Impact of different resistance training protocols on muscular oxidative stress parameters
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it