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Record W2093180995 · doi:10.1142/s0218957704001223

MUSCLE-INDUCED PATELLOFEMORAL JOINT LOADING RAPIDLY AFFECTS CARTILAGE mRNA LEVELS IN A SITE SPECIFIC MANNER

2004· article· en· W2093180995 on OpenAlexaff
Andrea L. Clark, Linda R. Mills, David A. Hart, Walter Herzog

Bibliographic record

VenueJournal of Musculoskeletal Research · 2004
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCartilageMessenger RNAIn vivoPatellaArticular cartilageIn vitroAnatomyChemistryOsteoarthritisCell biologyBiomedical engineeringBiologyMedicinePathologyBiochemistryGene

Abstract

fetched live from OpenAlex

Mechanical loading of articular cartilage affects the synthesis and degradation of matrix macromolecules. Much of the work in this area has involved mechanical loading of articular cartilage explants or cells in vitro and assessing biological responses at the mRNA and protein levels. In this study, we developed a new experimental technique to load an intact patellofemoral joint in vivo using muscle stimulation. The articular cartilages were cyclically loaded for one hour in a repeatable and measurable manner. Cartilage was harvested from central and peripheral regions of the femoral groove and patella, either immediately after loading or after a three hour recovery period. Total RNA was isolated from the articular cartilage and biological responses were assessed on the mRNA level using the reverse transcriptase-polymerase chain reaction. Articular cartilage from intact patellofemoral joints demonstrated heterogeneity at the mRNA level for six of the genes assessed independent of the loading protocol. Cyclical loading of cartilage in its native environment led to alterations in mRNA levels for a subset of molecules when assessed immediately after the loading period. However, the increases in TIMP-1 and decreases in bFGF mRNA levels were transient; being present immediately after load application but not after a three hour recovery period.

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)0.003
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 score0.137
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.363
Teacher spread0.272 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

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