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Record W2889714967 · doi:10.1038/s41598-018-32503-1

Menisci protect chondrocytes from load-induced injury

2018· article· en· W2889714967 on OpenAlexaff
Ziad Abusara, Stephen H. J. Andrews, Markus Von Kossel, Walter Herzog

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChemistryCell biologyBiology

Abstract

fetched live from OpenAlex

Menisci in the knee joint are thought to provide stability, increased contact area, decreased contact pressures, and offer protection to the underlying articular cartilage and bone during joint loading. Meniscal loss or injury is typically accompanied by degenerative changes in the knee, leading to an increased risk for osteoarthritis in animals including humans. However, the detailed mechanisms underlying joint degeneration and the development of osteoarthritis remain largely unknown, and the acute effects of meniscal loss have not been studied systematically. We developed a microscopy-based system to study microscale joint mechanics in living mice loaded by controlled muscular contractions. Here, we show how meniscal loss is associated with rapid chondrocyte death (necrosis) in articular cartilage within hours of injury, and how intact menisci protect chondrocytes in vivo in the presence of intense muscle-based joint loading and/or injury to the articular cartilage. Our findings suggest that loading the knee after meniscal loss is associated with extensive cell death in intact and injured knees, and that early treatment interventions should be aimed at preventing chondrocyte death.

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 score0.003

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 head0.018
GPT teacher head0.274
Teacher spread0.255 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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