Unveiling the Effects of Interval Resistance Training and Chlorella Vulgaris Supplementation on Meteorin-like Protein and Oxidative Stress in Obese Men
Bibliographic record
Abstract
Background: Dysregulation of adipocyte function occurs in obesity. Meteorin-like protein (Metrnl) is a newly discovered modulator of inflammation, metabolism, and differentiation of human adipocytes. The dietary supplement Chlorella Vulgaris (CV) reduces hyperlipidemia, hyperglycemia, and oxidative stress in clinical trials. Objectives: To explore the impact of 12 wks of interval resistance training (IRT) and CV supplementation on plasma levels of Metrnl and oxidative stress in males with obesity. Methods: = 11/group): control (CON), CV supplement (CV), IRT, and CV + IRT (CVIRT). The IRT was performed for 12 wks (3 sessions per week). The treatment consisted of a daily intake of CV (1800 mg capsule) or placebo capsules. Blood samples were collected 48 hours before and after the interventions to analyze biomedical measurements. Results: > 0.05). Conclusions: Although IRT and its combination with CV hold promise for improving Metrnl levels and oxidative status in obesity, combining IRT and CV do not yield greater benefits than IRT alone. Although standalone CV supplementation could favorably impact certain markers of oxidative stress, the effectiveness of CV supplementation appears to have a relatively limited effect across assessed biomarkers and requires further investigation.
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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) | 0.000 | 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 | 0.000 |
| 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".