Evidence map›Paper›PMID 38532129›Full record

ArticleArchives of virology2024

Proteomic characteristics of the treatment trajectory of patients with COVID-19.

Xue Li, Guilan Ding, Shilin Li, Chun Liu, Xiangde Zheng, Jinliang Luo, Sichun He, Fanwei Zeng, Xuan Huang, Fanxin Zeng

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of virology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 43% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Xue Li *Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
Guilan Ding *Department of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China.
Shilin Li *Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
Chun LiuDepartment of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China.
Xiangde ZhengDepartment of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China.
Jinliang LuoDepartment of General Practice, Dazhou Central Hospital, Dazhou, Sichuan, China.
Sichun HeDepartment of Laboratory Medicine, Dazhou Central Hospital, Dazhou, Sichuan, China.
Fanwei ZengDepartment of Orthopedics, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China. zengfx@pku.edu.cn.
Xuan HuangMedical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. huangxuan03@163.com.
Fanxin ZengDepartment of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China. 1244361462@qq.com.ORCID http://orcid.org/0000-0002-7337-4463
Dazhou Central Hospital · CNSichuan University · CNCapital Medical University · CN

Funding

Dazhou Science and Technology Bureau 21CDDZ-26Dazhou Science and Technology Bureau 21ZDYF0021Dazhou Science and Technology Bureau 22ZDYF0020Dazhou Science and Technology Bureau ZDYF0001
6 · The paper itself

Abstract

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has prompted global concern due to its profound impact on public health and the economy. Effective treatment of COVID-19 patients in the acute phase or of those with long COVID is a major challenge. Using data-independent acquisition (DIA) technology, we performed proteomic profiling on plasma samples from 22 COVID-19 patients and six healthy controls at Dazhou Central Hospital. Random forest and least absolute shrinkage and selection operator algorithms were used for analysis at various COVID-19 treatment stages. We identified 79 proteins that were differentially expressed between COVID-19 patients and healthy controls, mainly involving pathways associated with cell processes and binding. Across different treatment stages of COVID-19, five proteins-PI16, GPLD1, IGFBP3, KRT19, and VCAM1-were identified as potential molecular markers for dynamic disease monitoring. Furthermore, the proteins BTD, APOM, IGKV2-28, VWF, C4BPA, and C7 were identified as candidate biomarkers for distinguishing between SARS-CoV-2 positivity and negativity. Analysis of protein change profiles between the follow-up and healthy control groups highlighted cardiovascular changes as a concern for patients recovering from COVID-19. Our study revealed the infection profiles of SARS-CoV-2 at the protein expression level comparing different phases of COVID-19. DIA mass spectrometry analysis of plasma samples from COVID-19 patients undergoing treatment identified key proteins involved in signaling pathways that might be used as markers of the recovery phase. These findings provide insight for the development of therapy options and suggest potential blood biomarkers for COVID-19.

Indexed as

COVID-19BiomarkersCOVID-19 Drug TreatmentHumansPandemicsPost-Acute COVID-19 SyndromeProteomicsSARS-CoV-2Biomarkers

Identifiers

PMID38532129
OpenAlexW4393231687

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.