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Record W4322216951 · doi:10.1080/19490976.2023.2181922

The emerging roles of bacterial proteases in intestinal diseases

2023· review· en· W4322216951 on OpenAlexafffund
Alberto Caminero, Mabel Guzmán, Josie Libertucci, Alan Lomax

Bibliographic record

VenueGut Microbes · 2023
Typereview
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsKingston General HospitalQueen's UniversityMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchCrohn's and Colitis Canada
KeywordsProteasesProteaseBiologyIrritable bowel syndromeDiseaseImmunogenicityMicrobiologyImmunologyProteolytic enzymesEnzymeImmune systemBiochemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Proteases are an evolutionarily conserved family of enzymes that degrade peptide bonds and have been implicated in several common gastrointestinal (GI) diseases. Although luminal proteolytic activity is important for maintenance of homeostasis and health, the current review describes recent advances in our understanding of how overactivity of luminal proteases contributes to the pathophysiology of celiac disease, irritable bowel syndrome, inflammatory bowel disease and GI infections. Luminal proteases, many of which are produced by the microbiota, can modulate the immunogenicity of dietary antigens, reduce mucosal barrier function and activate pro-inflammatory and pro-nociceptive host signaling. Increased proteolytic activity has been ascribed to both increases in protease production and decreases in inhibitors of luminal proteases. With the identification of strains of bacteria that are important sources of proteases and their inhibitors, the stage is set to develop drug or microbial therapies to restore protease balance and alleviate disease.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.050
GPT teacher head0.380
Teacher spread0.330 · 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

Citations82
Published2023
Admission routes2
Has abstractyes

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