The effect of eight weeks of High Intensity Interval Training (HIIT) on the expression of lipasin in diabetic rats
Bibliographic record
Abstract
Background: Diabetes is a metabolic disorder recognized as one of the most common diseases in the world. The disease has also increased dramatically in Iran.Today, there are many ways to treat diabetes, one of which is the increase in the level of pancreatic beta cells. The increase in these cells is done in several ways Several studies have demonstrated that the lipasin or betatrophin gene, a liver-expressed peptide hormone, increases the proliferation of beta cells, and that overexpression of this gene can increase the number of beta cells. Materials and Methods: The study was conducted on 16 Wistar rats with a mean weight of 160 ± 10. They were induced by diabetes (seven months of diabetes mellitus was caused in rats). They then were divided into two groups of 6: Control (C) and High-Intensity Interval Training (HIIT). Eight weeks of exercise training was conducted on rats.The qRT-PCR technique was used to investigate changes in lipasin expression. An Independent t-test was used for data analysis, and Pearson correlation was used to determine the correlation between lipasin expression and insulin resistance index (P <0.05). Results: The results showed that expression of lipasin gene in the liver of rats in the training group was significantly higher than the control group rats after 8 weeks of training; Insulin resistance index of plasma, plasma insulin and plasma glucose decreased considerably after eight weeks of HIIT. Between lipasin expression and insulin resistance index in rats with type 2 diabetes in the training group, a consider correlation has been observed. Conclusion: This study showed that an 8-week HIIT training period, with increased lipasin expression, could increase beta cells and also recover type 2 diabetes, which had been destroyed by these cells, and as a result of this increase in Insulin secretion and there is a way to prevent the disease.
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) | 0.000 | 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".