Evidence map›Paper›PMID 39071490›Full record

ArticleOpen life sciences2024

Circular RNAs as potential biomarkers for male severe sepsis.

Liang Jun, Zhonghua Wang, Shouhong Wang, Xiaolong Liao, Tiehe Qin, Weixin Guo

Abstract read
In one paragraph

Article in Open life sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Liang JunDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Zhonghua WangDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Shouhong WangDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Xiaolong LiaoDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Tiehe QinDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Weixin GuoDepartment of Intensive Care, Guangdong Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Road, Guangzhou 510080, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circular RNAs (circRNAs) play important roles in many human diseases. However, their role in the development of severe sepsis, a condition that remains one of the main causes of death in intensive care units, has not yet been defined. In this study, we interrogated the molecular mechanisms of circRNAs in severe sepsis. We profiled the expression levels of 5,680 circRNAs in plasma extracted from blood samples of 9 severe sepsis cases or 9 controls (male, age 78 ± 7) using the Human circRNA Array. To enrich protein-coding genes hosting severe sepsis-related circRNAs, we conducted gene ontology and pathways analyses. Out of the identified 760 differentially expressed circRNAs, 404 were upregulated while 356 were downregulated (fold change [FC] ≥2 or ≤-2, and false discovery ratio <0.05). Circ-0008285 (located in exons of

Indexed as

circRNA arraycircular RNAsmicroRNAssevere sepsistranscriptome

Identifiers

PMID39071490
PMCPMC11282911

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.