Biochemical assessments of retinol, α-tocopherol, pyridoxal - 5-phosphate oxidative stress index and total antioxidant status in adolescent professional basketball players and sedentary controls
Bibliographic record
Abstract
Physical training is known to increase the antioxidant defence system and reduce exercise-induced oxidative stress. However, intense physical aerobic and anaerobic training with competition, such as those imposed on young professional basketball players can induce an increase of oxidative stress, which can be implicated with overtraining. The aim of this study was to test the effect of training and competition load on oxidative stress, antioxidant status, and vitamin levels in basketball players. Oxidative Stres Index (OSI 1), Total Peroxide (TPx) antioxidant (vitamin E, A and The total antioxidant status (TAC 1)), biochemical lipid parameters, as well as training results were measured. Results showed that all plasma vitamin levels were significantly higher in basketball players (vitamin A: 1.61 +/- 0.05 mmol/l, vitamin E: 26.45 +/- 0.72 mmol/l, vitamin B6: 10.58 +/- 0.7 mgr/l) than sedentary controls (vitamin A: 1.22 +/- 0.04 mmol /l, vitamin E: 19.24 +/- 0.73 mmol/l, vitamin B6: 6.0 +/- 0.35 mgr/l) (p < 0.01). In addition TAC 1 was 2.06 +/- 0.02 and 1.89 +/- 0.01 mmol Trolox eq/L in basketball players and controls, respectively (p < 0.01). Conversely OSI was 0.89 +/- 0.09 arbitrary unit and 0.88 +/- 0.071 arbitrary unit in basketball players and controls, respectively (p > 0.05). However, total plasma peroxide level (TPx) of basketball players and controls was not statistically different (18.55 +/- 2.07 and 17.18 +/- 1.61 micromol H2O2/L, respectively; p > 0.05). We conclude that physical exercise increase antioxidant levels and cause balance of the homeostasis. Training can not have positive or negative effects on oxidative stress depending on training load. The results suggested that oxidative stress and antioxidant measurement are significant in the biological follow-up of young basketball players.
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) | 0.000 | 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 | 0.000 |
| 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".