Glycogen storage in tissue-engineered cartilage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it