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Proteoglycan and ADAMTS expression during fibrogenic remodeling in the liver

2011· article· en· W3173400757 on OpenAlexaff
Sean B Maurice, Cody L Crick, Wan‐Cheol Kim, Christine Law, Paul Winwood

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsVersicanExtracellular matrixADAMTSProteoglycanMatrix metalloproteinaseFibrosisCell biologyHepatic stellate cellPathologyChemistryBiologyMetalloproteinaseMedicineThrombospondinBiochemistry

Abstract

fetched live from OpenAlex

Liver fibrosis and end stage cirrhosis are leading causes of morbidity and mortality worldwide. Changes in the structure and composition of the proteoglycan rich extracellular matrix (ECM) are known to play a role in these processes, though these changes are incompletely understood and characterized. An increased understanding of these events may one day allow early recognition and targeted therapy, leading to the reversibility of liver fibrosis as a future clinical outcome. In other tissues, proteoglycans including versican are key components of fibrotic ECM that modulates cellular functions. The ADAMTS enzymes are now known to cleave versican and other proteoglycans in a manner previously ascribed to the MMPs. This work focuses on the role of the hepatic stellate cell in synthesizing a proteoglycan rich fibroproliferative ECM in vitro and in vivo. We have investigated several candidate proteoglycans, including versican, along with several candidate ADAMTS enzymes, in two models of liver fibrosis. Using qPCR and confocal microscopy, we report a more thorough expression profile for these important ECM constituents than has previously been described. These findings underscore the need to better characterize the fibroproliferative liver ECM. Grant Funding Source : Northern Medical Program

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.207

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 head0.046
GPT teacher head0.249
Teacher spread0.203 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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