Effect of material geometry on cartilagenous tissue formationin vitro
Bibliographic record
Abstract
The effect of material geometry, as defined by average pore size, on chondrocyte phenotype and cartilagenous tissue formation in vitro was examined. Bovine articular chondrocytes were plated on porous titanium alloy (Ti6Al4V) discs of different average pore sizes (13, 43, and 68 microm) and grown in culture for 4 weeks. Chondrocyte phenotype was maintained as indicated by the synthesis of large proteoglycans (Kav +/- SD: 13 microm = 0.28 +/- 0.01; 43 microm = 0.29 +/- 0.01; 68 microm = 0.27 +/- 0.02) and type II collagen. Light microscopical examination of histological sections of the composites showed that cartilagenous tissue had formed on all discs. The cartilagenous tissue on the discs of the smallest average pore size (13 microm) was significantly thicker than the tissue on the discs of larger average pore sizes and also had greater amounts of proteoglycan [mean glycosaminoglycan content +/- SD microg/disc): 13 microm = 246.9 +/- 7.8; 43 microm = 190.4 +/- 10.2; 68 microm = 156.6 +/- 25.8, p = 0.002] and DNA [mean DNA content +/- SD microg/disc): 13 microm = 12.5 +/- 0.6; 43 microm = 8.3 +/- 0.2; 68 microm = 9.3 +/- 0.9, p = 0.0008]. However, the amount of proteoglycan accumulated per cell was similar in the tissues generated on the discs of different average pore sizes. In contrast, the amount of collagen in the cartilagenous tissues showed no significant differences between the different pore sizes, but the amount of collagen accumulated per cell was less in the tissue formed on the smallest pore size disc (13 microm) as compared with the tissue formed on the discs of the larger pore sizes [mean hydroxyproline content/DNA (microg/microg) +/- SD: 13 microm = 1.56 +/- 0.2; 43 microm = 2.19 +/- 0.2; 68 microm = 2.3 +/- 0.3]. These results suggest that material geometry, as defined by pore size, can affect the amount and composition of the cartilagenous tissue that forms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".