Exercise Training Prevents Endometrial Hyperplasia and Biomarkers for Endometrial Cancer in Rat Model of Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: Endometrial cancer is one of the most common types of gynecologic cancers. The ability of exercise to reduce the risk of endometrial cancer in women with type 2 diabetes has been established, but no studies have examined this link in type 1 diabetes.A randomized, controlled animal study was designed using a standard rat model of type 1 diabetes. The goal of this study was to investigate the ability of exercise to prevent increased levels of endometrial cancer biomarkers, estrogen receptor (ERα) and p16, and endometrial hyperplasia associated with diabetes. METHODS: FORTY FEMALE RATS WERE RANDOMIZED INTO FOUR GROUPS: sedentary control, exercise control, sedentary or exercised diabetic. Diabetes was induced by alloxan injection. A 4-week treadmill training program was initiated with the development of diabetes. Endometrial tissues were evaluated for hyperplasia and ERα and p16 levels and subcellular localization using microscopy. RESULTS: Severe diabetes lead to hyperplasia in the endometrial tissue in 70% of sedentary diabetic rats. Exercise-trained diabetic rats and the non-diabetic rats displayed no hyperplasia. The expression of ERα increased significantly (p < 0.02) while the expression level of p16 decreased significantly (p < 0.04) in the diabetic sedentary group compared to the non-diabetic groups. Exercise training led to a reversal in the percentage of p16 and ERα positive cells in diabetic rats. CONCLUSIONS: Severe diabetes leads to hyperplasia of the endometrial tissue and increased ERα levels and decreased p16 levels in rats, which can be prevented with aerobic exercise. KEYWORDS: Diabetes; Estrogen receptor alpha; P16; Endometrial hyperplasia; Endometrial cancer; Exercise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".