Mechanical And Glucose Stimulation Of Bovine Chondrocytes Affect ECM Synthesis
Bibliographic record
Abstract
Loss of articular cartilage is a cause of human morbidity which can potentially be cured with exogenous cartilage replacements. Although more research is required to create functional cartilage inserts, mechanical stimulation of chondrocytes (cartilage cells) in the correct media formulation may improve extracellular matrix (ECM) production, leading to a better-quality cartilage insert. This study aimed to identify the effect of unconfined dynamic compression of bovine chondrocytes seeded in agarose constructs on ECM production in different initial media glucose concentrations. Unstimulated constructs did not produce significantly different amounts of collagen or glycosaminoglycan (GAG) in response to different media glucose concentrations. Also, stimulated construct ECM production did not significantly differ from control constructs in each glucose concentration investigated. However, stimulated constructs in 10 mM glucose produced more collagen and GAG than in media containing 25 mM glucose. Analysis of the yield of lactate on glucose (YL/G) showed that stimulated constructs underwent more aerobic respiration as the glucose media concentration increased, which did not occur for control constructs. Culturing chondrocytes in media containing 10 mM glucose lead to more ECM production than media containing 25 mM glucose and should be used in future stimulation studies.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".