Peripheral blood markers for detecting severity of covid-19 acute kidney injury and application thereof

Abstract

The application belongs to the technical field of biotechnology, and particularly relates to peripheral blood markers for detecting severity of acute kidney injury of COVID-19 and application. The peripheral blood markers are any one or more selected from a first expression combination consisting of CD45RA, CD4 and CCR7, a second expression combination consisting of CD45RA, CD8 and CCR7, and a third expression combination consisting of CD3 and TIM3. By detecting the expression levels of the peripheral blood markers, i.e. the marker combinations, the functional state of T lymphocytes of a subject and the correlation between the functional state and the severity of kidney injury can be comprehensively evaluated, and real-time monitoring and risk assessment of dynamic changes of immune function can be realized. The application provides a non-invasive and efficient immune evaluation tool for clinical use, which is helpful for early intervention to reduce the risk of deterioration of kidney function, and has important clinical application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biotechnology, and particularly relates to a peripheral blood marker for detecting the severity of acute kidney injury caused by new coronavirus and application thereof. BACKGROUND

[0002] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to severe respiratory symptoms and multiple organ dysfunction. Although the clinical application of antiviral drugs and the widespread vaccination of viral vaccines can effectively control the infection rate and the severity of the disease, the mechanism of multiple organ injury in some severe infection patients is still unclear, and there is a lack of effective prevention and treatment strategies in clinic, especially a lack of test techniques that can easily be popularized in clinic and can early predict the severity of the disease. Acute kidney injury (AKI) is a common complication of severe SARS-CoV-2 infection. Severe SARS-CoV-2 patients can have different degrees of renal function impairment from simple proteinuria to acute kidney injury (AKI), and this injury is closely related to hospital mortality. Therefore, it is crucial to master the prognostic markers of acute kidney injury caused by SARS-CoV-2 and to carry out individualized treatment for patients.

[0003] In severe SARS-CoV-2 infection patients, the currently commonly used diagnostic and evaluation methods for acute kidney injury (AKI) caused by them still mainly rely on traditional blood and urine biochemical detection items (such as interleukin-6, serum creatinine, blood urea nitrogen, immunoglobulin, C-reactive protein, urine examination, etc.), which mainly focus on the monitoring of infection indicators and renal function indicators. Although they have good specificity and sensitivity for disease diagnosis, in the case of immune imbalance and inflammatory injury mode, the traditional methods cannot clearly determine the immune inflammatory state in the body, and there is a general lack of high sensitivity and specificity in the severity and progression risk of the disease. T lymphocytes are the most important immune effector cells for regulating immune homeostasis in the human body, and can be divided into various subtypes according to their surface markers and functions, and play different biological roles. Lymphopenia is not uncommon in severe SARS-CoV-2 infection patients in clinic, which is accompanied by changes in T cell subsets, manifested as an increase in the proportion of cytotoxic T helper (Th) cells (CD4 + T cells) responding to SARS-CoV-2 and a decrease in the proportion of regulatory T cells (Treg). Previous studies have confirmed that CD8 + T cells in severe SARS-CoV-2 infection patients have overexpression of TIM-3, and NK cells have overexpression of PD-1, while these markers are decreased in the recovery period. +T cell population recovered synchronously. These all indicate that T cell subpopulation changes are significant in the course of severe COVID-19 infection, but there is still a lack of understanding of its role in acute kidney injury (AKI) caused by severe COVID-19 infection. On this basis, flow cytometry is a new type of examination technology that has been promoted in the medical field in recent years. Flow cytometry can screen, isolate, and detect the number and proportion of T lymphocytes in patients, but the existing flow cytometry detection scheme has many deficiencies that need to be improved in the diagnosis and treatment of acute kidney injury (AKI). For example, the existing commonly used detection indicators are limited to the surface markers of T lymphocytes (CD3, CD4, CD8), which can only indicate the changes in the total number or proportion of T cells, and cannot accurately reflect the changes in the biological function of various T cell subpopulations and their chronic activation state, making it difficult to guide clinicians to make timely and effective judgments about the severity of acute kidney injury (AKI) and treatment effectiveness.

[0004] Multi-parameter flow cytometry can detect the expression level of target proteins by detecting the fluorescence intensity generated by cells according to the binding of fluorescently labeled antibodies to target proteins expressed by T cells. Then, according to the different expression levels of target proteins in different cells, a large class of T cells can be subdivided into multiple T cell subpopulations with different functions. Multi-parameter flow cytometry can detect multiple surface markers of cells at once, thereby systematically evaluating the number, proportion, and biological function of T cells, and making systematic judgments about the patient's disease state, infection risk, and drug efficacy. However, due to the large number of T cell surface proteins (markers) and the significant differences in cell biological function reflected by the different spatiotemporal combinations of protein expression, especially during disease progression and drug treatment, the combination changes form a geometric progression. Finding clinically significant expression combinations and rapidly and accurately analyzing the relationship between T cell functional activity and the activity and outcome of acute kidney injury caused by severe COVID-19 infection remains a clinical challenge that has not been solved.

[0005] The most widely used flow cytometry detection index in clinical application is limited to T cell surface markers CD3, CD4, CD8, etc. These detections can only reflect the changes in the number or proportion of total T cells, and cannot reflect the functional changes of different subgroups. Therefore, the current routine technology cannot quickly and effectively evaluate the relationship between the changes in the chronic pathological state of T cells and diseases. Secondly, in basic scientific research, cytokines are used to indirectly detect T cell function. Since T cells express a variety of cytokines, including the interleukin family and the interferon family, and a certain factor has expression in different subtypes of T cells, the biological function thereof has overlapping and multidirectional nature, the analysis method is complex and the results are difficult to interpret, which is not conducive to the promotion of clinical primary hospitals. Finally, the simultaneous determination of multiple cytokines inevitably has the influence of fluorescence interference between different channels and non-specific binding between various antibodies, which has the disadvantages of complex process, long time consumption, high cost, etc. A large amount of preliminary research is needed to optimize and improve the technology, which is the main technical problem in the clinical transformation of the current T cell chronic function evaluation method.

[0006] Therefore, there is a need in the art for a new peripheral blood marker for detecting the severity of COVID-19 acute kidney injury and applications thereof. SUMMARY

[0007] The present application provides a peripheral blood marker for detecting the severity of COVID-19 acute kidney injury, which is any one or more selected from the first expression combination consisting of CD45RA, CD4 and CCR7, the second expression combination consisting of CD45RA, CD8 and CCR7, and the third expression combination consisting of CD3 and TIM3.

[0008] In a specific embodiment, the peripheral blood marker is a fourth expression combination comprising CD45RA, CD4, CD8, CCR7, CD3 and TIM3.

[0009] The present application also provides an application of the peripheral blood marker as described above in the preparation of a product for predicting COVID-19 infection-induced acute kidney injury.

[0010] The present application also provides an application of a peripheral blood marker for detecting the severity of COVID-19 acute kidney injury, which comprises one or more of the first to fourth indicators, wherein the first indicator is CD4 + The number of Tem, i.e. the number of Tem cells when CD4 is positive, i.e. the number of cells positive for CD4 and negative for CD45RA and CCR7; the second indicator is CD8 + The number of Tem, i.e. the number of Tem cells when CD8 is positive, i.e. the number of cells positive for CD8 and negative for CD45RA and CCR7; the third indicator is CD8 +The ratio of Tem / Tcm, i.e. the ratio of the number of Tem cells to the number of Tcm cells when CD8 is positive, i.e. the ratio of the number of cells negative for CD45RA and CCR7 to the number of cells negative for CD45RA and positive for CCR7 when CD8 is positive; the fourth index is CD3 + TIM3 + The number of T cells, i.e. the number of T cells positive for CD3 and TIM3.

[0011] In the present application, when the first index CD4 + When the number of Tem is high, it indicates that the severity of acute kidney injury caused by COVID-19 is low or the T lymphocyte function state of the patient is good, or the prognosis of the patient is good; when the second index CD8 + When the number of Tem is high, it indicates that the severity of acute kidney injury caused by COVID-19 is low or the T lymphocyte function state of the patient is good, or the prognosis of the patient is good; when the third index CD8 + When the ratio of Tem / Tcm data is large, it indicates that the severity of acute kidney injury caused by COVID-19 is low or the T lymphocyte function state of the patient is good, or the prognosis of the patient is good; when the fourth index CD3 + TIM3 + When the number of T cells is high, it indicates that the severity of acute kidney injury caused by COVID-19 is high or the T lymphocyte function state of the patient is poor, or the prognosis of the patient is poor.

[0012] In a specific embodiment, the application comprises jointly detecting the severity of acute kidney injury caused by COVID-19 by combining the first index, the second index and the fourth index.

[0013] The present application can solve the following problems: 1) can efficiently, sensitively and specifically evaluate the severity, aggravation risk, drug efficacy and prognosis of the disease; 2) can systematically evaluate the proportion and functional changes of T cell subsets, and achieve the best potency ratio through optimization combination; 3) quickly realize the precise typing and immune activity detection of T cells, and effectively evaluate the chronic activation state of T cells; 4) simple operation, easy to understand, easy to popularize and apply in primary medical institutions. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a multi-channel flow cytometry peripheral blood T cell subset sorting strategy diagram, including figures A to F.

[0015] Figure 2 It is also a multi-channel flow cytometry peripheral blood T cell subset sorting strategy diagram, including figures A to C.

[0016] Figure 3Figure of the results of comparative analysis of T cell subsets in peripheral blood of 40 patients with severe COVID-19 infection-induced kidney injury and 40 patients with severe COVID-19 infection without kidney injury.

[0017] Figure 4 Figure of verification of the close correlation between changes in peripheral blood T lymphocyte subsets in 60 patients with acute kidney injury caused by novel coronavirus infection and the severity of COVID-19-related AKI.

[0018] Figure 5 Figure of receiver operating characteristic (ROC) analysis of T cell subsets closely related to estimated glomerular filtration rate (eGFR) to evaluate the predictive performance of the four indicators.

[0019] Figure 6 CD3 + TIM3 + T, CD4 + Tem, CD8 + Tem cell number, i.e., the ROC curve of the combination of the fourth indicator and the first and second indicators. DETAILED DESCRIPTION

[0020] Multi-channel flow cytometry detects labeled antibodies according to different single antibody sources, excitation light wavelengths, and fluorescent characteristics, including FITC, PE, PerCP, PE-Cy7, FITC, PE-Cy5, PerCP, PE-Cy7, Alexa Fluor 488, AF647, APC, APC-Cy7, BV421, and BV510. The conventional multi-channel flow cytometer can complete this program. The detection operation steps include: extracting 1-2 ml of peripheral blood, lysing red blood cells, isolating single nucleated cells and preparing a suspension, blocking cell surface Fc receptors (such as performing membrane rupture treatment for cytokine or nuclear transcription factor detection), incubating fluorescent antibodies for 30 minutes, washing free fluorescent antibodies and detecting on the machine, completing fluorescence compensation adjustment, and analyzing the detection results. All detection indicators are strictly set up with blank, Isotype, FMO, negative and positive control groups. The flow cytometry detection, blank control and analysis method are as follows, the specific steps are: extract 5 ml of peripheral blood from patients, use Ficoll density gradient centrifugation method at 2000 r / minute speed for 30 minutes, separate and extract peripheral blood mononuclear cells (PBMC). 70-90% of PBMC are lymphocytes, of which CD3 + T cells account for a large part of lymphocytes (45-70%). By extracting PBMC, CD3 + CD4 + T cells, CD3 + CD8 +T cells were subjected to further analysis, avoiding the influence of other blood components on the test results. Using the conventional clinical flow cytometry detection method, 2-5 x 10 6The cells are resuspended at a concentration of 1 x 106cells / mL, and the primary antibody (0.1-10 μg / mL) is added according to the recommended reference value range in the instructions, and incubated at room temperature or 4°C for 30-60 minutes in the dark. The present embodiment is detected by a 16-channel flow cytometer, and a commercial company can provide a variety of self-labeled primary antibodies or fluorescein secondary antibodies. The principle of antibody selection is that there is one fluorescein in each channel, and the fluorescein is reasonably distributed and selected according to the antigen expression strength and small spectral overlap between channels. The optimized fluorescein antibody collocation scheme can be used. The present scheme screens and verifies different combinations of 106 T cell markers by multi-channel flow cytometry detection technology, which covers cell surface marker proteins, cytokines, nuclear transcription factors and key signal pathway molecules. The test indicators specifically include: Bcl-6, CCR10, CCR3, CCR4, CCR5, CCR6, CCR7, CD127, CD137, CD152, CD154, CD16, CD161, CD21, CD23, CD25, CD27, CD28, CD3, CD38, CD4, CD40L, CD44, CD45, CD45RA, CD45RO, CD56, CD58, CD62L, CD69, CD8, CD80, CD86, CD95, CTLA-4, CXCR3, CXCR5, EOMES, FAS, FOXO1, FOXO3, FOXO4, FOXP1, FOXP3, GATA3, Granzyme, GZMA, HLA-DR, ICOS, IFN-gamma, IgD, IgM, IL-10, IL-12, IL-13, IL-17, IL-18, IL-2, IL-21, IL-22, IL-25, IL-26, IL-4, IL-5, IL-9, IL23R, IL2RA, IL2RB, IRF4, ITGAE, ITGAL, Ki67, KLRB1, LAG3, LEF1, Lymphotoxin, NCAM1, NK1.1, NKG2D, OX40, p-mTOR, PD1, PDL1, PECAM1, Perforin, PRDM1, PS6, PTGDR2, PU.1 / Spi1, RORC, RORγt, SELL, STAT1, STAT4, STAT5, T-bet, TBX21, TCF7, TCR Vα24, TCR Vβ11, TCRγ / δ, TGF-beta, TIM1, TIM3, TNF-alpha, TNF-beta.Further according to the detection data of more than 7000 clinical samples, 10 T cell markers and functional activity protein combinations were screened and verified, and it was first confirmed that the technology has important clinical value in the prognosis evaluation of severe novel coronavirus infection related AKI patients. The related test indexes include: CD45, CD45RA, CD3, CD4, CD8, CCR7, CD25, CD127, TIM3, LAG3 and cell activity protein pS6.

[0021] Figure 1 Lymphocytes in A: Lymphocytes, FSC: Forward scatter light, SSC: Side scatter light, FSC in the horizontal axis of the figure represents cell size, and SSC in the vertical axis represents cell granularity; FSC and SSC are used in combination to classify cell populations by drawing a scatter plot (FSC-SSC plot).

[0022] Figure 1 CD3 in B: CD3, CD45 in the vertical axis: CD45, the cells in the box represent T cells that are positive for both CD45 and CD3.

[0023] Figure 1 C comes from Figure 1 The cells in the box in B, the cells shown in the lower right corner of the figure are T cells that are positive for CD45, CD3 and TIM3. Inhibitory T-cell subsets: Inhibitory T-cell subsets.

[0024] Figure 1 D comes from Figure 1 The cells in the box in B, the cells shown in the lower right corner of the figure are T cells that are positive for CD45, CD3 and CD25. Regulatory T-cell subsets: Regulatory T-cell subsets.

[0025] Figure 1 E comes from Figure 1 The cells in the box in B, the cells shown in the lower right corner of the figure are T cells that are positive for CD45, CD3 and LAG3.

[0026] Figure 1 F comes from Figure 1 The cells in the box in D, the cells shown in the lower left corner of the figure are T cells that are positive for CD45, CD3 and CD25 and have low expression of CD127.

[0027] Figure 2 A comes from Figure 1 The cells in the box in B, the cells shown in the lower right corner of the figure are T cells that are positive for CD45, CD3 and CD4, and the cells shown in the upper left corner of the figure are T cells that are positive for CD45, CD3 and CD8.

[0028] Figure 2 B is from Figure 2 The cell in the lower right corner of A, i.e. the cell in the figure, is a T cell positive for both CD45 and CD3 and CD4, Figure 2 The cell in the upper left corner of B is a Tcm cell positive for CD45RA and negative for CCR7, Figure 2 The cell in the upper right corner of B is a Tn cell positive for both CD45RA and CCR7, Figure 2 The cell in the lower right corner of B is a Temra cell positive for CD45RA and negative for CCR7, Figure 2 The cell in the lower left corner of B is a Tem cell positive for both CD45RA and CCR7.

[0029] Gated on CD4 + T cells: Gated on CD4 + Gated on CD8

[0030] As known to those skilled in the art, CD45RA is a subtype of CD45. CD45RA is encoded by exon 4 of the CD45 gene, but lacks exons 5 and 6, and has a shorter molecular structure than CD45. Initial T cells (Tn) express CD45RA, while central memory T cells (Tcm) and effector memory T cells (Tem) express CD45RO. Figure 2 C is from Figure 2 The cell in the upper left corner of A, i.e. the cell in the figure, is a T cell positive for both CD45 and CD3 and CD8, Figure 2 The cell in the upper left corner of C is a Q1 cell positive for CD45RA and negative for CCR7, Figure 2 The cell in the upper right corner of C is a Q2 cell positive for both CD45RA and CCR7, Figure 2 The cell in the lower right corner of C is a Q3 cell positive for CD45RA and negative for CCR7, Figure 3 The cell in the lower left corner of C is a Q4 cell positive for both CD45RA and CCR7. Gated on CD8 + T cells: Gated on CD8 + Gated on CD8

[0031] The specific clinical application examples in the present application are listed as follows.

[0032] Example 1

[0033] The proportion and functional changes of peripheral blood T cell subsets of 40 patients with new crown infection and AKI were dynamically monitored for a long time by multi-channel flow cytometry detection technology, and the optimal combination of different indicators was screened and verified.

[0034] The specific combination includes: CD3+ TIM3 + cells (number of cells positive for CD3 and TIM3), CD8 + Tem / Tcm (ratio of Tem to Tcm when CD8 is positive), CD4 + / CD8 + (CD4 is the ratio of the number of cells positive for CD4 to the number of cells positive for CD8), CD4 + Tem cells (number of Tem cells when CD4 is positive, i.e., the number of cells positive for CD4 and negative for both CD45RA and CCR7), CD8 + Tem cells, CD3 + ps6, CD8 + Tcm % (percentage of Tcm cells out of all T cells when CD8 is positive), CD8 + Tem ps6, CD8 + T cells, Treg cells %, CD3 + T cells, CD8 + Tem %, CD3 + LAG3 + cells, CD4 + Tem / Tcm, Treg cells, CD4 + T cells, CD3 + LAG3 + %, NLR, CD4 + %, CD4 + Tem %, CD8 + %, CD4 + Tem ps6, CD8 + Tcm cells, CD4 + Tcm ps6, CD4 + Tcm %, CD8 + Tcm ps6, CD4 + Tcm cells, CD3 + %, CD3 + TIM3 + %. Wherein Tem refers to effector memory T cells (CD45RA - CCR7 - , i.e., negative for both CD45RA and CCR7); Tcm refers to central memory T cells (CD45RA - CCR7 + , i.e., negative for CD45RA and positive for CCR7); Treg refers to regulatory T cells CD3 + CD25+ CD127 low CD3 and CD25 are both positive while CD127 is low expression. In addition, T cells and cells in these codes refer to the number of T cells, because T in Tem and Tcm codes represents T cells, so the letter T in the corresponding combination code can be omitted. Figure 4 The results of the comparative analysis of T cell subsets in the peripheral blood of 40 patients with severe COVID-19-induced kidney injury and 40 patients with severe COVID-19 without kidney injury are shown in the figure. Figure A shows that CD4 + Tem cell count is lower in COVID-19-induced acute kidney injury (P<0.001), Figure B shows CD8 + Tem cell count is lower in COVID-19-induced acute kidney injury (P<0.001), Figure C shows CD8 + Tem / Tcm is lower in COVID-19-induced acute kidney injury (P<0.001), Figure D shows CD3 + TIM3 + T cell count is higher in COVID-19-induced acute kidney injury (P<0.0001).

[0035] Example 2

[0036] To verify our model, we again collected peripheral blood T cell subsets from 60 patients with COVID-19-induced acute kidney injury (AKI) and found that changes in peripheral blood T lymphocyte subsets were closely related to the severity of COVID-19-related AKI. Figure 5 To verify the changes in peripheral blood T lymphocyte subsets in 60 patients with COVID-19-induced acute kidney injury and the close relationship between the severity of COVID-19-related AKI and the changes in peripheral blood T lymphocyte subsets. Figure A shows that CD4 + Tem number (the number of Tem cells when CD4 is positive, i.e., the number of cells that are positive for CD4 and negative for CD45RA and CCR7) is positively correlated with eGFR (r 2 =0.216, p<0.001); Figure B shows CD8 + Tem number (the number of Tem cells when CD8 is positive, i.e., the number of cells that are positive for CD8 and negative for CD45RA and CCR7) is positively correlated with eGFR (r 2 =0.123, p=0.006); Figure C shows CD8 + Tem / Tcm ratio (ratio of Tem to Tcm when CD8 is positive) is positively correlated with eGFR (r 2 =0.184, p<0.001). Figure D shows CD3 + TIM3 +T cell number (number of cells positive for both CD3 and TIM3) was negatively correlated with eGFR (r 2 =0.326, p<0.0001). Wherein eGFR is estimated glomerular filtration rate. The above results show that the peripheral blood CD4 + Tem, CD8 + Tem, CD8 + Tem / Tcm, CD3 + TIM3 + Changes in T lymphocyte subsets are closely related to the severity of acute kidney injury (AKI).

[0037] Example 3

[0038] Figure 5 To perform a receiver operating characteristic (ROC) analysis on T cell subsets closely related to estimated glomerular filtration rate (eGFR) to evaluate the predictive performance of the four indicators. The results are: Figure A shows the AUC of CD4 + Tem number = 0.745 (95% Cl: 0.597-0.866), Figure B shows the AUC of CD8 + Tem number = 0.664 (95% Cl: 0.514-0.806), Figure C shows the AUC of CD8 + Tem / Tcm ratio = 0.786 (95% Cl: 0.649-0.891), and Figure D shows the AUC of CD3 + TIM3 + T cell number = 0.848 (95% Cl: 0.730-0.943). It is suggested that in acute kidney injury (AKI) caused by COVID-19 infection, the above indicators can be used as effective predictors of disease prognosis. In addition, Figure 6 The ROC curve of CD4 + Tem / Tcm ratio is not shown, in fact, its AUC = 0.571 (95% Cl: 0.396-0.725), showing that this indicator cannot be used as an effective predictor of disease prognosis.

[0039] Example 4

[0040] To improve the performance of the prediction model, a multi-index joint prediction strategy was used. After screening, it is suggested that: ​ CD3 + TIM3 + T, CD 4 +Tem, CD8 +The AUC of the combined ROC curve of the number of Tem cells reached 0.925 (95% CI: 0.841-1.00). This indicates that the combined detection of the number of CD3+TIM3+T, CD4+Tem and CD8+Tem cells has significant predictive value for the severity of renal function damage in acute kidney injury (AKI) caused by COVID-19 infection, and has important clinical guiding value.

[0041] In summary, the present application first screened and clinically verified a combination of 10 T cell markers and 1 functional activity protein from 106 T cell markers through multi-channel flow cytometry detection technology. The present application first confirmed that the peripheral blood markers have important clinical application value in the severity of disease, treatment outcome and prognosis evaluation of AKI patients related to novel coronavirus infection, and can effectively fill the existing clinical blank.

[0042] The T cell immunology research background, multi-channel flow cytometry detection scheme, big data model construction and single cell sequencing analysis method involved in the present application can refer to a series of research results published by the inventor's team in the early stage, which will not be repeated here.

[0043] In summary, the present application belongs to the field of biotechnology, and specifically relates to a peripheral blood marker for detecting the severity of COVID-19 acute kidney injury and application. The peripheral blood marker of the present application is any one or more selected from the first expression combination consisting of CD45RA, CD4 and CCR7, the second expression combination consisting of CD45RA, CD8 and CCR7, and the third expression combination consisting of CD3 and TIM3. By detecting the expression level of the above peripheral blood marker, i.e., the marker combination, the functional state of T lymphocytes of the subject and its correlation with the severity of kidney injury can be comprehensively evaluated, and real-time monitoring and risk assessment of dynamic changes of immune function can be realized. The present application provides a non-invasive and efficient immune evaluation tool for clinical use, which helps to intervene early to reduce the risk of worsening kidney function, and has important clinical application value.

[0044] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. The application of a peripheral blood biomarker in the preparation of a product for predicting acute kidney injury caused by SARS-CoV-2 infection, wherein the peripheral blood biomarker is an expression combination of CD45RA, CD4, CD8, CCR7, CD3 and TIM3, and the product includes the combined detection of a first indicator, a second indicator and a fourth indicator to detect the severity of acute kidney injury caused by SARS-CoV-2; the first indicator is the number of cells that are CD4 positive and CD45RA and CCR7 negative; the second indicator is the number of cells that are CD8 positive and CD45RA and CCR7 negative; and the fourth indicator is the number of T cells that are both CD3 and TIM3 positive.

Images