Altered cell metabolism in tissues of the knee joint in a rabbit model of Botulinum toxin A‐induced quadriceps muscle weakness
Bibliographic record
Abstract
Quadriceps muscle weakness is frequently associated with knee injuries in sports. The influence of quadriceps weakness on knee joint homeostasis remains undefined. We hypothesized that quadriceps weakness will lead to tissue-specific alterations in the cell metabolism of tissues of the knee. Quadriceps weakness was induced with repetitive injections of Botulinum toxin A in six 1-year-old New Zealand White rabbits for 6 months. Five additional animals served as controls with injections of saline/dextrose. Muscle weakness was assessed by muscle wet mass, isometric knee extensor torque, and histological morphology analysis. Cell metabolism was assessed for patellar tendon, medial and lateral collateral ligament, and medial and lateral meniscus by measuring the total RNA levels and specific mRNA levels for collagen I, collagen III, MMP-1, MMP-3, MMP-13, TGF-β, biglycan, IL-1, and bFGF by reverse transcription and polymerase chain reaction. While the total RNA levels did not change, tissue-specific mRNA levels were lower for relevant anabolic and catabolic molecules, indicating potential changes in tissue mechanical set points. Quadriceps weakness may lead to adaptations in knee joint tissue cell metabolism by altering a subset of anabolic and catabolic mRNA levels corresponding to a new functional and metabolic set point for the knee that may contribute to the high injury rate of athletes with muscle weakness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".