The effect of continuous culture on the growth and structure of tissue‐engineered cartilage
Bibliographic record
Abstract
The use of bioreactors for cartilage tissue engineering has become increasingly important as traditional batch-fed culture is not optimal for in vitro tissue growth. Most tissue engineering bioreactors rely on convection as the primary means to provide mass transfer; however, convective transport can also impart potentially unwanted and/or uncontrollable mechanical stimuli to the cells resident in the construct. The reliance on diffusive transport may not necessarily be ineffectual as previous studies have observed improved cartilaginous tissue growth when the constructs were cultured in elevated volumes of media. In this study, to approximate an infinite reservoir of media, we investigated the effect of continuous culture on cartilaginous tissue growth in vitro. Isolated bovine articular chondrocytes were seeded in high density, 3D culture on Millicell filters. After two weeks of preculture, the constructs were cultivated with or without continuous media flow (5-10 microL/min) for a period of one week. Tissue engineered cartilage constructs grown under continuous media flow significantly accumulated more collagen and proteoglycans (increased by 50-70%). These changes were similar in magnitude to the reported effect of through-thickness perfusion without the need for large volumetric flow rates (5-10microL/min as opposed to 240-800 microL/min). Additionally, tissues grown in the reactor displayed some evidence of the stratified morphology of native cartilage as well as containing stores of intracellular glycogen. Future studies will investigate the effect of long-term continuous culture in terms of extracellular matrix accumulation and subsequent changes in mechanical function.
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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) | 0.001 | 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 | 0.001 |
| 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".