Evidence map›Paper›PMID 41437945›Full record

ReviewResuscitation plus2026

Multi-omics in cardiac arrest: a systematic review.

Huaqing Ye, Kaiyi Wang, Wentao Sang, Luyao Gao, Zhizhou Liu, Chao Lan, Yuan Bian, Yuguo Chen, Feng Xu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Resuscitation plus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Huaqing YeDepartment of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Kaiyi WangDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Wentao SangDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Luyao GaoDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Zhizhou LiuDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Chao LanDepartment of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Yuan BianDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Yuguo ChenDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
Feng XuDepartment of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiac arrest (CA) poses significant public health challenges, with high incidence and mortality rates. Characterized by its rapid progression and high mortality, there is a lack of effective pharmacotherapy for CA, making prevention and early intervention crucial. Advancements in genomics, transcriptomics, proteomics, and metabolomics offer profound insights into the pathophysiology and etiology of CA, potentially leading to novel diagnostic biomarkers and therapeutic targets. This comprehensive review explores recent advancements in omics technologies and their applications in understanding CA, highlighting the role of genetic variations, RNA transcripts, protein profiles, and metabolites in CA. It also addresses the limitations of current studies and proposes future directions for omics research in unraveling the complexities of CA. This review aims to advance our understanding of CA and pave the way for precision treatment strategies.

Indexed as

BiomarkersCardiac arrestIntegrative analysisOmicsPrecision medicine

Identifiers

PMID41437945
PMCPMC12719694

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.