Direct effects of caffeine on osteoblastic cells metabolism: the possible causal effect of caffeine on the formation of osteoporosis
Bibliographic record
Abstract
BACKGROUND: Caffeine consumption has been reported to decrease bone mineral density (BMD), increase the risk of hip fracture, and negatively influence calcium retention. In this study, we investigated the influence of caffeine on the osteoblasts behaviour. METHOD: Osteoblasts derived from newborn Wistar-rat calvaria was used in this study. The effects of various concentrations of caffeine on bone cell activities were evaluated by using MTT assay. Alkaline phosphatase (ALP) staining, von Kossa staining and biochemical parameters including ALP, lactate dehydrogenase (LDH), prostaglandin E2 (PGE2) and total protein were performed at day 1, 3, and 7. DNA degradation analysis under the caffeine influence was also performed. RESULTS AND DISCUSSION: The results showed that the viability of the osteoblasts, the formation of ALP positive staining colonies and mineralization nodules formation in the osteoblasts cultures decreased significantly in the presence of 10 mM caffeine. The intracellular LDH, ALP and PGE2 content decreased significantly, the LDH and PGE2 secreted into the medium increased significantly. The activation of an irreversible commitment to cell death by caffeine was clearly demonstrated by DNA ladder staining. CONCLUSION: In summary, our results suggest that caffeine has potential deleterious effect on the osteoblasts viability, which may enhance the rate of osteoblasts apoptosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".