Structural Changes to Immune Organs in Rats after Intermittent Fasting Following a High Carb and Fat Diet
Bibliographic record
Abstract
Background:A diet high in carbs and fat puts people at risk of obesity. Obesity causes changes in the immune system and increases the risk of premature ageing, including in the lymphoid organs ― such as the thymus and spleen. Fasting is expected to improve the immune system. The purpose of this research is to determine the effects of intermittent fasting on images of the structure of the thymus, the number of fat cells, Hassall’s corpuscles of the thymus, the area and density of pulp white spleen, and the number of leukocytes in Wistar rats (Rattus norvegicus) fed a diet high in carbs and fat. Methods:An experimental study with post-test only control group design, with 15 male rat subjects aged 2.5 months were divided into three groups: first group had a diet that was ad libitum (AL); second group were given a diet high in fats and carbohydrates for 1 month then were fed ad libitum (HCL); and third group were given a diet high in fat and carbohydrates for 1 month continued with alternating 12 hour fasting periods for 72 days (F). Results: Total Hassall’s corpuscles of AL, HCL, and F groups were 1.33 ± 077; 2.58 ± 1.35; and 0.69 ± 0.27, respectively(p = 0.008). Fat cells were not found in the thymus. The largest white pulp in the spleen was found in group F, followed by AL, while the smallest was found in group of HCL (p = 0.01). The most depleted white pulp density was the HCL group. There is no significant difference in the number of leukocytes and different leukocyte count between the groups (p> 0.05). Conclusion: Intermittent fasting for 72 days affects the number of Hassall’s corpuscles in the thymus and the width of white pulp in the spleen of Wistar rats.
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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".