Tea flavonoids stimulate mineralization in osteoblast‐like cells (259.7)
Bibliographic record
Abstract
Epidemiological studies suggest that habitual tea consumption is associated with higher bone mineral density. Rooibos tea, in particular, is a rich source of flavonoids, including rutin, orientin, hyperoside, and luteolin, that may favorably modulate cellular processes such as hydroxyapatite production. The study objective was to determine if individual flavonoids (0.001µM‐100µM) promoted mineralization in Saos2 cells. Mineralization was quantified by alizarin red staining at day 21, while alkaline phosphatase activity, a marker of osteoblast cell activity, was determined at days 3, 7 and 10. Rutin (25µM), orientin (0.01µM), hyperoside (5µM) and luteolin (5µM) resulted in higher (p<0.05) mineral production than control. Higher mineral production was associated with higher alkaline phosphatase activity (p<0.05) for rutin (25µM), orientin (0.1µM), hyperoside (1µM) and luteolin (1µM) as early as 3 days into the mineralization process. Maximum enzyme activity was dependent on the flavonoid. Mitochondrial activity, a marker of cellular proliferation, reflected the mineralization data. In conclusion, flavonoids can positively influence mineral production in osteoblasts and this is, in part, due to higher osteoblast activity. Thus, tea flavonoids are a promising area for future research investigating dietary approaches for bone health. Grant Funding Source : Supported by a NSERC Discovery Grant for W. Ward
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".