System for differentiating between different lung diseases in children and applications

Abstract

This invention discloses a system and its application for differentiating different lung diseases in children. By detecting S100A8 / A9 and determining the corresponding diagnostic threshold, this invention achieves rapid, sensitive, and specific differentiation between children with tuberculosis and normal children, as well as children with pneumonia caused by other pathogens. It provides a reliable basis for clinical auxiliary diagnosis, improves diagnostic efficiency and accuracy, avoids misdiagnosis and missed diagnosis, and can thus guide rational clinical treatment and improve patient prognosis.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to a system and its application for differentiating different lung diseases in children. Background Technology

[0002] Tuberculosis is caused by Mycobacterium tuberculosis (Mycobacterium tuberculosis) Mycobacterium tuberculosis Tuberculosis (PTB) is a chronic infectious disease that can affect multiple organs throughout the body, with pulmonary tuberculosis being the most common. Children, whose immune systems are not yet fully developed, are particularly susceptible to tuberculosis. PTB patients often experience rapid disease progression, atypical clinical symptoms, and are difficult to diagnose, resulting in a heavy disease burden. Simultaneously, children are also susceptible to community-acquired pneumonia caused by various pathogens (such as viruses, bacteria, and mycoplasma). Because pulmonary tuberculosis (PTB) and pneumonia share highly overlapping clinical manifestations such as fever, cough, and changes in lung imaging, early and accurate differential diagnosis presents a significant challenge. Misdiagnosis or delayed treatment can lead to serious consequences.

[0003] Currently, the differentiation between tuberculosis and pneumonia in clinical practice still mainly relies on traditional methods, including: (1) Tuberculin skin test (PPD test): This method is greatly affected by BCG vaccination. In areas where BCG vaccination is widespread, the false positive rate of PPD test in children is high, making it difficult to accurately distinguish between the immune response after BCG vaccination and tuberculosis infection; it cannot distinguish between past infection and recent infection, and it is difficult to distinguish between latent tuberculosis infection and tuberculosis patients; at the same time, for children with low immune function, such as those with low immune function, those using immunosuppressants and glucocorticoids, those with severe infection, those with hematological diseases (leukemia, etc.), those with malnutrition, those with immunodeficiency diseases, and those infected with MTB within 8-12 weeks, PPD test may produce false negative results, leading to missed diagnosis; (2) Interferon-γ release assay (IGRAs): This method uses the ESAT-6 and CFP-10 specific antigens or antigenic peptides encoded by the RD1 region gene of Mycobacterium tuberculosis (MTB). Since ESAT-6 and CFP-10 are mainly present in the MTB complex and not in BCG strains and most NTMs (except Mycobacterium kansas, Mycobacterium marinum, and Mycobacterium surgaense), the IGRAS method has higher specificity than TST. Therefore, a positive IGRAS result is helpful in the diagnosis of MTB infection and can exclude BCG inoculation reaction and most NTM infections. Similar to TST, positive IGRAS results are difficult to distinguish between past and recent MTB infections, and between latent tuberculosis infection and tuberculosis. In addition, IGRAS has similar sensitivity to TST and is affected by immune function. For example, in cases of immunodeficiency, use of immunosuppressants and glucocorticoids, severe infection, hematological diseases (such as leukemia), malnutrition, children with immunodeficiency diseases, and patients infected with MTB within 8-12 weeks, IGRAS may produce false negative results, leading to missed diagnosis. (3) Sputum smear test: Children have less respiratory secretions, making sputum specimen collection difficult. In addition, the content of Mycobacterium tuberculosis in sputum is usually low, resulting in low sensitivity of this test method, which is prone to false negatives and delays the diagnosis of tuberculosis. (4) Imaging examination: Although it can show the condition of lung lesions, tuberculosis and other pathogen-induced pneumonia have overlapping imaging manifestations, such as lung infiltrates and nodular shadows, which are difficult to accurately distinguish based on imaging results alone and are prone to misdiagnosis. (5) Pathogen detection: Methods such as viral nucleic acid detection, bacterial culture, and Mycoplasma pneumoniae nucleic acid and antibody detection have a long detection cycle. For example, bacterial culture usually takes several days, and a positive sputum bacterial culture often cannot distinguish between pathogenic bacteria and locative bacteria, which cannot meet the needs of rapid clinical diagnosis. Some detection methods, such as Mycoplasma pneumoniae antibody detection, may produce false negative results in the early stage of infection when antibodies have not yet been produced or the antibody titer is low, which affects the accuracy of diagnosis.

[0004] S100A8 / A9 is known to be primarily expressed by neutrophils. When neutrophils are activated, destroyed, or die, this abundant cytoplasmic neutrophil protein is released and functions as a damage-related molecular pattern by binding to various receptors. It participates in the regulation of multiple physiological functions, including cell proliferation, differentiation, migration / invasion, inflammation, oxidative stress, calcium homeostasis, apoptosis, glycogen phosphorylation, and macrophage aggregation. S100A8 / A9 expression is increased in various infectious and inflammatory diseases (such as sepsis, inflammatory bowel disease, myocardial infarction, and autoimmune diseases) and is closely related to disease severity. In addition to its antimicrobial function, S100A8 / A9 also acts as a molecule with pro-tumor and anti-tumor properties associated with cell survival and growth, angiogenesis, DNA damage response, and extracellular matrix remodeling. As mentioned above, although S100A8 / A9 has been reported to be closely associated with a variety of diseases, these studies have focused on the relationship between S100A8 / A9 and a single disease. Currently, there are no studies on using S100A8 / A9 to differentiate between different diseases.

[0005] The information in the background section is merely intended to illustrate the general background of the invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] Existing diagnostic methods for differentiating tuberculosis from normal children and pneumonia caused by other pathogens suffer from high false-positive and false-negative rates, long testing cycles, complex operations, and stringent requirements for testing conditions. One objective of this invention is to provide the application of S100A8 / A9 in differentiating children with tuberculosis from normal children and children with pneumonia. By detecting the concentration of S100A8 / A9 in serum and determining the corresponding diagnostic threshold, rapid, sensitive, and specific differentiation between tuberculosis and pneumonia caused by other pathogens in normal children can be achieved. This provides a reliable basis for early clinical diagnosis, improves diagnostic efficiency and accuracy, avoids misdiagnosis and missed diagnosis, thereby guiding rational clinical treatment and improving patient prognosis. Specifically, this invention includes the following:

[0007] In a first aspect, the invention provides the use of a reagent in the preparation of a diagnostic product for distinguishing between tuberculosis and pneumonia in children, wherein the reagent includes a reagent for quantifying S100A8 / S100A9.

[0008] In some embodiments, according to the application described in the invention, the pneumonia includes pneumonia caused by infection with at least one selected from viruses, bacteria other than Mycobacterium tuberculosis, and Mycoplasma pneumoniae.

[0009] In some embodiments, according to the application described in the present invention, the reagent comprises at least one of an enzyme, a buffer solution, an antibody, or a functional fragment thereof.

[0010] In some embodiments, according to the application described in the invention, the child suffers from clinical symptoms including fever and / or cough.

[0011] In some embodiments, according to the application described in the present invention, the detection product includes test strips, reagent kits, or biochips.

[0012] A second aspect of the present invention provides a system for distinguishing different lung diseases in children, comprising: A data acquisition unit is configured to acquire biomarker data values ​​from a child's blood sample, wherein the biomarkers include S100A8 / S100A9; A storage unit configured to store a first threshold and a second threshold, wherein the first threshold is less than the second threshold; A data processing unit, configured to: communicate with the storage unit, retrieve the first threshold and / or the second threshold, compare the marker data value with the second threshold, classify the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold, and further compare the marker data value with the first threshold when the marker data value is less than the second threshold; if the marker data value is greater than the first threshold, classify the child as a high-risk group for tuberculosis. The result output unit is configured to output the corresponding classification result for the child.

[0013] In some embodiments, in the system for distinguishing different lung diseases in children according to the present invention, the first threshold and the second threshold are determined by analyzing database data, plotting subject operating characteristic curves, and calculating the area under the curve, sensitivity, and specificity.

[0014] In some embodiments, according to the system for distinguishing different lung diseases in children according to the present invention, the data acquisition unit is further configured to acquire the child's age data values, and The data processing unit is further configured to retrieve the child's age data value and adjust the first threshold and the second threshold according to the child's age data value.

[0015] A third aspect of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps: Obtain biomarker data values ​​from children's blood samples, including biomarkers S100A8 / S100A9; The system retrieves a first threshold and / or a second threshold stored in the memory, compares the biomarker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the biomarker data value is greater than the second threshold. If the biomarker data value is less than the second threshold, it is further compared with the first threshold; if the biomarker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

[0016] A fourth aspect of the present invention provides a computer storage medium or cloud, wherein a computer program is stored, and when the computer program is executed by a computer, it performs the following steps: Obtain biomarker data values ​​from children's blood samples, including biomarkers S100A8 / S100A9; The system retrieves the first threshold and / or the second threshold stored in the memory, compares the marker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold. When the marker data value is less than the second threshold, it is further compared with the first threshold. If the marker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

[0017] From a technical perspective, this invention has the following technical effects: 1. Rapid detection: This invention uses enzyme-linked immunosorbent assay (ELISA) to detect serum S100A8 / A9 concentration. The entire detection process can be completed within a few hours. Compared with traditional methods such as sputum culture for Mycobacterium tuberculosis (which takes several days) and nucleic acid detection of some pathogens (which takes several hours to a day), the detection cycle is greatly shortened, and results can be provided quickly for clinical diagnosis.

[0018] 2. Simple to operate: The detection method has relatively simple operation steps, does not require complicated instruments and equipment or professional technical personnel training, and can be carried out by primary medical institutions, making it easy to promote and apply.

[0019] 3. High sensitivity and specificity: In distinguishing tuberculosis from healthy children and other pathogen-infected pneumonia, the S100A8 / A9 concentration has an AUC greater than 0.8 in certain diagnostic scenarios, and has high sensitivity and specificity, which can effectively reduce false positive and false negative results and improve diagnostic accuracy.

[0020] 4. Samples are easy to obtain: Serum samples can be obtained by venous blood collection. For pediatric patients, venous blood collection is relatively easy to perform, has little trauma, and patients have good compliance, which solves the problem of difficult sputum sample collection in children.

[0021] At the application level, this invention has the following technical effects: 1. Assisting Early Clinical Diagnosis: This invention provides a health-related information system and cloud platform, offering clinicians a new and reliable diagnostic basis. It helps doctors quickly and accurately differentiate tuberculosis from normal children and pneumonia caused by other pathogens, avoiding treatment delays or inappropriate treatment due to unclear diagnoses. For example, in children suspected of having tuberculosis, if the serum S100A8 / A9 concentration is higher than the optimal threshold (307 ng / mL) for distinguishing tuberculosis from healthy children, but lower than the corresponding optimal threshold for distinguishing tuberculosis from pneumonia caused by other pathogens, the risk of having tuberculosis is relatively high.

[0022] 2. Guiding Treatment Plan Selection: By accurately distinguishing the type of disease, doctors can select targeted treatment plans based on the diagnostic results. For example, if tuberculosis is diagnosed, anti-tuberculosis treatment will be given promptly; if bacterial pneumonia is diagnosed, antibiotic treatment will be given; if viral pneumonia is diagnosed, symptomatic and supportive treatment will be the main approach to avoid the overuse of antibiotics and reduce the development of drug resistance.

[0023] 3. Reduce medical costs: Because this invention is fast, easy to operate, and easy to obtain samples, it can reduce unnecessary examinations and hospitalization time for patients, thereby reducing their medical expenses. It also alleviates the diagnostic and treatment pressure on medical institutions and improves the efficiency of medical resource utilization. Attached Figure Description

[0024] Figure 1 The value of S100A8 / A9 in distinguishing between tuberculosis (TB), community-acquired pneumonia (CAP), and healthy controls (HC) in Example 1. Here, A represents the serum S100A8 / A9 levels in the HC, TB, and CAP groups in Example 1; B represents the serum S100A8 / A9 levels in the CAP subgroup; data are expressed as median and interquartile ranges, p < 0.001; and C is the ROC curve of S100A8 / A9 levels used in Example 1 to distinguish between TB, CAP, and HC.

[0025] Figure 2 The value of S100A8 / A9 in distinguishing between tuberculosis (TB), community-acquired pneumonia (CAP), and healthy controls (HC) in Example 2. In Example 2, A represents the serum S100A8 / A9 levels in the HC, TB, and CAP groups; B represents the serum S100A8 / A9 levels in the CAP subgroup; data are expressed as median and interquartile ranges, p < 0.001; and C is the ROC curve of S100A8 / A9 levels used in Example 2 to distinguish between TB, CAP, and HC.

[0026] Figure 3The value of S100A8 / A9 in different age groups of children in Example 3 for distinguishing between tuberculosis (TB) and community-acquired pneumonia (CAP) and healthy controls (HC). In this study, A represents the serum S100A8 / A9 levels in the HC, TB, and CAP groups; B represents the serum S100A8 / A9 levels in the CAP subgroup; data are expressed as median and interquartile ranges, p < 0.001; C and D are the ROC curves for S100A8 / A9 in distinguishing between TB, CAP, and HC in children aged 0-5 years and 5-18 years, respectively.

[0027] Figure 4 Example 4 uses ROC curves (AEs) to differentiate between tuberculosis (TB) and community-acquired pneumonia (CAP) and CAP subgroups. Here, NLR is the neutrophil-to-lymphocyte ratio, and CRP is C-reactive protein. Detailed Implementation

[0028] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0029] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that the upper and lower limits of the range and each intermediate value between them are specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, are also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0030] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar to or equivalent to those described herein may be used in the implementation or testing of this invention.

[0031] markers In one aspect, the present invention provides a biomarker for distinguishing different lung diseases in children, the biomarker comprising S100A8 / S100A9, wherein the amount of said biomarker is used to indicate different lung diseases in children (tuberculosis and pneumonia).

[0032] In a preferred embodiment, the biomarkers of the present invention further include NLR (neutrophil-lymphocyte ratio) and / or CRP (C-reactive protein), thereby further improving the efficacy in distinguishing different lung diseases (tuberculosis and pneumonia) in children.

[0033] Unless otherwise stated, the term "detection" as used herein refers to methods that include quantifying markers in a sample. It should be noted that the term "quantity" as used herein should be interpreted broadly: it can refer to quantitative or semi-quantitative results, and it can refer to absolute or relative content.

[0034] application One aspect of the invention provides the use of reagents in the preparation of diagnostic products that differentiate between different lung diseases in children (tuberculosis and pneumonia), wherein the reagents comprise any suitable reagents that can be used to quantify S100A8 / S100A9, wherein the pneumonia is preferably pathogenic infectious pneumonia. The term "diagnostic product" as used herein includes any product, device, instrument, or apparatus for detecting S100A8 / S100A9, such as, but not limited to, test strips, kits, protein chips, chromatographic and / or mass spectrometric instruments, etc.

[0035] In this invention, the type of biological sample used for detection or identification is not particularly limited, and examples include, but are not limited to, one or more of the following: serum, plasma, blood components, cells, tissues, or other fluids produced by the body. In one preferred embodiment, the biological sample described herein is serum. In another preferred embodiment, the biological sample described herein is plasma.

[0036] In this document, the term "child" refers to an individual or subject under the age of 18. In some embodiments, a child is older than 28 days but under 18 years of age. In some embodiments, a child is under 5 years of age. In some embodiments, a child is between 5 and 18 years of age.

[0037] This invention, through extensive data analysis, discovered significant differences in serum S100A8 / A9 concentrations among children with tuberculosis, healthy children, and children with pneumonia caused by other pathogens. These differences are statistically significant and can serve as potential biomarkers for distinguishing these populations. Furthermore, the optimal diagnostic threshold for serum S100A8 / A9 concentrations in differentiating between tuberculosis and healthy children, as well as between children with pneumonia caused by different pathogens, was determined, and its good diagnostic efficacy (AUC, sensitivity, and specificity) was verified through ROC curve analysis. In this invention, "difference" refers to a situation where the amount of the biomarker detected in children with tuberculosis is abnormally higher than that in healthy individuals, but lower than that in children with pneumonia caused by other pathogens.

[0038] In a preferred embodiment, the reagent further includes reagents for detecting other biomarkers, thereby further improving the efficacy in distinguishing different lung diseases in children (tuberculosis and pneumonia), said other biomarkers including but not limited to NLR and / or CRP.

[0039] In this invention, the reagents and detection methods used to detect biomarkers (including S100A8 / A9, NLR, CRP) are not particularly limited, as long as they can quantify or semi-quantitatively measure the amount or concentration of the biomarker. Preferably, the detection reagents include, but are not limited to, primers, probes, detection antibodies, or their functional fragments. Examples of functional fragments include, but are not limited to, Fab, Fab', F(ab')2, scFv, or scFv Fc fragments. Detection methods include, but are not limited to, enzyme-linked immunosorbent assay (ELISA), immunoturbidimetry, lateral flow immunochromatography, Western blotting, immunohistochemistry, flow cytometry, real-time quantitative PCR, high-throughput sequencing, etc.

[0040] Product testing In one aspect, the present invention provides a detection product for differentiating different lung diseases in children, including reagents for quantifying S100A8 / S100A9 and instructions on how to detect S100A8 / S100A9 and differentiate different lung diseases in children, wherein the instructions include optimal diagnostic thresholds for differentiating tuberculosis from healthy children and pneumonia caused by different pathogens.

[0041] The detection products of this invention include, but are not limited to, test strips, kits, or protein chips. In a preferred embodiment, the detection product of this invention is a kit containing reagents for detecting S100A8 / S100A9, particularly antibodies for detecting specific antigens of S100A8 / S100A9, such as monoclonal antibodies that specifically (or target) S100A8 binding to specific antigen fragments, monoclonal antibodies that specifically (or target) S100A9 binding to specific antigen fragments, or monoclonal antibodies that specifically (or target) the S100A8 / A9 complex binding to specific antigen fragments. Those skilled in the art will understand that, in addition to the antibodies described above, the kit may also contain conventional reagent components such as standards and substrate solutions. The specific components mentioned above, such as antibodies, can use S100A8 / A9 monoclonal antibodies reported in the art or commercially available products, and are not particularly limited thereto.

[0042] It is understood that the detection products of the present invention may further include detection reagents or detection devices for detecting additional markers, including but not limited to NLR and / or CRP.

[0043] In addition to the components described above, the kit of the present invention may also include precautions related to the manufacture, use, or sale of the diagnostic kit. Furthermore, the kit of the present invention may also be provided with detailed instructions for use, storage, and troubleshooting. The kit may optionally be housed in a suitable device, preferably for high-throughput robotic operation.

[0044] In some embodiments, the components of the kit of the present invention may be provided as dry powder. When the reagents and / or components are provided as dry powder, the powder may be reconstituted by adding a suitable solvent. It is contemplated that the solvent may also be provided in another container. The container typically includes at least one vial, test tube, flask, bottle, syringe, and / or other container means in which the solvent may optionally be placed in equal portions. The kit may also include means for containing a second container of sterile, pharmaceutically acceptable buffers and / or other solvents.

[0045] In some embodiments, the components of the kit of the present invention may be provided in solution form, such as an aqueous solution. When present in aqueous solution form, the concentration or content of these components can be readily determined by those skilled in the art according to different needs. For example, for storage purposes, the reagent concentration may be higher, and when in operation or in use, the concentration can be reduced to the working concentration, for example, by diluting the higher concentration solution.

[0046] In kits containing more than one component, the kit typically also includes second, third, or other additional containers for individually holding other components. Additionally, combinations of multiple components may be contained within the containers. Any combination or reagent described herein may be a component of the kit.

[0047] A system used to differentiate between different lung diseases in children. In another aspect, the present invention provides a system or apparatus for distinguishing different lung diseases in children, comprising: A data acquisition unit is configured to acquire biomarker data values ​​from a child's blood sample, wherein the biomarkers include S100A8 / S100A9; A storage unit configured to store a first threshold and a second threshold, wherein the first threshold is less than the second threshold; A data processing unit, configured to: communicate with the storage unit, retrieve the first threshold and / or the second threshold, compare the marker data value with the second threshold, classify the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold, and further compare the marker data value with the first threshold when the marker data value is less than the second threshold; if the marker data value is greater than the first threshold, classify the child as a high-risk group for tuberculosis. The result output unit is configured to output the corresponding classification result for the child.

[0048] It is understood that the data acquisition unit may further include detection data for acquiring additional biomarkers from the child samples, including but not limited to NLR and / or CRP.

[0049] In a preferred embodiment of the present invention, when used to distinguish between healthy children and children with tuberculosis, the first threshold (first diagnostic threshold) is 307 ng / mL, and when used to distinguish between children with pathogen-infected pneumonia and children with tuberculosis, the second threshold (second diagnostic threshold) is 1876 ng / mL. It will be understood in the art that the diagnostic threshold is not necessarily a single point value mentioned in the present invention, but can also be a suitable range. Therefore, in a preferred embodiment of the present invention, when used to distinguish between healthy children and children with tuberculosis, the range of the first threshold (first diagnostic threshold) is 307-364 ng / mL, and when used to distinguish between children with pathogen-infected pneumonia and children with tuberculosis, the range of the second threshold (second diagnostic threshold) is 1687-1876 ng / mL.

[0050] Further, in a preferred embodiment of the present invention, when used to differentiate between children with tuberculosis and children with viral pneumonia, the third diagnostic threshold is 970 ng / mL; when used to differentiate between tuberculosis and bacterial pneumonia, the fourth diagnostic threshold is 1852 ng / mL; when used to differentiate between tuberculosis and mixed-infection pneumonia, the fifth diagnostic threshold is 1517 ng / mL; and when used to differentiate between tuberculosis and mycoplasma pneumoniae pneumonia, the sixth diagnostic threshold is 1926 ng / mL. In another preferred embodiment of the present invention, when used to differentiate between children with tuberculosis and children with viral pneumonia, the diagnostic threshold ranges from 970 to 1032 ng / mL; when used to differentiate between tuberculosis and bacterial pneumonia, the diagnostic threshold ranges from 1691 to 1852 ng / mL; when used to differentiate between tuberculosis and mixed-infection pneumonia, the diagnostic threshold ranges from 1517 to 1687 ng / mL; and when used to differentiate between tuberculosis and mycoplasma pneumoniae pneumonia, the diagnostic threshold ranges from 1809 to 1926 ng / mL.

[0051] In this invention, when the biomarker data value (sometimes also called the measurement value) is lower than the first threshold of this invention, the subject is judged to be a healthy child, or to have a low risk of tuberculosis or pneumonia; when the measurement value is higher than the first threshold of this invention and lower than the second threshold of this invention, the subject is judged to have a high risk of tuberculosis or a low risk of pneumonia; when the measurement value is higher than the second threshold of this invention, the subject is judged to have a high risk of pneumonia and / or a low risk of tuberculosis.

[0052] Example 1 I. Experimental Methods 1. Experimental subjects All subjects were drawn from Kunming Children's Hospital, the Seventh People's Hospital of Liangshan Yi Autonomous Prefecture, and Baoding Children's Hospital. This multi-center collaborative approach ensured the geographical representativeness and clinical diversity of the case samples, providing a foundation for the reliability and applicability of the research results. Specific groupings and case numbers are as follows: 1.1 Healthy control group (100 cases) Inclusion criteria: Healthy children who visited or underwent physical examinations at the above three hospitals were selected. Clinical examination (including physical examination and preliminary screening with chest imaging) confirmed that they had no lung diseases and no history or clinical manifestations of other infectious diseases (such as respiratory tract infections, digestive tract infections, etc.).

[0053] 1.2 Pneumonia CAP group (775 cases) General Description: This group comprises 775 cases, consisting of children diagnosed with various types of infectious pneumonia at the three hospitals mentioned above. These include four types: viral pneumonia, bacterial pneumonia, mixed infection pneumonia, and Mycoplasma pneumoniae infection pneumonia. Specific grouping and inclusion criteria are as follows: (1) Virus group (133 cases, belonging to the pneumonia group) Inclusion criteria: Children hospitalized in the pediatric departments of three hospitals who were diagnosed with viral pneumonia by etiological examination were selected.

[0054] The diagnostic criteria include: a positive viral nucleic acid test (such as real-time fluorescent RT-PCR for respiratory viral nucleic acid), meeting the diagnostic criteria for viral infection, and, in conjunction with the child's clinical symptoms (such as fever, cough, shortness of breath, etc.) and chest imaging findings, ruling out the possibility of infection by other pathogens.

[0055] (2) Bacterial group (86 cases, belonging to the pneumonia group) Inclusion criteria: Children hospitalized in the pediatric departments of three hospitals who were diagnosed with bacterial infectious pneumonia by etiological or immunological examination were selected.

[0056] Diagnostic criteria include: positive bacterial nucleic acid test (such as real-time fluorescence PCR to detect bacterial specific nucleic acid) and / or culture in sputum and / or bronchoalveolar lavage fluid specimens, meeting the diagnostic criteria for bacterial infection, and in combination with the child's clinical symptoms (such as high fever, purulent sputum, chest pain, etc.) and chest imaging findings, ruling out the possibility of infection by other pathogens such as viruses and Mycoplasma pneumoniae.

[0057] (3) Mixed infection group (298 cases, belonging to the pneumonia group) Inclusion criteria: Children with pneumonia admitted to three hospitals were selected, and those diagnosed with pneumonia caused by mixed infection of two or more pathogens through etiological or immunological examination. Common infection combinations include mixed infections of viruses and bacteria (such as influenza virus combined with Streptococcus pneumoniae infection) and mixed infections of viruses and Mycoplasma pneumoniae (such as respiratory syncytial virus combined with Mycoplasma pneumoniae infection). Diagnosis requires verification through multiple testing methods, such as simultaneous viral nucleic acid testing, bacterial culture, and Mycoplasma pneumoniae antibody or nucleic acid testing, with at least two tests showing positive results, combined with a comprehensive judgment based on clinical symptoms and imaging characteristics.

[0058] (4) Mycoplasma pneumoniae group (258 cases, belonging to the pneumonia group) Inclusion criteria: Children diagnosed with Mycoplasma pneumoniae pneumonia by three hospitals were selected. The diagnostic criteria were: ① Positive Mycoplasma pneumoniae IgM antibody test (titer ≥ 1:160), and the child's clinical symptoms (such as irritating dry cough, fever, etc.) and chest imaging findings (such as interstitial pneumonia, bronchopneumonia changes) were consistent with the characteristics of Mycoplasma pneumoniae pneumonia; ② Positive Mycoplasma pneumoniae nucleic acid test (such as PCR test), excluding infections by other pathogens such as bacteria and viruses.

[0059] 1.3 Tuberculosis group (234 cases) Inclusion criteria: Children diagnosed with tuberculosis by comprehensive clinical diagnosis from three hospitals. Diagnosis requires multiple pieces of evidence: ① Clinical manifestations (e.g., prolonged low-grade fever, night sweats, fatigue, weight loss, cough, and sputum production); ② Strongly positive tuberculin skin test (PPD test) or positive IGRA test; ③ Chest imaging examinations (e.g., chest CT or chest X-ray) showing characteristic tuberculosis lesions (e.g., primary syndrome, cavity formation, hilar lymphadenopathy); ④ Sputum examination for Mycobacterium tuberculosis (e.g., positive acid-fast staining of sputum smear, positive culture of sputum for Mycobacterium tuberculosis, or positive nucleic acid test for Mycobacterium tuberculosis). For children without sputum, gastric juice / bronchoalveolar lavage fluid and bronchoalveolar lavage fluid tests can be used to assist in diagnosis and ensure diagnostic accuracy.

[0060] All participants were selected in accordance with medical ethics principles, and informed consent was obtained from the participants or their guardians.

[0061] 2. Serum sample collection Collect 2 ml of venous blood from all subjects and place it in a vacuum blood collection tube without anticoagulant. Let it stand at room temperature for 30-60 minutes until the blood coagulates. Then, centrifuge at 3000 r / min for 10-15 minutes to separate the serum. Aliquot the serum into EP tubes and store them in a -80℃ freezer for later use, avoiding repeated freeze-thaw cycles.

[0062] 3. Serum S100A8 / A9 concentration detection The concentration of S100A8 / A9 in serum was detected using an enzyme-linked immunosorbent assay (ELISA). The specific steps are as follows: Reagent preparation: Prepare the standard, enzyme-labeled antibody, substrate solution, washing solution and other reagents according to the instructions of the S100A8 / A9 ELISA kit (manufacturer: abcam; product model: ab267628), and bring the reagents to room temperature.

[0063] Set up standard wells, sample wells, and blank control wells in a 96-well plate. Add standard solutions of different concentrations to the standard wells, add the serum samples to be tested to the sample wells, and add buffer solution to the blank control wells.

[0064] Add an appropriate amount of capture antibody to each well, seal the plate, and incubate it on a shaker at room temperature for 2.5 hours.

[0065] After incubation, wash away unbound antibodies, add detection antibodies (specific antibodies against S100A8 / A9), incubate again, and wash.

[0066] Add enzyme-labeled secondary antibody, incubate, wash, then add substrate solution, and incubate in the dark for a period of time until color development occurs.

[0067] The reaction was terminated by adding a stop solution, and the absorbance of each well was measured at 450 nm using an ELISA reader.

[0068] Calculate the average absorbance of each replicate of standards, controls, and samples. Subtract the mean zero standard density. Plot a standard curve on a log-logarithmic scale, with the x-axis representing standard concentration and the y-axis representing absorbance. Draw the best-fit straight line through the standard points and use the equation to calculate the concentration of S100A8 / A9 in the samples.

[0069] 4. Diagnostic efficacy assessment Based on the serum S100A8 / A9 concentration data obtained from different groups, statistical software (such as SPSS 27.0) was used for data analysis. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. The optimal cut-off value for distinguishing tuberculosis from healthy children and pneumonia caused by different pathogens was determined to evaluate the diagnostic efficacy of serum S100A8 / A9 concentration.

[0070] II. Experimental Results The comparison of serum S100A8 / A9 concentrations in different groups is shown in Table 1 below.

[0071] Table 1 The results above show that the serum S100A8 / A9 concentration in the tuberculosis group was significantly higher than that in the healthy control group and significantly lower than that in the virus group, bacterial group, mixed infection group and mycoplasma group, and the differences were statistically significant (P<0.001).

[0072] The diagnostic efficacy of serum S100A8 / A9 concentrations is shown in Table 2 below.

[0073] Table 2 The diagnostic efficacy data above show that serum S100A8 / A9 concentration has good diagnostic value in differentiating tuberculosis from pneumonia caused by other pathogens in healthy children. In some diagnostic scenarios, the AUC is even greater than 0.8 (P < 0.001), which is statistically significant. Among them, the AUC is the highest (0.905) in differentiating tuberculosis from Mycoplasma pneumoniae pneumonia, indicating that it has the best diagnostic efficacy in this scenario, while also having high sensitivity (0.808) and specificity (0.938).

[0074] Example 2 I. Methods 1. Experimental subjects The subjects in this embodiment were also selected from Kunming Children's Hospital, Liangshan Yi Seventh People's Hospital, and Baoding Children's Hospital through a multi-center collaborative approach. The specific groupings and number of cases are as follows: (1) Healthy control group (51 cases) The inclusion standard is the same as the implementation example.

[0075] (2) Pneumonia CAP group (390 cases) General Description: This group comprises 390 cases, consisting of children diagnosed with various types of infectious pneumonia at the three hospitals mentioned above. Specific grouping and inclusion criteria are the same as in the previous implementation plan. The number of cases in each group is as follows: Virus group (67 cases, belonging to the pneumonia group); Bacterial group (44 cases, belonging to the pneumonia group); Mixed infection group (150 cases, belonging to the pneumonia group); Mycoplasma group (129 cases, belonging to the pneumonia group).

[0076] (3) Tuberculosis group (106 cases) The inclusion criteria are the same as in Example 1.

[0077] All participants were selected in accordance with medical ethics principles, and informed consent was obtained from the participants or their guardians.

[0078] 2. Experimental Procedure The methods for serum sample collection and serum S100A8 / A9 concentration detection are the same as in Example 1.

[0079] II. Experimental Results The comparison of serum S100A8 / A9 concentrations in different groups in this embodiment is shown in Table 3 below.

[0080] Table 3 The results above show that the serum S100A8 / A9 concentration in the tuberculosis group was significantly higher than that in the healthy control group and significantly lower than that in the virus group, bacterial group, mixed infection group and mycoplasma group, and the differences were statistically significant (P<0.001).

[0081] The diagnostic efficacy of serum S100A8 / A9 concentration in this embodiment is shown in Table 4 below.

[0082] Table 4 The diagnostic efficacy data above show that serum S100A8 / A9 concentration still has good diagnostic value in differentiating tuberculosis from healthy children and pneumonia caused by other pathogens. The AUC was greater than 0.8 in all diagnostic scenarios, and P < 0.001, indicating statistical significance. Among these, the AUC for differentiating tuberculosis from Mycoplasma pneumoniae pneumonia was the highest (0.903), indicating the best diagnostic efficacy in this scenario, while also exhibiting high sensitivity (0.811) and specificity (0.953).

[0083] This embodiment is completely consistent with Embodiment 1 in terms of experimental methods, sample sources, detection procedures, and statistical analysis, with only a reduction in the number of samples in each group. The results show that despite the reduced sample size, the trend of serum S100A8 / A9 concentration differences among different groups is consistent with that of Embodiment 1. The diagnostic efficacy indicators such as AUC, sensitivity, and specificity remain at high levels, and the optimal diagnostic cutoff value is close to that of Embodiment 1. This indicates that the method provided by this invention, using serum S100A8 / A9 concentration as a diagnostic biomarker for tuberculosis, has good stability and reproducibility, unaffected by fluctuations in sample size within a certain range, further verifying its reliable diagnostic value in clinical applications.

[0084] Example 3 This study further evaluated the diagnostic efficacy of S100A8 / A9 in differentiating between pathogen-borne infectious pneumonia and tuberculosis based on age stratification. The results showed that serum S100A8 / A9 concentrations increased with age, while this trend was reversed in children with tuberculosis. Serum S100A8 / A9 levels were significantly higher in children under 5 years of age than in those aged 5–18 years (median 927 ng / mL vs 599 ng / mL, p<0.001). Correspondingly, S100A8 / A9 demonstrated higher efficacy in differentiating between pathogen-borne infectious pneumonia and tuberculosis in children aged 5–18 years compared to children under 5 years of age (AUC 0.784 vs 0.908). The relatively high S100A8 / A9 levels in young children with tuberculosis may be related to the immature immune system response pattern in young children. The immune system of children under 5 years old is not yet fully developed, and the symptoms of tuberculosis in young children are often atypical, making them easy to misdiagnose as ordinary pneumonia. Therefore, in the clinical diagnosis of suspected tuberculosis in children under 5 years old, if S100A8 / A9 is greater than 663 ng / mL (the cutoff value for distinguishing between PTB and CAP) and S100A8 / A9 is less than 2635 μg / mL, the diagnostic efficacy for distinguishing tuberculosis from pneumonia reaches 0.784, which can effectively avoid missed diagnosis of tuberculosis in young children (Table 5).

[0085] Table 5 Furthermore, the S100A8 / A9 levels in non-young tuberculosis children (5-18 years old) were lower than those in younger tuberculosis children (0-5 years old), but the difference was more significant compared to pneumonia, resulting in higher diagnostic efficacy. S100A8 / A9 levels greater than 284 ng / mL and less than 1788 ng / mL achieved a diagnostic efficacy of 0.908 in distinguishing tuberculosis from pneumonia (Table 6).

[0086] Table 6 Example 4 This embodiment further explores the efficacy of combinations of multiple biomarkers in differentiating between tuberculosis and pneumonia in children. In addition to S100A8 / S100A9, the biomarkers further include NLR and / or CRP. The combinations of biomarkers include: S100A8 / A9+NLR, S100A8 / A9+CRP, and S100A8 / A9+NLR+CRP. The results are shown in Table 7. The results show that S100A8 / A9 combined with other biomarkers can still effectively differentiate between tuberculosis and pneumonia in children. Moreover, compared with using any one of the biomarkers alone, the combination of multiple biomarkers can synergistically improve the diagnostic efficacy of tuberculosis and pneumonia in children.

[0087] Table 7 Example 5 This embodiment provides a system for distinguishing different lung diseases in children, wherein the system includes: A data acquisition unit is configured to acquire biomarker data values ​​from a child's blood sample, wherein the biomarkers include S100A8 / S100A9; A storage unit configured to store a first threshold and a second threshold, wherein the first threshold is less than the second threshold; A data processing unit, configured to: communicate with the storage unit, retrieve the first threshold and / or the second threshold, compare the marker data value with the second threshold, classify the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold, and further compare the marker data value with the first threshold when the marker data value is less than the second threshold; if the marker data value is greater than the first threshold, classify the child as a high-risk group for tuberculosis. The result output unit is configured to output the corresponding classification result for the child.

[0088] In this embodiment, the first threshold and the second threshold are determined by analyzing database data, plotting subject operating characteristic curves, and calculating the area under the curve, sensitivity, and specificity.

[0089] In this embodiment, the data acquisition unit is further configured to acquire the child's age data value, and the data processing unit is further configured to retrieve the child's age data value and adjust the first threshold and the second threshold according to the child's age data value.

[0090] This embodiment further provides a computer device, wherein the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain biomarker data values ​​from children's blood samples, including biomarkers S100A8 / S100A9; The system retrieves the first threshold and / or the second threshold stored in the memory, compares the marker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold. When the marker data value is less than the second threshold, it is further compared with the first threshold. If the marker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

[0091] It is understood that the data acquisition unit may further include detection data for acquiring additional biomarkers from the child samples, including but not limited to NLR and / or CRP.

[0092] This embodiment further provides a computer storage medium or cloud, wherein a computer program is stored, and when the computer program is executed by a computer, it performs the following steps: Obtain biomarker data values ​​from children’s blood samples, wherein the biomarkers include S100A8 / S100A9, and it is understood that the biomarkers further include other biomarkers besides S100A8 / S100A9, including but not limited to NLR and / or CRP. The system retrieves the first threshold and / or the second threshold stored in the memory, compares the marker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold. When the marker data value is less than the second threshold, it is further compared with the first threshold. If the marker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

[0093] Those skilled in the art will understand that the various exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, specific embodiments of this invention can be embodied in the form of a software product, which can be stored on a non-volatile storage medium or a non-transitory computer-readable storage medium (such as a CD). The method according to the invention is contained in ROM, USB flash drive, portable hard drive, etc. or on a network, and includes several instructions to cause a computing device (which may be a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the invention.

[0094] In exemplary embodiments, the program product of the present invention can employ any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include, but are not limited to: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disk read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0095] Accordingly, based on the same inventive concept, the present invention also provides an electronic device.

[0096] In an exemplary embodiment, the electronic device is manifested as a general-purpose computing device. Components of the electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including the memory and the processor).

[0097] The memory stores program code that can be executed by the processing unit to perform the detection method described in this invention. The processor includes at least the result judgment unit (also referred to as a "unit") of this invention. The memory may include a readable medium in the form of volatile memory cells, such as random access memory (RAM) and / or cache memory cells, and may further include read-only memory (ROM).

[0098] The memory of the present invention may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0099] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0100] Electronic devices can also communicate with one or more external devices (such as keyboards, displays, pointing devices, Bluetooth devices, etc.), and with one or more devices that enable users to interact with the electronic device, and / or with any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.).

[0101] This communication can be achieved through input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown herein, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0102] Although the invention has been described with reference to exemplary embodiments, it should be understood that the invention is not limited to the disclosed exemplary embodiments. Various adjustments or changes may be made to the exemplary embodiments described in this specification without departing from the scope or spirit of the invention. The scope of the claims should be interpreted in the broadest possible sense to cover all modifications and equivalent structures and functions.

Claims

1. The application of the reagent in the preparation of a detection product for differentiating between pulmonary tuberculosis and pneumonia in children, characterized in that, The reagents include those used for quantifying S100A8 / S100A9.

2. The application according to claim 1, characterized in that, The pneumonia includes pneumonia caused by infection with at least one of the following: viruses, bacteria other than Mycobacterium tuberculosis, and Mycoplasma pneumoniae.

3. The application according to claim 1, characterized in that, The reagents include at least one of enzymes, buffer solutions, antibodies, or functional fragments thereof.

4. The application according to claim 1, characterized in that, The child had clinical symptoms including fever and / or cough.

5. The application according to claim 4, characterized in that, The testing products include test strips, reagent kits, or biochips.

6. A system for differentiating different lung diseases in children, characterized in that, include: A data acquisition unit is configured to acquire biomarker data values ​​from a child's blood sample, wherein the biomarkers include S100A8 / S100A9; A storage unit configured to store a first threshold and a second threshold, wherein the first threshold is less than the second threshold; A data processing unit is configured to: communicate with the storage unit, retrieve the first threshold and / or the second threshold, compare the marker data value with the second threshold, classify the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold, and further compare the marker data value with the first threshold when the marker data value is less than the second threshold, classifying the child as a high-risk group for tuberculosis if the marker data value is greater than the first threshold. The result output unit is configured to output the corresponding classification result for the child.

7. The system for distinguishing different lung diseases in children according to claim 6, characterized in that, The first threshold and the second threshold are determined by analyzing database data, plotting subject operating characteristic curves, and calculating the area under the curve, sensitivity, and specificity.

8. The system for distinguishing different lung diseases in children according to claim 6, characterized in that, The data acquisition unit is further configured to acquire the child's age data value, and to adjust the first threshold and the second threshold according to the child's age data value.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps: Obtain biomarker data values ​​from children's blood samples, including biomarkers S100A8 / S100A9; The system retrieves the first threshold and / or the second threshold stored in the memory, compares the marker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold. When the marker data value is less than the second threshold, it is further compared with the first threshold. If the marker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

10. A computer storage medium or cloud, characterized in that, It stores a computer program, which, when executed by a computer, performs the following steps: Obtain biomarker data values ​​from children's blood samples, including biomarkers S100A8 / S100A9; The system retrieves the first threshold and / or the second threshold stored in the memory, compares the marker data value with the second threshold, and classifies the child as a high-risk group for pneumonia when the marker data value is greater than the second threshold. When the marker data value is less than the second threshold, it is further compared with the first threshold. If the marker data value is greater than the first threshold, the child is classified as a high-risk group for tuberculosis. Output the corresponding classification results for the children.

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