Impact of different resistance training protocols on muscular oxidative stress parameters
Bibliographic record
Abstract
This study analyzes oxidative stress in skeletal muscle using different resisted training protocols. We hypothesize that different types of training produce different specifics. To test our hypothesis, we defined 3 resistance training protocols and investigated the respective biochemical responses in muscle. Twenty-four male Wistar rats were distributed in 4 groups: untrained (UT), muscular resistance training (RT), hypertrophy training (HT), and strength training (ST). After 12 weeks of training on alternate days, the red portion of the brachioradialis was removed and the following parameters were assessed: lactate and glycogen content, superoxide production, antioxidant enzyme content, and activities (superoxide dismutase, SOD; catalase, CAT; GPx, glutathione peroxidase). Thiobarbituric acid-reactive substances (TBARS), carbonyl, and thiol groups were also measured. Results showed increased superoxide production (UT = 5.348 ± 0.889; RT = 5.117 ± 0,651; HT = 8.412 ± 0.431; ST = 6.354 ± 0.552), SOD (UT = 0.078 ± 0.0163; RT = 0.101 ± 0.013; HT = 0.533 ± 0.109; ST = 0.388 ± 0.058), GPx (UT = 0.290 ± 0.023; RT = 0.348 ± 0.014; HT = 0.529 ± 0.049; ST = 0.384 ± 0.038) activities, and content of GPx (HT = 3.8 times; ST = 3.0 times) compared with the UT group. CAT activity was lower (UT = 3.966 ± 0.670; RT = 3.474 ± 0.583; HT = 2.276 ± 0.302; ST = 2.028 ± 0.471) in HT and ST groups. Oxidative damage was observed in the HT group (TBARS = 0.082 ± 0.009; carbonyl = 0.73 ± 0.053; thiol = 12.78 ± 0.917) compared with the UT group. These findings indicate that HT causes an imbalance in oxidative parameters in favor of pro-oxidants, causing oxidative stress in skeletal muscle.
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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) | 0.000 | 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".