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Record W2053021647 · doi:10.1080/03008200490278070

Collagen Fibrillogenesis and mRNA Levels in the Maturing Rabbit Medial Collateral Ligament and Patellar Tendon

2004· article· en· W2053021647 on OpenAlexaff
Ian K.Y. Lo, Linda L. Marchuk, Kate E. Leatherbarrow, Cyril B. Frank, David A. Hart

Bibliographic record

VenueConnective Tissue Research · 2004
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFibrillogenesisFibrilDecorinTendonChemistryMedial collateral ligamentMessenger RNACollagen, type I, alpha 1Procollagen peptidaseExtracellular matrixConnective tissueCollagen fibrilType I collagenAnatomyLumicanLigamentCell biologyMolecular biologyEndocrinologyBiologyPathologyBiochemistryProteoglycanMedicine

Abstract

fetched live from OpenAlex

This study compared collagen fibril diameter and mRNA changes in a subset of molecules involved in collagen fibrillogenesis during postnatal development and at maturity of rabbit medial collateral ligament (MCL) and patellar tendon (PT). Tissue was analyzed by RT-PCR for mRNA levels and collagen fibril diameters were measured using transmission electron microscopy. Collagen fibril diameters increased from 3 to 14 weeks with mean fibril diameters of PT significantly greater than MCL at 9, 12, and 14 weeks and maturity. RT-PCR analysis showed decorin and lumican mRNA levels were significantly higher in PT than MCL at all ages. Type I collagen, MMP-11, and procollagen C proteinase enhancer mRNA levels also were higher in the PT than the MCL between 3 and 14 weeks but not at maturity. Further understanding of collagen fibrillogenesis by studying protein synthesis and matrix turnover during maturation may provide insight into the mechanism(s) by which fibrils accrete in maturing connective tissues and how they are altered during healing following injury.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.098
GPT teacher head0.400
Teacher spread0.302 · 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
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

Citations28
Published2004
Admission routes1
Has abstractyes

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