Effects of combined exercise training and <i>Chlorella</i> intake on vasorelaxation mediated by nitric oxide in aged mice
Bibliographic record
Abstract
Chronic Chlorella intake and aerobic exercise training reduce arterial stiffness and increase circulating nitric oxide (NO) levels, which has beneficial effects. This study aimed to clarify the combined aortic NO-mediated effects of chronic Chlorella intake and aerobic exercise training on endothelial vasorelaxation in aged mice. In this study, 38-week-old male senescence-accelerated mouse prone 1 (SAMP1) mice were divided into aged sedentary control (Con), aerobic exercise training (AT; voluntary wheel running for 12 weeks), Chlorella intake (CH; 0.5% Chlorella powder in normal diet), and AT and CH combined (AT+CH) groups. Endothelium-dependent vasorelaxation by addition of acetylcholine to the isolated mouse aortic rings was significantly higher in the AT, CH, and AT+CH groups than in the Con group; a significantly greater effect was seen in the AT+CH group than in the AT and CH groups. Similarly, plasma and arterial nitrite/nitrate levels and arterial endothelial NO synthase phosphorylation were significantly higher in the AT, CH, and AT+CH groups than in the Con group; the AT+CH group had higher values than the AT and CH groups. Thus, chronic Chlorella intake combined with aerobic exercise training had pronounced effects on endothelial vasorelaxation in aged mice via an additive increase in arterial NO production. Novelty: Endothelium-dependent vasorelaxation was improved by Chlorella intake and exercise. Chlorella intake and exercise increased arterial Akt/eNOS/NO signaling. This combination approach further improved vasorelaxation via arterial NO production.
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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 | 0.001 |
| 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".