Prevalence of Depression in Animal Model of High Fat Diet Induced Obesity
Bibliographic record
Abstract
The prevalence of obesity is substantially increased in developing countries and it is considerably associated with type 2 diabetes (T2DM), dyslipidemia, and hypertension. These symptoms are clustered to form metabolic syndrome. In accordance with the Researchers opinion, obese people are more likely to suffer from depression, a mental affliction that appears due to chronic stress, disturbs thoughts, behavior, and feelings. It has been addressed that the physiological impairments which are undergoing due to obesity can affect the metabolic activities which in turn give impact on brain and affect it’s functioning, because obesity itself seems to constitute a chronic stressful state thus, exacerbates the risk of depression. Present study intended to illuminate the anticipated links between obesity and stress. To make possible the study, animal model of obesity was accomplished by subjecting the Albino wistar rats with energy-dense diet (high fat diet) for 5 weeks; later on, chronic mild stress paradigm was implemented along with high fat feeding for 2 weeks. As expected, high fat feeding increased the adiposity in rodents. Obese animals presented the depressive symptoms more prominent than normal fat feeding rats. Present findings suggest that obesity could increase the depressive symptoms potentially involve in the recruitment of depression.
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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".