Evidence map›Paper›PMID 41744755›Full record

ReviewCells2026

New and Emerging Research Models for Sepsis.

Saichaitanya Nallajennugari, Xiang Li, Mingui Fu

Abstract readReview
In one paragraph

Review in Cells, 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. Review
  2. 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

3 authors.

Saichaitanya NallajennugariDepartment of Biomedical Science, Shock/Trauma Research Center, School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA.
Xiang LiDepartment of Biomedical Science, Shock/Trauma Research Center, School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA.ORCID 0009-0001-7260-8133
Mingui FuDepartment of Biomedical Science, Shock/Trauma Research Center, School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA.ORCID 0000-0001-6315-8372

Funding

UMKC School of Medicine Bridge 2025
6 · The paper itself

Abstract

Human sepsis is a complex disease that manifests with a diverse range of phenotypes and inherent variability among individuals, making it hard to develop a comprehensive animal model. Despite this difficulty, numerous animal models have been developed that capture many key aspects of human sepsis. Though the animal models have contributed to the fundamental advances in understanding the pathogenesis of septic patients, the translational value of these models has been constantly questioned because many clinical trials of targeted therapies based on the advances in animal models have failed, highlighting the urgent need for developing new research models or refining previous animal models for sepsis research. In this review, we will summarize recent advances in new and emerging research models for sepsis, including human-based in vitro systems, highly tailored animal models, AI and digital models analyzing vast datasets to define patient subgroups and predict outcomes, and the FAMOUS framework ensuring that therapies are tested against the specific mechanism they are designed to target. We will discuss the strengths and limitations of these models, reflecting the clinical course of sepsis, and discuss the future directions in this subject area.

Indexed as

Models, BiologicalSepsisAnimalsDisease Models, AnimalHumansanimal modelsimmune responseinfectionorgan-on-chipsepsis

Identifiers

PMID41744755
PMCPMC12939978

What OpenQuestion 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 OpenQuestion graph.