Evidence map›Paper›PMID 40877945›Full record

ArticleVirology journal2025

From correlation to causation: unraveling the role of long non-coding RNAs in COVID-19 pathogenesis.

Tianfei Yu, Yunhan Zhang, Haolan Zhang, Ming Li

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in Virology journal, 2025. 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
–field-weighted citation impact
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. 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

4 authors.

Tianfei YuDepartment of Biotechnology, College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, 161006, China. yutianfei2001@163.com.
Yunhan ZhangDepartment of Biotechnology, College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, 161006, China.
Haolan ZhangDepartment of Biotechnology, College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, 161006, China.
Ming LiDepartment of Computer Science and Technology, College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, China. fionalee629@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heydari et al. present an intriguing study examining the role of three long non-coding RNAs (lncRNAs)-H19, taurine upregulated gene 1 (TUG1), and colorectal neoplasia differentially expressed (CRNDE)-in the context of Coronavirus Disease 2019 (COVID-19), focusing on their diagnostic potential and biological significance. The authors argue that these lncRNAs play a role in inflammatory and fibrotic processes associated with COVID-19 and demonstrate their potential utility as biomarkers using machine learning-based predictive models. While the study offers significant contributions to the field, there are limitations in its methodology, interpretative depth, and generalizability that merit closer examination. This commentary critically evaluates the findings, suggesting avenues for refinement and further research.

Indexed as

COVID-19RNA, Long NoncodingBiomarkersHumansSARS-CoV-2BiomarkersRNA, Long NoncodingCOVID-19 biomarkersDiagnostic potentialInflammation and fibrosisLong non-coding RNAs (lncRNAs)Machine learning applications

Identifiers

PMID40877945
PMCPMC12392523

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.