Models and methods for predicting rat age

Abstract

The present application relates to a model for predicting the age of a rat, which considers data of immune cells, or data of immune cells, and data of cytokine and / or chemokine levels to predict the age of a rat. The model of the present application significantly improves the accuracy and reliability of age prediction, provides strong support for personalized health management, and helps to promote the development of precision medicine, and ultimately aims to achieve the goal of extending healthy life.

Description

Technical Field

[0001] This application relates to a model and method for predicting the age of rats. Background Technology

[0002] With the aging population, there is a growing concern about extending healthy lifespan. Current mainstream age prediction models primarily rely on epigenetic clocks (such as DNA methylation mapping) and transcriptome analysis, assessing an individual's biological age by detecting specific biomarkers. However, these methods struggle to accurately characterize the aging state of individual organs, leading to a lack of organ-specific aging assessment.

[0003] The decline in the function of the immune system, as the central regulatory network for maintaining systemic homeostasis, has become a key factor in systemic aging. With increasing age, immune capacity gradually weakens, leading to impaired protective immune responses. This process involves dynamic changes in multiple immune tissues and organs, such as the spleen, thymus, and lymph nodes, as well as corresponding cytokine networks. However, the potential of immune cell profiles and cytokine characteristics as innovative biomarkers for assessing biological age has not yet been fully explored.

[0004] To address the shortcomings of existing models, it is imperative to establish a model that can comprehensively assess the aging status of an individual's various organs. Summary of the Invention

[0005] In view of the problems existing in the prior art, the purpose of this application is to provide an effective model and method for predicting the age of rats.

[0006] Specifically, this application relates to the following aspects:

[0007] 1. A model for predicting age, comprising:

[0008] The data acquisition module is used to acquire data on the subject's immune cells;

[0009] A data processing module is used to perform data standardization processing on the data information acquired by the data acquisition module; and

[0010] An age calculation module is used to calculate the age (Y) of the subject by processing the data information processed in the data processing module.

[0011] The subjects were rats.

[0012] 2. The model according to claim 1, wherein:

[0013] In the data acquisition module, the collected immune cell data refers to the proportion and / or number of different types of immune cells from mesenteric lymph nodes, spleen, thymus, and peripheral blood detected on any day after the subject's birth.

[0014] 3. The model according to claim 2, wherein:

[0015] The different types of immune cells are selected from any two or more of the following: T cells, Tc cells, DP cells, Th cells, Th1 cells, Th2 cells, Th17 cells, Treg cells, B cells, NK cells, dendritic cells, macrophages, and activated T cells.

[0016] Preferably, the different types of immune cells include B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, and activated T cells;

[0017] More preferably, the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, and activated T cells; or the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, Th1 cells, and activated T cells.

[0018] 4. The model according to any one of claims 1-3, wherein:

[0019] The age calculation module contains a pre-stored formula for calculating age (Y) based on data fitted from the immune cells of subjects in an existing database.

[0020] 5. The model according to claim 4, wherein:

[0021] The formula is as follows: Formula 1

[0022]

[0023] Where Y is the calculated age of the subject, β jA For unitless parameters, b1 is a constant, X 1A The proportion of B cells in mesenteric lymph nodes, X 2A The proportion of T cells in mesenteric lymph nodes, X 3A The proportion of Th cells in the spleen, X 4A The proportion of Treg cells in the spleen, X 5A The proportion of dendritic cells in the spleen, X 6A X represents the number of splenic macrophages. 7A The proportion of spleen macrophages, X 8AThe proportion of DP cells in the spleen, X 9A The ratio of Tc in the spleen, X 10A The proportion of spleen B cells, X 11A The proportion of thymic dendritic cells, X 12A The proportion of thymic Tc cells, X 13A The proportion of thymic NK cells, X 14A The proportion of thymic B cells, X 15A The proportion of thymic macrophages, X 16A X represents the number of activated T cells in the thymus. 17A The number of peripheral blood T cells, X 18A The proportion of activated T cells in peripheral blood, X 19A This represents the proportion of Th cells in peripheral blood.

[0024] 6. The model according to claim 5, wherein:

[0025] b1 is selected from any value from 6.361831 to 6.561831, preferably 6.461831;

[0026] β 1A The value is selected from any value in the range of -0.080980 to 0.119020, preferably 0.019020;

[0027] β 2A Any value selected from -2.719323 to -2.519323, preferably -2.619323;

[0028] β 3A The value is selected from any value in the range of 2.939118 to 3.139118, preferably 3.039118;

[0029] β 4A The value is selected from any value between 1.923731 and 2.123731, preferably 2.023731;

[0030] β 5A The value is selected from any value in the range of 0.823716 to 1.023716, preferably 0.923716;

[0031] β 6A The value is selected from any value in the range of 0.003778 to 0.203778, preferably 0.103778;

[0032] β 7A The value is selected from any value in the range of -0.039901 to 0.160099, preferably 0.060099;

[0033] β 8AThe value is selected from any value between -0.955700 and -0.755700, preferably -0.855700;

[0034] β 9A Any value selected from -2.919561 to -2.719561, preferably -2.819561;

[0035] β 10A Any value selected from -1.625000 to -1.425000, preferably -1.525000;

[0036] β 11A The value is selected from any value between 0.156859 and 0.356859, preferably 0.256859;

[0037] β 12A The value is selected from any value in the range of -0.066058 to 0.133942, preferably 0.033942;

[0038] β 13A The value is selected from any value between -0.039637 and 0.160363, preferably 0.060363;

[0039] β 14A The value is selected from any value in the range of -0.093984 to 0.106016, preferably 0.006016;

[0040] β 15A The value is selected from any value in the range of -0.084509 to 0.115491, preferably 0.015491;

[0041] β 16A The value is selected from any value between -0.167287 and 0.032713, preferably -0.067287;

[0042] β 17A The value is selected from any value in the range of -0.024124 to 0.175876, preferably 0.075876;

[0043] β 18A The value is selected from any value between -0.868056 and -0.668056, preferably -0.768056;

[0044] β 19A The value is selected from -3.167211 to -2.967211, preferably -3.067211.

[0045] 7. The model according to claim 4, wherein:

[0046] The formula is as follows: Formula 2:

[0047]

[0048] Where Y is the calculated age of the subject, β jB b2 is a unitless parameter, and X is a constant. 1B The proportion of T cells in mesenteric lymph nodes, X 2B The proportion of Th cells in the spleen, X 3B The proportion of Treg cells in the spleen, X 4B The proportion of dendritic cells in the spleen, X 5B The proportion of DP cells in the spleen, X 6B The proportion of spleen B cells, X 7B The proportion of Tc cells in the spleen, X 8B The proportion of thymic dendritic cells, X 9B X represents the number of thymic DP cells. 10B The proportion of thymic NK cells, X 11B The proportion of thymic macrophages, X 12B The proportion of thymic B cells, X 13B The proportion of thymic activated T cells, X 14B This represents the proportion of Th cells in peripheral blood.

[0049] 8. The model according to claim 7, wherein:

[0050] b2 is selected from any value from 3.518588 to 3.718588, preferably 3.618588;

[0051] β 1B Any value selected from -1.604289 to -1.404289, preferably -1.504289;

[0052] β 2B Any value selected from 3.232314 to 3.432314, preferably 3.332314;

[0053] β 3B The value is selected from any value in the range of 2.021060 to 2.221060, preferably 2.121060;

[0054] β 4B The value is selected from any value between 0.609868 and 0.809868, preferably 0.709868;

[0055] β 5B The value is selected from any value between -0.365396 and -0.165396, preferably -0.265396;

[0056] β 6BAny value selected from -1.376704 to -1.176704, preferably -1.276704;

[0057] β 7B Any value selected from -3.114965 to -2.914965, preferably -3.014965;

[0058] β 8B The value is selected from any value between 0.004950 and 0.204950, preferably 0.104950;

[0059] β 9B Any value selected from -0.214899 to -0.014899, preferably -0.114899;

[0060] β 10B The value is selected from any value in the range of -0.064790 to 0.135210, preferably 0.035210;

[0061] β 11B The value is selected from any value between -0.094624 and 0.105376, preferably 0.005376;

[0062] β 12B The value is selected from any value between -0.093941 and 0.106059, preferably 0.006059;

[0063] β 13B The value is selected from any value between -0.917150 and -0.717150, preferably -0.817150;

[0064] β 14B The value is selected from any value between -1.185096 and -0.985096, preferably -1.085096.

[0065] 9. The model according to any one of claims 1-3, wherein:

[0066] The data acquisition module is also used to acquire data on the levels of cytokines and / or chemokines in the subjects.

[0067] 10. The model according to claim 9, wherein:

[0068] In the data acquisition module, the collected data on cytokine and / or chemokine levels refers to the concentration of cytokines and / or chemokines in the serum of the subject on any day after birth.

[0069] 11. The model according to claim 10, wherein:

[0070] In the data acquisition module, the cytokines are selected from any one or more of IL-1α, G-CSF, IL-10, IL-17A, IL-1β, IL-6, TNF-α, GM-CSF, IL-4, IFN-γ, IL-2, IL-5, IL-13, and IL-12p70.

[0071] Preferably, the cytokines include IL-1α;

[0072] More preferably, the cytokines are IL-1α, G-CSF, IL-13, and TNF-α.

[0073] 12. The model according to claim 10, wherein:

[0074] In the data acquisition module, the chemokine is selected from any one or more of GRO-α, MCP-1, MCP-3, MIP-1α, MIP-2, Rantes, Eotaxin, and IP-10.

[0075] 13. The model according to any one of claims 9-12, wherein:

[0076] The age calculation module contains a pre-stored formula for calculating age (Y) that is fitted based on data of the subject's immune cells and cytokine and / or chemokine levels in an existing database.

[0077] 14. The model according to claim 13, wherein:

[0078] The formula is as follows: Formula 3:

[0079]

[0080] Where Y is the calculated age of the subject, β jC For unitless parameters, b3 is a constant, and X 1C The proportion of B cells in mesenteric lymph nodes, X 2C X represents the number of mesenteric lymph node macrophages. 3C The proportion of Th1 cells in mesenteric lymph nodes, X 4C The proportion of T cells in mesenteric lymph nodes, X 5C The proportion of Treg cells in the spleen, X 6C The proportion of dendritic cells in the spleen, X 7C X represents the number of dendritic cells in the spleen. 8C The proportion of Th cells in the spleen, X 9C The proportion of DP cells in the spleen, X 10C The proportion of Tc cells in the spleen, X 11C The proportion of spleen B cells, X12C The proportion of thymic dendritic cells, X 13C X represents the number of activated T cells in the thymus. 14C The proportion of thymic NK cells, X 15C The proportion of peripheral blood Tc cells, X 16C The proportion of Th cells in peripheral blood, X 17C The proportion of activated T cells in peripheral blood, X 18C The number of peripheral blood T cells, X 19C The serum IL-1α level, X 20C For serum G-CSF levels, X 21C The serum IL-13 level, X 22C This represents the serum TNF-α level.

[0081] 15. The model according to claim 14, wherein:

[0082] b3 is selected from any value from 7.832772 to 8.032772, preferably 7.932772;

[0083] β 1C The value is selected from any value between 0.241801 and 0.441801, preferably 0.341801;

[0084] β 2C The value is selected from any value in the range of -0.055621 to 0.144379, preferably 0.044379;

[0085] β 3C Any value selected from -0.216718 to -0.016718, preferably -0.116718;

[0086] β 4C The value is selected from any value between -2.582550 and -2.382550, preferably -2.482550;

[0087] β 5C The value is selected from any value between 1.653708 and 1.853708, preferably 1.753708;

[0088] β 6C The value is selected from any value between 0.094184 and 0.294184, preferably 0.194184;

[0089] β 7C The value is selected from any value in the range of 0.053549 to 0.253549, preferably 0.153549;

[0090] β 8CThe value is selected from any value in the range of 3.713789 to 3.913789, preferably 3.813789;

[0091] β 9C The value is selected from any value between -0.909406 and -0.709406, preferably -0.809406;

[0092] β 10C Any value selected from -3.168760 to -2.968760, preferably -3.068760;

[0093] β 11C Any value selected from -2.463371 to -2.263371, preferably -2.363371;

[0094] β 12C The value is selected from any value in the range of 0.291829 to 0.491829, preferably 0.391829;

[0095] β 13C The value is selected from any value between -0.200740 and -0.000740, preferably -0.100740;

[0096] β 14C The value is selected from any value in the range of -0.003577 to 0.196423, preferably 0.096423;

[0097] β 15C The value is selected from any value between -0.522425 and -0.322425, preferably -0.422425;

[0098] β 16C Any value selected from -4.336476 to -4.136476, preferably -4.236476;

[0099] β 17C Any value selected from -0.311944 to -0.111944, preferably -0.211944;

[0100] β 18C The value is selected from any value between -0.073234 and 0.126766, preferably 0.026766;

[0101] β 19C The value is selected from any value between 0.218171 and 0.418171, preferably 0.318171;

[0102] β 20C The value is selected from any value between -0.081046 and 0.118954, preferably 0.018954;

[0103] β 21C The value is selected from any value in the range of -0.093710 to 0.106290, preferably 0.006290;

[0104] β 22C The value is selected from any value between -0.097227 and 0.102773, preferably 0.002773.

[0105] 16. The model according to claim 13, wherein:

[0106] The formula is as follows: Formula Four:

[0107]

[0108] Where Y is the calculated age of the subject, β jD For unitless parameters, b4 is a constant, X 1D The proportion of T cells in mesenteric lymph nodes, X 2D The proportion of Th cells in the spleen, X 3D The proportion of Treg cells in the spleen, X 4D The proportion of dendritic cells in the spleen, X 5D The proportion of DP cells in the spleen, X 6D The proportion of spleen B cells, X 7D The proportion of Tc cells in the spleen, X 8D The proportion of thymic dendritic cells, X 9D The proportion of thymic NK cells, X 10D The proportion of thymic B cells, X 11D The proportion of activated T cells in peripheral blood, X 12D The proportion of Th cells in peripheral blood, X 13D This represents the serum IL-1α level.

[0109] 17. The model according to claim 16, wherein:

[0110] b4 is selected from any value from 2.148733 to 2.348733, preferably 2.248733;

[0111] β 1D The value is selected from any value between -1.105501 and -0.905501, preferably -1.005501;

[0112] β 2D The value is selected from any value in the range of 4.908543 to 5.108543, preferably 5.008543;

[0113] β 3DThe value is selected from any value in the range of 2.077282 to 2.277282, with 2.177282 being preferred;

[0114] β 4D The value is selected from any value in the range of 0.213109 to 0.413109, preferably 0.313109;

[0115] β 5D The value is selected from any value between -0.357812 and -0.157812, preferably -0.257812;

[0116] β 6D Any value selected from -2.401495 to -2.201495, preferably -2.301495;

[0117] β 7D Any value selected from -3.233323 to -3.033323, preferably -3.133323;

[0118] β 8D The value is selected from any value between 0.009070 and 0.209070, preferably 0.109070;

[0119] β 9D The value is selected from any value between -0.078264 and 0.121736, preferably 0.021736;

[0120] β 10D The value is selected from any value in the range of -0.097597 to 0.102403, preferably 0.002403;

[0121] β 11D The value is selected from any value between -0.352896 and -0.152896, preferably -0.252896;

[0122] β 12D Any value selected from -1.235006 to -1.035006, preferably -1.135006;

[0123] β 13D The value is selected from any value in the range of 0.094109 to 0.294109, preferably 0.194109.

[0124] Beneficial effects:

[0125] This application, based on a multidimensional dataset of immune cell profiles, cytokines, and chemokine characteristics, identified age-related feature curves and developed an age prediction model based on Lasso regression. This resulted in a unified aging prediction index, identifying specific parameters most valuable for age prediction. The model significantly improves the accuracy and reliability of age prediction, providing strong support for personalized health management, promoting the development of precision medicine, and ultimately aiming to extend healthy lifespan. Attached Figure Description

[0126] Figure 1 This is a flow cytometry diagram of the gating strategy of rat mesenteric lymph nodes.

[0127] Figure 2 This is a flow cytometry diagram of the gating strategy in the spleen of a rat.

[0128] Figure 3 This is a flow cytometry gating strategy diagram of the rat thymus.

[0129] Figure 4 This is a flow cytometry gating strategy diagram of peripheral blood in rats.

[0130] Figures 5A-5C The graph shows the Lasso age prediction model built based on dataset "I", the residual histogram of the Lasso age prediction model built based on dataset "I" and selecting the minimum MSE, and the relationship between the residuals and the predicted values. Figure 5A A diagram of the Lasso age prediction model built based on dataset "I";

[0131] Figure 5B The residual histogram constructed to select the minimum MSE; Figure 5C A graph showing the relationship between residuals and predicted values ​​constructed to select the minimum MSE.

[0132] Figures 6A-6B This section presents the residual histogram and the relationship between residuals and predicted values ​​for a Lasso age prediction model built based on dataset "I" with the minimum number of variables selected. Figure 6A For residual histograms; Figure 6B This is a graph showing the relationship between residuals and predicted values.

[0133] Figures 7A-7E The graph shows the Lasso age prediction model built based on dataset "F", the residual histogram of the Lasso age prediction model built based on dataset "F" by selecting the minimum MSE or the fewest number of variables, and the graph showing the relationship between the residuals and the predicted values. Figure 7A A diagram of the Lasso age prediction model built based on dataset "F"; Figure 7B The residual histogram constructed to select the minimum MSE; Figure 7C A graph showing the relationship between residuals and predicted values ​​constructed to select the minimum MSE; Figure 7D The residual histogram constructed to select the fewest number of variables; Figure 7E A graph showing the relationship between residuals and predicted values ​​constructed to select the minimum number of variables.

[0134] Figures 8A-8C The graph shows the Lasso age prediction model built based on dataset "IF", the residual histogram of the Lasso age prediction model built based on dataset "F" and selecting the minimum MSE, and the relationship between the residuals and the predicted values. Figure 8A A diagram of the Lasso age prediction model built based on the dataset "IF"; Figure 8B The residual histogram constructed to select the minimum MSE; Figure 8C A graph showing the relationship between residuals and predicted values ​​constructed to select the minimum MSE.

[0135] Figures 9A-9B This section presents the residual histogram and the relationship between residuals and predicted values ​​for a Lasso age prediction model constructed based on the dataset "IF" with the minimum number of variables selected. Figure 9A For residual histograms; Figure 9B This is a graph showing the relationship between residuals and predicted values.

[0136] Figures 10A-10B This section describes the external validation results of the model built on dataset "I" and the model built on dataset "IF" using the test set. Figure 10A The external validation results are for the model built based on dataset "I". Figure 10B These are the external validation results for the model built on the "IF" dataset. Detailed Implementation

[0137] The present application is further illustrated below with reference to embodiments. It should be understood that the embodiments are only used to further illustrate and explain the present application and are not intended to limit the present application.

[0138] Unless otherwise defined, technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art. While similar or identical methods and materials may be applied in experimental or practical applications, materials and methods are described herein. In case of conflict, the definitions included herein shall prevail. Furthermore, materials, methods, and examples are for illustrative purposes only and are not intended to be limiting. The present application is further described below with reference to specific embodiments, but is not intended to limit the scope of the application.

[0139] definition

[0140] As used herein, the term "immune cell" generally encompasses any cell derived from hematopoietic stem cells that plays a role in the immune response. Immune cells can originate from many organs and tissues, such as the thymus, spleen, lymph nodes, and lymphoid tissue clusters (e.g., in the gastrointestinal tract and bone marrow). Immune cells include, but are not limited to, lymphocytes (such as T cells and B cells), antigen-presenting cells (APCs), dendritic cells, monocytes, macrophages, natural killer (NK) cells, mast cells, basophils, eosinophils, or neutrophils, and any progenitor cells of such cells.

[0141] As used herein, the term "T cell" (i.e., T lymphocyte) is intended to include all cells within the T cell lineage, including thymocytes, immature T cells, mature T cells, etc. The term "T cell" may include CD4+. + and / or CD8 + T cells, T helper (Th) cells (such as Th1, Th2 and Th17 cells), and T regulator (Treg) cells.

[0142] As used herein, the term "cytokine" refers to small molecular weight regulatory proteins secreted by cells that influence cellular behavior (activation, proliferation, differentiation, migration, etc.). Examples of such cytokines include, but are not limited to, interferons (e.g., α-interferon, β-interferon, γ-interferon (IFN-γ)), interleukins (e.g., IL-1, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-11, IL-12, IL-12p70, IL-13, IL-14, IL-15, IL-16, IL-17, IL-17A, IL-18, IL-19, IL-20, IL-24), tumor necrosis factor (e.g., TNF-α), transforming growth factor-β, TRAIL, granulocyte colony-stimulating factor (G-CSF), macrophage colony-stimulating factor (GM-CSF), etc.

[0143] As used herein, the term "chemokines" refers to a specific class of low-molecular-weight proteins that influence leukocyte chemotaxis and other cellular behaviors, playing a crucial role in the mid-stage of an inflammatory response. Examples of chemokines include, but are not limited to, macrophage inflammatory proteins (e.g., MIP-1, MIP-1β, MIP-1α, MIP-2), growth regulatory proteins (e.g., GRO-α), monocyte chemotactic proteins (e.g., MCP-1, MCP-3), activating factors (Rantes) that regulate the expression and secretion of normal T cells, eosinophil chemotactic proteins (Eotaxin), and interferon-induced proteins (e.g., IP-10).

[0144] As used in this article, the term "continuous variable" refers to variables in statistics that can be categorized into continuous variables and categorical variables based on whether their values ​​are continuous. A continuous variable is one that can take any value within a certain interval; its values ​​are continuous, and any two adjacent values ​​can be infinitely divided, resulting in an infinite number of possible values. Examples include the specifications and dimensions of manufactured parts, and measurements such as height, weight, and chest circumference. These are continuous variables, and their values ​​can only be obtained through measurement or quantification. Conversely, discrete variables are those whose values ​​can only be calculated using natural numbers or integer units. Examples include the number of businesses, employees, and equipment; these can only be counted in units of measurement, and their values ​​are generally obtained using counting methods.

[0145] As used in this article, the term "categorical variable" refers to variables such as geographical location and demographics, which are used to group survey respondents. Descriptive variables describe the differences between one customer group and other customer groups. Most categorical variables are descriptive variables. Categorical variables can be divided into two main categories: unordered categorical variables and ordered categorical variables. Unordered categorical variables refer to categories or attributes that do not differ in degree or order. They can be further divided into ① binary categories, such as gender (male, female), drug response (negative and positive), etc.; ② multi-category categories, such as blood type (O, A, B, AB), occupation (worker, farmer, merchant, student, soldier), etc. Ordered categorical variables, on the other hand, have differences in degree between categories. For example, urine glucose test results are classified as -, ±, +, ++, +++; treatment efficacy is classified as cured, significantly effective, improved, ineffective. For ordered categorical variables, they should first be grouped according to rank, the number of observation units in each group should be counted, and a frequency table of the ordered variable (each rank) should be compiled. The resulting data is called ordinal data.

[0146] The type of variable is not static; it can be transformed between different types depending on the research objective. For example, hemoglobin level (g / L) is originally a numerical variable. If it is divided into two categories—normal and low—it can be analyzed as binary categorical data. If it is divided into five levels—severe anemia, moderate anemia, mild anemia, normal, and elevated hemoglobin—it can be analyzed as ordinal data. Sometimes, categorical data can also be quantified. For instance, if a patient's nausea is represented by 0, 1, 2, and 3, it can be analyzed as numerical variable data (quantitative data).

[0147] As used in this paper, the term "linear regression" refers to a statistical, linear method used to model the relationship between a dependent variable and one or more independent variables. In linear regression, the relationship is modeled using a linear predictor function estimated from the data using its unknown model parameters. This model is called a linear model. Typically, for the case of a single independent variable, the (x, y) data points are plotted graphically as a scatter plot, where x is the independent variable and y is the dependent variable. Linear regression aims to obtain a "best-fit line" representing the relationship between the dependent and independent variables. Linear regression models are typically fitted using the least squares method, but they can also be fitted in other ways, such as by minimizing a "cost function" in some other norm (e.g., L1-norm penalty or L2-norm penalty).

[0148] As used in this paper, the term "minimum absolute shrinkage and operator selection regression (often simply called Lasso regression)" is a compression estimation method based on the idea of ​​reducing the variable set (order reduction). It constructs a penalty function to compress the coefficients of variables and make some regression coefficients zero, thereby achieving variable selection. It is an algorithm that uses a penalty function to improve the predictive power of a model. This algorithm, using 1-norm constraints, not only solves problems of high dimensionality and collinearity but also makes the established model "sparse," meaning the algorithm has an automatic wavelength selection effect during modeling.

[0149] This application provides a model for predicting age, comprising:

[0150] The data acquisition module is used to acquire data on the subject's immune cells;

[0151] A data processing module is used to perform data standardization processing on the data information acquired by the data acquisition module; and

[0152] An age calculation module is used to calculate the age (Y) of the subject by processing the data information processed in the data processing module.

[0153] The subjects were rats.

[0154] In the data processing module, the immune cell data of the subject are standardized. Specifically, standardization is performed using the mean of immune cell data from 1-month-old rats tested in the same batch. The number of 1-month-old rats used for standardization is at least 2, more specifically, at least 5. The 1-month-old rats used for standardization can be female or male.

[0155] In some implementations, the data processing module is also used to process missing values ​​in the standardized data. Specifically, missing values ​​in the standardized data are processed using a multiple interpolation method, such as a chain equation multiple interpolation method.

[0156] In the age calculation module, the calculated age (Y) of the subject can be expressed as months, for example.

[0157] In the age calculation module, the subject's age (Y) is calculated using data from the subject's immune cells. Specifically, in the age calculation module, the subject's immune cell data is used as a continuous variable.

[0158] The data on immune cells refers to the proportion and / or quantity of different types of immune cells from mesenteric lymph nodes, spleen, thymus, and peripheral blood detected on any day after the subject's birth. "Different types of immune cells" can refer to the same type of immune cell from different samples (e.g., from mesenteric lymph nodes, spleen, thymus, and peripheral blood), or it can refer to different types of cells from different samples (e.g., from mesenteric lymph nodes, spleen, thymus, and peripheral blood).

[0159] The proportion and number of different types of immune cells can be detected using any method known in the art, such as flow cytometry, single-cell sequencing, immunohistochemistry (IHC), and multiplex immunofluorescence (mIF). Specifically, in this application, flow cytometry is used to detect different types of immune cells and their corresponding antigen molecular markers. Here, "antigen molecular markers" refer to specific proteins or other molecules that can specifically recognize and distinguish different types of immune cells. These markers are usually located on the cell surface or inside the cell and can be detected by antibodies that specifically bind to them.

[0160] Furthermore, those skilled in the art will understand that this application is not intended to limit the specific selection of antigen molecular markers used, as long as the selected antigen molecular marker can effectively distinguish the target cell from other cells, it meets the requirements of this application.

[0161] In some embodiments, the different types of immune cells are selected from any two or more of T cells, Tc cells, DP cells, Th cells, Th1 cells, Th2 cells, Th17 cells, Treg cells, B cells, NK cells, dendritic cells, macrophages, and activated T cells.

[0162] The antigenic molecular marker of the T cells is, for example, CD45. + CD3 + The antigenic molecular marker of the Tc cells is, for example, CD45. + CD3 + CD4 - CD8 + The antigenic molecular marker of the DP cells is, for example, CD45.+ CD3 + CD4 + CD8 + The antigenic molecular marker of the Th cells is, for example, CD45. + CD3 + CD4 + CD8 - The antigenic molecular marker of the Th1 cells is, for example, CD45. + CD3 + CD4 + CD8 - IFN-γ + The antigenic molecular marker of the Th2 cells is, for example, CD45. + CD3 + CD4 + CD8 - IL-4 + The antigenic molecular marker of the Th17 cells is, for example, CD45. + CD3 + CD4 + CD8 - IL-17 + The antigen molecular marker of the Treg cells is, for example, (CD45). + CD3 + CD4 + CD8 - CD25 + FoxP3 + The antigenic molecular marker of the B cells is, for example, CD45. + CD3 - CD45RA + The antigenic molecular marker of the NK cells is, for example, CD45. + CD3 - CD161 + The antigenic molecular marker of the dendritic cells is, for example, CD45. + CD3 - CD103 + The antigenic molecular marker of the macrophages is, for example, CD45. + CD3 - CD68 + The antigenic molecular marker for the activated T cells is, for example, CD45. + CD3 + CD71 + .

[0163] In some embodiments, the different types of immune cells include B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, and activated T cells.

[0164] In some embodiments, the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, and activated T cells. In some embodiments, the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, Th1 cells, and activated T cells.

[0165] The age calculation module contains a pre-stored formula for calculating age (Y) based on data fitted from the immune cells of subjects in an existing database.

[0166] In this application, the existing database refers to a database consisting of subjects who meet the following criteria for quantity, age, and gender in the first, second, and third batches.

[0167] The age calculation module calculates the subject's age (Y) using, for example, the following formula (a):

[0168]

[0169] Where Y is the calculated age of the subject, β jA For unitless parameters, b1 is a constant, X 1A The proportion of B cells in mesenteric lymph nodes, X 2A The proportion of T cells in mesenteric lymph nodes, X 3A The proportion of Th cells in the spleen, X 4A The proportion of Treg cells in the spleen, X 5A The proportion of dendritic cells in the spleen, X 6A X represents the number of splenic macrophages. 7A The proportion of spleen macrophages, X 8A The proportion of DP cells in the spleen, X 9A The ratio of Tc in the spleen, X 10A The proportion of spleen B cells, X 11A The proportion of thymic dendritic cells, X 12A The proportion of thymic Tc cells, X 13A The proportion of thymic NK cells, X 14A The proportion of thymic B cells, X 15A The proportion of thymic macrophages, X 16A X represents the number of activated T cells in the thymus. 17A The number of peripheral blood T cells, X 18A The proportion of activated T cells in peripheral blood, X 19A This represents the proportion of Th cells in peripheral blood.

[0170] Specifically, in the age calculation module, the subject's immune cell data, β... jA The values ​​of b1 and b1 can be directly calculated by substituting them into formula (1).

[0171] Where b1 is selected from any value between 6.361831 and 6.561831; β 1A Selected from any value between -0.080980 and 0.119020; β 2A Any value selected from -2.719323 to -2.519323; β 3A Selected from any value between 2.939118 and 3.139118; β 4A Any value selected from 1.923731 to 2.123731; β 5A Selected from any value between 0.823716 and 1.023716; β 6A Selected from any value between 0.003778 and 0.203778; β 7A Selected from any value between -0.039901 and 0.160099; β 8A Selected from any value between -0.955700 and -0.755700; β 9A Any value selected from -2.919561 to -2.719561; β 10A Selected from any value between -1.625000 and -1.425000; β 11A Selected from any value between 0.156859 and 0.356859; β 12A Selected from any value between -0.066058 and 0.133942; β 13A Selected from any value between -0.039637 and 0.160363; β 14A Selected from any value between -0.093984 and 0.106016; β 15A Selected from any value between -0.084509 and 0.115491; β 16A Selected from any value between -0.167287 and 0.032713; β 17A Selected from any value between -0.024124 and 0.175876; β 18A Selected from any value between -0.868056 and -0.668056; β 19A Any value selected from -3.167211 to -2.967211.

[0172] Specifically, for example, b1 is 6.461831; β 1A It is 0.019020; β 2A -2.619323; β3A It is 3.039118; β 4A It is 2.023731; β 5A It is 0.923716; β 6A It is 0.103778; β 7A It is 0.060099; β 8A -0.855700; β 9A -2.819561; β 10A -1.525000; β 11A It is 0.256859; β 12A It is 0.033942; β 13A It is 0.060363; β 14A It is 0.006016; β 15A It is 0.015491; β 16A -0.067287; β 17A It is 0.075876; β 18A -0.768056; β 19A It is -3.067211.

[0173] The age calculation module calculates the subject's age (Y) using, for example, the following formula (II):

[0174]

[0175] Where Y is the calculated age of the subject, β jB b2 is a unitless parameter, and X is a constant. 1B The proportion of T cells in mesenteric lymph nodes, X 2B The proportion of Th cells in the spleen, X 3B The proportion of Treg cells in the spleen, X 4B The proportion of dendritic cells in the spleen, X 5B The proportion of DP cells in the spleen, X 6B The proportion of spleen B cells, X 7B The proportion of Tc cells in the spleen, X 8B The proportion of thymic dendritic cells, X 9B X represents the number of thymic DP cells. 10B The proportion of thymic NK cells, X 11B The proportion of thymic macrophages, X 12B The proportion of thymic B cells, X 13B The proportion of thymic activated T cells, X 14B This represents the proportion of Th cells in peripheral blood.

[0176] Specifically, in the age calculation module, the subject's immune cell data, β... jBThe values ​​of b2 and b2 can be directly calculated by substituting them into formula (II).

[0177] Where b2 is selected from any value between 3.518588 and 3.718588; β 1B Selected from any value between -1.604289 and -1.404289; β 2B Any value selected from 3.232314 to 3.432314; β 3B Any value selected from 2.021060 to 2.221060; β 4B Selected from any value between 0.609868 and 0.809868; β 5B Selected from any value between -0.365396 and -0.165396; β 6B Selected from any value between -1.376704 and -1.176704; β 7B Selected from any value between -3.114965 and -2.914965; β 8B Selected from any value between 0.004950 and 0.204950; β 9B Selected from any value between -0.214899 and -0.014899; β 10B Selected from any value between -0.064790 and 0.135210; β 11B Selected from any value between -0.094624 and 0.105376; β 12B Selected from any value between -0.093941 and 0.106059; β 13B Selected from any value between -0.917150 and -0.717150; β 14B Any value selected from -1.185096 to -0.985096.

[0178] Specifically, for example, b2 is 3.618588; β 1B -1.504289; β 2B 3.332314; β 3B 2.121060; β 4B It is 0.709868; β 5B -0.265396; β 6B -1.276704; β 7B -3.014965; β 8B It is 0.104950; β 9B -0.114899; β 10B It is 0.035210; β 11B It is 0.005376; β 12B It is 0.006059; β13B -0.817150; β 14B It is -1.085096.

[0179] This application also provides a method for predicting age, comprising: a data acquisition step, which acquires data on the immune cells of a subject; a data processing step, which standardizes the data acquired in the data acquisition step; and an age calculation step, which calculates the age (Y) of the subject by calculating the processed data in the data processing step. The subject is a rat.

[0180] In some embodiments, the method further includes processing missing values ​​in the standardized data during the data processing step. Specifically, missing values ​​in the standardized data are processed using a multiple interpolation method, such as a chain equation multiple interpolation method.

[0181] As described above, the specific details of the steps performed in the method of this application, including the acquisition and processing of the subject's immune cell data, can all refer to the steps performed in each module of the model involved in this application.

[0182] This application provides another model for predicting age, which includes:

[0183] The data acquisition module is used to acquire data on the subject's immune cells, as well as data on the levels of cytokines and / or chemokines;

[0184] A data processing module is used to perform data standardization processing on the data information acquired by the data acquisition module; and

[0185] An age calculation module is used to calculate the age (Y) of the subject by processing the data information processed in the data processing module.

[0186] The subjects were rats.

[0187] In the data processing module, the data on the subject's immune cells, as well as the data on cytokine and / or chemokine levels, are standardized. Specifically, standardization is performed using the mean of the immune cell, cytokine, and chemokine levels data from the same batch of 1-month-old rats tested. The number of 1-month-old rats used for standardization is at least 2, more specifically, at least 5. The 1-month-old rats used for standardization can be female or male.

[0188] In some implementations, the data processing module is also used to process missing values ​​in the standardized data. Specifically, missing values ​​in the standardized data are processed using a multiple interpolation method, such as a chain equation multiple interpolation method.

[0189] In the age calculation module, the calculated age (Y) of the subject can be expressed as months, for example.

[0190] In the age calculation module, the subject's age (Y) is calculated using data on the subject's immune cells and data on the levels of cytokines and / or chemokines. Specifically, in the age calculation module, the subject's immune cell data and data on the levels of cytokines and / or chemokines are used as continuous variables.

[0191] The data on immune cells refers to the proportion and / or quantity of different types of immune cells from mesenteric lymph nodes, spleen, thymus, and peripheral blood detected on any day after the subject's birth. Specifically, the "data on immune cells" and "different types of immune cells" are as defined above. In some embodiments, the different types of immune cells include B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, and activated T cells. In some embodiments, the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, and activated T cells. In some embodiments, the different types of immune cells are B cells, T cells, Th cells, Treg cells, dendritic cells, DP cells, Tc cells, NK cells, macrophages, Th1 cells, and activated T cells.

[0192] The data on cytokine and / or chemokine levels refer to the concentrations of cytokines and / or chemokines in the serum of the subject on any day after birth. The concentrations of cytokines and / or chemokines in serum can be detected using any method known in the art, such as enzyme-linked immunosorbent assay (ELISA), multiplex bead analysis (e.g., Luminex xMAP technology), or protein microarrays. Specifically, in this application, multiplex bead analysis using Luminex xMAP technology is employed for detection.

[0193] In some embodiments, the cytokines are selected from any one or more of IL-1α, G-CSF, IL-10, IL-17A, IL-1β, IL-6, TNF-α, GM-CSF, IL-4, IFN-γ, IL-2, IL-5, IL-13, and IL-12p70. In some embodiments, the cytokines include IL-1α. In some embodiments, the cytokines are IL-1α, G-CSF, IL-13, and TNF-α.

[0194] In some embodiments, the chemokine is selected from any one or more of GRO-α, MCP-1, MCP-3, MIP-1α, MIP-2, Rantes, Eotaxin, and IP-10.

[0195] The age calculation module contains a pre-stored formula for calculating age (Y) that is fitted based on data of the subject's immune cells and cytokine and / or chemokine levels in an existing database.

[0196] In this application, the existing database refers to a database consisting of subjects who meet the following criteria for quantity, age, and gender in the first, second, and third batches.

[0197] The age calculation module calculates the subject's age (Y) using, for example, the following formula (iii):

[0198]

[0199] Where Y is the calculated age of the subject, β jC For unitless parameters, b3 is a constant, and X 1C The proportion of B cells in mesenteric lymph nodes, X 2C X represents the number of mesenteric lymph node macrophages. 3C The proportion of Th1 cells in mesenteric lymph nodes, X 4C The proportion of T cells in mesenteric lymph nodes, X 5C The proportion of Treg cells in the spleen, X 6C The proportion of dendritic cells in the spleen, X 7C X represents the number of dendritic cells in the spleen. 8C The proportion of Th cells in the spleen, X 9C The proportion of DP cells in the spleen, X 10C The proportion of Tc cells in the spleen, X 11C The proportion of spleen B cells, X 12C The proportion of thymic dendritic cells, X 13C X represents the number of activated T cells in the thymus. 14C The proportion of thymic NK cells, X 15CThe proportion of peripheral blood Tc cells, X 16C The proportion of Th cells in peripheral blood, X 17C The proportion of activated T cells in peripheral blood, X 18C The number of peripheral blood T cells, X 19C The serum IL-1α level, X 20C For serum G-CSF levels, X 21C The serum IL-13 level, X 22C This represents the serum TNF-α level.

[0200] Specifically, the age calculation module incorporates data on the subject's immune cells, cytokine levels, and β-cell levels. jC The values ​​of b and b3 can be directly calculated by substituting them into formula (III).

[0201] Where b3 is selected from any value between 7.832772 and 8.032772; β 1C Selected from any value between 0.241801 and 0.441801; β 2C Selected from any value between -0.055621 and 0.144379; β 3C Selected from any value between -0.216718 and -0.016718; β 4C Selected from any value between -2.582550 and -2.382550; β 5C Selected from any value between 1.653708 and 1.853708; β 6C Selected from any value between 0.094184 and 0.294184; β 7C Selected from any value between 0.053549 and 0.253549; β 8C Any value selected from 3.713789 to 3.913789; β 9C Selected from any value between -0.909406 and -0.709406; β 10C Selected from any value between -3.168760 and -2.968760; β 11C Any value selected from -2.463371 to -2.263371; β 12C Selected from any value between 0.291829 and 0.491829; β 13C Selected from any value between -0.200740 and -0.000740; β 14C Selected from any value between -0.003577 and 0.196423; β 15C Selected from any value between -0.522425 and -0.322425; β 16CSelected from any value between -4.336476 and -4.136476; β 17C Selected from any value between -0.311944 and -0.111944; β 18C Selected from any value between -0.073234 and 0.126766; β 19C Selected from any value between 0.218171 and 0.418171; β 20C Selected from any value between -0.081046 and 0.118954; β 21C Selected from any value between -0.093710 and 0.106290; β 22C Select any value from -0.097227 to 0.102773.

[0202] Specifically, for example, b3 is 7.932772; β 1C It is 0.341801; β 2C It is 0.044379; β 3C -0.116718; β 4C -2.482550; β 5C It is 1.753708; β 6C It is 0.194184; β 7C It is 0.153549; β 8C It is 3.813789; β 9C -0.809406; β 10C -3.068760; β 11C -2.363371; β 12C It is 0.391829; β 13C -0.100740; β 14C It is 0.096423; β 15C -0.422425; β 16C -4.236476; β 17C -0.211944; β 18C It is 0.026766; β 19C It is 0.318171; β 20C It is 0.018954; β 21C It is 0.006290; β 22C It is 0.002773.

[0203] The age calculation module calculates the subject's age (Y) using, for example, the following formula (iv):

[0204]

[0205] Where Y is the calculated age of the subject, βjD For unitless parameters, b4 is a constant, X 1D The proportion of T cells in mesenteric lymph nodes, X 2D The proportion of Th cells in the spleen, X 3D The proportion of Treg cells in the spleen, X 4D The proportion of dendritic cells in the spleen, X 5D The proportion of DP cells in the spleen, X 6D The proportion of spleen B cells, X 7D The proportion of Tc cells in the spleen, X 8D The proportion of thymic dendritic cells, X 9D The proportion of thymic NK cells, X 10D The proportion of thymic B cells, X 11D The proportion of activated T cells in peripheral blood, X 12D The proportion of Th cells in peripheral blood, X 13D This represents the serum IL-1α level.

[0206] Specifically, the age calculation module incorporates data on the subject's immune cells, cytokine levels, and β-cell levels. jD The values ​​of b4 and b4 can be directly calculated by substituting them into formula (IV).

[0207] Where b4 is selected from any value between 2.148733 and 2.348733; β 1D Selected from any value between -1.105501 and -0.905501; β 2D Selected from any value between 4.908543 and 5.108543; β 3D Selected from any value between 2.077282 and 2.277282; β 4D Selected from any value between 0.213109 and 0.413109; β 5D Selected from any value between -0.357812 and -0.157812; β 6D Selected from any value between -2.401495 and -2.201495; β 7D Any value selected from -3.233323 to -3.033323; β 8D Selected from any value between 0.009070 and 0.209070; β 9D Selected from any value between -0.078264 and 0.121736; β 10D Selected from any value between -0.097597 and 0.102403; β 11D Selected from any value between -0.352896 and -0.152896; β 12D Selected from any value between -1.235006 and -1.035006; β13D Any value selected from 0.094109 to 0.294109.

[0208] Specifically, for example, b4 is 2.248733; β 1D -1.005501; β 2D It is 5.008543; β 3D It is 2.177282; β 4D It is 0.313109; β 5D -0.257812; β 6D -2.301495; β 7D -3.133323; β 8D It is 0.109070; β 9D It is 0.021736; β 10D It is 0.002403; β 11D -0.252896; β 12D -1.135006; β 13D It is 0.194109.

[0209] This application also provides a method for predicting age, comprising: a data acquisition step, which acquires data on the immune cells of a subject, as well as data on the levels of cytokines and / or chemokines; a data processing step, which standardizes the data information acquired in the data acquisition step; and an age calculation step, which calculates the age (Y) of the subject based on the processed data information in the data processing step. The subject is a rat.

[0210] In some embodiments, the method further includes processing missing values ​​in the standardized data during the data processing step. Specifically, missing values ​​in the standardized data are processed using a multiple interpolation method, such as a chain equation multiple interpolation method.

[0211] As described above, the specific details of the steps performed in the method of this application, including the acquisition and processing of data on the subject's immune cells and the levels of cytokines and / or chemokines, can all refer to the steps performed in each module of the model involved in this application.

[0212] This application establishes a mathematical model for predicting rat age, which considers data on immune cells, or data on immune cells along with data on cytokine and / or chemokine levels. All models in this application are effective in predicting rat age. The model that integrates data on immune cells along with data on cytokine and / or chemokine levels demonstrates superior accuracy and reliability due to the inclusion of more biomarker information.

[0213] Example

[0214] The following description, in conjunction with specific embodiments, illustrates the content of this application, but the scope of this application is not limited thereto. Unless otherwise specified, the reagents and instruments used in the following embodiments are all conventional reagents and instruments in the art and can be obtained commercially. The methods used are all conventional experimental methods, and those skilled in the art can undoubtedly implement the described schemes and obtain corresponding results based on the embodiments.

[0215] Main instruments and reagents

[0216] Low-temperature high-speed centrifuge (Heraeus, Germany), Luminex 200 system (Thermo Fisher, USA), flow cytometer (Beckman Coulter, Gallios, USA); erythrocyte lysis buffer (RT122, Beijing Tiangen Biotech Co., Ltd.), eBioscience TM Flow cytometry intracellular fixation and permeabilization buffer (88-8824-00, Thermo Fisher, USA), eBioscience TM Foxp3 / transcription factor flow cytometry immobilization and perforation buffer (00-5523-00, Thermo Fisher, USA), UltraComp eBeads Plus compensating microspheres (01-3333-41, Thermo Fisher, USA), fetal bovine serum FBS (Beijing TransGen Biotech Co., Ltd.).

[0217] Animal selection

[0218] In this study, 119 SPF-grade SD rats were used, divided into four batches: the first batch consisted of 10 rats at 1 month old, 20 rats at 3 months old (half male and half female), and 10 female rats at 9 months old; the second batch consisted of 10 rats at 1 month old, 20 rats at 6 months old (half male and half female); the third batch consisted of 14 rats at 1 month old, 20 rats at 12 months old (half male and half female); and the fourth batch consisted of 5 male rats at 1 month old and 10 male rats at 9 months old. All animals were provided by the Department of Animal Science, Peking University School of Medicine. The animals were housed in a barrier environment, provided with SPF-grade rat and mouse maintenance feed, and had free access to food and water. The barrier environment temperature was (23±2)℃, and the relative humidity was (55±15)%, with a light-dark cycle every 12 hours. The animal experiments complied with the relevant regulations of the Laboratory Animal Welfare Ethics Branch of the Biomedical Ethics Committee of Peking University.

[0219] The specific steps of some of the experimental operations involved in the embodiment are as follows:

[0220] Immune cell typing

[0221] The animal samples selected for immune cell typing were as follows: the first batch consisted of 10 animals aged 1 month, 20 animals aged 3 months (half male and half female), and 10 females aged 9 months; the second batch consisted of 10 animals aged 1 month, 20 animals aged 6 months (half male and half female); the third batch consisted of 10 animals aged 1 month, 20 animals aged 12 months (half male and half female); and the fourth batch consisted of 5 males aged 1 month and 10 males aged 9 months, for a total of 115 animals.

[0222] All animals were allowed to grow naturally to the required age for the experiment. After isoflurane anesthesia, whole blood was collected via the abdominal aorta for later use. The animals were then euthanized by cervical dislocation. The spleen, thymus, and mesenteric lymph nodes of the rats were aseptically collected and immediately placed in Hanks' solution. The spleen, thymus, and mesenteric lymph nodes were separately ground into single-cell suspensions. Peripheral blood and spleen cells were subjected to erythrocyte lysis. The cells were washed with PBS, centrifuged, the supernatant discarded, and the cells resuspended. Flow cytometry was used to count the cells. Based on the obtained cell concentration, the sample cell count was adjusted to 1 × 10⁻⁶. 6 Prepare a homogeneous single-cell suspension by adding 100 μL of PBS containing 2% FBS to each tube for later use.

[0223] Add the corresponding surface molecular flow cytometry antibody (as shown in Table 1 below) to the single-cell suspension, mix well, and incubate at 4°C in the dark for 30 minutes. After incubation, wash the cells with PBS, centrifuge and discard the supernatant. If intracellular and nuclear staining is not required, add 200 μL of PBS containing 2% FBS to resuspend the cells, filter through a 300-mesh nylon sieve, and place them in a Gallios analytical flow cytometer for detection.

[0224] If intracellular and nuclear staining is required, after completing surface molecular staining and incubation, wash cells with PBS, centrifuge and discard the supernatant. Add 100 μL of intracellular flow cytometry fixation buffer and 100 μL of Loxp3 / transcription factor flow cytometry fixation buffer to each sample, mix well, and incubate at room temperature in the dark for 90 minutes. After incubation, add 1 mL of flow cytometry permeabilization buffer, centrifuge at room temperature and discard the supernatant. Resuspend the precipitate and add the corresponding intracellular and nuclear molecular flow cytometry antibodies (as shown in Table 1 below). Mix well and incubate at room temperature in the dark for 30 minutes. After incubation, add another 1 mL of flow cytometry permeabilization buffer, centrifuge at room temperature and discard the supernatant. Resuspend the precipitate in 200 μL of PBS containing 2% FBS, filter through a 300-mesh nylon sieve, and place in a Gallios analytical flow cytometer for detection.

[0225] Before performing immunocytotyping, tissue and whole blood from 1-month-old male rats were collected according to the above method to prepare single-cell suspensions. Antibody single-label staining was performed on each fluorescence channel. The corresponding voltage and compensation of the Gallios analytical flow cytometer were adjusted in advance. The specific fluorescence channels and protocols are shown in Table 1 below.

[0226] Table 1. Flow cytometry antibodies used in this embodiment.

[0227]

[0228]

[0229] Different immune cells and antigen molecules were selected: T cells (CD45) + CD3 + ), Tc cells (CD45) + CD3 + CD4 - CD8 + ), DP cells (CD45) + CD3 + CD4 + CD8 + ), Th cells (CD45) + CD3 + CD4 + CD8 - ), Th1 cells (CD45) + CD3 + CD4 + CD8 - IFN-γ + ), Th2 cells (CD45) + CD3 + CD4 + CD8 - IL-4 + ), Th17 cells (CD45) + CD3 + CD4 + CD8 - IL-17 + ), Treg cells (CD45) + CD3 + CD4 + CD8 - CD25 + FoxP3 + ), B cells (CD45) + CD3 - CD45RA + NK cells (CD45) + CD3 - CD161 + ), dendritic cells (CD45) + CD3 - CD103 + ), macrophages (CD45) + CD3 - CD68 + ), activated T cells (CD45) + CD3 +CD71 + After typing the immune cells, the resulting cell proportions are used to calculate the number of each type of immune cell step by step using the following formula: N A =P A ×P s ×N s , where N A To calculate the number of T cells, B cells, NK cells, dendritic cells, and macrophages, P A P represents the proportions of T cells, B cells, NK cells, dendritic cells, and macrophages obtained after immune cell typing. s CD45 in different tissues or peripheral blood + Cell percentage, N s The total number of cells in different tissues or peripheral blood; N B =P B ×N A , where N B To calculate the number of Tc cells, Th cells, activated T cells, and DP cells, P B The proportions of Tc cells, Th cells, activated T cells, and DP cells obtained after immunophenotyping, N A To calculate the number of T cells in different tissues or peripheral blood; N C =P C ×N B , where N C To calculate the number of Th1 cells, Th2 cells, Th17 cells, and Treg cells, P C The proportions of Th1 cells, Th2 cells, Th17 cells, and Treg cells after immunophenotyping, N B This is to calculate the number of Th cells in different tissues or peripheral blood.

[0230] Cytokine and chemokine detection

[0231] Whole blood was collected from rats and centrifuged at 3000 rpm for 10 minutes to collect serum. A total of 103 serum samples were obtained, specifically: 40 samples from the first batch; 16 samples from the second batch, consisting of 6 one-month-old rats (half male and half female) and 10 six-month-old males; 32 samples from the third batch, consisting of 20 twelve-month-old rats (half male and half female) and 7 one-month-old males and 5 one-month-old females; and 15 samples from the fourth batch, consisting of 5 one-month-old males and 10 nine-month-old males. The obtained serum samples were frozen at -80℃ for later use.

[0232] The ProcartaPlex Multiplex Rat Cytokine and Chemokine Assay Kit (EPX220-30122-901, Leitz Biotechnology Co., Ltd.) was used for high-throughput liquid-phase protein chip detection on a Luminex 200 instrument. The results were read and analyzed. The specific cytokines and chemokines were: IL-1α, G-CSF, IL-10, IL-17A, IL-1β, IL-6, TNF-α, GM-CSF, IL-4, IFN-γ, IL-2, IL-5, IL-13, IL-12p70, GRO-α, MCP-1, MCP-3, MIP-1α, MIP-2, Rantes, Eotaxin, and IP-10.

[0233] Statistical analysis

[0234] Flow cytometry results were processed using FlowJo v10.8. To eliminate the influence of animal batches, each batch of samples was standardized using the following formula: Where X a-n(adj.) X represents the proportion, number, or cytokine level of immune cells in animals of age n after standardization in batch a. a-n This refers to the raw data on the proportion, number, or cytokines of immune cells in animals of age n in batch a. This refers to the original data on the proportion, number, or cytokines of immune cells in animals aged 1 month in batch a.

[0235] Animal samples from the first, second, and third batches were used as datasets. The proportions and numbers of each immune cell, cytokine levels, and sex were used as independent variables. These variables were fitted with the dependent variable (age in months) using the R v4.4 "tidyverse" package. Statistically significant regression coefficients (P < 0.05) were retained, while those without statistical significance were removed. Missing values ​​were identified in the retained dataset and imputed using IBM SPSS Statistics 27.0.

[0236] The R v4.4 "glmnet" package was used to screen variables for Lasso regression. First, different λ and mean squared error (MSE) were obtained through cross-validation. Based on the recommended number of variables to be retained, two models were built by selecting the minimum MSE and the λ corresponding to the minimum number of variables. The optimal Lasso regression model was selected using the residual histogram and the relationship between residuals and predicted values.

[0237] After the model was built, the fourth batch of animal sample data (5 males aged 1 month and 10 males aged 9 months) was used as the test set. The data was standardized in the same way, the required indicators were selected, and the model was validated outside the model.

[0238] Example 1: Preliminary screening of immune cell markers in SD rats of different ages

[0239] Taking the immunocytometric classification of 1-month-old male SD rats as an example, the flow cytometry gating strategy for mesenteric lymph nodes, spleen, thymus, and peripheral blood is as follows: Figures 1-4 As shown.

[0240] The proportions of major immune cells in the mesenteric lymph nodes, spleen, thymus, and peripheral blood of rats of different ages are shown in Tables 2-5. Based on these proportions and Table 6, the number of each type of immune cell can be calculated. The number (±s) of each type of immune cell in the mesenteric lymph nodes of rats of different ages is approximately 10. 6 ±10 4 ~10 9 ±10 5 The number of various immune cells in the spleen (±s) was approximately 10. 4 ±10 3 ~10 8 ±10 5 The number of various immune cells in the thymus (±s) is approximately 10. 6 ±10 3 ~10 9 ±10 4 The number of various immune cells in peripheral blood (±s) was approximately 10. 2 ±19~10 7 ±10 4 A total of 91 indicators (including gender) were included in the analysis. After standardizing the 100 dataset samples, considering the large number of variables, small sample size, missing values, and the fact that many indicators were not related to age, the univariate linear regression method was first used to analyze the relationship between each indicator and age.

[0241] Table 2. Proportion of major immune cells in mesenteric lymph nodes of rats of different ages.

[0242]

[0243] Table 3. Proportion of major immune cells in the spleen of rats of different ages.

[0244]

[0245] Table 4. Proportion of major immune cells in the thymus of rats of different ages.

[0246]

[0247]

[0248] Table 5. Proportions of major immune cells in peripheral blood of rats of different ages.

[0249]

[0250]

[0251] Table 6. CD45 levels in rats of different ages + Proportion of immune cells and total number of cells

[0252]

[0253]

[0254] Statistically significant regression coefficients were retained for further analysis, while those without statistical significance were removed. The results showed that 56 independent variables were retained out of 91 indicators (as shown in Table 7 below). For example, the proportion of thymic T cells in rats decreased significantly with age (r = -40.861, P = 1.369 × 10⁻⁶). -3 The proportion of Th cells in the spleen was significantly positively correlated with age (r = 14.249, P = 7.608 × 10⁻⁶). -19 The missing values ​​in the retained data are supplemented using multiple imputation, and the final immune cell dataset (referred to as dataset "I") is obtained.

[0255] Table 7. Immune cell independent variables significantly related to age after screening for the univariate linear regression model.

[0256]

[0257]

[0258]

[0259] Example 2: Lasso age prediction model built based on rat immune cell dataset

[0260] Lasso regression was performed using dataset "I", and different values ​​of λ and mean squared error (MSE) were obtained through cross-validation. The results are as follows: Figure 5A As shown in Table 8, the model was first constructed using λ (0.1883694) corresponding to the minimum MSE, retaining 19 variables. The resulting model is shown in Table 8 below, with the coefficient of determination (R²)... 2 The value is 0.9332166.

[0261] Table 8. Immune cell independent variables included in the Lasso age prediction model constructed using minimal MSE.

[0262]

[0263]

[0264] The model was examined, and the residual histogram is shown below. Figure 5B As shown, the residuals exhibit good normality; the relationship between residuals and predicted values ​​is shown in the graph. Figure 5C As shown, the homogeneity of variance is good. The model fitting equation is: Y (rat age in months) = 0.019020X1 (proportion of B cells in mesenteric lymph nodes) - 2.619323X2 (proportion of T cells in mesenteric lymph nodes) + 3.039118X3 (proportion of Th cells in spleen) + 2.023731X4 (proportion of Treg cells in spleen) + 0.923716X5 (proportion of dendritic cells in spleen) + 0.103778X6 (number of macrophages in spleen) + 0.060099X7 (proportion of macrophages in spleen) - 0.855700X8 (proportion of DP cells in spleen) - 2.819561X9 (proportion of Tc cells in spleen) - 1.525000X 10 (Percentage of B cells in the spleen) +0.256859X 11 (Proportion of thymic dendritic cells) +0.033942X 12 (Proportion of thymic Tc cells) +0.060363X 13 (Ratio of thymic NK cells) +0.006016X 14 (Ratio of thymic B cells) +0.015491X 15 (Ratio of thymic macrophages) -0.067287X 16 (Number of activated thymic T cells) +0.075876X 17 (Peripheral blood T cell count) -0.768056X 18 (Percentage of activated T cells in peripheral blood) -3.067211X 19 (Percentage of Th cells in peripheral blood) +6.461831.

[0265] Secondly, we attempted to build a model using λ (0.4775851) corresponding to the minimum number of variables, retaining 14 variables. The constructed model is shown in Table 9 below. R 2 It is 0.890923.

[0266] Table 9 shows the immune cell independent variables included in the Lasso prediction model constructed with the minimum number of variables.

[0267]

[0268]

[0269] The model was examined, and the residual histogram is shown below. Figure 6A As shown, the normality is slightly poor, and the residuals are significantly skewed; the relationship between residuals and predicted values ​​is shown in the graph below. Figure 6BAs shown, the homogeneity of variance is good, but compared with the MSE model, the residuals show an increasing trend with the increase of predicted values, indicating insufficient linear fitting. The model fitting equation is: Y (rat age in months) = -1.504289X1 (proportion of T cells in mesenteric lymph nodes) + 3.332314X2 (proportion of Th cells in spleen) + 2.121060X3 (proportion of Treg cells in spleen) + 0.709868X4 (proportion of dendritic cells in spleen) - 0.265396X5 (proportion of DP cells in spleen) - 1.276704X6 (proportion of B cells in spleen) - 3.014965X7 (proportion of Tc cells in spleen) + 0.104950X8 (proportion of dendritic cells in thymus) - 0.114899X9 (number of DP cells in thymus) + 0.035210X 10 (Ratio of thymic NK cells) +0.005376X 11 (Proportion of thymic macrophages) +0.006059X 12 (Ratio of thymic B cells) -0.817150X 13 (Percentage of activated T cells in peripheral blood) -1.085096X 14 (Percentage of Th cells in peripheral blood) +3.618588.

[0270] In summary, the above results preliminarily suggest that the Lasso age prediction model constructed using dataset "I" and the minimum MSE model is relatively good.

[0271] Example 3: Constructing a Lasso age prediction model based on serum cytokine and chemokine indices from SD rats of different ages.

[0272] The serum cytokine and chemokine levels of rats at different ages are shown in Table 10 below. A total of 23 indicators (including sex) were included in the analysis. After standardizing the data of 88 samples, the relationship between each indicator and age was first analyzed using the univariate linear regression method.

[0273] Table 10 Serum cytokine and chemokine levels (pg / mL) in rats of different ages

[0274]

[0275]

[0276]

[0277] Note: "—" indicates that the overall level of this group of samples is below the detection limit.

[0278] The results showed that 14 of the 23 indicators were retained (as shown in Table 11 below), indicating a significant correlation with age. For example, in the serum of rats aged 1-12 months, the levels of GM-CSF, IP-10, GRO-α, and IL-1α all increased with age (P<0.05). Missing values ​​in the retained data were imputed using multiple imputation to obtain the cytokine and chemokine dataset (referred to as dataset "F").

[0279] Table 11. Cytokines and chemokines significantly related to age after screening using the univariate linear regression model.

[0280]

[0281]

[0282] Lasso regression was performed using the cleaned dataset "F", and different λ and MSE were obtained through cross-validation. The results are as follows. Figure 7A As shown. First, we try to build a model using λ (0.08692318) corresponding to the minimum MSE, retaining 8 independent variables, R. 2 The value is 0.6263407. The model is examined, and the residual histogram is shown below. Figure 7B As shown, the residuals are skewed; the relationship between residuals and predicted values ​​is shown in the graph below. Figure 7C As shown, the homogeneity of variance is poor. Next, we tried building a model using λ (0.6132259) corresponding to the minimum number of variables, retaining 7 independent variables. R0 2 The value is 0.5579011. The model is examined, and the residual histogram is shown below. Figure 7D As shown, the residuals also exhibit a significant skewness; the relationship between residuals and predicted values ​​is shown in the graph below. Figure 7E As shown, the model does not fit linearly well.

[0283] The independent variables of the models constructed in the two ways are shown in Table 12 below. The results indicate that the Lasso regression model constructed using the dataset "F" is not ideal for predicting age.

[0284] Table 12 lists the cytokine and chemokine independent variables included in the Lasso age prediction model constructed using the minimum MSE and the fewest number of variables.

[0285] GRO-α 1.708924 GRO-α 1.227652 MCP-1 -1.630539 MCP-1 -0.552664 IP-10 0.857078 IP-10 0.560906 IL-1α 0.569976 IL-1α 0.559953 Rantes -0.254783 IL-5 0.113249 IL-5 0.216515 IL-2 0.049387 IL-2 0.057930 IL-13 0.005803 IL-13 0.017564 Intercept (b) 1.770956 Intercept (b) 1.231875

[0286] Example 4: Lasso age prediction model constructed based on rat immune cell-cytokine shared dataset

[0287] Datasets "I" and "F" were merged according to animal ID to generate a rat immune cell-cytokine shared dataset (i.e., dataset "IF"), covering 80 samples and 70 independent variables. Different λ and MSE values ​​were obtained through cross-validation, and the results are as follows: Figure 8A As shown.

[0288] First, we tried to build a model using λ (0.1107523) corresponding to the minimum MSE, retaining 22 variables. The constructed model is shown in Table 13 below. R 2 It is 0.9565656.

[0289] Table 13 shows the immune cells and cytokines independent variables included in the Lasso age prediction model constructed using the minimum MSE.

[0290]

[0291] The model was examined, and the residual histogram is shown below. Figure 8B As shown, the residuals exhibit good normality; the relationship between residuals and predicted values ​​is shown in the graph. Figure 8C As shown, the variance homogeneity is good. The model fitting equation is: Y (rat age in months) = 0.341801X1 (proportion of B cells in mesenteric lymph nodes) + 0.044379X2 (number of macrophages in mesenteric lymph nodes) - 0.116718X3 (proportion of Th1 cells in mesenteric lymph nodes) - 2.482550X4 (proportion of T cells in mesenteric lymph nodes) + 1.753708X5 (proportion of Treg cells in spleen) + 0.194184X6 (proportion of dendritic cells in spleen) + 0.153549X7 (number of dendritic cells in spleen) + 3.813789X8 (proportion of Th cells in spleen) - 0.809406X9 (proportion of DP cells in spleen) - 3.068760X 10 (Percentage of Tc cells in spleen) -2.363371X 11 (Percentage of B cells in the spleen) +0.391829X 12 (Ratio of thymic dendritic cells) -0.100740X 13 (Number of activated thymic T cells) +0.096423X 14 (Ratio of thymic NK cells) -0.422425X 15 (Percentage of peripheral blood Tc cells) -4.236476X 16 (Percentage of Th cells in peripheral blood) -0.211944X 17 (Percentage of activated T cells in peripheral blood) +0.026766X 18 (Peripheral blood T cell count) +0.318171X 19 (serum IL-1α level / pg·mL) -1 )+0.018954X20 (serum G-CSF level / pg·mL) -1 +0.006290X 21 (serum IL-13 level / pg·mL) -1 +0.002773X 22 (serum TNF-α level / pg·mL) -1 )+7.932772.

[0292] Next, we tried to build a model using λ (0.4907017) corresponding to the minimum number of variables, retaining 13 variables. The model is shown in Table 14 below. R 2 It is 0.9033772.

[0293] Table 14 lists the independent variables of immune cells and cytokines included in the Lasso prediction model constructed with the fewest possible variables.

[0294]

[0295] The model was examined, and the residual histogram is shown below. Figure 9A As shown, the residuals exhibit good normality; the relationship between the residuals and the predicted values ​​is as follows: Figure 9B As shown, the linear fit is poor. The model fitting equation is: Y (rat age in months) = -1.005501X1 (proportion of T cells in mesenteric lymph nodes) + 5.008543X2 (proportion of Th cells in spleen) + 2.177282X3 (proportion of Treg cells in spleen) + 0.313109X4 (proportion of dendritic cells in spleen) - 0.257812X5 (proportion of DP cells in spleen) - 2.301495X6 (proportion of B cells in spleen) - 3.133323X7 (proportion of Tc cells in spleen) + 0.109070X8 (proportion of dendritic cells in thymus) + 0.021736X9 (proportion of NK cells in thymus) + 0.002403X 10 (Ratio of thymic B cells) -0.252896X 11 (Percentage of activated T cells in peripheral blood) -1.135006X 12 (Percentage of Th cells in peripheral blood) +0.194109X 13 (serum IL-1α level / pg·mL) -1 )+2.248733.

[0296] In summary, the results indicate that the Lasso model built on the dataset "IF" and the minimum MSE model performs better than the Lasso prediction model built using dataset "I" or dataset "F" alone.

[0297] Example 6: External Validation of the Rat Lasso Age Prediction Model

[0298] The immunophenotyping and cytokine detection methods for the rats in the test set were the same as those for the dataset. First, the test set data was filtered for the required immune cell indicators according to the prediction model constructed based on dataset "I" (i.e., Table 8). The filtering results are shown in Table 15 below. The same standardization and missing value imputation were performed, and the indicators were substituted into the model one by one to obtain the predicted age (Age_pre) for the animal. The out-of-model validation results for the immune cell construction are as follows: Figure 10A As shown, R 2 The value is 0.87364, and the residual normal probability plot shows that it follows a normal distribution.

[0299] Table 15 shows the required metrics for the prediction model built according to dataset "I" in the test set.

[0300]

[0301]

[0302] Furthermore, the test set data was filtered according to the prediction model constructed based on the dataset "IF" (i.e., Table 13), and the results after filtering are shown in Table 16 below. The data processing method is the same as above. External validation results are as follows. Figure 10B As shown, R 2 The value is 0.88682 (slightly higher than the prediction model for database "I"), and the residual normal probability plot also shows that it follows a normal distribution.

[0303] Table 16 shows the required proportion and number of immune cells and cytokine levels (pg / mL) selected by the prediction model constructed according to the dataset "IF" for the test set.

[0304]

[0305]

[0306]

[0307] Note: "—" indicates that the sample is below the detection limit.

[0308] Therefore, the external validation results show that the model built on dataset "IF" performs better than the model built on dataset "I".

[0309] In summary, the Lasso optimal prediction model constructed in this application, which utilizes 18 immune cell variables from the mesenteric lymph nodes, spleen, thymus, and peripheral blood of SD rats in conjunction with 4 serum cytokine variables, can accurately predict the actual age of SD rats. The model fitting equation is: Y (rat age in months) = 0.341801X1 (proportion of B cells in mesenteric lymph nodes) + 0.044379X2 (number of macrophages in mesenteric lymph nodes) - 0.116718X3 (proportion of Th1 cells in mesenteric lymph nodes) - 2.482550X4 (proportion of T cells in mesenteric lymph nodes) + 1.753708X5 (proportion of Treg cells in spleen) + 0.194184X6 (proportion of dendritic cells in spleen) + 0.153549X7 (number of dendritic cells in spleen) + 3.813789X8 (proportion of Th cells in spleen) - 0.809406X9 (proportion of DP cells in spleen) - 3.068760X 10 (Percentage of Tc cells in spleen) -2.363371X 11 (Percentage of B cells in the spleen) +0.391829X 12 (Ratio of thymic dendritic cells) -0.100740X 13 (Number of activated thymic T cells) +0.096423X 14 (Ratio of thymic NK cells) -0.422425X 15 (Percentage of peripheral blood Tc cells) -4.236476X 16 (Percentage of Th cells in peripheral blood) -0.211944X 17 (Percentage of activated T cells in peripheral blood) +0.026766X 18 (Peripheral blood T cell count) +0.318171X 19 (serum IL-1α level / pg·mL) -1 )+0.018954X 20 (serum G-CSF level / pg·mL) -1 +0.006290X 21 (serum IL-13 level / pg·mL) -1 +0.002773X 22 (serum TNF-α level / pg·mL) -1 )+7.932772.

[0310] The above description is merely a preferred embodiment of this application and is not intended to limit the application in any other way. Any person skilled in the art may make changes or modifications to the disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the protection scope of this application.

Claims

1. A model for predicting age, comprising: The data acquisition module is used to acquire data on the subject's immune cells; A data processing module is used to perform data standardization processing on the data information acquired by the data acquisition module. as well as An age calculation module is used to calculate the age (Y) of the subject by processing the data information processed in the data processing module. The subjects were rats; In the data acquisition module, the collected immune cell data refers to the proportion and number of different types of immune cells from mesenteric lymph nodes, spleen, thymus, and peripheral blood detected on any day after the subject's birth. The data acquisition module is also used to acquire data on the cytokine levels of the subject. In the data acquisition module, the collected cytokine level data refers to the concentration of cytokines in the serum of the subject on any day after birth. The age calculation module contains a pre-stored formula for calculating age (Y) based on data from the subjects' immune cells and cytokine levels in an existing database. This formula is as follows: Formula 3: jC X jC + b3 (Formula 3) Where Y is the calculated age of the subject. β jC For unitless parameters, b3 is a constant, and X 1C The proportion of B cells in mesenteric lymph nodes, X 2C X represents the number of mesenteric lymph node macrophages. 3C The proportion of Th1 cells in mesenteric lymph nodes, X 4C The proportion of T cells in mesenteric lymph nodes, X 5C The proportion of Treg cells in the spleen, X 6C The proportion of dendritic cells in the spleen, X 7C X represents the number of dendritic cells in the spleen. 8C The proportion of Th cells in the spleen, X 9C The proportion of DP cells in the spleen, X 10C The proportion of Tc cells in the spleen, X 11C The proportion of spleen B cells, X 12C The proportion of thymic dendritic cells, X 13C X represents the number of activated T cells in the thymus. 14C The proportion of thymic NK cells, X 15C The proportion of peripheral blood Tc cells, X 16C The proportion of Th cells in peripheral blood, X 17C The proportion of activated T cells in peripheral blood, X 18C The number of peripheral blood T cells, X 19C The serum IL-1α level, X 20C For serum G-CSF levels, X 21C The serum IL-13 level, X 22C This represents the serum TNF-α level.

2. The model according to claim 1, wherein: b3 is selected from any value between 7.832772 and 8.032772; β 1C Any value selected from 0.241801 to 0.441801; β 2C Select any value from -0.055621 to 0.144379; β 3C Select any value from -0.216718 to -0.016718; β 4C Select any value from -2.582550 to -2.382550; β 5C Any value selected from 1.653708 to 1.853708; β 6C Any value selected from 0.094184 to 0.294184; β 7C Select any value from 0.053549 to 0.253549; β 8C Any value selected from 3.713789 to 3.913789; β 9C Select any value from -0.909406 to -0.709406; β 10C Any value selected from -3.168760 to -2.968760; β 11C Any value selected from -2.463371 to -2.263371; β 12C Any value selected from 0.291829 to 0.491829; β 13C Select any value from -0.200740 to -0.000740; β 14C Select any value from -0.003577 to 0.196423; β 15C Select any value from -0.522425 to -0.322425; β 16C Select any value from -4.336476 to -4.136476; β 17C Select any value from -0.311944 to -0.111944; β 18C Select any value from -0.073234 to 0.126766; β 19C Any value selected from 0.218171 to 0.418171; β 20C Select any value from -0.081046 to 0.118954; β 21C Select any value from -0.093710 to 0.106290; β 22C Select any value from -0.097227 to 0.102773.

3. The model according to claim 2, wherein: b3 is 7.932772; β 1C It is 0.341801; β 2C It is 0.044379; β 3C It is -0.116718; β 4C It is -2.482550; β 5C It is 1.753708; β 6C It is 0.194184; β 7C It is 0.153549; β 8C It is 3.813789; β 9C It is -0.809406; β 10C The value is -3.068760; β 11C The value is -2.363371; β 12C It is 0.391829; β 13C It is -0.100740; β 14C It is 0.096423; β 15C It is -0.422425; β 16C The value is -4.236476; β 17C It is -0.211944; β 18C It is 0.026766; β 19C It is 0.318171; β 20C It is 0.018954; β 21C It is 0.006290; β 22C It is 0.002773.

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