Effects of glucose and its modulation by insulin and estradiol on BMSC differentiation into osteoblastic lineages
Bibliographic record
Abstract
It is well known that diabetes affects bone in human and animal models, and leads to osteopenia and osteoporosis. Bone-mineral density and other biochemical markers of bone turnover are very much affected in people with diabetes. Reduced bone mass, occurring with increased frequency in diabetes mellitus, has been attributed to poor glycemic control, but the pathogenic mechanisms remain unknown. High concentrations of glucose (hyperglycemia) in diabetics leads to this complication. Very few in vitro studies using bone-cell lines have been carried out to address this problem. In this study, we examined the effects of different doses of glucose concentration (5.5, 16.5, and 49.4 mmol/L), alone, with insulin (0.6 microg/mL), or with 17beta-estradiol (E2) (10 nmol/L), on rat bone-marrow stromal cells (BMSCs) in the presence of an osteogenic medium. BMSC proliferation and alkaline phosphatase (ALP) were studied after 3 and 7 d of culture, respectively; the area stained for collagen and mineralized nodules was studied after 28 d of culture. With high concentrations of glucose, BMSC proliferation, ALP activity, the number of nodules formed, and the area stained for collagen were greatly reduced. Insulin treatment alone was able to increase [3H]-thymidine uptake or ALP activity, whereas both insulin and estradiol were able to increase the number of mineralized nodules and the area stained for collagen and mineralization. In conclusion, this study suggests that insulin and estradiol are able to contain the deleterious effect of high concentrations of glucose on BMSC-derived osteoblast proliferation and 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) | 0.000 | 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".