Evidence map›Paper›PMID 41868133›Full record

ReviewFrontiers in pharmacology2026

Early biomarkers for predicting sepsis-induced shock: insights from inflammatory pathways and immune response.

Jia Li, Qiufang Zhao, Haiyun Gao, Hongjun Wang, Cong Guo, Xiaoling Feng

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2026. 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.

Jia LiDepartment of Emergency Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, China.
Qiufang ZhaoDepartment of Emergency Medicine, Hospital of Traditional Chinese Medicine, Shijiazhuang, China.
Haiyun GaoDepartment of Emergency Medicine, Hospital of Traditional Chinese Medicine, Shijiazhuang, China.
Hongjun WangDepartment of Pharmacy, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, China.
Cong GuoDepartment of Pharmacy, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, China.
Xiaoling FengHebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe sepsis-induced shock is one of the most challenging problems in critical care despite the progress made in treatment. Recognizing high-risk patients early on is critical for successful results, and the standard diagnostic approaches to such an ailment fail to identify it prior to shock setting in. Biomarkers have become promising diagnostic, prognostic predictors and treatment surveillance platforms in sepsis in the past few years. This review discusses the significance of biomarkers, e.g., cytokines, chemokines, acute-phase proteins and immune dysfunction markers in the pathogenesis of sepsis-induced shock. Additionally, we investigate the potential of new biomarkers, including microRNAs, circular RNAs, endothelial biomarkers, gene signatures, a combination of multimarker panels and machine learning models to improve the diagnostic and prognostic proficiency. As effective as they may seem, they (biomarkers) create challenges in clinical application, including variability, standardization, cost and regulatory approval. This review discusses future approaches to sepsis biomarker research, focusing on personalized medicine, global availability, and clinical validation to address barriers currently experienced in improving sepsis management worldwide.

Indexed as

diagnostic biomarkersearly biomarkersimmune responseinflammatory pathwayssepsis-induced shock

Identifiers

PMID41868133
PMCPMC12999895

What Socratic 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 Socratic graph.