The effect of compressive loading magnitude on in situ chondrocyte calcium signaling
Bibliographic record
Abstract
Chondrocyte metabolism is stimulated by deformation and is associated with structural changes in the cartilage extracellular matrix (ECM), suggesting that these cells are involved in maintaining tissue health and integrity. Calcium signaling is an initial step in chondrocyte mechanotransduction that has been linked to many cellular processes. Previous studies using isolated chondrocytes proposed loading magnitude as an important factor regulating this response. However, calcium signaling in the intact cartilage differs compared to isolated cells. The purpose of this study was to investigate the effect of loading magnitude on chondrocyte calcium signaling in intact cartilage. We hypothesized that the percentage of cells exhibiting at least one calcium signal increases with increasing load. Fully intact rabbit femoral condyle and patellar bone/cartilage samples were incubated in calcium-sensitive dyes and imaged continuously under compressive loads of 10-40 % strain. Calcium signaling was primarily associated with the dynamic loading phase and greatly increased beyond a threshold deformation of about 10 % nominal tissue strain. There was a trend toward more cells exhibiting calcium signaling as loading magnitude increased (p = 0.133). These results provide novel information toward identifying mechanisms underlying calcium-dependent signaling pathways related to cartilage homeostasis and possibly the onset and progression of osteoarthritis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".