Evidence map›Paper›PMID 40297590›Full record

ReviewFrontiers in immunology2025

Targeting the immuno-inflammatory-microbial network: a key strategy for sepsis treatment.

Yue Xu, Jiaxin Wang, Rui Yuan, Zhu Qin, Kunlan Long, Peiyang Gao

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. 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.

Yue XuHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jiaxin WangHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Rui YuanHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Zhu QinHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Kunlan LongHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Peiyang GaoHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a life-threatening condition caused by a dysregulated host response to infection, remaining a major global health challenge despite clinical advances. Therapeutic challenges arise from antibiotic misuse, incomplete understanding of its complex pathophysiology, and the unresolved interplay of immune dysregulation and microbiota disruption. Investigating microbial homeostasis in the shift from cytokine storm to immunosuppression may elucidate the interplay between microbial metabolites, immune dysfunction, and organ injury, providing a foundation for targeted therapies and drug development. Traditional Chinese Medicine (TCM) has demonstrated significant advantages in mitigating sepsis-associated cytokine storms and modulating gut microbiota homeostasis, offering a promising strategy for developing highly effective and less toxic targeted monomeric compounds. Elucidating the interactions within the immune-inflammation-microbiota network in sepsis paves the way for biomarker-driven personalized therapeutic approaches.

Indexed as

Gastrointestinal MicrobiomeSepsisAnimalsDrugs, Chinese HerbalHumansInflammationMedicine, Chinese TraditionalDrugs, Chinese Herbalcytokine stormimmune balanceprobioticstraditional Chinese medicine (TCM)treatment outcome

Identifiers

PMID40297590
PMCPMC12034552

What OpenQuestion holds

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LicenceCC BY
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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.