Evidence map›Paper›PMID 41567206›Full record

ReviewFrontiers in immunology2025

The endothelial-immunothrombotic storm in viral sepsis: lessons from COVID-19.

Kaihuan Zhou, Yin Chen, Jielong Pang, Jianfeng Zhang, Junyu Lu

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Current issues in molecular biology · 2026
    Article
  3. Observational
  4. Article
  5. Review
  6. mFrontiers in immunology · 2026
    Review
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

5 authors.

Kaihuan Zhou *Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yin Chen *Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jielong PangDepartment of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jianfeng ZhangDepartment of Emergency Medicine, Wuming Hospital of Guangxi Medical University, Nanning, China.
Junyu LuIntensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Taking COVID-19 as an illustrative example, this review systematically elucidates the central pathological mechanism of viral sepsis, termed the endothelial-immunothrombotic storm. This mechanism is initiated by the direct viral infection of endothelial cells, which provokes excessive immune activation and disrupts coagulation through immunothrombosis, including cytokine storms, NETosis, and complement activation. Meanwhile, these processes establish a vicious cycle leading to multiple organ failure. Compared with classical bacterial sepsis, viral sepsis exhibits distinctive features such as interferon dysregulation, direct endothelial damage, a hypercoagulable state, and T-cell exhaustion. This review integrates the latest research findings, contrasts the pathophysiological differences between viral and bacterial sepsis, and proposes precision strategies focused on endothelial protection, immune modulation, and anticoagulation. Finally, we discuss the clinical translational prospects of these approaches and suggests directions for future research.

Indexed as

COVID-19Cytokine Release SyndromeEndothelial CellsEndothelium, VascularSARS-CoV-2SepsisThrombosisAnimalsComplement ActivationHumansT-Cell ExhaustionCOVID-19cytokine stormendothelial injuryimmunothrombosisviral sepsis

Identifiers

PMID41567206
PMCPMC12816216

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.