Black and Green Tea as Well as Specialty Teas Increase Osteoblast Mineralization with Varying Effectiveness
Bibliographic record
Abstract
Many human studies suggest a benefit of tea consumption on bone health. The study objective was to compare the ability of different tea types to promote mineralization. Saos-2 cells underwent mineralization (5 days) in the presence of tea (white: WT, green: GT, black: BT, green rooibos: GR, or red rooibos: RR; 1 μg/mL of polyphenols) or control. Total polyphenol content (TPC, Folin-Ciocalteu's reagent), antioxidant capacity (2,2-diphenyl-1-picrylhydrazyl [DPPH] scavenging), mineralization (Alizarin Red staining), gene expression quantitative reverse transcription PCR (RT-qPCR), and cell activity (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay) were determined. TPC was highest in GT and BT. The ability of each tea to inhibit DPPH also differed (WT, GT > RR) after normalizing for polyphenol quantity. Each tea increased mineralization and differences were observed among types (GT/BT/GR/RR > WT, GT = BT = GR, RR > BT/GT). mRNA expression of alkaline phosphatase ( ALP ) and ectonucleotide pyrophosphatase/phosphodiesterase ( NPP1 ) remained unchanged, whereas osteopontin ( OPN ) and sclerostin ( SOST ) were reduced in cells treated with tea, regardless of type. At 24- and 48-h postexposure to tea, cell activity was greater in cells receiving any of the teas compared with vehicle control. Supplementation increased mineralization regardless of tea type with both rooibos teas and black tea stimulating greater mineralization than WT, whereas green tea is similar to the others. While future study is needed to confirm in vivo effects, the results suggest that consuming any of the teas studied may benefit bone health.
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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 | 0.001 |
| 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".