Caffeine alters mitochondrial dehydrogenase and alkaline phosphatase activity of human gingival fibroblasts <i>in vitro</i>
Bibliographic record
Abstract
AIM: Caffeine is one of the most widely consumed behaviorally active substances in the world. Although its effects on the central nervous system and bone metabolism have been documented, as yet there is no report on its effect on tissues in the oral cavity. In this study we analyzed the viability of human gingival fibroblasts (HGF) and alkaline phosphatase (ALP) enzyme activity after exposure to different concentrations of caffeine for different exposure time periods. METHODS: The HGF were cultured with different concentrations of caffeine. Viability of cells exposed to caffeine was analyzed by the 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay to assess mitochondrial dehydrogenase activity. The activity of ALP was analyzed at specific time intervals after caffeine addition. RESULTS: Our results showed that caffeine of concentrations <1 mm did not affect the viability of HGF and the ALP enzyme activity. Nevertheless, caffeine at 5 and 10 mm dramatically decreased the viability and ALP activity of the cells after 4 days such that, by day 9, the viability of cells declined to near zero in the 10 mm group. CONCLUSION: These results provided evidence that caffeine in high concentrations can decrease cellular viability and ALP activity in HGF.
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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".