The Effect of High‐Intensity Interval Training and Mixed Probiotic Supplementation on SMOC‐1 Gene Expression, Insulin Resistance, and Blood Glucose in Male Rats With Induced Diabetes
Bibliographic record
Abstract
Background and Aims In this study, we investigated whether high‐intensity interval training (HIIT) and mixed probiotic consumption, either autonomously or synergistically, could regulate the expression of calcium‐binding protein‐1 (SMOC‐1), insulin resistance (IR), and blood glucose (BG) in male rats with induced diabetes. Methods Thirty healthy male Wistar rats, aged about 8 weeks, were randomly divided into five groups of six rats each, including control group (C, 1G), diabetic control group (CD, 2G), probiotic supplement group (Pro, 3G), HIIT group (Ex, 4G), and HIIT and probiotic supplement group (Pro + Ex, 5G). Each strain of the mixed probiotic supplement, containing Lactobacillus rhamnosus GG (PTCC1637), Lactobacillus reuteri , and Lactococcus casei enriched with L‐cysteine HCl, was administered at a concentration of 10 10 colony‐forming units (CFU) per milliliter to Groups 3G and 5G. Groups 4G and 5G underwent HIIT to evaluate the effect of supplementation and HIIT on SMOC‐1, IR, and BG. Results Mixed probiotics and HIIT did not affect SMOC‐1 expression in liver muscle ( η 2 = 0.00, p = 0.965, F (1, 15) = 0.002); however, they synergistically lowered IR ( η 2 = 0.23, p = 0.048, F (1, 15) = 4.65) and BG ( η 2 = 0.32, p = 0.013, F (1, 15) = 7.79). Conclusion We found no significant effect of mixed probiotic supplementation or its combination with HIIT on SMOC‐1. Notably, the HIIT and mixed probiotics reduced IR and BG. Future studies can help assess the accurate synergistic effects of HIIT and mixed probiotics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".