Comparison of Different Tea Types on Osteoblast Activity
Bibliographic record
Abstract
Many studies report that tea consumption is associated with higher bone mineral density in women. Polyphenols in tea may mediate such benefits. We examined if English Breakfast (EB) and Golden Monkey (GM) black tea, green tea (GT) or rooibos tea (RT) increased osteoblast activity in human osteoblast‐like cells. Preclinical studies typically use alcohol‐derived polyphenol extracts although tea for human consumption is prepared with water. We determined that polyphenol levels are higher with methanol versus water extraction (161 mg/g vs. 86 mg/g gallic acid equivalents, p < 0.001). Water extraction reduced polyphenol levels of GT, EB, and GM by more than 50%, whereas RT maintained 65% of its polyphenols. GT (117‐235 mg/g) and EB (121‐202 mg/g) had the highest polyphenol content, regardless of solvent (p < 0.001). For mineralization, the effect of water‐extracted polyphenols (1 or 10 µg polyphenols/mL media) were measured using the Alizarin Red assay. Cellular activity was examined by reduction of thiazolyl blue tetrazolium bromide (MTT). Two‐way ANOVA indicated a favorable effect of dose (1 or 10 µg/mL, p<0.001) and treatment (p < 0.001) on mineralization. 10 µg/mL tea extract produced less mineral than 1 µg/mL (132% vs. 157% of control, p < 0.001). Both doses of GT (p < 0.001, p < 0.001) and RT (p < 0.05, p < 0.01), but only 1 µg/mL of EB (p < 0.001) and GM (p < 0.001) increased mineralization. Both doses of GT (p < 0.05, p < 0.05) and RT (p < 0.001, p < 0.01) and 1 µg/mL of EB (p < 0.05) promoted greater cellular activity at 24 hrs. Neither dose of GM, nor 10 µg/mL of EB resulted in significant differences of MTT. Differing effects among tea types may be due to diverse polyphenol profiles. (Funded by NSERC Discovery Grant)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".