Plasma protein marker, detection reagent or detection tool for diagnosing severe-to-critical symptom of new coronal virus pneumonia

A technology for turning critical illnesses and proteins, which is applied in biological testing, disease diagnosis, and microbial detection/testing, and can solve the problems of no biomarkers for the occurrence, development, and outcome of coronavirus disease

Active Publication Date: 2020-10-20
WUHAN INST OF VIROLOGY CHINESE ACADEMY OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Currently, there are no biomarkers for the on...

Method used

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  • Plasma protein marker, detection reagent or detection tool for diagnosing severe-to-critical symptom of new coronal virus pneumonia
  • Plasma protein marker, detection reagent or detection tool for diagnosing severe-to-critical symptom of new coronal virus pneumonia
  • Plasma protein marker, detection reagent or detection tool for diagnosing severe-to-critical symptom of new coronal virus pneumonia

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 7 critically ill patients (S1-S7, referred to as S), and whole blood collected from 5 dead patients at 4 time points during hospitalization (the last one The time points were taken before death, F1T1-F1T4, F2T1-F2T4, F3T1-F3T4, F4T1-F4T4, F5T1-F5T4, referred to as FT1-FT4), a total of 27 whole blood samples.

[0037] The clinical classification is as follows: severe, meeting any of the following: 1. shortness of breath, RR ≥ 30 times / min; 2. resting state, oxygen saturation ≤ 93%; 3. partial pressure of oxygen in arterial blood (Pa02) / Oxygen inhalation concentration (Fi02)≤300mg1mmHg=0.1Pa), high altitude (over 1000 meters above sea level) areas should correct Pa02 / Fi02 according to the following formula: Pa02 / F102×[at...

Embodiment 2

[0044] Example 2: Screening markers for critical illness to critical illness by machine learning

[0045] 1. Experimental materials

[0046] Mass spectrum data of 27 plasma samples (sample described in 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.

[0047] 2. Experimental process

[0048] (1) To screen for differential proteins, to screen for the absolute value of the fold change (FC) of the proteins in the S group and the FT1-FT4 group to be greater than 0.8, and the two-tailed unpaired Welch T test to be less than 0.01 (|log2(FC)|>0.8, unpaired two-sided Welch's t-test, P<0.01).

[0049] (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.

[0050] (3) For each candidate OBC,...

Embodiment 3

[0058] Example 3: Validation of OBC Predictor Accuracy

[0059] 1. Experimental materials

[0060] Mass spectrum data of 27 plasma samples (samples described in Example 1), Python 3.7 (https: / / www.anaconda.com / ).

[0061] 2. Experimental process

[0062] (1) The identified proteins in OBC are divided into true positive (TruePositive, TP), true negative (TrueNegative, TN), false positive (False Positive, FP) and false negative (FalseNegative, FalseNegative, FN) ratio for chaos logic analysis.

[0063] (2) Draw the chaotic matrix through Scikit learn 0.22.1 software. Principal component analysis (PCA) was used to test the degree of separation of the two sets of data predicted by OBC.

[0064] The result is as image 3 As shown, the OBC combination predicts the true positive rate of 88% and the false positive rate of 12%.

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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 detection tool for diagnosing severe-to-critical symptomsof the new coronalpneumonia. ELISA detection finds that concentrations of cholesterol ester transfer protein, calcium binding protein S100A8, calcium binding protein S100A9 and C reactive protein have significant differences in plasma of a severe patient and a critical patient, so that the accuracy of the OBC combination for predicting severe cases to critical cases is further determined.

Description

technical field [0001] The present invention relates to the field of biotechnology, in particular to a plasma protein marker, a detection reagent or a detection tool for diagnosing a new coronary pneumonia from severe to critical. 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, and 15% to 20% of severe patients can develop into critically ill patients (disease death). Currently, 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 detection tools for diagnosing the transition from severe to critical of COVID-19. This protein combination is used for the...

Claims

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Application Information

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IPC IPC(8): G01N33/68C12Q1/70
CPCC12Q1/701C12Q2600/158G01N33/56983G01N33/6893G01N2800/50
Inventor 周溪尚游张定宇薛宇邱洋舒婷吴迪黄霂晗
Owner WUHAN INST OF VIROLOGY CHINESE ACADEMY OF SCI
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