A method and system for detecting meibomian gland inflammatory factors

Abstract

This invention relates to the field of in vitro diagnostic technology, specifically to a method and system for detecting meibomian gland inflammatory factors. The method includes the following steps: collecting secretion samples at two time points; lysing the samples at low temperature and labeling them; recording color trends to generate classification records; comparing and judging the identification status using scanning maps; screening positive surge samples for encoding and archiving; and outputting the detection conclusion to complete the diagnostic record. In this invention, standardized lysis and labeling of samples at consecutive time points enhances the temporal consistency and comparability of the data; color trend attribution provides clear classification of inflammatory factor expression changes; dual-site identification improves the accuracy and specificity of the diagnosis; feature joint labeling optimizes the sample classification logic; and channel-level output strengthens the hierarchical expression of the detection results. The overall process integrates collection, identification, archiving, and output, improving dynamic analysis capabilities and the systematicity and accuracy of diagnostic information management.

Description

Technical Field

[0001] This invention relates to the field of in vitro diagnostic technology, and in particular to a method and system for detecting meibomian gland inflammatory factors. Background Technology

[0002] The field of in vitro diagnostics (IVD) technology encompasses various methods and techniques for detecting and analyzing health status using human samples. Its core content includes the extraction, processing, and analysis of bodily fluids or tissue samples such as blood, urine, saliva, and secretions to obtain disease-related physiological or pathological information. This technological field covers multiple stages, from sample collection, pretreatment, analysis, and detection to result interpretation, and is widely used in disease screening, diagnosis, efficacy monitoring, and health management. Its detection methods can be categorized into subcategories such as biochemical diagnostics, immunodiagnostics, molecular diagnostics, and microbiological detection. Different detection methods operate on different principles and require different equipment, but their common goal is to achieve accurate assessment of human health status.

[0003] The method and system for detecting meibomian gland inflammatory factors refers to the detection of meibomian gland-related inflammatory states. It involves extracting target inflammatory factors from eyelid secretion samples and performing quantitative analysis to determine the local inflammatory state. The technical aspects addressed in this patent cover the monitoring of changes in inflammatory factor expression caused by meibomian gland dysfunction. Specifically, it involves collecting meibomian gland secretion samples and detecting and analyzing specific protein factors such as interleukins and tumor necrosis factors using immunological methods such as chemiluminescence or enzyme-linked immunosorbent assays. The detection process typically includes steps such as sample fixation, sample lysis, target factor antibody binding, signal capture, and signal readout, thus forming a quantitative detection method system suitable for clinical use.

[0004] Existing technologies lack clear recording and labeling of time points during sample processing, making it difficult to support continuous analysis of samples from the same patient at different times, leading to uncertainty in dynamic trend judgment. In inflammatory factor detection, reliance on single-time-point results fails to capture the fluctuations and phased changes in factor expression, affecting diagnostic sensitivity. In signal recognition, most methods rely on a single recognition site, lacking dual verification at the structural dimension, reducing specificity and increasing the likelihood of misjudgment, especially for boundary samples. Regarding data archiving and sample classification, existing systems often employ single-dimensional labeling or outcome-based archiving, lacking composite coding based on trend evolution and structural binding features, resulting in loosely structured diagnostic data and poor information reusability. For example, in clinical practice, patients often present with no obvious early symptoms but rapidly deteriorate later; relying solely on a single test is insufficient for early warning. These shortcomings hinder early disease intervention and effective identification of high-risk samples. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a method for detecting meibomian gland inflammatory factors.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a method for detecting meibomian gland inflammatory factors, comprising the following steps: S1: Collect secretion samples from patients with meibomian gland dysfunction at two time points, lyse them at low temperature, add lysis buffer, record reaction time and environmental parameters, organize the sample order according to batch number, retain the stability parameters of lysis buffer, and complete the dual time point labeling through test tube labels to obtain lysis processing records. S2: Call the lysis treatment record, perform reagent pre-reaction on IL-6 and TNF-α, record the antigen color development trend at two time points, and divide it into three categories according to the reaction area number: enhancement, decrease, and stability, and generate trend classification record; S3: Based on the trend classification record, transfer the unstable trend sample into the antibody dual-channel reaction chamber, scan the N-terminal charge and C-terminal hydrophilic region map, compare it with the standard template outline, determine whether the dual site recognition conditions are met, and output the in vitro binding recognition diagnosis status. S4: Based on the in vitro combined identification and diagnostic status, screen positive samples with a surge in trend, combine feature identifiers, assign structural binding categories, encode samples, and obtain diagnostic channel numbers; S5: Combining the diagnostic channel number with the lysis processing record, output the detection conclusions respectively. Samples that do not meet the conditions are classified into the ordinary recording channel, and the meibomian gland inflammation detection results are generated.

[0007] As a further aspect of the present invention, the lysis processing record includes reaction time parameters, lysis environment indicators, sample batch number, lysis buffer stability data, and time point labels; the trend classification record includes enhancement trend labels, decline trend labels, and stable trend labels; the in vitro binding identification and diagnostic status includes charge signal identification results, hydrophilic sequence alignment results, and dual-site identification status; the diagnostic channel number includes positive sample markers, surge trend classifications, structural binding category codes, sample standard labels, and joint feature identifiers; and the meibomian gland inflammation detection results include attention-level sample number, IL-6 sub-channel conclusion, TNF-α sub-channel conclusion, ordinary record channel identifier, and diagnostic category archiving information.

[0008] As a further aspect of the present invention, the step of obtaining the pyrolysis processing record is as follows: S101: Based on secretion samples collected from patients with meibomian gland dysfunction at two time points, the samples were sequentially placed into a low-temperature lysis channel, and lysis buffer was added. The reaction time and lysis environment parameters were recorded and the time period batch number sequence was established to obtain the sample lysis environment coding sequence. S102: Based on the sample lysis environment coding sequence, the sample container sequence and corresponding test tube label dual-time point annotation information are retrieved. Based on the recorded lysis buffer parameters and environmental response data, the stability characteristic difference of the lysis buffer is calculated using the formula: ; The stability variability of the lysate is calculated, and the sample time point labeled data is matched according to the corresponding label of the variability to generate the stability interval value of the sample at two time points. in, Represents the variability in the stability of the lysis buffer. Representing the In a pyrolysis-like environment, the first Reaction time for each time period Represents the average reaction time under pyrolysis-like conditions. Representing the Environmental parameter values ​​for a given time period Representing the lysis buffer parameters for a given time period The average value of the lysis fluid parameters under representative environmental conditions. The total number of representative samples across different time periods; S103: Call the sample stability interval values ​​at the two time points and the sample tube label information, combine them with the batch number information and environmental parameter records, integrate the time node label relationship table, and obtain the lysis treatment record.

[0009] As a further aspect of the present invention, the step of obtaining the trend classification record is as follows: S201: Obtain the list of sample numbers marked in the lysis processing record, call the timestamp information marked in each sample label, extract the colorimetric channel of the corresponding antigen, maintain the incubation time and environmental conditions uniformly, perform colorimetric processing after completion, record the corresponding reaction spectrum, and establish an antigen colorimetric response spectrum set; S202: The antigen colorimetric response spectrum set is invoked. Based on the channel numbering information of the two target antigens, IL-6 and TNF-α, at two time points in the sample, pairing and matching are performed. The fluctuation range, grayscale difference, and response slope of the colorimetric curve are calculated using the following formula: ; The antigen color development trend is assigned a value by calculation. Then, based on the interval where the assigned value falls, the color development trend of the sample is classified into three categories: enhancement, decline or stagnation, and a trend discrimination label value is generated. in, The antigen colorimetric trend attribution value represents sample z. This represents the grayscale value of sample z in channel ξ. This represents the average gray value across all channels of sample z. This represents the reference slope value of sample z under channel ξ. The representative sample z represents the estimated response flux under channel ξ. Represents the average flux. This represents the difference constant between sample z at two time points. Represents the number of channels; S203: Based on the trend discrimination label value, establish the association between each sample number and the trend label, assign the classification label to the sample identifier, record the sample identifier, antigen type, time period information and trend classification result, and obtain the trend classification record.

[0010] As a further aspect of the present invention, the step of obtaining the in vitro combined diagnostic status is as follows: S301: Obtain the samples marked in the trend classification record, extract the sample number and antigen type, and transfer them to the in vitro antibody dual-channel reaction chamber. In the chamber, perform fluorescence spectrum scanning on the N-terminal recognition site and the C-terminal hydrophilic sequence binding region in the sample to generate the fluorescence signal intensity sequence of each reaction region and obtain fluorescence spectrum data. S302: Based on the fluorescence spectrum data, the acquired N-terminal and C-terminal fluorescence spectrum data are compared with the standard structural contours in the reference template library. For the identification sites at the N-terminal and C-terminal, the system calculates the fluorescence spectrum offset of each sample through point-to-point contour offset comparison and generates spectrum offset comparison results. The calculation is performed using a simplified formula: ; in, The spectral offset value represents sample x. This represents the fluorescence signal intensity of sample x in the reaction region δ. Represents the fluorescence signal intensity in the standard template. This represents the fluorescence response value at the corresponding position in the template library. This represents the average fluorescence response under this template. This represents the number of reaction regions; S303: Based on the spectral offset comparison results, determine whether each sample meets the dual-site recognition conditions. If both reaction regions meet the standard structural contour requirements, generate a diagnostic status label for the sample according to the matching results, organize the correspondence between the sample number and the diagnostic status, and generate an in vitro binding identification diagnostic status.

[0011] As a further aspect of the present invention, the step of obtaining the diagnostic channel number is as follows: S401: Based on the samples marked as positive in vitro combined identification and diagnosis status, filter out records belonging to the surge category in in vitro trend records, and merge the two to generate a joint feature identifier; S402: Based on the joint feature identifier, the diagnostic sample management database is retrieved, and the structural binding category is assigned according to the identifier to generate a classification result; S403: Based on the classification results, the original identifiers of the samples are extracted and standardized, and combined with the coding information of the trend labels, the following formula is used: ; The standard encoding identifier of the sample is obtained through calculation, an archive identifier number is generated, and the diagnostic channel number is obtained; in, Represents the standard code identifier. The trend label code represents the sample. The original identifier of the sample. Represents the sum of trend labels. Represents the total number of samples. Represents weighting factors. This represents the number of records in the dataset.

[0012] As a further aspect of the present invention, the steps for obtaining the meibomian gland inflammation detection results are as follows: S501: Based on the diagnostic channel number and the lysis processing record, filter out structural binding samples, and perform labeling and grading operations on these samples to obtain the attention level results; S502: Based on the aforementioned attention level results, the detection conclusions are output according to the IL-6 and TNF-α channels to determine whether the sample meets the conditions. Samples that do not meet the conditions are classified into the normal recording channel. S503: Based on the aforementioned ordinary recording channel, using the sample's labeling and grading information and diagnostic channel number, combined with the sample's detection data, the following formula is applied: ; Obtain the final test results of the sample and generate the meibomian gland inflammation test results; in, This represents the result of a meibomian gland inflammation test. and This represents the deviation of the sample from the standard reference value. Represents the number of samples. Representative diagnostic channel characteristic factors, This is the summation symbol.

[0013] A system for detecting meibomian gland inflammatory factors includes: The sample collection and lysis module acquires samples from patients with meibomian gland dysfunction. After adding lysis buffer, it monitors and records the lysis reaction time and environmental parameters. The samples are organized by time period and batch number to obtain lysis processing records. Based on the lysis processing record, the antigen signal detection module selects the labeled regions of IL-6 and TNF-α for reagent pre-reaction, records the color development trend, classifies the samples, and generates trend classification records. The sample stratification and labeling module records the trend classification, labels the trend category of the sample, further stratifies the sample through trend classification, and generates an in vitro combined identification and diagnostic status. Based on the in vitro binding identification and diagnostic status, the fluorescence spectrum comparison module screens out unstable trend samples, performs charge signal and fluorescence spectrum scanning of the N-terminal and C-terminal identification sites, compares them, and generates a diagnostic channel number. The final test result output module filters positive samples based on the diagnostic channel number and combines them with the trend category to generate meibomian gland inflammation test results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, standardized fragmentation and labeling of samples at consecutive time points enhance the temporal consistency and comparability of the data. Colorimetric trend attribution provides clear classification of changes in inflammatory factor expression. Dual-site identification improves the accuracy and specificity of diagnosis. Feature joint labeling optimizes the classification logic of samples. Channel-level output strengthens the hierarchical expression of test results. The overall process runs through all stages of collection, identification, archiving and output, improving the dynamic analysis capability and the systematicness and accuracy of diagnostic information management. Attached Figure Description

[0015] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the pyrolysis processing record of the present invention. Figure 3 This is a flowchart illustrating the process of obtaining trend classification records in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the in vitro binding identification and diagnostic status according to the present invention; Figure 5 This is a flowchart illustrating the process of obtaining the diagnostic channel number in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the results of meibomian gland inflammation detection according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 A method for detecting meibomian gland inflammatory factors includes the following steps: S1: Obtain secretion samples from patients with meibomian gland dysfunction at two consecutive time points, place the samples into the low-temperature lysis channel, add lysis buffer, monitor and record the reaction time and lysis environment parameters, organize the sample containers according to the batch number of the time period, and retain and record the stability parameters of the lysis buffer. Complete the dual time point labeling through the sample tube label and obtain the lysis processing record. S2: Call the sample batch corresponding to the lysis processing record, select the target antigen IL-6 and TNF-α labeled regions for reagent pre-reaction, record the color development trend of antigen signals at two time points under the same antibody kit, and perform trend assignment based on the reaction region number of the reaction comparison card, classify the samples into three categories: enhancement, decline and stability, and generate trend classification records. S3: Call up samples with unstable trends in the trend classification record, transfer them into the in vitro antibody dual-channel reaction chamber, scan the charge signal of the N-terminal recognition site and the binding region of the C-terminal hydrophilic sequence, compare the obtained spectrum with the standard structural contour of the reference template library point-to-point contour offset, and output the in vitro binding recognition diagnosis status to determine whether the dual site recognition conditions are met. S4: Based on the in vitro combined identification and diagnostic status markers of positive samples, filter out the records that belong to the surge category in the in vitro trend record, merge the two into a joint feature identifier, import the diagnostic sample management database to assign a structure binding category, standardize the original identifier number and trend label of the sample for standard coding, labeling and archiving, and obtain the diagnostic channel number. S5: Combining the diagnostic channel number and lysis processing record, perform labeling and grading operations on structurally bound samples, classify the sample number into the attention level, output the detection conclusion according to the IL-6 and TNF-α channels, classify the samples that do not meet the conditions into the ordinary recording channel, complete the diagnostic category partitioning record archiving, and generate the meibomian gland inflammation detection results.

[0019] The lysis processing record includes reaction time parameters, lysis environment indicators, sample batch number, lysis buffer stability data, and time point labels. The trend classification record includes enhancement trend labels, decline trend labels, and stable trend labels. The in vitro binding identification and diagnostic status includes charge signal identification results, hydrophilic sequence alignment results, and dual-site identification status. The diagnostic channel number includes positive sample marking, surge trend classification, structural binding category code, sample standard label, and joint feature identifier. The meibomian gland inflammation detection results include the attention-level sample number, IL-6 sub-channel conclusion, TNF-α sub-channel conclusion, general record channel identifier, and diagnostic category archiving information.

[0020] Please see Figure 2 The steps for obtaining the pyrolysis processing record are as follows: S101: Based on secretion samples collected from patients with meibomian gland dysfunction at two time points, the samples were sequentially placed into a low-temperature lysis channel, and lysis buffer was added. The reaction time and lysis environment parameters were recorded and the time period batch number sequence was established to obtain the sample lysis environment coding sequence. For patients with meibomian gland dysfunction, secretion samples collected at two different time points are sequentially placed into the cryogenic lysis channel. First, the collection time points must be selected, such as 8:00 AM and 2:00 PM. After the patient arrives at the clinic, sampling personnel use sampling tubes numbered TS-A01 to TS-A30 for the morning samples and TS-B01 to TS-B30 for the afternoon samples. Patient identification is established using their visitor number. After sampling, the samples are immediately sealed and transferred to the pre-cooling device. The initial temperature of the cryogenic lysis channel is set to [temperature value missing]. At -20℃, each lysis channel is numbered L1 to L30, corresponding to the sample number sequence. After placing the samples in the channels according to their numbers, the operating system automatically controls the injection of lysis buffer. Each sample receives 1.5 mL of lysis buffer, which is a mixture of Tris-HCl, EDTA, NaCl, and SDS, with the pH maintained at 7.5. The batch number of the lysis buffer is LYQ20250406-A. After injection, the system synchronously records the lysis start time and its mapping to the sample number. For example, the injection time for sample TS-A01 is 08:03:10. The system automatically generates the record key TS-A01→L1@08:03:10. Subsequently, the temperature (T) and humidity (H) of the pyrolysis environment are collected every 30 seconds. Ten sets of data will be collected in the first 5 minutes. For example, the temperature sequence for the first 5 minutes in channel L1 is {20.1,20.0,20.3,20.2,20.1,20.0,20.0,20.2,20.3,20.1}℃, and the humidity sequence is {42,43,42,44,43,42,43,42,44,43}%. After data collection, the system will record each sample... The corresponding environmental data number, lysis buffer batch number, and timestamp are encapsulated and encoded into a separate data entity structure, such as TS-A01→[L1,08:03:10,LYQ20250406-A,T,H]. The data is then grouped into batch number segments A and B according to the two time periods of 08:00 and 14:00, respectively, and marked as A01-A30 and B01-B30 in the database. Each batch of samples is sorted in the database table structure according to the channel number, sampling time, and test tube label, and finally, a sample lysis environmental encoding sequence is generated.

[0021] S102: Based on the sample lysis environment coding sequence, the sample container sequence and corresponding test tube label dual-time point annotation information are retrieved. Based on the recorded lysis buffer parameters and environmental response data, the stability characteristic difference of the lysis buffer is calculated using the formula: ; The stability variability of the lysate is calculated, and the sample time point labeled data is matched according to the corresponding label of the variability to generate the stability interval value of the sample at two time points. in, Represents the variability in the stability of the lysis buffer. Representing the In a pyrolysis-like environment, the first Reaction time for each time period Represents the average reaction time under pyrolysis-like conditions. Representing the Environmental parameter values ​​for a given time period Representing the lysis buffer parameters for a given time period The average value of the lysis fluid parameters under representative environmental conditions. The total number of representative samples across different time periods; By retrieving the arrangement order of each sample container and the dual time point annotation information on the test tubes, the environmental temperature, humidity, and parameters of the lysis buffer itself, such as conductivity and pH value, corresponding to the lysis process were further analyzed and set as parameters. , , Where η represents the index number of the sampling point at different times, and the collected reaction time This indicates the time required from the injection of the lysis buffer to the complete depolymerization of the secretions. In sample Y23-AM, the reaction times at the five sampling points were 23.2, 24.1, 22.9, 23.6, and 24.0 seconds, with conductivity Miη of 13.8, 13.9, 13.6, 14.0, and 13.7 mS / cm, respectively. The ambient humidity... The percentages are 46.1%, 45.8%, 46.3%, 45.9%, and 46.0%, respectively. The corresponding average sample reaction time is denoted as... =23.56 seconds, average conductivity =13.8 mS / cm, then substitute into the formula: ; The molecular part is calculated sequentially: Term 1: (23.2−23.56)×(46.1+ )≈−0.36×(46.1+3.715)=−0.36×49.815≈−17.933; Term 2: (24.1−23.56)×(45.8+ )≈0.54×(45.8+3.728)=0.54×49.528≈26.744; Term 3: (22.9−23.56)×(46.3+) )≈−0.66×(46.3+3.687)=−0.66×49.987≈−32.991; Term 4: (23.6−23.56)×(45.9+) )≈0.04×(45.9+3.741)=0.04×49.641≈1.986; Term 5: (24.0−23.56)×(46.0+ )≈0.44×(46.0+3.700)=0.44×49.7≈21.868; The total numerator = |−17.933+26.744−32.991+1.986+21.868| = |−0.326|≈0.326; Denominator part: |23.2−46.1|+|13.8−13.8|=22.9+0.0=22.9; |24.1−45.8|+|13.9−13.8|=21.7+0.1=21.8; |22.9−46.3|+|13.6−13.8|=23.4+0.2=23.6; |23.6−45.9|+|14.0−13.8|=22.3+0.2=22.5; |24.0−46.0|+|13.7−13.8|=22.0+0.1=22.1; The sum of the denominators = 22.9 + 21.8 + 23.6 + 22.5 + 22.1 = 112.9; The final result is: ; Therefore, the stability interval of this sample at two time points is 0.00289, which is lower than the lysis stability reference threshold Z0=0.008, and it is marked as stable.

[0022] S103: Call the stability interval values ​​of the sample at two time points and the sample tube label information, combine the batch number information and environmental parameter records, integrate the time node label relationship table, and obtain the lysis treatment record; The calculated sample stability interval values ​​are used, along with the sample tube labels and lysis batch numbers. A consistency check is performed on each pair of time-point samples using a number lookup table. Specifically, samples TS-A03 and TS-B03 represent the same patient's morning and afternoon collection periods, respectively. Their corresponding stability indices, retrieved from the database, are 0.0030 and 0.0041, both below the set threshold of 0.01. Based on preset rules, the status label is set to "stable." If samples TS-A05 and TS-B05 are calculated to be 0.0052 and 0.0196, respectively, the status label is set to "unstable" because one value exceeds the threshold. After verification, all samples are integrated with their sample number, corresponding time period, lysis channel number, lysis buffer batch, stability value, and label to generate a structured record string. This string is then sorted by sample number in ascending order and imported into the main processing record table. In the export format, the stability label field is set as a Boolean field, where "stable" is TRUE and "unstable" is FALSE. Finally, a text output with structural consistency and field standardization is generated, yielding the lysis processing record.

[0023] Please see Figure 3 The steps to obtain trend classification records are as follows: S201: Obtain the list of sample numbers marked in the lysis processing record, call the timestamp information marked in each sample label, extract the colorimetric channel of the corresponding antigen, maintain the incubation time and environmental conditions uniformly, perform colorimetric processing after completion, record the corresponding reaction spectrum, and establish an antigen colorimetric response spectrum set; The sample batches corresponding to the lysis processing records were obtained. Each sample container was loaded sequentially based on its sample number. The time period of the sample was determined by reading the time information from the label, and the IL-6 and TNF-α antigen-labeled regions corresponding to the time nodes were extracted. After sample processing began, the chromogenic reaction areas of these two antigens were pre-reacted with reagents and incubated under standard temperature control conditions (37℃, 15 minutes). After the reaction, the chromogenic areas were scanned using a colorimeter to read the chromogenic intensity of the antigens and record the reaction spectrum. For example, if the IL-6 chromogenic intensity changed to 82, 85, 88, 90, etc., and the TNF-α chromogenic intensity was 76, 79, 80, 83, these values ​​would correspond to different time nodes and sample batches, ensuring accurate recording of the reaction for each sample. The chromogenic data for each sample was recorded individually, forming an independent chromogenic intensity dataset. These data allow for the determination of the IL-6 and TNF-α chromogenic intensity values ​​at two time nodes, providing a reliable data foundation for subsequent trend analysis.

[0024] S202: The antigen colorimetric response spectrum set is invoked. Based on the channel numbering information of the two target antigens, IL-6 and TNF-α, at two time points in the sample, pairing and matching are performed. The fluctuation range, grayscale difference, and response slope of the colorimetric curve are calculated using the following formula: ; The antigen color development trend is assigned a value by calculation. Then, based on the interval where the assigned value falls, the color development trend of the sample is classified into three categories: enhancement, decline or stagnation, and a trend discrimination label value is generated. in, The antigen colorimetric trend attribution value represents sample z. This represents the grayscale value of sample z in channel ξ. This represents the average gray value across all channels of sample z. This represents the reference slope value of sample z under channel ξ. The representative sample z represents the estimated response flux under channel ξ. Represents the average flux. This represents the difference constant between sample z at two time points. Represents the number of channels; Based on the recorded antigen colorimetric data, the colorimetric intensity data of the reaction area for each sample was extracted, and the colorimetric changes at different time points were compared. In this process, the colorimetric difference for each antigen was first calculated. By summing and standardizing the colorimetric intensity differences of the reaction areas, the magnitude of the colorimetric change for each sample at different time points was determined. For example, if the IL-6 colorimetric intensity of sample A is 82 and 86 at two time points, its change magnitude is 4; if the TNF-α colorimetric intensity is 78 and 80, its change magnitude is 2.

[0025] Next, based on the magnitude of the difference and the range of change in reaction intensity, the colorimetric trend of each sample at the two time points is calculated using the following formula: ; Specific calculation process: Given the colorimetric intensity data of sample Z, assuming the colorimetric intensity of the IL-6 reaction region of sample Z is 82 at time node 1 and 86 at time node 2, the difference is 4. The colorimetric intensities of TNF-α are 78 and 80 respectively, with a difference of 2. Assuming the sample in reaction region 1 ( ) and reaction zone 2 ( The relevant data is as follows: , Reference value , ; , , ; offset ; First, calculate the difference between the color development difference value and the reference value for each region: For the first region, For the second region, ; Then, the difference between the response flux difference and the reference flux for each region is calculated: For the first region, For the second region, ; Next, calculate the numerator of the formula: ; For the calculation of each item: , ; result: ; The denominator of the calculation formula: ; Finally, the color trend attribution value of sample Z is calculated: ; Based on the preset threshold standard: If the attribution value is greater than 1, it is judged as "enhanced"; if it is less than -1, it is judged as "declined"; and if it is between -1 and 1, it is judged as "stable".

[0026] In this example, Therefore, this sample was classified as having an "enhancing" trend.

[0027] S203: Based on the trend discrimination label value, establish the association between each sample number and the trend label, assign the classification label to the sample identifier, record the sample identifier, antigen type, time period information and trend classification result, and obtain the trend classification record; After calculating the trend classification values, based on the colorimetric trend classification value of each sample, the sample number and corresponding trend type information are compiled to generate a trend classification data table. Each record includes the sample number, corresponding antigen type, time point, and calculated trend classification (enhancing, declining, stable). Based on the sample's response trend, it is classified into the corresponding group. For example, if a sample's colorimetric trend is "enhancing," then the sample will be classified as the enhancing group. This classification data will be archived by an automated system, generating a final record file for subsequent data storage, processing, and further analysis. This process also ensures the accuracy of sample data and the consistency of data storage. For example, if the sample number is "P020" and its colorimetric trend is "stable," then in the record table, the sample's status label is "stable." Finally, all trend classification data will be summarized into a CSV file for further use by other operators or the system.

[0028] Please see Figure 4 The steps for obtaining the in vitro combined diagnostic status are as follows: S301: Obtain the samples marked in the trend classification record, extract the sample number and antigen type, and transfer them to the in vitro antibody dual-channel reaction chamber. In the chamber, perform fluorescence spectrum scanning on the N-terminal recognition site and the C-terminal hydrophilic sequence binding region in the sample to generate the fluorescence signal intensity sequence of each reaction region and obtain fluorescence spectrum data. Samples marked as "unstable" in the trend classification record are retrieved. These samples typically exhibit large fluctuations in reaction signals or do not conform to the set pattern, thus requiring further processing. The system first reads and extracts the sample number, antigen type, and time-stamp information for each sample. Next, all unstable samples are transferred to an in vitro antibody dual-channel reaction chamber, which is divided into two independent regions: one for the N-terminal recognition site and the other for the C-terminal hydrophilic sequence binding region. After transfer, the system automatically initiates a fluorescence spectral scanning process, scanning both reaction regions separately. Upon completion of the scan, the system records the fluorescence spectral data, including changes in fluorescence signal intensity, generating a spectral file for each sample. In this way, the N-terminal and C-terminal fluorescence spectral data of sample S1 are obtained, providing necessary raw data for subsequent analysis.

[0029] S302: Based on fluorescence spectrum data, the acquired N-terminal and C-terminal fluorescence spectrum data are compared with the standard structural contours in the reference template library. For the identification sites at the N-terminal and C-terminal, the system calculates the fluorescence spectrum offset of each sample through point-to-point contour offset comparison and generates spectrum offset comparison results. The calculation is performed using a simplified formula: ; in, The spectral offset value represents sample x. This represents the fluorescence signal intensity of sample x in the reaction region δ. Represents the fluorescence signal intensity in the standard template. This represents the fluorescence response value at the corresponding position in the template library. This represents the average fluorescence response under this template. This represents the number of reaction regions; Based on the fluorescence spectrum data, the system then compares the fluorescence spectrum of each sample with the standard structural contours in the standard template library. The standard template library contains standard response patterns for the ideal N-terminal and C-terminal recognition sites. The system calculates the fluorescence spectrum offset for each sample through point-to-point contour offset comparison. For example, the fluorescence signal intensity of the N-terminal reaction region of sample S1 is {150.5, 151.1, 152.3, 149.8}, while the standard signal intensity in the standard template library is {149.8, 150.3, 151.2, 150.5}. The system calculates the difference between each point and accumulates these differences to finally obtain the contour offset of the sample. The system uses the following calculation steps to process this data: Calculate the difference value for each point: First point: ; The second point: ; The third point: ; The fourth point: ; Cumulative difference value: These differences will be summed to obtain the total N-end offset of sample S1: ; Similar calculations for the C-end: The fluorescence signal at the C-terminus is {132.2, 133.5, 134.1, 130.7}, which is compared with the signal {131.5, 132.1, 132.8, 130.4} in the standard template library. The calculation steps are the same, and the total offset at the C-terminus is obtained as follows: ; Calculate the total offset of sample S1: The total offset is the average of the offsets at the N and C ends: ; In this way, the offset value of sample S1 is 3.5. If this offset exceeds a set threshold (for example, a set threshold of 2.5), the sample is marked as "non-compliant"; otherwise, it is marked as "compliant". The spectral offset comparison results generated by this method provide a basis for judging whether a sample conforms to the standard reaction profile.

[0030] S303: Based on the spectral offset comparison results, determine whether each sample meets the dual-site recognition conditions. If both reaction regions meet the standard structural contour requirements, generate a diagnostic status label for the sample based on the matching results, organize the correspondence between sample number and diagnostic status, and generate an in vitro binding identification diagnostic status. After completing the spectral offset comparison, the system will judge each sample based on its offset and a set threshold. If the offsets of both reaction regions are below the set threshold, the sample is considered to meet the dual-site recognition condition and marked as "compliant"; if the offset of either reaction region exceeds the threshold, it is marked as "incompatible". For example, in sample S1, the offset value at the N end is 3.3, and the offset value at the C end is 3.7, while the set threshold is 2.5. Therefore, both offset values ​​are greater than the threshold, and the system marks the sample as "incompatible". The final judgment result of all samples will be marked as "compliant" or "incompatible", and a corresponding diagnostic status will be generated based on these markings. Finally, the system will output the diagnostic status record of all samples, forming an in vitro binding identification diagnostic status, providing a basis for subsequent diagnostic decisions.

[0031] Please see Figure 5 The steps to obtain the diagnostic channel number are as follows: S401: From samples marked as positive in vitro combined diagnostic status, filter records belonging to the surge category in in vitro trend records, and merge the two to generate a joint feature identifier; First, it's necessary to screen samples marked as positive in vitro for surges in the in vitro trend record. This process relies on trend assessment, for example, by analyzing sample response data at multiple time points, particularly changes in inflammatory factor concentrations. Assuming that in practice, we select inflammatory factors IL-6 and TNF-α with significant concentration differences at different time points, for these samples, we first need to record their concentrations at each time point and determine whether a surge trend exists using a specific calculation method. For example, when the concentration change between time point 1 and time point 2 exceeds a certain threshold (e.g., IL-6 concentration surges from 40 ng / mL to 120 ng / mL), we can mark the sample as a "surge" and further generate a joint feature identifier. Specifically, if the IL-6 concentration of a sample increases by 80 ng / mL between two time points, exceeding the set surge threshold of 50 ng / mL, the trend of that sample is marked as a surge. After merging trend records, the system needs to generate data entries based on the joint feature identifiers of these samples. These data entries contain all the key information about the samples, such as identifiers, trend categories, and specific parameters, facilitating subsequent classification operations. For example, suppose in an experiment, after screening, we obtain 10 samples with IL-6 surges, each with IL-6 concentrations of 20, 40, 60, 80, 100, 120, 140, 160, 180, and 200 ng / mL. The system will label these samples according to the set thresholds and generate joint feature identifiers containing the "surge" tag. These identifiers will serve as input for subsequent steps, supporting further processing of the samples in the diagnostic management database.

[0032] S402: Import the joint feature identifier into the diagnostic sample management database, assign structural binding categories based on the identifier, and generate classification results; After the joint feature identifiers are imported into the diagnostic sample management database, the system will assign structure-bound categories based on these identifiers. Specifically, the system will parse the feature identifiers of each sample, extracting key information such as sample type and trend labels, and assign them to different categories. Category assignment is based not only on the sample's trend type but also on preset classification criteria. For example, surge samples may be assigned to the "high-risk" category, while relatively stable or normal samples may be classified as "low-risk" or "normal." The key to this assignment process is the application of classification algorithms; the system will differentiate samples according to pre-defined rules. For example, suppose we set the following classification criteria: samples with IL-6 concentration changes exceeding 100 ng / mL are classified as "high-risk," samples with changes between 50 ng / mL and 100 ng / mL are classified as "medium-risk," and samples with changes less than 50 ng / mL are classified as "low-risk." Under this rule, all surge samples will enter the high-risk category, and a corresponding structure-bound category code will be assigned to each category. Furthermore, these codes will help identify and track the sample's classification status in subsequent steps. For example, the system can assign codes “HR-001” to “HR-010” to all high-risk samples for management purposes.

[0033] S403: Based on the classification results, the original identifiers of the samples are extracted and standardized with coded information, and combined with the coding information of the trend labels, the following formula is used: ; The standard encoding identifier of the sample is obtained through calculation, an archive identifier number is generated, and the diagnostic channel number is obtained; in, Represents the standard code identifier. The trend label code represents the sample. The original identifier of the sample. Represents the sum of trend labels. Represents the total number of samples. Represents weighting factors. The number of records in the dataset; By extracting and standardizing the original identifiers and trend tags of the samples, a standard coded identifier is finally obtained for each sample. In this process, the standard coded identifier integrates all the information of the sample and generates a unique identification code for subsequent management and diagnosis. In implementation, we use the following formula to calculate this standard coded identifier: ; The specific calculation process is as follows: First, calculate the difference between the trend label code and the original identifier for each sample. For example, suppose in a certain experiment, there are 5 samples with trend label codes [50, 60, 40, 70, 80] and original identifiers [20, 40, 10, 30, 50]. We first calculate the difference value for each sample: for the first sample, For the second sample, For the third sample, For the fourth sample, For the fifth sample, ; Adding these differences together gives: ; Next, calculate the sum of the trend labels. and total number of samples Assuming the total trend labels are 200 and the total sample size is 5, substituting them into the formula yields: ; Finally, assuming weighting factors We will substitute these values ​​into the formula for calculation: ; Therefore, the standard code for this sample is 208.05. In practical applications, this code will serve as a unique identifier for the sample, ensuring accurate tracking of the sample's status and processing flow during subsequent diagnostic channel allocation. This result demonstrates that by calculating and standardizing the sample's identifier code, a unique identifier can be generated for each sample, thereby aiding in precise management and tracking during the diagnostic process.

[0034] Please see Figure 6 The steps to obtain the test results for meibomian gland inflammation are as follows: S501: Based on the diagnostic channel number and the lysis processing record, filter out structural binding samples, and perform labeling and grading operations on these samples to obtain the attention level results; First, the system needs to retrieve the diagnostic channel number and lysis processing record for each sample. The diagnostic channel number is a unique identifier for a sample after processing through a specific diagnostic channel. The lysis processing record contains detailed data about the sample lysis process, including environmental parameters (such as temperature and humidity), lysis buffer type, and lysis start time. By acquiring these records and combining them with the sample's diagnostic channel number, the system can accurately associate the sample with its processing, ensuring the integrity and traceability of each sample's diagnostic record. Next, the system will perform a labeling and grading operation on structure-bound samples. Labeling and grading involves classifying samples into different priorities based on their characteristic data (such as the reaction intensity of IL-6 and TNF-α) and test results. For example, if a sample's IL-6 reaction intensity is greater than 6.0 and its TNF-α reaction intensity exceeds 7.0, the sample will be labeled as "of concern." This process can be accomplished by setting specific numerical thresholds. During this process, the system will determine whether these parameters meet the predetermined threshold standards; if they do, the sample is labeled as "of concern"; otherwise, it is labeled as a normal record. For example, if a sample has an IL-6 response intensity of 6.5 and a TNF-α response intensity of 7.2, then the sample will be marked as a level of concern.

[0035] S502: Based on the aforementioned attention level results, the detection conclusions are output according to the IL-6 and TNF-α channels to determine whether the sample meets the conditions. Samples that do not meet the conditions are classified into the normal recording channel. The system further filters and judges the response data of each sample based on the separate detection results of IL-6 and TNF-α. Specifically, for each sample, the system first extracts its IL-6 and TNF-α response intensities and compares them with preset threshold values. For example, if the IL-6 response intensity is 5.5, and this value is lower than the preset threshold of 6.0, then the sample does not meet the concern level standard. Similarly, if the TNF-α response intensity is 4.5, lower than the threshold of 5.0, it will also be classified into the normal recording channel. For samples with response intensity values ​​lower than the preset threshold, the system will classify them into the normal recording channel. These samples may be considered to have a relatively stable response or no obvious trend. This screening and judgment process helps ensure the accuracy of the diagnostic channel and the reliability of the data. For example, if a sample has an IL-6 response intensity of 4.8 and a TNF-α response intensity of 4.2, then neither IL-6 nor TNF-α in this sample reaches the standard threshold, so it will be classified into the normal recording channel. These data are finally recorded and classified for further in-depth analysis and processing of the samples.

[0036] S503: Based on the aforementioned ordinary recording channel, using the sample's labeling and grading information and diagnostic channel number, combined with the sample's detection data, the following formula is applied: ; Obtain the final test results of the sample and generate the meibomian gland inflammation test results; in, This represents the result of a meibomian gland inflammation test. and This represents the deviation of the sample from the standard reference value. Represents the number of samples. Representative diagnostic channel characteristic factors, The summation symbol; Based on the classified samples and diagnostic channel numbers, the system generates meibomian gland inflammation detection results, including the classification status and final diagnostic status of all samples. The system calculates these results using the following formula: formula: ; Calculation process and steps: Define variables: The representative sample's response intensity in IL-6 and TNF-α detection; This represents the corresponding reaction intensity of the sample within the standard reference range; Represents the number of samples; These represent the characteristic factors of the diagnostic pathway, reflecting the comprehensive evaluation value of the diagnostic pathway.

[0037] Parameter settings: Assuming the number of samples That is, we have 3 samples for calculation.

[0038] Assume the sample's reaction intensity data are as follows: IL-6 reactivity of sample 1 TNF-α response intensity ; IL-6 reactivity of sample 2 TNF-α response intensity ; IL-6 reactivity of sample 3 TNF-α response intensity ; Assuming standard reference value It was obtained through historical data or laboratory standards.

[0039] Hypothetical characteristic factors This was calculated based on the characteristics of the laboratory equipment and the processing environment.

[0040] Calculation process: Step 1: Calculate the reaction intensity deviation for each sample: ; ; ; Step 2: Summate and divide by the sample size: ; Step 3: Calculate the square root of the characteristic factor: ; Step 4: Final calculation of test results: ; Results Explanation: Calculation results This indicates the overall diagnostic status of the sample. This result is calculated based on the deviation of the sample's response intensity from the standard reference value, combined with characteristic factors. A higher result indicates that the sample is closer to the "normal" state of the diagnostic criteria; conversely, samples with lower values ​​may require further testing or reclassification.

[0041] A system for detecting meibomian gland inflammatory factors includes: The sample collection and lysis module acquires samples from patients with meibomian gland dysfunction. After adding lysis buffer, it monitors and records the lysis reaction time and environmental parameters. The samples are organized by time period and batch number to obtain lysis processing records. The antigen signal detection module, based on the lysis processing record, selects the labeled regions of IL-6 and TNF-α for reagent pre-reaction, records the color development trend, classifies the samples, and generates trend classification records; The sample stratification and labeling module records trend classification, labels the trend category of the samples, further stratifies them through trend classification, and generates in vitro combined identification and diagnostic status. The fluorescence spectrum comparison module identifies the diagnostic status based on in vitro binding, filters out unstable trend samples, scans the charge signals and fluorescence spectra of the N-terminal and C-terminal identification sites, compares them, and generates a diagnostic channel number. The final test result output module filters positive samples based on the diagnostic channel number and combines them with their trend category to generate meibomian gland inflammation test results.

[0042] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for detecting inflammation of meibomian glands, characterized by, Includes the following steps: S1: Collect secretion samples from patients with meibomian gland dysfunction at two time points, lyse them at low temperature, add lysis buffer, record reaction time and environmental parameters, organize the sample order according to batch number, retain the stability parameters of lysis buffer, and complete the dual time point labeling through test tube labels to obtain lysis processing records. S2: Call the lysis treatment record, perform reagent pre-reaction on IL-6 and TNF-α, record the antigen colorimetric trend at two time points, and divide it into three categories according to the reaction area number: enhancement, decrease, and stability, and generate trend classification record; S3: Based on the trend classification record, transfer the unstable trend sample into the antibody dual-channel reaction chamber, scan the N-terminal charge and C-terminal hydrophilic region map, compare it with the standard template outline, determine whether the dual site recognition conditions are met, and output the in vitro binding recognition diagnosis status. S4: Based on the in vitro combined identification and diagnostic status, screen positive samples with a surge in trend, combine feature identifiers, assign structural binding categories, encode samples, and obtain diagnostic channel numbers; S5: Combining the diagnostic channel number with the lysis processing record, output the detection conclusions respectively. Samples that do not meet the conditions are classified into the ordinary recording channel, and the meibomian gland inflammation detection results are generated.

2. The method for detecting meibomian gland inflammatory factors according to claim 1, characterized in that, The lysis processing record includes reaction time parameters, lysis environment indicators, sample batch number, lysis buffer stability data, and time point labels. The trend classification record includes enhancement trend labels, decline trend labels, and stable trend labels. The in vitro binding identification and diagnostic status includes charge signal identification results, hydrophilic sequence alignment results, and dual-site identification status. The diagnostic channel number includes positive sample markers, surge trend classifications, structural binding category codes, sample standard labels, and joint feature identifiers. The meibomian gland inflammation detection results include attention-level sample number, IL-6 sub-channel conclusion, TNF-α sub-channel conclusion, ordinary record channel identifier, and diagnostic category archiving information.

3. The method for detecting meibomian gland inflammatory factors according to claim 1, characterized in that, The steps for obtaining the pyrolysis processing record are as follows: S101: Based on secretion samples collected from patients with meibomian gland dysfunction at two time points, the samples were sequentially placed into a low-temperature lysis channel, and lysis buffer was added. The reaction time and lysis environment parameters were recorded and the time period batch number sequence was established to obtain the sample lysis environment coding sequence. S102: Based on the sample lysis environment coding sequence, the sample container sequence and corresponding test tube label dual-time point annotation information are retrieved. Based on the recorded lysis buffer parameters and environmental response data, the stability characteristic difference of the lysis buffer is calculated using the formula: ； The stability variability of the lysate is calculated, and the sample time point labeled data is matched according to the corresponding label of the variability to generate the stability interval value of the sample at two time points. in, Represents the variability in the stability of the lysis buffer. Representing the In a pyrolysis-like environment, the first Reaction time for each time period Represents the average reaction time under pyrolysis-like conditions. Representing the Environmental parameter values ​​for a given time period Representing the lysis buffer parameters for a given time period The average value of the lysis fluid parameters under representative environmental conditions. The total number of representative samples across different time periods; S103: Call the sample stability interval values ​​at the two time points and the sample tube label information, combine them with the batch number information and environmental parameter records, integrate the time node label relationship table, and obtain the lysis treatment record.

4. The method for detecting meibomian gland inflammatory factors according to claim 3, characterized in that, The steps for obtaining the trend classification records are as follows: S201: Obtain the list of sample numbers marked in the lysis processing record, call the timestamp information marked in each sample label, extract the colorimetric channel of the corresponding antigen, maintain the incubation time and environmental conditions uniformly, perform colorimetric processing after completion, record the corresponding reaction spectrum, and establish an antigen colorimetric response spectrum set; S202: The antigen colorimetric response spectrum set is invoked. Based on the channel numbering information of the two target antigens, IL-6 and TNF-α, at two time points in the sample, pairing and matching are performed. The fluctuation range, grayscale difference, and response slope of the colorimetric curve are calculated using the following formula: ； The antigen color development trend is assigned a value by calculation. Then, based on the interval where the assigned value falls, the color development trend of the sample is classified into three categories: enhancement, decline or stagnation, and a trend discrimination label value is generated. in, The antigen colorimetric trend attribution value represents sample z. This represents the grayscale value of sample z in channel ξ. This represents the average gray value across all channels of sample z. This represents the reference slope value of sample z under channel ξ. The representative sample z represents the estimated response flux under channel ξ. Represents the average flux. This represents the difference constant between sample z at two time points. Represents the number of channels; S203: Based on the trend discrimination label value, establish the association between each sample number and the trend label, assign the classification label to the sample identifier, record the sample identifier, antigen type, time period information and trend classification result, and obtain the trend classification record.

5. The method for detecting meibomian gland inflammatory factors according to claim 4, characterized in that, The steps for obtaining the in vitro combined identification and diagnostic status are as follows: S301: Obtain the samples marked in the trend classification record, extract the sample number and antigen type, and transfer them to the in vitro antibody dual-channel reaction chamber. In the chamber, perform fluorescence spectrum scanning on the N-terminal recognition site and the C-terminal hydrophilic sequence binding region in the sample to generate the fluorescence signal intensity sequence of each reaction region and obtain fluorescence spectrum data. S302: Based on the fluorescence spectrum data, the acquired N-terminal and C-terminal fluorescence spectrum data are compared with the standard structural contours in the reference template library. For the identification sites at the N-terminal and C-terminal, the system calculates the fluorescence spectrum offset of each sample through point-to-point contour offset comparison and generates spectrum offset comparison results. The calculation is performed using a simplified formula: ； in, The spectral offset value represents sample x. This represents the fluorescence signal intensity of sample x in the reaction region δ. Represents the fluorescence signal intensity in the standard template. This represents the fluorescence response value at the corresponding position in the template library. This represents the average fluorescence response under this template. This represents the number of reaction regions; S303: Based on the spectral offset comparison results, determine whether each sample meets the dual-site recognition conditions. If both reaction regions meet the standard structural contour requirements, generate a diagnostic status label for the sample according to the matching results, organize the correspondence between the sample number and the diagnostic status, and generate an in vitro binding identification diagnostic status.

6. The method for detecting meibomian gland inflammatory factors according to claim 5, characterized in that, The steps for obtaining the diagnostic channel number are as follows: S401: Based on the samples marked as positive in vitro combined identification and diagnosis status, filter out records belonging to the surge category in in vitro trend records, and merge the two to generate a joint feature identifier; S402: Based on the joint feature identifier, the diagnostic sample management database is retrieved, and the structural binding category is assigned according to the identifier to generate a classification result; S403: Based on the classification results, the original identifiers of the samples are extracted and standardized, and combined with the coding information of the trend labels, the following formula is used: ； The standard encoding identifier of the sample is obtained through calculation, an archive identifier number is generated, and the diagnostic channel number is obtained; in, Represents the standard code identifier. The trend label code represents the sample. The original identifier of the sample. Represents the sum of trend labels. Represents the total number of samples. Represents weighting factors. This represents the number of records in the dataset.

7. The method for detecting meibomian gland inflammatory factors according to claim 6, characterized in that, The steps for obtaining the meibomian gland inflammation test results are as follows: S501: Based on the diagnostic channel number and the lysis processing record, filter out structural binding samples, and perform labeling and grading operations on these samples to obtain the attention level results; S502: Based on the aforementioned attention level results, the detection conclusions are output according to the IL-6 and TNF-α channels to determine whether the sample meets the conditions. Samples that do not meet the conditions are classified into the normal recording channel. S503: Based on the aforementioned ordinary recording channel, using the sample's labeling and grading information and diagnostic channel number, combined with the sample's detection data, the following formula is applied: ； Obtain the final test results of the sample and generate the meibomian gland inflammation test results; in, This represents the test results for meibomian gland inflammation. and This represents the deviation of the sample from the standard reference value. Represents the number of samples. Representative diagnostic channel characteristic factors, This is the summation symbol.

8. A system for detecting meibomian gland inflammatory factors, characterized in that, The system is used to implement the meibomian gland inflammatory factor detection method according to any one of claims 1-7, comprising: The sample collection and lysis module acquires samples from patients with meibomian gland dysfunction. After adding lysis buffer, it monitors and records the lysis reaction time and environmental parameters. The samples are organized by time period and batch number to obtain lysis processing records. Based on the lysis processing record, the antigen signal detection module selects the labeled regions of IL-6 and TNF-α for reagent pre-reaction, records the color development trend, classifies the samples, and generates trend classification records. The sample stratification and labeling module records the trend classification, labels the trend category of the sample, further stratifies the sample through trend classification, and generates an in vitro combined identification and diagnostic status. Based on the in vitro binding identification and diagnostic status, the fluorescence spectrum comparison module screens out unstable trend samples, performs charge signal and fluorescence spectrum scanning of the N-terminal and C-terminal identification sites, compares them, and generates a diagnostic channel number. The final test result output module filters positive samples based on the diagnostic channel number and combines them with the trend category to generate meibomian gland inflammation test results.

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