Evidence map›Paper›PMID 27432263›Full record

ReviewCurrent infectious disease reports2016

Differential Paradigms in Animal Models of Sepsis.

S Manoj Kumar Kingsley, B Vishnu Bhat

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current infectious disease reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  13. A model of porcine polymicrobial septic shock.Intensive care medicine experimental · 2023
    Article
  14. Review
  15. Development of a High-Throughput Urosepsis Mouse Model.Pathogens (Basel, Switzerland) · 2023
    Article
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  17. Review
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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

2 authors.

S Manoj Kumar KingsleyDepartment of Neonatology, Jawaharlal Institute of Post Graduate Medical Education and Research (JIPMER), Puducherry, 605006, India.
B Vishnu BhatDepartment of Neonatology, Jawaharlal Institute of Post Graduate Medical Education and Research (JIPMER), Puducherry, 605006, India. drvishnubhat@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a serious clinical problem involving complex mechanisms which requires better understanding and insight. Animal models of sepsis have played a major role in providing insight into the complex pathophysiology of sepsis. There have been various animal models of sepsis with different paradigms. Endotoxin, bacterial infusion, cecal ligation and puncture, and colon ascendens stent peritonitis models are the commonly practiced methods at present. Each of these models has their own advantages and also confounding factors. We have discussed the underlying mechanisms regulating each of these models along with possible reasons why each model failed to translate into the clinic. In animal models, the timing of development of the hemodynamic phases and the varied cytokine patterns could not accurately resemble the progression of clinical sepsis. More often, the exuberant and transient pro-inflammatory cytokine response is only focused in most models. Immunosuppression and apoptosis in the later phase of sepsis have been found to cause more damage than the initial acute phase of sepsis. Likewise, better understanding of the existing models of sepsis could help us create a more relevant model which could provide solution to the currently failed clinical trials in sepsis.

Indexed as

Animal modelsClinical sepsisClinical trialsConfounding factorsPathophysiology

Identifiers

What OpenQuestion holds

Textmetadata
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.