Preventive Effects of Health-Food Drinks on the Obesity and DNA Damage of Mice Fed a High-Fat Diet under a Mild Stress
Bibliographic record
Abstract
The body weight gain of mice fed an obesity-inducing diet is suppressed by a mild restraint stress, but at the same time, as previously reported, the stress induces DNA damage in the cells of multiple organs. In the present study, we attempted to prevent not only the obesity but also the DNA damage of mice fed an obesity-inducing diet under a mild restraint with commercially available health-food drinks such as fruit and vegetable juices, soymilks, vinegars and lactic acid bacteria drinks, which are rich in antioxidants. The body weight gain of young female mice fed a high-fat diet containing 20% fat for 4 weeks was considerably inhibited by restraint for 15 min per day in weeks 2 to 4 of 4-week period. The inhibition was further promoted with the concomitant administration of health-food drinks noted above, and was accompanied by a decrease of periovular fat, a major abdominal fat in the female mice. The definite loss of energy intake in the mice given health-food drinks was approximately compensated by the energy of the drink administered. On the other hand, the increase of DNA damage generated by restraint in the cells of five organs – the liver, pancreas, spleen, heart and bone marrow – was markedly suppressed with the administration of these drinks. The results suggest that a combination of mild stress and intake of suitable health-food containing some antioxidants may inhibit lifestyle-related diseases including hyperlipidemia and obesity, which may contribute to the inhibition of metabolic syndrome and childhood obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".