Daily Consumption of a Chocolate Rich in Flavonoids Decreases Cellular Genotoxicity and Improves Biochemical Parameters of Lipid and Glucose Metabolism
Bibliographic record
Abstract
In recent years, Atherosclerotic Cardiovascular Disease (ACVD), Obesity and Diabetes, have increase exponentially worldwide. In the present work, we evaluate the genoprotective effect of consuming a flavonoids-rich chocolate and the improvement in the biochemical parameters related to the prevention and treatment of cardiovascular risk and metabolic syndrome in young Mexican adults. A randomized, placebo-controlled, double-blind study was undertaken in the Autonomous University of Baja California. The treatments were a daily intake of 2 grams of dark chocolate containing 70% cooca or milk chocolate. Total phenolic compounds and flavonoids were evaluated in both chocolates. Anthropometrical and Biochemical parameters were measured in the 84 participants before and after the study. Buccal epithelial genotoxicity was also evaluated from the beginning to the end of the experiment in the participants. Result suggested that flavonoids of cocoa intake have protective effects against DNA damage, and Biochemical parameters (total cholesterol, triglycerides, and LDL-cholesterol level in blood) and anthropometrical parameters (waist circumference) were also improved after six months of daily intake of 2 grams of dark chocolate with a 70% of cocoa.
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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".