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Plasma protein marker, detection reagent or reagent tool for diagnosing severe change of new coronal virus pneumonia from mild symptom

A protein, mild technology

Active Publication Date: 2020-10-20
武汉市金银潭医院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Currently, there are no biomarkers for the onset, progression and outcome of coronavirus disease

Method used

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  • Plasma protein marker, detection reagent or reagent tool for diagnosing severe change of new coronal virus pneumonia from mild symptom
  • Plasma protein marker, detection reagent or reagent tool for diagnosing severe change of new coronal virus pneumonia from mild symptom
  • Plasma protein marker, detection reagent or reagent tool for diagnosing severe change of new coronal virus pneumonia from mild symptom

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] Example 1: Mass Spectrometry Analysis of Plasma Samples from Patients with New Coronary Pneumonia

[0035] 1. Experimental materials

[0036] Plasma samples from patients with new coronary pneumonia, from a hospital, including whole blood collected from 10 mild patients (M1-M10, referred to as M) and whole blood collected from 7 severe patients (S1-S7, referred to as S), total 17 whole blood samples. According to the "Diagnosis and Treatment Plan for Pneumonia Infected by Novel Coronavirus (Trial Version 6)" jointly issued by the General Office of the National Health Commission and the Office of the State Administration of Traditional Chinese Medicine, the clinical classification is as follows: mild, mild clinical symptoms, no pneumonia manifestations in imaging ;Severe, meeting any of the following: 1. Shortness of breath, RR≥30 times / min; 2. In a resting state, oxygen saturation ≤93%; 3. Arterial partial pressure of oxygen (Pa02) / inhaled oxygen concentration (Fi02)≤...

Embodiment 2

[0043] Example 2: Machine learning screening for markers from mild disease to severe disease

[0044] 1. Experimental materials

[0045] Mass spectrometry data of 17 plasma samples (see Example 1), Python 3.7 (https: / / www.anaconda.com / ), Scikit learn 0.22.1 (https: / / scikit-learn.org / stable / ). The source code of this experiment is https: / / github.com / Ning-310 / POC-19.

[0046] 2. Experimental process

[0047] (1) To screen for differential proteins, to screen for the absolute value of the fold change (FC) of proteins in the S group and the M group is greater than 0.8, and the two-tailed unpaired Welch T test is less than 0.01 (|log2(FC)|>0.8, unpaired two-sided Welch's t-test, P<0.01).

[0048] (2) Randomly select no more than 5 proteins from the differential proteins to form a potential optimal marker combination (OBC). The initial weight value of each protein is set to 1, and 1000 OBC candidates are set.

[0049] (3) For each candidate OBC, we randomly generate a training d...

Embodiment 3

[0057] Example 3: Validation of OBC Predictor Accuracy

[0058] 1. Experimental materials

[0059] Mass spectrum data of 17 plasma samples (see Example 1), Python 3.7 (https: / / www.anaconda.com / ).

[0060] 2. Experimental process

[0061] (1) Identify the true positive (TruePositive, TP), true negative (True Negative, TN), false positive (False Positive, FP) and false negative (False Negative, FN) ratio for chaotic logic analysis.

[0062] (2) Draw the chaotic matrix through Scikit learn 0.22.1 software.

[0063] The result is as image 3 As shown, the OBC combination predicts that the true positive rate of mild to severe disease is 100%, and the false positive rate is 0.

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Abstract

The invention relates to the technical field of biology, in particular to a plasma protein marker, a detection reagent or a reagent tool for diagnosing severe change of new coronal virus pneumonia from mild symptoms. The plasma protein marker comprises zinc-alpha2-glycoprotein, mucoid 1, mucoid 2 and a complement factor I. Results show that the area (AUC) value under a subject working characteristic curve (ROC) for predicting the severe illness of a patient suffering from mild symptoms through combination of the four proteins is 1, the true positive rate for predicting the severe symptoms of the patient suffering from mild symptoms is 100%, and the false positive rate for predicting the severe symptoms of the patient suffering from mild symptoms is 0. In addition, enzyme-linked immunosorbent assay (ELISA) experiments find that the concentrations of the four proteins have significant differences in blood plasma of patients with mild symptoms and critical patients, so that the accuracy of predicting the severe case of mild symptoms by using the combination is further determined.

Description

technical field [0001] The present invention relates to the field of biotechnology, in particular to plasma protein markers, detection reagents or reagent tools for the diagnosis of COVID-19 from mild to severe. Background technique [0002] The novel coronavirus disease (COVID-19) caused by SARS-CoV-2 infection has a wide range of symptoms, from mild cold and fever, to respiratory syndrome, pneumonia, systemic multiple organ failure and even death; about 10% to 20% Mild patients can develop into severe patients. At present, there are no biomarkers for the occurrence, development and outcome of coronavirus disease. Contents of the invention [0003] In view of this, the present invention provides plasma protein markers, detection reagents or reagent tools for the diagnosis of COVID-19 from mild to severe. Zinc-alpha-2-glycoprotein, Alpha-1-acid glycoprotein 1, Alpha-1-acid glycoprotein 2, and Complement factor I protein concentrations were significantly different in the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/68G01N33/569
CPCG01N33/68G01N33/56983G01N2333/165G01N2333/4716G01N2333/4725G01N2333/4728G01N2800/56Y02A50/30
Inventor 张定宇周溪尚游薛宇邱洋舒婷吴迪
Owner 武汉市金银潭医院
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