CN114250285B

CN114250285B - Interleukin 37-based respiratory tract virus infection (risk) severe pre-warning

Info

Publication number: CN114250285B
Application number: CN202010994903.1A
Authority: CN
Inventors: 徐建青; 张晓燕; 李昂
Original assignee: SHANGHAI PUBLIC HEALTH CLINICAL CENTER
Current assignee: SHANGHAI PUBLIC HEALTH CLINICAL CENTER
Priority date: 2020-09-21
Filing date: 2020-09-21
Publication date: 2024-10-15
Anticipated expiration: 2040-09-21
Also published as: CN114250285A

Abstract

Provided herein are interleukin 37-based respiratory viral infection (risk) severe pre-warnings. In particular, the application provides the use of a substance that detects interleukin 37 levels in a sample in the preparation of a product (e.g., a kit or detection system) for prognosis evaluation of respiratory viral infection, corresponding products, systems and methods thereof. The application takes the interleukin 37 as a basic evaluation index, obviously improves the accuracy and specificity of severe prediction, can effectively reduce the incidence rate and death rate of severe after the infection of the respiratory viruses, and has important clinical application value and wide application prospect.

Description

Interleukin 37-based respiratory tract virus infection (risk) severe pre-warning

Technical Field

The present application is in the field of biological diagnostics, immunology and medical data analysis. In particular, the application relates to a quantifiable interleukin 37-based model, a method and related products capable of carrying out clinical early warning on (critically) ill patients after respiratory tract virus infection.

Background

Worldwide, acute respiratory diseases account for a large proportion of all acute morbidity and mortality, and the main cause of this is acute viral respiratory infections.

According to the concept proposed by WHO in 2003, a new infection is an infection caused by a new species or species of pathogenic microorganisms and in recent years leading to regional or international public health problems. Viruses are the primary causative agent of the new infectious disease. The pathogen of the new coronavirus disease currently in the global pandemic stage is the new coronavirus which is not found in human before, and no effective prevention and specific treatment method is available for the new coronavirus disease.

The acute respiratory viral infectious diseases have strong transmission capacity and high transmission speed, and are extremely easy to cause fulminant epidemic. Moreover, acute respiratory viral infections can easily develop as severe, such as hypercytokine blood, acute respiratory distress syndrome, etc., with a substantial increase in mortality. The hypercytokine blood disease, also called as 'cytokine storm', is a phenomenon that a plurality of inflammatory cytokines are rapidly and abundantly produced in a short period due to overactivation of an immune system in the process of removing virus infection, is extremely easy to cause infectious shock and multi-organ failure, and is one of main reasons of illness progress and death of patients suffering from acute respiratory tract virus infection. Acute respiratory distress syndrome is usually caused by acute diffuse lung injury, often accompanied by increased pulmonary vascular permeability and decreased air-bearing lung tissue due to pulmonary inflammation.

Acute respiratory viral infections greatly burden the disease, including, for example: epidemiological burden such as high morbidity, high mortality, high morbidity density, high cumulative mortality, and the like; the life health burden of the patient with health loss; and loss of income, loss of production, etc. Therefore, the early warning of whether the respiratory tract virus infection can progress to severe or not can be realized, so that the patient can get early therapeutic intervention, and the method has important significance for reducing the death rate and relieving the disease burden.

At present, although there are many early warning indexes for serious and critical diseases of respiratory virus infection clinically, the judgment standard is fuzzy, the subjectivity is strong, and the disease state of patients reaching the serious early warning indexes is usually not optimistic.

In the new coronavirus pneumonia diagnosis and treatment scheme (7 th edition of trial), the serious and critical clinical early warning index of adults is the progressive decline of peripheral blood lymphocytes; peripheral blood inflammatory factors such as IL-6, C-reactive protein are progressively elevated; progressive elevation of lactic acid and rapid progression of intrapulmonary lesions in the short term. Early warning indexes of children are respiratory rate increase; poor mental reaction and somnolence; progressive elevation of lactic acid and imaging revealed bilateral or multiple lobular infiltration, pleural effusion or short term lesions, etc. It can be seen that similar early warning indexes do not have clear and quantifiable judgment standards, and a more accurate severe early warning method is needed.

In view of the foregoing, there is still a great need in the art to develop early warning indicators, methods and products that can effectively treat respiratory tract viral infections that will develop (critically) severe disease, so that patients can get early therapeutic intervention, reduce mortality, and reduce disease burden.

Disclosure of Invention

The application provides an early warning index, a method and a product of respiratory tract virus infection (risk) severe symptoms based on interleukin 37.

In a first aspect of the application there is provided the use of a substance for detecting interleukin 37 levels in a sample in the preparation of a product (e.g. a kit or detection system) for prognosis evaluation of respiratory viral infection.

In some embodiments, the sample is obtained from a mammalian subject selected from the group consisting of: a mammalian subject suffering from or suspected of suffering from a respiratory viral infection (e.g., a mammalian subject treated or not treated with a respiratory viral infection, such as a node of the earliest time at which the patient was initially admitted without therapeutic intervention), a mammalian subject at risk of a respiratory viral infection, a mammalian subject that had suffered from a respiratory viral infection but had been cured.

In some embodiments, the mammal is selected from the group consisting of: primates, rodents, farm mammals, mammalian pets, and the like, e.g., humans, apes, gorillas, monkeys, cows, sheep, horses, camels, pigs, dogs, cats, rabbits, mice, and the like.

In some embodiments, the sample is one or more samples selected from the group consisting of: tissue, cells, blood (e.g., whole blood, serum, plasma), sputum, nasal swab, pharyngeal swab, alveolar lavage.

In some embodiments, the sample is a fresh sample, a frozen sample, a fixed sample (e.g., formalin fixed sample), an embedded sample (e.g., paraffin embedded sample).

In some embodiments, the level of interleukin 37 is one or more levels selected from the group consisting of: protein level of interleukin 37, mRNA level of interleukin 37,

In some embodiments, the method of detecting interleukin 37 is one or more methods selected from the group consisting of: immunohistochemical methods (such as immunofluorescence assay, ELISA, immune colloidal gold method), western blotting, RNA blotting, RT-PCR, in situ hybridization, biochip method.

In some embodiments, the agent is selected from agents specific for interleukin 37, such as an anti-interleukin 37 antibody or antigen-binding fragment thereof, preferably a monoclonal antibody; interleukin 37 specific probes, gene chips, PCR primers, gRNA, etc.

In some embodiments, the substance carries a detectable label, e.g., the detectable label is selected from the group consisting of: radioisotopes, fluorophores, chemiluminescent moieties, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, or ligands (e.g., biotin or haptens).

In some embodiments, the respiratory virus is a virus capable of transmitting through the respiratory tract, infecting and replicating and proliferating in human upper and lower respiratory epithelial cells, causing acute infection and possibly developing severe.

In some embodiments, the infection is an infection caused by a virus selected from the group consisting of: rhinoviruses, coronaviruses (e.g., SARS-CoV-2, MERS), influenza viruses (e.g., H1N1, H3N2, H7N9, H5N 1), hendra viruses, nipah viruses, adenoviruses, human metapneumoviruses, respiratory syncytial viruses.

In some embodiments, the prognostic evaluation includes one or more selected from the group consisting of: the risk assessment of respiratory viral infection developing severe or critical or even death, the risk assessment of complications occurring, the risk assessment of cytokine storm developing, the assessment of recovery time.

In some embodiments, wherein the severe condition has one or more characteristics selected from the group consisting of: the oxygen saturation in the shortness of breath and resting state is lower than the lower limit value, the arterial blood oxygen partial pressure and the oxygen inhalation concentration are lower than the lower limit value, and the pulmonary imaging shows that the focus obviously progresses by more than 50% within 24-48 hours.

In some embodiments, the criticality has one or more characteristics arising from respiratory viral infection selected from the group consisting of: respiratory failure and requires mechanical ventilation, shock, incorporation of other organ failure, and ICU monitoring therapy.

In some embodiments, the respiratory virus is a novel coronavirus of SARS-CoV-2. The severe symptoms are characterized by one or more of the items listed in the new coronavirus pneumonia diagnosis and treatment protocol (e.g. trial seventh edition), e.g. according to any one of the following:

① Shortness of breath occurs, RR is more than or equal to 30 times/min

② In a resting state, the oxygen saturation is less than or equal to 93 percent

③ Arterial blood oxygen partial pressure (PaO ₂)/oxygen uptake concentration (FiO ₂) less than or equal to 300mmHg (1 mmhg=0.133 kPa)

④ Pulmonary imaging shows significant lesion progression >50% within 24-48 hours.

In some embodiments, the respiratory virus is a novel coronavirus of SARS-CoV-2. The critical symptoms are characterized by (refer to the new coronavirus pneumonia diagnosis and treatment scheme (seventh edition of trial):

One of the following conditions is met:

① Respiratory failure occurs and mechanical ventilation is required

② Shock occurs

③ ICU monitoring therapy is required to incorporate other organ failure.

In some embodiments, the disease is respiratory viral pneumonia. The severity was graded according to severity of the disease as follows:

Light and moderate symptoms:

the general state is better, no high risk factor exists, vital signs are stable, and the organ functions are not obviously abnormal.

(Dangerous) severe cases:

Disturbance of consciousness

Respiratory rate >30 times/min

PaO ₂<60mm Hg、PaO₂/FiO₂ <300, mechanical ventilation therapy

Blood pressure <90/60mm Hg

Chest radiographs show bilateral or multiple lung lobe involvement, or lesions enlarged more than or equal to 50% within 48 hours of admission

Oliguria: urine volume <20ml/h, or <80ml/h, or acute renal failure requires dialysis treatment.

In some embodiments, the product further comprises a substance that detects one or more of the other clinical indicators or essential features for respiratory viral infection detection, such as:

The basic features are selected from: sex, age, underlying disease; and/or

The other clinical index is selected from: clinical test index, biochemical index and/or immune index; for example:

The clinical test index is one or more selected from the group consisting of: white blood cell count, red blood cell count, platelet count, lymphocyte count, monocyte count, eosinophil (base) character, neutrophil count, hypersensitive C-reactive protein (HS-CRP), activated partial prothrombin time, D-dimer, erythrocyte sedimentation rate; in some embodiments is a hypersensitive CRP;

the biochemical index is one or more selected from the following group: blood pH, blood carbon dioxide partial pressure, blood oxygen partial pressure, lactic acid, alanine aminotransferase, aspartic acid aminotransferase, alkaline phosphatase, lactate dehydrogenase, cholinesterase, total bilirubin, albumin, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, urea, creatine, glucose, creatine kinase, uric acid, haptoglobin, serum cystatin C, amylase, troponin, myoglobin;

The immune index is one or more selected from the following group: interleukin 6, interleukin 8, interleukin 1 beta, interleukin 2, interleukin 4, interleukin 5, interleukin 10, interleukin 12p70, interleukin 17, tumor necrosis factor alpha, interferon gamma, CD3 absolute, CD4 absolute, CD8 absolute, NK cell absolute, CD19 absolute, procalcitonin, anti-ribonucleoprotein antibody, anti-histone antibody, anti-nucleosome antibody, immunoglobulin a, immunoglobulin G, immunoglobulin M, complement, rheumatoid factor, thyroxine; in some embodiments are interleukin 6 and interleukin 8.

In some embodiments, the indicator is a combination of interleukin 37 levels with interleukin 6 (IL-6), interleukin 8 (IL-8), and/or hypersensitive C response protein (HS-CRP). In some embodiments, the index is: combination of IL-37 and IL-6; combination of IL-37 with IL-8; combination of IL-37 with HS-CRP; combination of IL-37 with IL-6 and IL-8; combination of IL-37 with IL-6 and HS-CRP; combination of IL-37 with IL-8 and HS-CRP; or IL-37 in combination with IL-6, IL-8 and HS-CRP.

In some embodiments, the prognosis evaluation is performed by quantifying detection indexes in a model and substituting the detection indexes into a model equation to perform probability calculation, so as to realize accurate judgment;

For example, the analytical model includes: general linear model, generalized linear model, log linear model, weight estimation model, cluster analysis model, binary Logistics regression analysis model, multiple Logistics regression analysis model, neural network, such as binary Logistics regression analysis model.

In some embodiments, the auxiliary software for model analysis includes, but is not limited to SPSS, medcalc, python, SAS, metlab, STATA. Preferably, the auxiliary software for model analysis is SPSS, medcalc, and Python.

In some embodiments, in the binary Logistics regression analysis model, the analysis results are represented by a subject's working characteristic curve (Receiver Operating Characteristic), abbreviated ROC curve. Further, the evaluation indexes of the ROC curve further comprise sensitivity, specificity, area under the curve, about index (Youden index), recall rate, accuracy rate, positive prediction rate, negative prediction rate, cut-off value and the like. Further, the evaluation index of the ROC curve also includes sensitivity, specificity, area under the curve and about index (Youden index).

In some aspects of the application, a product for prognosis evaluation of respiratory viral infection is provided, the product comprising a substance that detects interleukin 37 levels in a sample, and optionally, one or more other substances that detect other clinical indicators for respiratory viral infection detection.

In some embodiments, the product is a kit or system (e.g., a detection and/or analysis system).

In some embodiments, the kit further comprises one or more substances selected from the group consisting of: container, buffer, adjuvant, solvent, negative control, positive control, and instructions for use.

In one embodiment, the kit is a kit for detecting interleukin 37 expression in a biological sample based on immunohistochemical method of immunodetection, comprising: blocking fluid (e.g., 10% goat serum), anti-interleukin 37 antibody (e.g., anti-interleukin 37 monoclonal antibody), secondary antibody (e.g., anti-rabbit biotinylated secondary antibody), labeled conjugate (e.g., HRP labeled streptavidin), substrate buffer (e.g., DAB substrate buffer), chromogenic fluid (e.g., DAB chromogenic fluid), and/or substrate solution.

Features in this aspect may be as described elsewhere herein.

In some aspects of the application, there is provided a system for prognosis evaluation of respiratory viral infection, comprising the following apparatus:

(a) Means for collecting and/or receiving interleukin 37 level data in the sample;

(b) A means for analyzing the data to prognosis the respiratory viral infection in the subject, wherein an interleukin 37 level above a control level indicates that the respiratory viral infection in the subject is well predicted, and an interleukin 37 level below a control level indicates that the respiratory viral infection in the subject is poorly predicted.

In some embodiments, the system further comprises (a') means for collecting and/or receiving data of other clinical indicators or essential characteristics of respiratory viral infection in the sample.

In some embodiments, the device described in (b) is used to combine interleukin 37 level data with other clinical indicators or baseline characteristic data of respiratory viral infection for prognostic evaluation of respiratory viral infection in the subject.

Or in some embodiments, the system further comprises means for analyzing other clinical indicators or essential characteristic data of respiratory viral infection; and/or means for performing a combination analysis of the interleukin 37 level data with other clinical indicators or essential characteristic data of the respiratory viral infection to prognostic evaluate the respiratory viral infection of the subject.

In some embodiments, the system further comprises one or more devices selected from the group consisting of: means for inputting and/or outputting and/or storing data; means for outputting and/or storing the analysis results, e.g. means for uploading and/or storing the analysis results to a cloud database or a corresponding cloud database means thereof; and the device intelligently analyzes and recommends the treatment scheme according to the prognosis evaluation result.

Features in this aspect may be as described elsewhere herein.

In some aspects of the application, methods are provided for prognostic evaluation of respiratory viral infection, the methods comprising detecting a substance at an interleukin 37 level in a sample, and analyzing the level to prognostic evaluate respiratory viral infection in the subject, wherein if the interleukin 37 level is higher than a control level, it is indicative of a good prognosis of respiratory viral infection in the subject, and if the interleukin 37 level is lower than the control level, it is indicative of a poor prognosis of respiratory viral infection in the subject.

Features in this aspect may be as described elsewhere herein.

Any combination of the technical solutions and features described above can be made by a person skilled in the art without departing from the inventive concept and the scope of protection of the present invention. Other aspects of the invention will be apparent to those skilled in the art in view of the disclosure herein.

Drawings

The present invention will be further described with reference to the accompanying drawings, wherein these drawings are provided only for illustrating embodiments of the present invention and are not intended to limit the scope of the present invention.

Fig. 1: relationship between plasma interleukin 37 levels and clinical prognosis in patients with new crown pneumonia

Fig. 1A: the plasma interleukin 37 of 36 healthy individuals and 254 new patients with the coronary pneumonia are obviously different, and the total of the new patients with the coronary pneumonia presents with abnormally high level;

FIGS. 1B-1E: a comparison of the performance of the interleukin 37 low secreting group and the high secreting group in a clinical prognosis-related evaluation index, comprising: hospitalization days (fig. 1B), viral nucleic acid to negative days (fig. 1C), lung CT improvement days (fig. 1D), and cough elimination days (fig. 1E). The dividing standard of the interleukin 37 secretion amount is based on the median of the interleukin 37 secretion values of plasma of 254 patients;

fig. 1F: an ROC curve predicted by using an interleukin 37 single index, wherein an AUC represents the area under the curve, and the closer the AUC value is to 1, the greater the possibility of correctly classifying a prediction model is; the ordinate is Sensitivity (Sensitivity), which represents the true positive rate, the closer the value is to 100%, indicating that the higher the accuracy of judging a positive sample as a positive sample; the abscissa is "100-specific", wherein: specificity (SPECIFICITY) represents the false negative rate, the closer the value is to 100%, indicating a higher accuracy in judging the negative sample as negative; the diagonal is the reference line. About index = sensitivity + specificity-1, also known as correct index, is a method of evaluating the authenticity of a screening test.

The statistical analysis methods all adopt unpaired Mann-Whitney test, and the significance expression method is as follows: ". Times.," P <0.05; ". Times.," P <0.01; "/x", P <0.001; ", P <0.0001.

Fig. 2: correlation between plasma interleukin 37, inflammatory factor level and clinically typed (mild) and (dangerous) severe symptoms of patients with new coronary pneumonia

Fig. 2A: plasma interleukin 37 levels in patients with new crown pneumonia are extremely significantly inversely correlated with inflammatory factor interleukin 6 levels;

Fig. 2B: 254 patients were classified into (mild) moderate and (dangerous) severe based on clinical typing, and analysis showed that (dangerous) severe patients exhibited high levels of IL-6.

Fig. 2C: plasma interleukin 37 levels in patients with new crown pneumonia are significantly inversely correlated with hypersensitive C-reactive protein levels;

Fig. 2D: 254 patients were classified into (mild) moderate and (critical) severe based on clinical typing, and analysis showed that (critical) severe patients exhibited high levels of HS-CRP.

Fig. 2E: plasma interleukin 37 levels in patients with new crown pneumonia are very significantly inversely correlated with inflammatory factor IL-8 levels;

fig. 2F: 254 patients were classified into (mild) moderate and (dangerous) severe based on clinical typing, and analysis showed that (dangerous) severe patients exhibited high levels of IL-8.

Definition of mild to moderate and critical conditions: light middle-aged: clinical symptoms are mild, and the symptoms are not manifested by pneumonia in imaging; or symptoms such as fever and respiratory tract, and the like, and the symptoms are visible in the imaging; critical illness: shortness of breath occurs, RR is more than or equal to 30 times/minute; in a resting state, the oxygen saturation is less than or equal to 93 percent; arterial blood oxygen partial pressure (PaO ₂)/oxygen uptake concentration (FiO ₂) less than or equal to 300mmHg (1 mmhg=0.133 kPa); pulmonary imaging shows that lesions significantly progress >50% within 24-48 hours; or respiratory failure occurs and mechanical ventilation is required; shock occurs; ICU monitoring therapy is required to incorporate other organ failure.

2A-2C employ linear regression analysis, "r" represents the correlation coefficient, and r <0 represents the linear negative correlation; FIGS. 2D-2E employ the unpaired Mann-Whitney test, significance representation method: ". Times.," P <0.05; ". Times.," P <0.01; "/x", P <0.001; ", P <0.0001.

Fig. 3: ROC curve predicted by interleukin 37, hypersensitive C reaction protein and interleukin 6 in combination

The solid line represents the IL-37 and HS-CRP two-factor ROC curve, the dotted line represents the IL-37, HS-CRP and IL-6 three-factor ROC curve, and the AUC value predicted by the three-factor combination is larger, so that the prediction effect is better.

Fig. 4: ROC curve predicted by interleukin 37, interleukin 8 and interleukin 6 in combination

The solid line represents the IL-37 and IL-6 two-factor ROC curve, the dotted line represents the IL-37, IL-6 and IL-8 three-factor ROC curve, and the AUC value predicted by the three-factor combination is larger, so that the prediction effect is better.

Fig. 5: ROC curve predicted by interleukin 37, interleukin 8 and hypersensitive C reaction protein

The solid line represents the IL-37 and IL-8 two-factor ROC curve, the dotted line represents the IL-37, IL-8 and hypersensitive CRP three-factor ROC curve, and the AUC value predicted by the three-factor combination is larger, so that the prediction effect is better.

Fig. 6: tracking and predicting result of each stage of disease course development of new patient with coronatine

The axis of abscissa is the number of days the patient progresses, i.e., the number of hospitalization days. Taking interleukin 37, interleukin 8 and hypersensitive C reactive protein three-factor combined prediction model as an example, substituting each index of the total 62 patients (59 patients of mild and severe patients and 3 patients of critical patients) at different hospital day time points into a prediction formula to obtain a prediction probability P value. The solid lines represent the predicted probabilities for 3 of the critically ill patients, all greater than 0.5, and the dashed lines represent the predicted probabilities for 59 of the light and medium ill patients, all less than 0.5. The result shows that the prediction model obtains very accurate prediction results in different hospitalization stages of the patient and does not change with the result of therapeutic intervention of the patient. The prediction model and the related indexes thereof are prompted to be used as tracking evaluation standards of the illness state of the patient in the disease course development stage, and the method has great auxiliary capability of evaluating the illness state of the patient.

Detailed Description

In the early warning model, interleukin 37 is adopted as a prediction base index, and inflammatory factors and inflammatory markers are adopted as auxiliary prediction indexes. Interleukin 37 is the only potent inhibitory cytokine in interleukin 1 family that performs the function of inhibiting inflammation, has extremely strong ability to inhibit inflammation, and can influence the transcription of pro-inflammatory factor genes by intracellular binding to TGF-beta downstream Smad3 to form a complex, or inhibit activation of pro-inflammatory signaling pathways by intracellular signaling through binding to cell surface IL-18R or IL-1R8, and promote activation of anti-inflammatory signaling pathways. However, the correlation of interleukin 37 with prognosis of respiratory tract infectious diseases has not been studied or revealed in the art.

The present disclosure provides a quantifiable (risk) severe pre-alarm model of respiratory viral infection based on interleukin 37 alone or in combination with other respiratory infection indicators (e.g., inflammatory factors, cytokines). The early warning model can predict severe symptoms at the earliest stage of disease occurrence. Clinically, the existing early warning indexes of severe and critical diseases aiming at respiratory tract virus infection cannot be effectively quantified, the subjectivity is high, and various physiological indexes of patients cannot be optimistic when the patients reach the severe pre-judging standard. Therefore, there is a need for a quantifiable accurate predictor, method and product that can perform early warning in the early stages of disease.

The early warning index, the early warning method and the early warning product provided by the application are based on the level of interleukin 37 in early blood plasma or serum of a patient, and one or more of other clinical indexes are also included in the model. The model has the advantages that interleukin 37 is used as a base evaluation index, the accuracy and the specificity of severe prediction are greatly improved, all parameters (such as various indexes and basic characteristics which are detected) in the model are numerical values in laboratory examination or clinical laboratory detection results, and the numerical values can be substituted into a model equation to calculate the prediction probability, so that severe early warning becomes a data visualization, and the result is a quantized accurate model, thereby effectively reducing the occurrence rate and the death rate of severe after infection of respiratory viruses, and having important clinical application value and wide application prospect. By the combined analysis of plasma interleukin 37, HS-CRP and IL-8 levels of 254 SARS-CoV-2 infected patients just before the early stage admission treatment intervention, 100% of all 20 serious patients can be accurately identified, and the specificity is 91.2%. The model can accurately screen the severe patients in early stage of disease occurrence so as to perform clinical therapeutic intervention measures earlier, thereby greatly reducing the occurrence rate and death rate of the severe patients and having extremely high clinical application value.

The early warning model has two important advantages, namely, early warning time is advanced to the earliest stage of patient admission, namely, before the worsening symptoms appear, accurate pre-judgment is carried out by combining detection indexes of the patient, so that the patient obtains the earliest therapeutic intervention, and the risk of serious illness and even death is reduced to the greatest extent. Secondly, different and fuzzy early warning indications in the past, the early warning mechanism is completely quantized, a threshold value is set, whether a patient is at risk of developing (dangerous) severe symptoms is judged more clearly and definitely through the value, the clinical universality is stronger, the defect of insufficient accuracy and stronger subjectivity caused by empirical judgment is reduced to the greatest extent, and the method has important social value and wide application prospect.

All numerical ranges provided herein are intended to expressly include all values and ranges of values between the endpoints of the range. The features mentioned in the description or the features mentioned in the examples can be combined. All of the features disclosed in this specification may be combined with any combination of the features disclosed in this specification, and the various features disclosed in this specification may be substituted for any alternative feature serving the same, equivalent or similar purpose. Thus, unless expressly stated otherwise, the disclosed features are merely general examples of equivalent or similar features.

As used herein, "comprising," having, "or" including "includes" including, "" consisting essentially of … …, "" consisting essentially of … …, "and" consisting of … …; "consisting essentially of … …", "consisting essentially of … …" and "consisting of … …" are under the notion of "containing", "having" or "including".

Interleukin 37 and detecting substance thereof

As used herein, the term "interleukin 37 (IL-37) protein or polypeptide" is used interchangeably with "protein or polypeptide encoded by interleukin 37 (IL-37) gene" and refers to a protein or polypeptide encoded by IL-37 gene, a conservatively variant polypeptide thereof, or a homologous protein or polypeptide thereof, or an active fragment thereof. Interleukin 37 proteins are known in the art as interleukin 1 family members, for example, the sequence of human interleukin 37 may be as shown in Gene ID: 27178.

In some embodiments, the nucleotide sequence of the IL-37 encoding gene may be: has Gene ID 27178 or SEQ ID NO: 6-10 or a degenerate sequence thereof; or a spliceosome nucleotide sequence thereof; or a nucleotide sequence obtained by deriving at least one nucleotide by substitution, deletion or addition in a nucleotide sequence defined in a nucleotide sequence having a Gene ID of 27178 or SEQ ID NO of 6 to 10 or a degenerate sequence thereof or a spliceosome nucleotide sequence thereof, and encodes a protein having the same or similar function as a nucleotide sequence having a Gene ID of 27178 or SEQ ID NO of 6 to 10 or a degenerate sequence thereof.

In some embodiments, the IL-37 protein may be: a protein having an amino acid sequence shown in any one of SEQ ID NOs 1 to 5; an IL-37 protein encoded by an IL-37 encoding gene as described above; or a spliceosome amino acid sequence thereof; or a protein having one or more (e.g., 1,2, 3, 4, 5, 6, 7, 8, 9, 10) amino acid substitutions and/or deletions and/or additions in the amino acid sequence defined in the aforementioned protein having the amino acid sequence or a spliceosome amino acid sequence thereof, which is functionally identical or similar to the protein represented by the amino acid sequence represented by SEQ ID NO: 1-5.

As used herein, the terms interleukin 37 "detecting substance", "detecting reagent" or "reagent for detecting interleukin 37 molecule" or "reagent for detecting interleukin 37 expression amount" are used interchangeably, and refer to a substance that is specific for interleukin 37 molecule and can be used to directly or indirectly detect the content of interleukin 37 molecule. These detection substances can detect interleukin 37 at the gene level or protein level.

Since the sequence of interleukin 37 molecule is known in the art, one of ordinary skill in the art can prepare reagents specific for interleukin 37 molecule based on conventional means or by commercially available reagents. For example, detection reagents useful in the present invention include, but are not limited to: antibodies with detection specificity for interleukin 37 molecules.

Also, to facilitate detection, the detection reagents of the present invention may also carry detectable labels, including but not limited to: radioisotopes, fluorophores, chemiluminescent moieties, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, ligands (e.g., biotin or haptens), and the like.

The detection reagents of the invention may be present in solution, immobilized on a carrier (e.g., substrate, adsorbate) or in other ways conventional in the art, provided that the manner of presence is suitable for detection of interleukin 37 in a biological sample. For example, when the detection reagent of the present invention is a nucleotide probe, it may exist in the form of a biochip (or "microarray").

Prognosis evaluation and method for interleukin 37 in respiratory tract virus infection

According to the disclosure of the present application, the level of interleukin 37 is closely related to the prognosis of respiratory tract virus infection, and thus can be used as a basic index for prognosis evaluation and drug screening.

As used herein, the terms "pre-warning" and "prognosis" are used interchangeably to refer to predicting the likely course and outcome of a disease, which includes judging the particular outcome of a disease (e.g., recovery, appearance or disappearance of certain symptoms, signs, and complications, among other abnormalities, and death).

The poor prognosis described herein includes, but is not limited to: respiratory viral infections develop as severe or critical or even death, develop complications, develop into cytokine storms, and heal for long periods of time. After predicting a patient's prognosis, the patient's prognosis can be improved in combination with a treatment that reduces the amount of interleukin 37 molecules.

In general, prognosis of respiratory viral infection can be assessed by: detecting the level of interleukin 37 molecule in a subject to be tested or in a sample obtained from the subject and comparing the level with a control level; if the comparison shows that the level of interleukin 37 molecule in the subject is higher than the control level, the subject is indicated to have a poor prognosis of respiratory viral infection. In some embodiments, the methods of the present application further optionally comprise: obtaining a sample to be tested from a subject; the test sample is contacted with a reagent or kit for detecting interleukin 37 levels.

As used herein, the term "normal control" refers to the level of interleukin 37 molecules used as a reference, including but not limited to: levels of interleukin 37 measured in a normal biological sample (e.g., a sample obtained from a healthy person or a subject during a normal period) not infected with respiratory viruses, a population standard level determined statistically, or a normalized level.

In accordance with the use of the application, the application also provides a method of assessing the prognosis of a respiratory viral infection in a mammalian subject, the method comprising:

(a') collecting and/or receiving interleukin 37 level data in the sample;

(b ') analyzing the data to assess the prognosis of the subject's respiratory viral infection, wherein an interleukin 37 level above a control level indicates that the subject's respiratory viral infection is well predicted, and an interleukin 37 level below a control level indicates that the subject's respiratory viral infection is poorly predicted.

In some embodiments, the method further comprises collecting and/or receiving data of other clinical indicators or essential characteristics of respiratory viral infection in the sample, and analyzing in combination with interleukin 37 level data.

In some embodiments, the good prognosis comprises: the respiratory tract virus infection is only mild or moderate, has no complications, does not develop into cytokine storm and has short healing time.

In some embodiments, the poor prognosis comprises: respiratory viral infections develop as severe or critical or even death, develop complications, develop into cytokine storms, and heal for long periods of time.

In some embodiments, the control level is selected from the group consisting of: average interleukin 37 levels in healthy subjects, interleukin 37 levels in the subject assessed prior to the absence of respiratory viral infection.

In some embodiments, the control level is: levels of interleukin 37 measured in a normal biological sample (e.g., a sample obtained from a healthy person or a subject during a normal period) not infected with respiratory viruses, a population standard level determined statistically, or a normalized level.

In some embodiments, the analysis of the data includes substituting the data into a model equation for probability calculation, the model including: general linear model, generalized linear model, log linear model, weight estimation model, cluster analysis model, binary Logistics regression analysis model, multiple Logistics regression analysis model, neural network, such as binary Logistics regression analysis model.

In some embodiments, the method further comprises the steps of: a data input and/or output and/or storage step; an analysis result outputting and/or storing step, such as a step of uploading and/or storing to a cloud database; and intelligently analyzing and recommending the treatment scheme according to the prognosis evaluation result.

Further, provided herein is a method of screening for a candidate drug that improves prognosis of respiratory viral infection, the method comprising testing the effect of the candidate drug on interleukin 37 levels in a subject or a sample obtained from the subject, wherein a decrease in interleukin 37 levels after use of the candidate drug is indicative of the candidate drug having an effect of improving prognosis of a tumor. The various features involved in the candidate drug screening methods of the application may be as defined or set forth herein. In some embodiments, the drug candidate is interleukin 37, a potentiator or a promoter thereof.

Detection product

The application also provides a product for prognosis evaluation of respiratory viral infection, comprising: substances for detecting interleukin 37 levels and, optionally, other substances associated with respiratory viral infections, such as detection substances for existing respiratory infection indicators.

Depending on the requirements of the assay used, an appropriate interleukin 37 assay may be selected and formulated into a product, such as a kit, suitable for the assay used. The detection means and reagents contained in the product can be adjusted and changed by one of ordinary skill in the art according to actual conditions and needs.

Thus, also provided herein is a product (e.g., a kit) comprising: (i) Detecting an effective amount of one or more reagents for detecting interleukin 37; (ii) Optionally, one or more substances selected from the group consisting of: a container, instructions for use, positive controls, negative controls, buffers, adjuvants or solvents, such as solutions for suspending or immobilizing cells, detectable labels or tags, solutions for facilitating hybridization of nucleic acids, solutions for lysing cells, or solutions for nucleic acid purification.

In one example, provided herein is a detection kit suitable for detecting the expression of interleukin 37 in a biological sample by immunohistochemical methods. The detection kit may comprise: blocking fluid, such as 10% goat serum; primary antibodies, such as interleukin 37 monoclonal antibodies; secondary antibodies, e.g., labeled (e.g., HRP-labeled) or unlabeled goat anti-rabbit secondary antibodies; substrate buffers, such as DAB substrate buffer; developing solution; and optionally a container containing the above reagents and instructions for use.

The detection kit can be also provided with a kit using instruction, wherein the instruction describes how to detect by using the kit, and how to judge the prognosis of the infection of the respiratory virus by using the detection result and select a treatment scheme.

Of course, the kit can also contain other reagents that are clinically useful for the judgment of the progression of respiratory viral infection, the selection of a therapeutic regimen, and/or the prognostic assessment in a subject to aid or verify the results obtained by detecting interleukin 37. One of ordinary skill in the art can make routine selections based on the particular needs.

Respiratory tract virus infection prognosis evaluation system

The application also provides a system corresponding to the method, which comprises the following devices:

It should be appreciated that one skilled in the art may add other conventional devices or programs to the system as desired.

Examples

The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Appropriate modifications and variations of the invention may be made by those skilled in the art, and are within the scope of the invention.

The experimental methods, which do not address specific conditions in the following examples, may employ methods conventional in the art, such as enzyme-linked immunosorbent assay or conditions suggested by the supplier.

Percentages and parts are by weight unless otherwise indicated. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, any methods and materials similar or equivalent to those described herein can be used in the methods of the present invention. The preferred methods and materials described herein are presented for illustrative purposes only.

EXAMPLE 1 close relationship of plasma Interleukin 37 levels in patients with New coronaries to clinical prognosis

The patient plasma tested in this example was derived from randomly selected 254 patient plasma samples from Shanghai public health clinical center 2020, month 2 to 5, which were positively diagnosed as COVID-19 by viral nucleic acid detection. The age of the patient is 42.76 +/-0.95, 114 of which are female and 140 of which are male. This study was approved by the ethics committee, and all patients in the group signed informed consent.

The plasma of 254 new patients with coronaries at the earliest stage of admission (before therapeutic intervention) is selected for freeze thawing, 36 healthy individuals are used as control, and an interleukin 37 enzyme-linked immunosorbent assay kit (purchased from Beijing four cypress biotechnology Co., ltd.) is used for quantitative analysis of plasma interleukin 37 level, the detection method is carried out according to the specification of the kit, and the data is read by an enzyme-labeling instrument. As a result, it was found that the plasma of the patients with new coronaries exhibited significantly abnormally high levels of interleukin 37 (fig. 1A).

We further divided 254 patients into two groups, IL-37 high and IL-37 low, by median of plasma IL-37 secretion, and found that patients with high plasma interleukin 37 levels had significantly better clinical prognosis than patients with low plasma water for no reason interleukin 37, which could be manifested as a very significant shortening of the days of hospitalization including patients (fig. 1B), viral nucleic acid to negative (fig. 1C), lung CT image improvement (fig. 1D) and cough elimination (fig. 1E).

Taking the interleukin 37 secretion value of 254 patients measured by an ELISA test as an independent variable, taking (light) medium and (dangerous) severe as dependent variables, and carrying out binary Logistics regression analysis modeling to obtain the prediction probability of interleukin 37 single index.

The results of the ROC curves are shown in the curves in fig. 1F, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like, as shown in table 1. AUC is 0.727.

The formula of the prediction model is:

Logit(P)＝-1.339-0.068*IL-37(pg/mL)＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

When P is more than 0.5, the tested person is or is candidate to be a new (critically) patients with coronatine, otherwise, the subject is or is candidate to be a patient with new (mild) moderate coronary pneumonia. The sensitivity of the combined prediction model was 100% and the specificity was 52.99%.

Table 1: each evaluation index of interleukin 37 single index prediction

Variable(s)	IL-37
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.727
		Standard error of ^a	0.0374
Confidence interval of 95% ^b	0.668-0.781
		Z statistics	6.069
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.5299
		Correlation standard	＞0.06319
Sensitivity to	100.00
		Specificity (specificity)	52.99

According to the parameters in Table 1, the prediction sensitivity of the single interleukin 37 index prediction reaches 100%, and the specificity also reaches more than 50%, which indicates that the single interleukin 37 index can predict 100% of severe patients. The results suggest that high plasma interleukin 37 levels are beneficial to the realization of better clinical outcome for patients with new coronaries and can be used as a potential clinical outcome prediction index.

Example 2 significant negative correlation of plasma Interleukin 37 levels in patients with New coronal pneumonia with the inflammatory indicators Interleukin 6 (IL-6), interleukin 8 (IL-8) and hypersensitive C-reactive protein (HS-CRP)

The patient plasma sources used in this example were the same as in example 1.

Further, a correlation study was conducted between the plasma interleukin 37 level and inflammation related indicators including HS-CRP, IL-6 and IL-8 of patients with new coronary pneumonia, wherein the HS-CRP detection results are from clinical test reports, the IL-6 and IL-8 detection results are from plasma cytokine detection results, and the detection method is to determine the plasma IL-6 and IL-8 levels of patients by using Simoa CorPlex human cytokine detection kit, and the operation steps are according to the experimental procedure of American manufacturer Quanterix.

Linear correlation regression analysis was performed and showed that patient plasma interleukin 37 levels were significantly inversely correlated with all of the patient's in vivo IL-6 levels (fig. 2A), IL-8 levels (fig. 2B) and HS-CRP levels (fig. 2C).

The 254 patients were further clinically typed as (light) medium-sized and (dangerous) heavy, and the characteristic levels of the above inflammation-related index in both clinical typing were analyzed, respectively. The results show that: the (risk) heavy patients all had very significant increases in HS-CRP levels (FIG. 2D), plasma IL-6 levels (FIG. 2E) and plasma IL-8 levels (FIG. 2F).

The results suggest that the levels of the inflammation-related indicators IL-6, IL-8 and HS-CRP can be used as predictors to jointly predict (risk) severe indicators with interleukin 37.

Example 3, interleukin 37, hypersensitive C response protein (HS-CRP) and Interleukin 6 (IL-6) in combination to predict patients with severe Crohn's disease

The ROC related charts are all chart drawing through Medcalc software after calculating each prediction probability through SPSS software.

A. Double factor combination of interleukin 37 and hypersensitive C response protein (HS-CRP)

The interleukin 37 secretion amount value and the HS-CRP secretion amount value of the patient measured in the example 1 are taken as independent variables together, and (light) medium and (dangerous) severe symptoms are taken as dependent variables, and binary Logistics regression analysis modeling is performed to obtain the joint prediction probability of interleukin 37 and HS-CRP.

The results of the ROC curves are shown in the il_37_hs_crp curve in fig. 3, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like as shown in table 2. AUC for the two-factor combination was 0.934.

The formula of the prediction model is:

Logit(P)＝-2.505-0.077^＊IL-37(pg/mL)+0.049^＊HS-CRP＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

when P is more than 0.5, the tested person is or is candidate to be a severe patient with the new coronaries (or the risk) and vice versa. The sensitivity of the combined prediction model was 92.86% and the specificity was 91.86%.

At present, according to clinical observation of new cases of coronaries pneumonia, patients are classified into two indexes of (mild) moderate symptoms and (dangerous) severe symptoms, and clinical definitions are respectively as follows: light middle-aged: clinical symptoms are mild, and the symptoms are not manifested by pneumonia in imaging; or symptoms such as fever and respiratory tract, and the like, and the symptoms are visible in the imaging; critical illness: shortness of breath occurs, RR is more than or equal to 30 times/minute; in a resting state, the oxygen saturation is less than or equal to 93 percent; arterial blood oxygen partial pressure (PaO ₂)/oxygen uptake concentration (FiO ₂) less than or equal to 300mmHg (1 mmhg=0.133 kPa); pulmonary imaging shows that the focus significantly progresses by more than 50% within 24-48 hours; or respiratory failure occurs and mechanical ventilation is required; shock occurs; ICU monitoring therapy is required to incorporate other organ failure.

B. Three factors of interleukin 37, hypersensitive C reactive protein (HS-CRP) and interleukin 6 are combined to further improve the sensitivity of the prediction model, we performed three factors of interleukin 37, HS-CRP and interleukin 6 combined prediction analysis modeling. And (3) taking the interleukin 37 secretion value, the HS-CRP and the IL-6 secretion value of the patient as independent variables, and taking the (light) medium and (dangerous) severe symptoms as dependent variables to carry out binary Logistics regression analysis to obtain the joint prediction probability of the IL-37, the HS-CRP and the IL-6.

The results of ROC curves are shown in fig. 3 as il_37_hs_crp_il_6, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like, as shown in table 2, wherein the AUC of the three-factor combination is 0.955.

The formula of the prediction model is:

Logit(P)＝-3.052-0.072^＊IL-37(pg/mL)+0.043^＊HS-CRP+0.067^＊IL-6＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

when P is more than 0.5, the tested person is or is candidate to be a severe patient with the new coronaries (or the risk) and vice versa. The sensitivity of the combined prediction model was 100% and the specificity was 83.59%.

TABLE 2 evaluation of the respective evaluation index of the two-factor combination of interleukin 37 with hypersensitive C-reactive protein or the three-factor combination prediction of interleukin 37 with hypersensitive C-reactive protein and interleukin 6

Variable(s)	IL-37+HS-CRP
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.936
		Standard error of ^a	0.0255
Confidence interval of 95% ^b	0.897-0.964
		Z statistics	17.107
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.8471
		Correlation standard	＞0.07932
Sensitivity to	92.86
		Specificity (specificity)	91.86

Variable IL-37+HS-CRP+IL-6
	Classification of medium (light) and (dangerous) severe symptoms

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.955
		Standard error of ^a	0.0162
Confidence interval of 95% ^b	0.918-0.979
		Z statistics	28.075
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.8359
		Correlation standard	＞0.05273
Sensitivity to	100.00
		Specificity (specificity)	83.59

^aDeLong ER,DeLong DM,Clarke-Pearson DL.Comparing the areas under two or more correlated receiver operating characteristic curves：a nonparametric approach.Biometrics.1988;44(3)：837-845.

According to the data analysis in the embodiment, the two-factor combination of interleukin 37 and hypersensitive C reaction protein or the three-factor combination of interleukin 37 and hypersensitive C reaction protein and interleukin 6 can be used for effectively predicting a severe patient with new coronaries (critically ill), wherein the sensitivity of the two-factor combination of interleukin 37 and hypersensitive C reaction protein is 92.86 percent, and the specificity is 91.86 percent; the sensitivity of the three-factor combined prediction group of interleukin 37, hypersensitive C reaction protein and interleukin 6 can reach 100 percent, and the specificity is 83.59 percent. According to the specific clinical requirements for the prediction of severe patients with respiratory viral infection, a three-factor combined prediction model can be selected to predict all possible severe patients as far as possible, and a two-factor prediction model with relatively high specificity can be selected.

Example 4, interleukin 37, interleukin 6 (IL-6) and Interleukin 8 (IL-8) in combination to predict patients with Crohn's disease (Critical)

A. dual factor combination of interleukin 37 and interleukin 6

Taking the interleukin 37 secretion value and the plasma IL-6 secretion value of a patient as independent variables, taking (light) medium and (dangerous) severe as dependent variables, and carrying out binary Logistics regression analysis modeling to obtain the joint prediction probability of IL-37 and IL-6.

The results of the ROC curves are shown as il_37_il_6 in fig. 4, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like as shown in table 3. The two-factor combined AUC was 0.885.

The formula of the prediction model is:

Logit(P)＝-2.349-0.06^＊IL-37(pg/mL)+0.098^＊IL-6＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

when P is more than 0.5, the tested person is or is candidate to be a severe patient of the new coronaries pneumonia, otherwise, the tested person is or is candidate to be a patient of the new coronaries pneumonia. The sensitivity of this joint prediction model was 95% and the specificity was 67%.

B. Three factor combinations of interleukin 37, interleukin 6 and interleukin 8

To further enhance specificity we performed three factor joint predictive analytical modeling of interleukin 37, IL-6 and IL-8. And (3) carrying out binary Logistics regression analysis by taking the interleukin 37 secretion value, the IL-6 secretion value and the IL-8 secretion value of a patient as independent variables and taking the (light) medium and (dangerous) severe symptoms as dependent variables to obtain the joint prediction probability of the IL-37, the IL-6 and the IL-8.

The results of ROC curves are shown in fig. 4 as il_37_il_6_il_8, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like, as shown in table 3, and the three-factor combined AUC is 0.937. The formula of the prediction model is:

Logit(P)＝-2.834-0.061^＊IL-37(pg/mL)+0.104^＊IL-6+0.012^＊IL-8＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

When P is more than 0.5, the tested person is or is candidate to be a severe patient of the new coronaries pneumonia, otherwise, the tested person is or is candidate to be a patient of the new coronaries pneumonia. The analysis result shows that the sensitivity of the three parameters of interleukin 37, IL-6 and IL-8 for the combined prediction of the severe cases of the new coronaries of pneumonia patients is 100 percent, and the specificity is 76.24 percent.

Table 3: each evaluation index of interleukin 37 and interleukin 6 two-factor combination or interleukin 37 and interleukin 6 and interleukin 8 three-factor combination prediction

Variable(s)	IL-37+IL-6
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.885
		Standard error of ^a	0.0323
Confidence interval of 95% ^b	0.836-0.924
		Z statistics	11.927
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.6200
		Correlation standard	＞0.06835
Sensitivity to	95.00
		Specificity (specificity)	67.00

Variable(s)	IL-37+IL-6+IL-8
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.937
		Standard error of ^a	0.0205
Confidence interval of 95% ^b	0.896-0.965
		Z statistics	21.310
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.7624
		Correlation standard	＞0.05845
Sensitivity to	100.00
		Specificity (specificity)	76.24

According to the data analysis in the embodiment, the two-factor combination of interleukin 37 and interleukin 6 or the three-factor combination of interleukin 37 and interleukin 6 and interleukin 8 can be used for effectively predicting the severe patients with the new coronary pneumonia, wherein the sensitivity and the specificity of the two-factor combination of interleukin 37 and interleukin 6 are respectively 95% and 67%, and the sensitivity of the three-factor combination after the interleukin 8 is added is 100%, and the specificity is also improved to 76.24%. The three factors of interleukin 37, interleukin 6 and interleukin 8 have better combined prediction effect.

Example 5, interleukin 37, interleukin 8 (IL-8) and hypersensitive C response protein (HS-CRP) in combination prediction of severe patients with Crohn's disease

A. two factor combination of interleukin 37 and interleukin 8

Taking the interleukin 37 secretion value and the plasma IL-8 secretion value of a patient as independent variables, taking (light) medium and (dangerous) severe symptoms as dependent variables, and carrying out binary Logistics regression analysis modeling to obtain the joint prediction probability of interleukin 37 and IL-8.

The results of the ROC curves are shown as il_37_il_8 in fig. 5, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like as shown in table 4. The two-factor combined AUC was 0.9.

The formula of the prediction model is:

Logit(P)＝-1.768-0.067^＊IL-37(pg/mL)+0.017^＊IL-8＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

when P is more than 0.5, the tested person is or is candidate to be a severe patient of the new coronaries pneumonia, otherwise, the tested person is or is candidate to be a patient of the new coronaries pneumonia. The sensitivity of the combined prediction model was 100% and the specificity was 56.93%.

B. Three factor combination of interleukin 37, interleukin 8 and hypersensitive C response protein (HS-CRP)

To further improve the specificity of the assay, we performed a joint predictive analytical modeling of interleukin 37, HS-CRP, and IL-8. And (3) taking the interleukin 37 secretion value, the HS-CRP and the IL-8 secretion value of the patient as independent variables, and taking the (light) medium and (dangerous) severe symptoms as dependent variables to perform binary Logistics regression analysis to obtain the joint prediction probability of interleukin 37, HS-CRP and IL-8.

The results of ROC curves are shown in fig. 5 as il_37_hs_crp_il_8, and the test parameters corresponding to each ROC curve include sensitivity, specificity, area under the curve, standard error, confidence interval, significance, and the like as shown in table 4. The formula of the prediction model is:

Logit(P)＝-2.770-0.072^＊IL-37(pg/mL)+0.048^＊HS-CRP+0.009^＊IL-8＝ln[P/(1-P)]

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

When P is more than 0.5, the tested person is or is candidate to be a severe patient of the new coronaries pneumonia, otherwise, the tested person is or is candidate to be a patient of the new coronaries pneumonia. The sensitivity of the combined prediction model was 100% and the specificity was 91.24%. The analysis result shows that the sensitivity and the specificity of the three parameters of interleukin 37, HS-CRP and IL-8 for jointly predicting the (critical) severe disease of the patient suffering from the new coronaries are the highest, the area under the curve is 0.959, and P is less than 0.0001.

Table 4: each evaluation index of dual factor combination of interleukin 37 and interleukin 8 or three factor combination prediction of interleukin 37 and interleukin 8 and hypersensitive C reaction protein

Variable(s)	IL-37+IL-8
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.876
		Standard error of ^a	0.0364
Confidence interval of 95% ^b	0.825-0.916
		Z statistics	10.334
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.5693
		Correlation standard	＞0.0699
Sensitivity to	100.00
		Specificity (specificity)	56.93

Variable(s)	IL-37+HS-CRP+IL-8
		Classification variable	(Light) middle (dangerous) severe cases

Area under ROC curve (AUC)

Area under ROC curve (AUC)	0.959
		Standard error of ^a	0.0137
Confidence interval of 95% ^b	0.922-0.981
		Z statistics	33.396
Significance level P (area=0.5)	＜0.0001

^aDeLong et al.，1988

^b Binomial type accuracy

Youden index

Youden index J	0.9124
		Correlation standard	＞0.07708
Sensitivity to	100.00
		Specificity (specificity)	91.24

As can be seen from the data analysis in this example, the two-factor combination of interleukin 37 and interleukin 8 or the three-factor combination of interleukin 37 and interleukin 8 and hypersensitive C reaction protein can be used for effectively predicting the severe patients with the new coronary pneumonia, wherein the sensitivity of interleukin 37 and interleukin 8 in combination is 100%, and the specificity is 56.93%. In the three-factor combination added with the hypersensitive C reaction protein, the sensitivity is still 100 percent, but the specificity is obviously improved to 91.24 percent. Therefore, the three-factor combined prediction effect of interleukin 37, interleukin 8 and hypersensitive C reaction protein is better.

Example 6 tracking prediction results of various stages of disease progression in patients with New coronaries

Taking interleukin 37, interleukin 8 and hypersensitive C reactive protein three-factor combined prediction model as an example, tracking and predicting are carried out according to each index of the disease course development of a patient.

In this example, a total of 62 patients were predicted, and the plasma sources of the 62 patients were the same as in example 1, and were randomly selected from 254 patients with ages of 45.28+0.81, 33 females and 29 males. Of these, 59 patients with (mild) middle-aged symptoms and 3 patients with (critical) severe symptoms. Each patient carries out tracking follow-up according to different stages of hospitalization, the treatment method during the follow-up is not different from that of other patients, the prediction probability of each point is calculated from independent results of interleukin 37, interleukin 8 and hypersensitive C reaction protein in the plasma of the patient extracted at the detection time point, and the application formula is calculated as a three-factor combined prediction model formula in the embodiment 5:

P＝EXP[Logit(P)]/(1+EXP[Logit(P)])

the results show that with the continuous development of the disease course along with the continuous intervention of the treatment means, the prediction results of the prediction model in different time phases are kept highly consistent, namely 100% of 3 severe patients are predicted, and 100% of 59 mild patients are correctly predicted, which indicates that the disease condition of the 3 severe patients is always optimistic and the continuous treatment intervention is required. The early warning model has great auxiliary potential for tracking and evaluating the disease condition and adjusting the treatment scheme in the course of the disease development of the patient.

All documents mentioned in this disclosure are incorporated by reference in this disclosure as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.

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Glu Pro Val Gly Val Thr Asp Lys Phe Glu Asn Arg Lys His Ile Glu

145 150 155 160

Phe Ser Phe Gln Pro Val Cys Lys Ala Glu Met Ser Pro Ser Glu Val

165 170 175

Ser Asp

<210> 5

<211> 192

<212> PRT

<213> Homo sapiens (Homo sapiens)

<400> 5

Met Ser Gly Cys Asp Arg Arg Glu Thr Glu Thr Lys Gly Lys Asn Ser

1 5 10 15

Phe Lys Lys Arg Leu Arg Gly Pro Lys Val Lys Asn Leu Asn Pro Lys

20 25 30

Lys Phe Ser Ile His Asp Gln Asp His Lys Val Leu Val Leu Asp Ser

35 40 45

Gly Asn Leu Ile Ala Val Pro Asp Lys Asn Tyr Ile Arg Pro Glu Ile

50 55 60

Phe Phe Ala Leu Ala Ser Ser Leu Ser Ser Ala Ser Ala Glu Lys Gly

65 70 75 80

Ser Pro Ile Leu Leu Gly Val Ser Lys Gly Glu Phe Cys Leu Tyr Cys

85 90 95

Asp Lys Asp Lys Gly Gln Ser His Pro Ser Leu Gln Leu Lys Lys Glu

100 105 110

Lys Leu Met Lys Leu Ala Ala Gln Lys Glu Ser Ala Arg Arg Pro Phe

115 120 125

Ile Phe Tyr Arg Ala Gln Val Gly Ser Trp Asn Met Leu Glu Ser Ala

130 135 140

Ala His Pro Gly Trp Phe Ile Cys Thr Ser Cys Asn Cys Asn Glu Pro

145 150 155 160

Val Gly Val Thr Asp Lys Phe Glu Asn Arg Lys His Ile Glu Phe Ser

165 170 175

Phe Gln Pro Val Cys Lys Ala Glu Met Ser Pro Ser Glu Val Ser Asp

180 185 190

<210> 6

<211> 657

<212> DNA

<213> Homo sapiens (Homo sapiens)

<400> 6

atgtcctttg tgggggagaa ctcaggagtg aaaatgggct ctgaggactg ggaaaaagat 60

gaaccccagt gctgcttaga agacccggct ggaagccccc tggaaccagg cccaagcctc 120

cccaccatga attttgttca cacaagtcca aaggtgaaga acttaaaccc gaagaaattc 180

agcattcatg accaggatca caaagtactg gtcctggact ctgggaatct catagcagtt 240

ccagataaaa actacatacg cccagagatc ttctttgcat tagcctcatc cttgagctca 300

gcctctgcgg agaaaggaag tccgattctc ctgggggtct ctaaagggga gttttgtctc 360

tactgtgaca aggataaagg acaaagtcat ccatcccttc agctgaagaa ggagaaactg 420

atgaagctgg ctgcccaaaa ggaatcagca cgccggccct tcatctttta tagggctcag 480

gtgggctcct ggaacatgct ggagtcggcg gctcaccccg gatggttcat ctgcacctcc 540

tgcaattgta atgagcctgt tggggtgaca gataaatttg agaacaggaa acacattgaa 600

ttttcatttc aaccagtttg caaagctgaa atgagcccca gtgaggtcag cgattag 657

<210> 7

<211> 593

<212> DNA

<213> Homo sapiens (Homo sapiens)

<400> 7

atgtcctttg tgggggagaa ctcaggagtg aaaatgggct ctgaggactg ggaaaaagat 60

gaaccccagt gctgcttaga aggtccaaag gtgaagaact taaacccgaa gaaattcagc 120

attcatgacc aggatcacaa agtactggtc ctggactctg ggaatctcat agcagttcca 180

gataaaaact acatacgccc agagatcttc tttgcattag cctcatcctt gagctcagcc 240

tctgcggaga aaggaagtcc gattctcctg ggggtctcta aaggggagtt ttgtctctac 300

tgtgacaagg ataaaggaca aagtcatcca tcccttcagc tgaagaagga gaaactgatg 360

aagctggctg cccaaaagga atcagcacgc cggcccttca tcttttatag ggctcaggtg 420

ggctcctgga acatgctgga gtcggcggct caccccggat ggttcatctg cacctcctgc 480

aattgtaatg agcctgttgg ggtgacagat aaatttgaga acaggaaaca cattgaattt 540

tcatttcaac cagtttgcaa gctgaaatga gccccagtga ggtcagcgat tag 593

<210> 8

<211> 474

<212> DNA

<213> Homo sapiens (Homo sapiens)

<400> 8

atgtcctttg tgggggagaa ctcaggagtg aaaatgggct ctgaggactg ggaaaaagat 60

gaaccccagt gctgcttaga agagatcttc tttgcattag cctcatcctt gagctcagcc 120

tctgcggaga aaggaagtcc gattctcctg ggggtctcta aaggggagtt ttgtctctac 180

tgtgacaagg ataaaggaca aagtcatcca tcccttcagc tgaagaagga gaaactgatg 240

aagctggctg cccaaaagga atcagcacgc cggcccttca tcttttatag ggctcaggtg 300

ggctcctgga acatgctgga gtcggcggct caccccggat ggttcatctg cacctcctgc 360

aattgtaatg agcctgttgg ggtgacagat aaatttgaga acaggaaaca cattgaattt 420

tcatttcaac cagtttgcaa agctgaaatg agccccagtg aggtcagcga ttag 474

<210> 9

<211> 537

<212> DNA

<213> Homo sapiens (Homo sapiens)

<400> 9

atgtcctttg tgggggagaa ctcaggagtg aaaatgggct ctgaggactg ggaaaaagat 60

gaaccccagt gctgcttaga agacccggct ggaagccccc tggaaccagg cccaagcctc 120

cccaccatga attttgttca cacaaagatc ttctttgcat tagcctcatc cttgagctca 180

gcctctgcgg agaaaggaag tccgattctc ctgggggtct ctaaagggga gttttgtctc 240

tactgtgaca aggataaagg acaaagtcat ccatcccttc agctgaagaa ggagaaactg 300

atgaagctgg ctgcccaaaa ggaatcagca cgccggccct tcatctttta tagggctcag 360

gtgggctcct ggaacatgct ggagtcggcg gctcaccccg gatggttcat ctgcacctcc 420

tgcaattgta atgagcctgt tggggtgaca gataaatttg agaacaggaa acacattgaa 480

ttttcatttc aaccagtttg caaagctgaa atgagcccca gtgaggtcag cgattag 537

<210> 10

<211> 579

<212> DNA

<213> Homo sapiens (Homo sapiens)

<400> 10

atgtcaggct gtgataggag ggaaacagaa accaaaggaa agaacagctt taagaagcgc 60

ttaagaggtc caaaggtgaa gaacttaaac ccgaagaaat tcagcattca tgaccaggat 120

cacaaagtac tggtcctgga ctctgggaat ctcatagcag ttccagataa aaactacata 180

cgcccagaga tcttctttgc attagcctca tccttgagct cagcctctgc ggagaaagga 240

agtccgattc tcctgggggt ctctaaaggg gagttttgtc tctactgtga caaggataaa 300

ggacaaagtc atccatccct tcagctgaag aaggagaaac tgatgaagct ggctgcccaa 360

aaggaatcag cacgccggcc cttcatcttt tatagggctc aggtgggctc ctggaacatg 420

ctggagtcgg cggctcaccc cggatggttc atctgcacct cctgcaattg taatgagcct 480

gttggggtga cagataaatt tgagaacagg aaacacattg aattttcatt tcaaccagtt 540

tgcaaagctg aaatgagccc cagtgaggtc agcgattag 579

Claims

1. Use of a substance for detecting a combination of interleukin 37 level, interleukin 8 level and hypersensitive C response protein level in a sample obtained from a patient suffering from a novel coronavirus infection with SARS-CoV-2 in the manufacture of a product for prognostic evaluation of the novel coronavirus infection of said patient.

2. The use of claim 1, wherein the sample is obtained from a patient treated or not treated for SARS-CoV-2 novel coronavirus infection.

3. The use of claim 1, wherein the patient is a patient initially admitted to an earliest time node without therapeutic intervention.

4. The use of claim 1, wherein the level of interleukin 37 is one or more levels selected from the group consisting of: protein level of interleukin 37, nucleic acid level of interleukin 37.

5. The use of claim 1, wherein the level of interleukin 37 is the mRNA level of interleukin 37.

6. The use of claim 1, wherein the prognostic evaluation comprises one or more selected from the group consisting of: the development of a novel coronavirus infection of SARS-CoV-2 is a risk assessment of severe or critical illness or even death, or an assessment of recovery process and/or recovery time.

7. The application of claim 6, wherein the recovery process is selected from the group consisting of: viral nucleic acid to turn negative, lung CT improvement and/or cough elimination; the recovery time is the number of hospitalization days.

8. The use of claim 6, wherein the severe disorder has one or more characteristics from the group consisting of: the oxygen saturation in the shortness of breath and resting state is lower than the lower limit value, the arterial blood oxygen partial pressure and the oxygen inhalation concentration are lower than the lower limit value, and the pulmonary imaging shows that the focus obviously progresses by more than 50% within 24-48 hours;

The critical condition has one or more characteristics selected from the group consisting of: respiratory failure and requires mechanical ventilation, shock, incorporation of other organ failure, and ICU monitoring therapy.

9. The use according to claim 1, wherein the prognosis evaluation is performed by quantifying the detection index in the model and substituting it into the model equation for probability calculation, thereby achieving accurate judgment.

10. The use of claim 9, wherein said model comprises: a general linear model, a generalized linear model, a log linear model, a weight estimation model, a cluster analysis model, a binary Logistics regression analysis model, a multivariate Logistics regression analysis model, or a neural network.

11. Use according to any one of claims 1-10, wherein the product is a kit and/or a detection system.

12. A product for prognostic evaluation of a SARS-CoV-2 novel coronavirus infection in a patient suffering from a SARS-CoV-2 novel coronavirus infection, said product consisting of a substance that detects interleukin 37 levels, interleukin 8 levels and hypersensitive C-reactive protein levels in a sample.