Glycogen storage in tissue-engineered cartilage
Bibliographic record
Abstract
Recent focus in cartilage tissue engineering has been to develop functional tissue that can survive after implantation. One such determinant is the ability of the engineered tissue to be able to sustain its metabolic activity post-implantation. In vivo, chondrocytes contain stores of intracellular glycogen to support metabolism and it is unknown whether these cells can store glycogen during tissue growth in vitro. Thus, the purpose of this study was to determine the appropriate nutrient conditions to elicit glycogen storage in tissue-engineered cartilage. Isolated bovine articular chondrocytes were seeded in scaffold-free, 3D culture and grown under different nutrient conditions (glucose concentrations and media volumes) for 4 weeks. Intracellular glycogen storage, glucose utilization and extracellular matrix (ECM) accumulation of the engineered tissues were then evaluated. Glucose concentration (5-10 mM) and media volume (1-4 ml) had no apparent effect on cartilaginous tissue formation. However, glucose consumption by the cells increased in proportion to the volume of medium provided. Lactate production was similarly affected but in direct proportion to the glucose consumed, indicating a change in glucose utilization. Similarly, under elevated medium volume, engineered tissues stained positive for intracellular glycogen, which was also confirmed biochemically (1 ml, 1 +/- 2; 2 ml, 13 +/- 4; 4 ml, 13 +/- 3 microg/construct). The storage of intracellular glycogen in engineered cartilage can be elicited by culturing the constructs in elevated volumes of medium (>or=1 ml medium/million cells), which might help to ensure appropriate metabolic function after implantation.
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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) | 0.001 | 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".