Evidence map›Paper›PMID 39040114›Full record

ReviewFrontiers in immunology2024

The potential immunological mechanisms of sepsis.

Xinyu Zhang, Yujing Zhang, Shiying Yuan, Jiancheng Zhang

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–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

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Xinyu Zhang *Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yujing Zhang *Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shiying YuanDepartment of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiancheng ZhangDepartment of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is described as a life-threatening organ dysfunction and a heterogeneous syndrome that is a leading cause of morbidity and mortality in intensive care settings. Severe sepsis could incite an uncontrollable surge of inflammatory cytokines, and the host immune system's immunosuppression could respond to counter excessive inflammatory responses, characterized by the accumulated anti-inflammatory cytokines, impaired function of immune cells, over-proliferation of myeloid-derived suppressor cells and regulatory T cells, depletion of immune effector cells by different means of death, etc. In this review, we delve into the underlying pathological mechanisms of sepsis, emphasizing both the hyperinflammatory phase and the associated immunosuppression. We offer an in-depth exploration of the critical mechanisms underlying sepsis, spanning from individual immune cells to a holistic organ perspective, and further down to the epigenetic and metabolic reprogramming. Furthermore, we outline the strengths of artificial intelligence in analyzing extensive datasets pertaining to septic patients, showcasing how classifiers trained on various clinical data sources can identify distinct sepsis phenotypes and thus to guide personalized therapy strategies for the management of sepsis. Additionally, we provide a comprehensive summary of recent, reliable biomarkers for hyperinflammatory and immunosuppressive states, facilitating more precise and expedited diagnosis of sepsis.

Indexed as

SepsisAnimalsBiomarkersCytokinesHumansImmune ToleranceBiomarkersCytokinesbiomarkerscell death modeimmune cellsmachine learningsepsis

Identifiers

PMID39040114
PMCPMC11260823

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Registered trials

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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.