Red Rooibos Tea Stimulates Osteoblast Mineralization in a Dose-Dependent Manner
Bibliographic record
Abstract
Most studies show a positive association between higher tea intake and bone mineral density (BMD), while associations with fracture are inconclusive. Although a few studies have investigated the potential effects of rooibos tea on the bone cell metabolism, only 1 or 2 doses were tested or specific flavonoids were evaluated in isolation. A dose-response study is needed to comprehensively identify whether a level that modulates bone cell activity is associated with dietary or supplemental levels of red rooibos (RR) tea. We investigated if RR tea stimulates mineralization in a dose-dependent manner. Mineralization was induced in Saos-2 cells in the presence of RR tea (0.1–10 µg/mL of polyphenols) or control (dH2O) for 5 days. Cell activity, mRNA, protein expression and activity of key regulators of mineralization were measured. A positive dose-dependent relationship was observed between RR tea concentration and mineralization accompanied with similar trends in cell activity. With high concentrations of tea, protein expression of enzymes directly regulating mineralization (ALP and NPP1) were unchanged but ALP activity was significantly elevated. Osteopontin (OPN) was significantly downregulated midway through mineralization while sclerostin (SOST) was upregulated in response to higher concentrations of RR tea. In conclusion, RR tea stimulated osteoblast mineralization in a dose-dependent manner and were accompanied with downregulation of OPN, increased ALP activity, and increased cell activity.
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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) | 0.002 | 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".