Effects of a single bout of high-intensity interval exercise on C1q/TNF-related proteins
Bibliographic record
Abstract
High-intensity interval training (HIIT) is known to be an effective exercise training regimen to improve energy substrate metabolism and insulin sensitivity. However, the underlying mechanisms of improvement in insulin sensitivity due to HIIT have not yet been fully clarified. C1q/tumor necrosis factor-related protein (CTRP) 1 and CTRP9, which are adiponectin paralogs and novel adipokines, have favorable effects on energy substrate metabolism and insulin sensitivity. The purpose of this study was to investigate the effects of a single bout of HIIT on CTRP1 and CTRP9 secretions in healthy men. Eight healthy male subjects (mean ± SE: age, 23.4 ± 1.1 years; height, 172.1 ± 1.7 cm; body mass, 68.0 ± 2.0 kg; body mass index, 22.9 ± 0.5 kg/m2) participated in this study. They performed a single bout of HIIT consisted of four 30-s maximal cycling bouts with 4 min of rest between bouts using a cycle ergometer. Blood samples were collected before the exercise, at 0 (immediately after the exercise), 15, 30, and 120 min after the single bout of HIIT. Serum CTRP1, CTRP9, and high-molecular-weight (HMW) adiponectin concentrations were measured using enzyme-linked immunosolvent assay kits. CTRP1 concentration significantly increased at 120 min after the HIIT. CTRP9 concentration also significantly increased immediately after the single bout of HIIT. In contrast, there were no significant differences in HMW adiponectin concentration before and after the acute HIIT. These findings suggest that a single bout of HIIT may stimulate CTRP1 and CTRP9 secretions in healthy men.
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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) | 0.001 | 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".