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Analysis of Aggrecan Catabolism by Immunoblotting and Immunohistochemistry

2011· article· en· W51342837 on OpenAlexafffund
Peter J. Roughley, John S. Mort

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsShriners Hospitals for Children - Canada
FundersCanadian Institutes of Health Research
KeywordsAggrecanKeratan sulfateAggrecanaseChondroitin sulfateADAMTSChemistryExtracellular matrixCartilageMatrix metalloproteinaseProteolysisCell biologyOsteoarthritisBiochemistryMetalloproteinaseGlycosaminoglycanBiologyAnatomyArticular cartilagePathologyMedicineThrombospondinEnzyme

Abstract

fetched live from OpenAlex

Aggrecan is essential for the normal function of articular cartilage and intervertebral disc, where it provides the ability for the tissues to withstand compressive loading. This property depends on both the high charge density endowed by its numerous chondroitin sulfate and keratan sulfate chains and its ability to form large molecular aggregates via interaction with hyaluronan. Degradation of aggrecan via the action of proteases takes place throughout life and the degradation products accumulate in the tissue and impair its function. Such degradation is exacerbated in degenerative or inflammatory joint disorders. The use of antibodies recognizing the various regions of aggrecan and the neoepitopes generated upon proteolytic cleavage has shown that matrix metalloproteinases and aggrecanases, members of the ADAMTS family, are responsible for aggrecan degradation, both throughout life and in disease. By using immunoblotting techniques, it is possible to determine the extent of aggrecan degradation and to identify the degradation products that have accumulated in the tissue, and immunohistochemistry allows the location of the aggrecan degradation to be established.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.013
GPT teacher head0.338
Teacher spread0.325 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations9
Published2011
Admission routes2
Has abstractyes

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