Cartilage and chondrocyte response to extreme muscular loading and impact loading: Can in vivo pre-load decrease impact-induced cell death?
Bibliographic record
Abstract
BACKGROUND: Impact loading causes cartilage damage and cell death. Pre-loading prior to impact loading may protect cartilage and chondrocytes. However, there is no systematic evidence and understanding of the effects of pre-load strategies on cartilage damage and chondrocyte death. This study aimed at determining the effects of the pre-load history on impact-induced chondrocyte death in an intact joint. METHODS: Patellofemoral joints from 42 rabbits were loaded by controlled quadriceps muscle contractions and an external impacter. Two extreme muscular loading conditions were used: (i) a short-duration, high intensity, static muscle contraction, and (ii) a long-duration, low-intensity, cyclic muscle loading protocol. A 5-Joule centrally-oriented, gravity-accelerated impact load was applied to the joints. Chondrocyte viability was quantified following the muscular loading protocols, following application of the isolated impact loads, and following application of the impact loads that were preceded by the muscular pre-loads. Joint contact pressures were measured for all loading conditions by a pressure-sensitive film. FINDINGS: Comparing to cartilage injured by impact loading alone, cartilage pre-loaded by static, maximal intensity, short-term muscle loads had lower cell death, while cartilage pre-loaded by repetitive, low-intensity, long-term muscular loads has higher cell death. The locations of peak joint contact pressures were not strongly correlated with the locations of greatest cell death occurrence. INTERPRETATION: Static, high intensity, short muscular pre-load protected cells from impact injury, whereas repetitive, low intensity, prolonged muscular pre-loading to the point of muscular fatigue left the chondrocytes vulnerable to injury. However, cell death seems to be unrelated to the peak joint pressures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".