the m attributes exceeds detection thresholds of relative abundance. An oral microbiome testing system for early diagnosis of periodontal disease in pets, configured to generate health status data of the specimen by comparing the first heatmap and the second heatmap with the standard samples.

6. In Paragraph 3, The processing device generates correlation data by calculating m correlation values ​​between m microbial communities present in the sample and a representative microbial community corresponding to the m genera, and An oral microbiome testing system for the early diagnosis of periodontal disease in pets, configured to generate health status data of the specimen by comparing the m correlation values ​​with the standard samples.

7. In Paragraph 1, The processing device determines the standard sample closest to the abundance data, the frequency data, and the correlation data for the sample using a data classification model trained to output the standard sample closest to the input data among the standard samples, and An oral microbiome testing system for the early diagnosis of periodontal disease in pets, configured to generate health status data based on a standard sample determined to be closest to the above specimen.

8. In Paragraph 7, The above processing device is configured to generate the oral health report by considering metadata including the species, age, gender, weight, oral care status, and specific diseases of the pet subject to examination, in an oral microbiome testing system for early diagnosis of periodontal disease in pets.

9. In Paragraph 1, The processing device determines an evaluation indicator corresponding to the microbiome data based on the PCoA cluster of the pet standard database, and An oral microbiome testing system for the early diagnosis of periodontal disease in companion animals, configured to generate the oral health report by additionally considering the above evaluation indicators.

10. In a method for testing the oral microbiome for the early diagnosis of periodontal disease in companion animals, A step of generating microbiome data based on a sample collected from the oral cavity of a pet subject to examination using a genomic analysis device; A step of generating abundance data, occurrence frequency data, and correlation data of the microbiome data based on the list of microorganisms of interest in the pet standard database through a processing device; A step of generating health status data of the specimen by comparing the abundance data, the frequency of occurrence data, and the correlation data through the processing device with standard samples of the pet standard database; and The method includes the step of generating an oral health report for the pet subject to examination based on the health status data through the processing device. The standard samples of the above pet standard database are, Primary standard samples including multiple disease-specific abundance data, incidence data, and correlation data including bad breath stage, plaque stage, tartar stage, gingivitis stage, and periodontal disease stage, and For each of the above primary standard samples, the disease progression stage is subdivided and constructed into additional secondary standard samples including abundance data, incidence data, and correlation data by disease progression level, and A method for testing the oral microbiome for the early diagnosis of periodontal disease in companion animals, characterized in that the above occurrence frequency data is provided as a heatmap that visually displays the frequency of appearance of each microorganism in two dimensions by indicating the probability that a specific microorganism in the specimens of the above primary standard samples and the above secondary standard samples exceeds a preset threshold in different colors.

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