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Granzyme B contributes to extracellular matrix degradation in UV‐treated skin (1046.1)

2014· article· en· W1494301439 on OpenAlexafffund
Leigh G. Parkinson, Ana Isabel Toro-Montoya, David J. Granville

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsExtracellular matrixGranzyme BDecorinFibronectinChemistryInflammationMatrix metalloproteinaseCell biologyImmunologyKnockout mouseProteoglycanBiologyImmune systemBiochemistryCD8

Abstract

fetched live from OpenAlex

Extracellular matrix (ECM) degradation is a hallmark of many chronic inflammatory diseases that can lead to a loss of function, aging and disease progression. Granzyme B (GzmB), a serine protease that is expressed by a variety of cells, has been shown to accumulate in the extracellular space during chronic inflammation and cleave a number of ECM proteins. Using a model of UV‐induced chronic inflammation in the skin, we hypothesized that GzmB contributes to ECM degradation through both direct cleavage of ECM proteins and indirectly through the induction of other proteinases, which leads to a phenotype of aged skin. Wild‐type and GzmB‐knockout (KO) mice were repeatedly exposed to minimal erythemal doses of solar simulated UV‐irradiation for up to 20 weeks. GzmB expression was significantly increased in wild‐type treated skin compared to controls. GzmB deficiency significantly protected against the formation of wrinkles and the loss of dermal collagen density, which was related to the cleavage of decorin, an abundant proteoglycan involved in collagen fibrillogenesis and integrity. GzmB also cleaved fibronectin, and fibronectin fragments have been shown to increase the expression of collagen‐degrading matrix metalloproteinases (MMPs) in fibroblasts. Collectively, these findings indicate a significant role of GzmB in ECM degradation, which may have implications in many chronic inflammatory diseases. Grant Funding Source : Supported by CIHR

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.265
Teacher spread0.252 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2014
Admission routes2
Has abstractyes

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