Controlling Medium Osmolality Improves the Expansion of Human Articular Chondrocytes in Serum-Free Media
Bibliographic record
Abstract
To investigate the effects of medium osmolality on the expansion of human articular chondrocytes (HACs) with serum-free media (SFM), proprietary SFM of various osmolalities (290, 320, 350, 400, and 450 mOsm/kg), supplemented with components known to enhance chondrocyte growth, were constructed by the adjustment of NaCl concentration. It was found that HACs obtained better expansion in SFM at osmolalites lower than the average osmolality (400 mOsm/kg) of human articular cartilage in vivo. SFM at 290, 320, and 350 mOsm/kg showed similar growth, attaining up to a 1.55-fold increase in the proliferation rate compared with SFM at 400 mOsm/kg. Increasing SFM osmolality to 450 mOsm/kg resulted in a proliferation rate of 0.65-fold lower than at 400 mOsm/kg. Chondrogenic capacity was also examined via three-dimensional pellet cultures in a chondrogenic medium with HACs expanded in 320 and 400 mOsm/kg SFM. Biochemical, histological, and immunohistochemical analyses revealed similar glycosaminoglycan and collagen type II contents in both groups. Taken together, these results show that the expansion of HACs in SF cultures can be improved by adjusting the medium osmolality to be within the range of 290-350 mOsm/kg and that controlling medium osmolality during monolayer cultures does not deter the tissue-forming capability of the cells.
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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) | 0.000 | 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 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".