TWM652658U - System using number of immune cells to interpret disease information of tested living thing - Google Patents

Abstract

A system that uses immune cell numbers to interpret tested biological disease information, including an immune cell detection device for outputting immune cell number data of a tested subject and a data analysis workstation that is communicatively connected to it. The data analysis workstation includes a central processor and its The signal connects the comparison cell database, the calculation module and the regression correction module; the calculation module reads the immune cell quantity data and calculates the risk prediction data based on it; the regression correction module has a function based on the comparison cells The regression model established by the database, the regression correction module reads the risk prediction data to obtain the risk prediction correction data according to the regression correction of the comparison cell database. In this way, more accurate and effective immune cell disease risk prediction results can be obtained.

Description

A system that uses the number of immune cells to interpret tested biological disease information

This new type of system is related to the detection of immune cell numbers, and particularly refers to a system that uses immune cell numbers to interpret disease information of tested organisms.

In the medical field, the number of immune cells is considered an important indicator related to disease risk. When the number of immune cells is abnormal, it means that the health status of the tested organism may be affected by certain diseases, such as infectious diseases, cancer, allergies, and autoimmune diseases.

With the advancement of technology, the number of immune cells can now be measured in a variety of ways to obtain disease risk information of the tested organism. Current technologies for measuring the number of immune cells mainly include routine blood tests and enzyme-linked immunosorbent assay (ELISA). However, these existing assay techniques have their limitations. For example, although routine blood tests are simple to operate, their results are easily affected by many factors, such as the quality of the sample and the patient's health status. Although ELISA technology can provide more accurate immune cell quantity data, it usually requires specialized equipment and operating skills, and is costly. Additionally, these techniques require special processing of cells or serum, which may affect the accuracy of test results.

It is worth noting that flow cytometry has obvious advantages. Flow cytometry can not only measure the number and function of various immune cells in the blood in real time, but also analyze individual cells, providing more levels and higher resolution data. However, flow cytometry still presents certain challenges in determining immune cell numbers, such as the selection of dyes and reagents, as well as highly specialized operational requirements.

According to reports, although there are currently a variety of technologies that can be used to measure immune cell numbers to assess disease risk, these methods still have many limitations. As an advanced technology, flow cytometry has the potential to solve these problems, but further optimization and research are still needed.

The purpose of this new model is to provide a system that uses the number of immune cells to interpret tested biological disease information. By using a flow cytometer as an immune cell detection device, the obtained immune cell number data is calculated to obtain risk prediction data for immune cell diseases. , and then perform regression correction on the risk prediction data based on the comparison cell database to obtain more accurate and effective immune cell disease risk prediction results for the tested organisms.

In order to achieve the aforementioned purpose, the present invention provides a system for interpreting biological disease information by using the number of immune cells, which includes: an immune cell detection device, which is a first-flow cytometer, and is used to output the number of immune cells of a subject under test. Data; a data analysis workstation, communicatively connected with the immune cell detection device, the data analysis workstation includes a central processor and a comparison cell database, a calculation module and a regression correction module signally connected to it; the calculation module The team reads the immune cell quantity data through the central processor and calculates it to obtain a risk prediction data; the regression correction module is equipped with a regression model based on the comparison cell database, and the regression correction module uses the central processing unit The processor reads the risk prediction data to obtain a risk prediction correction data based on the comparison cell database regression correction.

Optionally, in this novel embodiment, the immune cell detection device includes another central processor and a detection module signal connected thereto; the detection module is used to output a cell fluorescence value of the tested sample The data is sent to the central processing unit for an immune cell quantity data conversion unit to read and convert the cell fluorescence numerical data to form the immune cell quantity data.

Optionally, in this novel embodiment, the cell fluorescence value data includes the fluorescence value of an antibody of the same isotype and the fluorescence value of a specific type of antibody.

Optionally, in this novel embodiment, the immune cell detection device further includes an analysis chart output unit connected to the central processor via a signal for reading the immune cell quantity data and outputting a sample cell analysis chart accordingly. to the data analysis workstation.

Optionally, in the new embodiment of the present invention, the data analysis workstation further includes a test report output unit connected to the central processor signal, and the test report output unit reads the risk prediction correction data and the sample cell analysis The chart is used to output an immune cell disease risk prediction report.

Optionally, in this novel embodiment, the immune cell detection device is further provided with a human-machine interface module and a storage module that are signally connected to the central processor.

Optionally, in the new embodiment of the present invention, the data analysis workstation is further provided with a human-machine interface module and a storage module signally connected to the central processor.

Optionally, in this novel embodiment, the immune cell detection device and the data analysis workstation are each provided with a communication module, and the immune cell detection device and the data analysis workstation communicate and transmit data through the communication module.

Optionally, in this novel embodiment, the comparison cell database is a database established based on cell data of specific human genetic characteristics.

Optionally, in this novel embodiment, the comparison cell database is a database established based on specific animal cell data.

To sum up, this new system that uses the number of immune cells to interpret tested biological disease information can use flow cytometry as the immune cell detection device, solving the technical problems existing in routine blood tests and ELISA to measure the number of immune cells. , not only is the detection convenient and the detection cost is reduced, but also a more accurate number of immune cells of the tested organism can be obtained quickly and accurately, thereby providing accurate and effective information and judgment results of the tested organism's disease information.

Regarding the technologies, means and other effects adopted by the present invention to achieve the above objectives, the best feasible embodiments are described in detail below along with the drawings.

In order to facilitate the understanding of the present invention, the following description is given in conjunction with the embodiments.

As shown in Figure 1, the system provided by the present invention for using the number of immune cells to interpret tested biological disease information mainly includes an immune cell detection device 10 and a data analysis workstation 20. The immune cell detection device 10 can be a first-flow cytometer, and the data analysis workstation 20 can be a local computer device or server, or a data analysis system or server in the cloud.

In this novel embodiment, as shown in Figure 1 , the immune cell detection device 10 may include a central processor 11 and a detection module 12 signal-connected thereto, an immune cell quantity data conversion unit 13, a human-machine interface module 14, and a storage module. Group 15, analysis chart output unit 16 and communication module 17. The data analysis workstation 20 may specifically include a central processor 21 and a comparison cell database 22 connected to the signal, a calculation module 23, a regression correction module 24, a human-machine interface module 25, a storage module 26, and a communication module 27 and the inspection report output unit 28. Among them, the immune cell detection device 10 and the data analysis workstation 20 can realize wired or wireless data transmission or communication through the communication modules 17 and 27.

As shown in Figure 1, the calculation module 23 reads the immune cell quantity data through the central processor 21 and calculates it to obtain a risk prediction data; the regression correction module 24 is provided with a risk prediction method based on the comparison cell database 22. Regression model, the regression correction module 24 reads the risk prediction data through the central processor 21 to perform regression correction based on the comparison cell database 22 to obtain a risk prediction correction data.

In the embodiment of the present invention, the new system is mainly used to detect and predict immune cell disease risks including tumor risk, allergic immunity risk, autoimmune risk, immune depression risk, infection risk, etc., but is not limited thereto. Therefore, as shown in Figure 2, the calculation module 23 of the data analysis workstation 20 further includes a tumor risk assessment unit 231, an allergy immune risk assessment unit 232, an autoimmune risk assessment unit 233, and an immune hyporisk assessment unit 234. and infection risk assessment unit 235 and other immune cell disease risk assessment units; each risk assessment unit is provided with a corresponding algorithm formula according to the immune cell disease to be assessed, whereby the comparison cell database 22 reads the immune cell disease After collecting the quantitative data, filter out the immune cell quantity data that affects the disease to be tested, and import the filtered data into the corresponding risk assessment unit to calculate and obtain the risk prediction data using the algorithm formula.

The role of immune cells in the tested organism is to defend against abnormalities or external threats in the body to maintain the health of the individual organism. Immune cells include T cells, B cells, and NK cells, which have the ability to identify and eliminate pathogens or abnormal cells; therefore, important information on the health status of the tested organism can be obtained through changes in the number of immune cells. For example, in the direction of tumor monitoring, the number of immune cells (such as NK cells and T cells) decreases, and the body's ability to clear abnormal cells may decrease, increasing the risk of tumor development; in the part of allergic immunity, abnormal activation or changes in the number of immune cells may Causes excessive reaction to external antigens (such as pollen or food), forming allergic symptoms; if some immune cells are overactive or excessive in number, they may increase the risk of autoimmune diseases; the number of overall immune cells decreases (such as T cells, B cells ) may weaken the defense against pathogens; and, the reduced number of immune cells (such as macrophages, neutrophils) may reduce the body's ability to fight infection and increase the risk of infection. From the above, it can be seen that changes in the number of immune cells can be regarded as an indicator of disease risk, and can also reflect disease progression and treatment response. Therefore, by detecting and analyzing changes in immune cells in detail, a more accurate assessment of health status and disease risk can be made. Predict or provide direction for future treatment strategies.

Furthermore, since cell data in existing flow cytometers mainly come from Western populations, there are inevitable biases when applied to immune cell disease risk prediction for non-Western populations; therefore, the regression of the data analysis workstation 20 The correction module 24 is used to perform regression correction on the risk prediction data output by the aforementioned calculation module 23 to obtain risk prediction correction data that is more accurate and effective than the risk prediction data. Specifically, as shown in Figure 3, the regression correction module 24 mainly includes a tumor risk regression correction module 241, an allergic immunity risk regression correction module 242, and an autoimmune risk assessment unit corresponding to each risk assessment unit of the calculation module 23. Risk regression correction module 243, immunocompromised risk regression correction module 244 and infection risk regression correction module 245; each risk regression correction module is equipped with a regression model established based on the comparison cell database 22.

In this novel embodiment, the comparison cell database 22 can be a database established based on human cell data; the human cells can be screened according to specific genetic characteristics and then established to form the comparison cell database 22, in order to improve the number of cells with these characteristics. The accuracy of mitochondrial testing for people with specific genetic characteristics can increase the effectiveness of interpreting the physiological information of the tested organisms; in practical applications, the specific genetic characteristics can be limited to ethnic Chinese (English: Ethnic Chinese), that is, Chinese in a broad sense. Descendants of ethnic groups, or by region, can be ethnic descendants of East Asia, thereby reducing the data bias caused by the analysis of immune cell number data provided by flow cytometry based on Western ethnic cell data.

In the embodiment of the present invention, the comparison cell database 22 can be a database established based on specific animal cell data; the specific animal cells can be established according to species classification to form the comparison cell database 22; in practical applications, so The specific species can be common pet species (such as dogs, cats) or endangered species in cultivation, etc., thereby improving the accuracy of detecting the number of immune cells of the tested species and increasing the effectiveness of interpreting the physiological information of the tested species.

As shown in FIG. 1 , the detection module 12 of the immune cell detection device 10 is used to output cell fluorescence numerical data of the tested sample to the central processing unit 11 , so that the immune cell quantity data conversion unit 13 passes through the central processing unit 11 . The device 11 reads and converts the cell fluorescence numerical data to form the immune cell quantity data. Wherein, the cell fluorescence value data includes the fluorescence value of an antibody of the same isotype and the fluorescence value of a specific type of antibody.

As shown in Figure 1, the analysis chart output unit 16 reads the immune cell quantity data through the central processor 11, analyzes it and outputs a specimen cell analysis chart; the specimen cell analysis chart is sent back to the central processor. 11, and then transmitted to the data analysis workstation 20 through the communication modules 17 and 27. Thereby, the test report output unit 28 of the data analysis workstation 20 can read the risk prediction correction data and the specimen cell analysis chart through the central processor 21, and integrate and output an immune cell disease risk prediction report accordingly. .

In this novel embodiment, the human-machine interface module 14 is used by personnel to control and operate the immune cell detection device 10 to perform detection and operate the built-in analysis software of the immune cell detection device 10 , and the storage module 15 is used to store the data obtained during the detection. and a cell database matching the built-in analysis software of the immune cell detection device 10 .

In this novel embodiment, the human-machine interface module 25 is used for personnel to control and operate the data analysis workstation 20 to perform data analysis and review and produce test reports; the storage module 26 is used to store subject information, experimental data, and test reports. and the data and reports obtained after analysis.

The specific structure and data transmission relationship of the new system are explained above. The detection process of the new system is explained below. The following detection process is based on the example that the tested organism is a human being, but the present invention is not limited to this. Taking the tested organism as a human being as an example, the steps of the testing process include:

Collect blood samples: 8ml of venous blood is drawn, and the anticoagulant Heparin is added.

Isolate mononuclear cells (PBMC): Take 6ml of whole blood from the specimen, add PBS (Phosphate buffered saline; phosphate buffered saline) to dilute; then, take a centrifuge tube (SepMateTM) and add lymphocyte separation solution (LymphoprepTM) 3.5ml, and then add the aforementioned PBS diluted whole blood to the centrifuge tube; add an appropriate amount of PBS into the centrifuge tube and centrifuge the centrifuge tube and its contents; then count the number of PBMC viable cells in the specimen to obtain PBMC specimen.

Cell surface antigen-fluorescent antibody reaction: Take two tubes of the aforementioned PBMC specimens (the cell content of each tube is 1×10 ⁶ ); add an isotype antibody (Isotype antibody) to one of the PBMC specimens as a control example; the other tube Add specific antibodies to the aforementioned PBMC samples as a reaction example (the types of specific antibodies can be 2 or more than 12 types, the specific selection is shown in Table 1 below). After the aforementioned control sample and reaction sample were reacted in the dark for 10-15 minutes, PBS was added to wash the cells.

Instrumental detection: Use a flow cytometer (BD Company, USA) to analyze and obtain the cell fluorescence numerical data of the control sample and the reaction sample. In the detection step of this instrument, antibody panels for different immune cell surface antigens are designed for the detection of different diseases.

Numerical conversion and analysis: Use a flow cytometer to convert the aforementioned cell fluorescence values to obtain corresponding immune cell number data, which contains the number ratio of a specific immune cell group (expressed as a percentage).

Numerical analysis: Use a flow cytometer to analyze the immune cell quantity data and output the sample cell analysis chart to the data analysis workstation 20 .

Risk assessment: The data analysis workstation 20 is used to predict the risk of a specific disease or physiological state based on the aforementioned immune cell quantity data. In the risk assessment step, the data analysis workstation 20 can read and calculate the risk prediction data through the calculation module 23, and then regress and correct the risk prediction data through the regression correction module 24 to form the predicted risk correction data.

Produce an inspection report: Based on the aforementioned risk prediction data or predicted risk correction data and the specimen cell analysis chart, produce an immune cell disease risk prediction report, which includes: (1) tumor risk, (2) allergic immunity risk, ( Risk assessment results such as 3) autoimmune risk, (4) immunocompromised risk, and (5) infection risk.

Table 1: List of specific antibodies that can be used in this new system to detect the number of immune cells. Among them, the present invention uses two or more specific cell surface markers in Table 1 to identify specific immune cell populations in a flow cytometer. For example: use CD3 ⁺ /CD56 ^- to calibrate T cells, use CD3 ^- /CD19 ⁺ to calibrate B cells, use CD3 ^- /CD56 ⁺ to calibrate NK cells, etc. BD company material number Antibody clone name Fluorescent dye Cell surface markers (specific) Titer(µl) first mixture 559866 G46-6 APC HLA-DR 3.5 566752 2-L1-A BV711 CCR7 5 560675 HI100 PE-Cy7 CD45RA 4 564488 B159 BB515 CD56 4 563877 SK3 BV786 CD4 4 561417 UCHT1 V500 CD3 4 560717 3G8 PerCP-Cy5.5 CD16 3 564054 M5E2 BV605 CD14 3 565935 EH12.1 BV421 PD-1 3 567056 RPA-T8 R718 CD8 2.5 560177 SJ25C1 APC-H7 CD19 3 566919 6H6 PE CD123 4 565909 Clone 3.9 PE CD11c 4 Second mixture 555531 FN50 PE CD69 20 564771 1B5 APC CCR10 5 740737 RF8B2 BV711 CXCR5 5 560225 M-A251 APC-H7 CD25 5 563877 SK3 BV786 CD4 4 566533 1C6 BB700 CXCR3 4 562724 11A9 BV605 CCR6 4 562576 2D7 BV421 CCR5 4 560675 HI100 PE-Cy7 CD45RA 4 564488 B159 BB515 CD56 4 561417 UCHT1 V500 CD3 4 567056 RPA-T8 R718 CD8 2.5

The above detection process is based on the example that the tested organism is a human being, but the present invention is not limited to this. For example, for animals, the specimen can be a specimen in which animal cells are placed in a culture medium, and the additives may be other types of anticoagulants or other agents, and the added diluent is not necessarily PBS, or even fluorescent stains. Fluorescent dyes other than those listed above may be used.

In summary, this new system that uses the number of immune cells to interpret disease information of tested organisms can accurately detect the number of immune cells of species with specific genetic characteristics, and based on this, accurately interpret the risk of immune cell diseases of the tested organisms, thereby achieving Provide effective suggestions for promoting physical health.

10: Immune cell detection device 11:Central processing unit 12:Detection module 13: Immune cell number data conversion unit 14: Human-machine interface module 15:Storage module 16: Analysis chart output unit 17:Communication module 20:Data analysis workstation 21:Central processing unit 22: Compare cell database 23: Calculation module 231:Oncology Risk Assessment Unit 232: Allergy Immunity Risk Insurance Assessment Unit 233: Autoimmune Risk Assessment Unit 234: Immunocompromised Risk Assessment Unit 235: Infection Risk Assessment Unit 24: Regression correction module 241: Tumor risk regression correction module 242: Allergy immune risk regression correction module 243:Autoimmune risk regression correction module 244: Immunity low risk regression correction module 245: Infection risk regression correction module 25: Human-machine interface module 26:Storage module 27:Communication module 28: Inspection report output unit

Figure 1 is a schematic diagram of the architecture of this new system that uses the number of immune cells to interpret tested biological disease information; Figure 2 is a schematic diagram of the calculation module architecture of this new data analysis workstation; Figure 3 is a schematic diagram of the regression correction module architecture of this new data analysis workstation.

10: Immune cell detection device

11:Central processing unit

12: Detection module

13: Immune cell number data conversion unit

14: Human-machine interface module

15:Storage module

16: Analysis chart output unit

17:Communication module

20:Data analysis workstation

21:Central processing unit

22: Compare cell database

23: Calculation module

24: Regression correction module

25: Human-machine interface module

26:Storage module

27:Communication module

28: Inspection report output unit

Claims (10)

A system that uses the number of immune cells to interpret tested biological disease information, including: An immune cell detection device, which is a flow cytometer, is used to output data on the number of immune cells of a subject; A data analysis workstation is communicatively connected to the immune cell detection device. The data analysis workstation includes a central processor and a comparison cell database, a calculation module and a regression correction module signally connected to the central processor; The calculation module reads the immune cell quantity data through the central processor and calculates it to obtain a risk prediction data; The regression correction module is equipped with a regression model based on the comparison cell database. The regression correction module reads the risk prediction data through the central processor to obtain a risk prediction correction data based on the regression correction of the comparison cell database. . The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the immune cell detection device includes a central processor and a detection module connected to its signal; the detection module is used to output the The cell fluorescence value data of the subject is sent to the central processing unit for an immune cell quantity data conversion unit to read and convert the cell fluorescence value data to form the immune cell quantity data. As claimed in claim 2, the system for interpreting tested biological disease information using the number of immune cells, wherein the cell fluorescence value data includes an isotype antibody fluorescence value and a specific type antibody fluorescence value. The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the immune cell detection device further includes an analysis chart output unit connected to the central processor via a signal for reading the immune cell number data. , and accordingly output a sample cell analysis chart to the data analysis workstation. As described in claim 4, the system for interpreting tested biological disease information using the number of immune cells, wherein the data analysis workstation also includes a test report output unit connected to the central processor signal, and the test report output unit reads The risk prediction correction data and the sample cell analysis chart are used to output an immune cell disease risk prediction report. As claimed in claim 2, the system for interpreting tested biological disease information by using the number of immune cells, wherein the immune cell detection device is further provided with a human-machine interface module and a storage module that are signally connected to the central processor. The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the data analysis workstation is also provided with a human-machine interface module and a storage module connected to the central processor signal. The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the immune cell detection device and the data analysis workstation are respectively provided with a communication module, and the immune cell detection device and the data analysis workstation communicate through The communication module performs data communication and transmission. The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the comparison cell database is a database established based on cell data of specific human genetic characteristics. The system for interpreting tested biological disease information by using the number of immune cells as described in claim 1, wherein the comparison cell database is a database established based on specific animal cell data.