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Record W4229033136 · doi:10.1139/bcb-2021-0284

Working from within: how secretory leukocyte protease inhibitor regulates the expression of pro-inflammatory genes

2021· review· en· W4229033136 on OpenAlexaffvenue
Tinsley Claire Douglas, Sari S. Hannila

Bibliographic record

VenueBiochemistry and Cell Biology · 2021
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSLPIElafinSerine proteaseCell biologyBiologyTranscription factorProtease inhibitor (pharmacology)ProteaseRegulatorLipopolysaccharideGeneNeutrophil elastaseInflammationImmunologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Secretory leukocyte protease inhibitor (SLPI) is a small but powerful member of the serine protease inhibitor family, which includes proteins such as elafin and α1-antitrypsin. These proteins all have similar structures and antiprotease abilities, but SLPI has been found to have an additional role as an anti-inflammatory factor. It can inhibit the production of pro-inflammatory cytokines in cells stimulated with lipopolysaccharide, prevent neutrophil infiltration in murine models of lung and liver injury, and regulate the activity of the transcription factor NF-κB. In this review, we will revisit SLPI’s unique biochemistry, and then explore how its anti-inflammatory functions can be linked to more recent findings showing that SLPI can localize to the nuclei of cells, bind DNA, and act as a regulator of gene expression.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.040
GPT teacher head0.276
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2021
Admission routes2
Has abstractyes

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