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Record W2299188756 · doi:10.2741/s430

Degradomics of matrix metalloproteinases in inflammatory diseases

2015· review· en· W2299188756 on OpenAlexaff
Antoine Dufour

Bibliographic record

VenueFrontiers in Bioscience-Scholar · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProteasesMatrix metalloproteinaseInflammationProteolysisChemokineImmune systemCell biologyInflammatory responseBiologyImmunologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Organisms have evolved to react to stress, tissue damage and pathogen invasion to assure their survival. Leukocytes are the primary responders and they regulate repair, immune defense and inflammation with the aid of a wide variety of other cells (e.g. epithelial, fibroblasts). To assure proper responses, a plethora of proteins are involved including signaling molecules, chemokines and proteases to orchestrate a step-by-step reaction. Inflammation is an essential biological process, however, when it persists, it can lead to various diseases that are challenging to heal or cure. The technologies and techniques covered in this book chapter can be applied to study all proteases and their inhibitors although will be centered on the matrix metalloproteinases (MMPs). It will focus on the proteolysis performed by MMPs, their various beneficial and detrimental effects in inflammation and the novel methods to study their roles on human diseases.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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.

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

Citations19
Published2015
Admission routes1
Has abstractyes

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