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Record W3092432246 · doi:10.1097/shk.0000000000001679

Calpain Activation and Organ Failure in Sepsis: Molecular Insights and Therapeutic Perspectives

2020· review· en· W3092432246 on OpenAlexaff
Yifan Huang, Grace Wang, Tianqing Peng

Bibliographic record

VenueShock · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalpain Protease Function and Regulation
Canadian institutionsWestern UniversityLondon Health Sciences CentreLawson Health Research InstituteUniversity of Toronto
Fundersnot available
KeywordsCalpastatinCalpainSepsisOrgan dysfunctionProteasesMedicineImmunologyCysteine proteasePathogenesisBioinformaticsBiologyEnzyme

Abstract

fetched live from OpenAlex

ABSTRACT: Sepsis is a severe systemic response to infection; its ensuing organ failure commonly portends an unfavorable prognosis. Despite the fact that sepsis has been studied for decades, the molecular mechanisms underlying sepsis-induced organ dysfunction remain elusive and more complex than previously thought, and effective therapies are extremely limited. Calpain is a type of calcium-dependent cysteine protease that includes dozens of isoforms. Calpain, as well as its endogenous-specific inhibitor calpastatin, have been implicated in the pathogenesis of sepsis-induced organ dysfunction. Further, there is an accumulating body of evidence supporting the beneficial effect of calpain inhibition or regulation on multiple organ failure in sepsis. Better understanding of the underlying molecular mechanisms is helpful in the development of calpain/calpastatin-targeted therapeutic strategies to protect against sepsis-induced organ injury. The aim of this review is to summarize the recent literature and evidence surrounding the role of the calpain/calpastatin system in the process of organ dysfunction caused by sepsis-including regulation of cell death, modulation of inflammatory response, and disruption of critical proteins-to provide guidance for future research and therapy development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.266
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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