Cinnamon attenuates adiposity and affects the expression of metabolic genes in Diet-Induced obesity model of zebrafish
Bibliographic record
Abstract
The prevalence of obesity is increasing at an alarming rate worldwide with about 30% of the world population classified as obese. Obese body structure results when energy intake exceeds energy expenditure in an individual. Increase in the consumption of high-energy eatables, in the context of portion and energy provided, has resulted in obese populations which is becoming the leading cause of metabolic disorders related to morbidity. The obesity-related comorbidity is an enormous liability on health services and will affect measures taken in tackling the increasing obesity rate. Prevention of an obese phenotype is the most suitable long-term strategy. Another approach towards the treatment of obesity is weight management through phytotherapeutics. In this study, we explored the anti-obesity effects of Cinnamon (Cinnamomum zeylanicum) in adult male zebrafish. Through BMI measurements, blood glucose level analyses, serum triglyceride analyses, Oil Red O staining as well as quantitative Real Time-PCR, the ability of cinnamon to reduce metabolic disorders associated with obesity is investigated for the first time in a zebrafish model. Our studies indicate that cinnamon ameliorates the genotypic and phonotypic characteristics associated with obesity through lowering of BMI, blood glucose, triglyceride levels, lipid levels in the liver and through gene modulation.
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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 | 0.001 | 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".