Substrate porosity enhances chondrocyte attachment, spreading, and cartilage tissue formation<i>in vitro</i>
Bibliographic record
Abstract
Tissue engineering is being explored as a new approach to treat damaged cartilage. As the biomaterial used may influence tissue formation, the effects of substrate geometry on chondrocyte behavior in vitro were examined. Articular chondrocytes were isolated and cultured on the surface of smooth, rough, porous-coated, and fully porous Ti-6Al-4V substrates. The percentage of chondrocytes that attached to each substrate at 24 h was determined. After 24 and 72 h, chondrocytes were visualized by scanning electron microscopy and cell areas were measured. Collagen and proteoglycan accumulation within the first 24 h was determined by incorporation with [3H]-proline and [35S]-SO4, respectively. Chondrocyte attachment as well as matrix accumulation was enhanced as substrate surface area increased. Cell areas on the fully porous substrate were over four times greater than on any other substrate by 72 h in culture. After 8 weeks in culture, a continuous layer of cartilaginous tissue formed only on the surface of the fully porous substrate. This suggests that fully porous Ti-6Al-4V substrates provide the conditions that favor cartilage tissue formation by influencing cell attachment and extent of cell spreading. Understanding how substrate porosity influences chondrocyte behavior may help identify methods to further enhance cartilage tissue formation in vitro.
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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 | 0.000 |
| 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".