Effects of a 6-wk Sprint Interval Training Protocol at Different Altitudes on Circulating Extracellular Vesicles
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the modulation of circulating exosome-like extracellular vesicles (ELVs) after 6 wk of sprint interval training (SIT) at sea level and at 2000, 3000, and 4000 m. METHODS: Thirty trained endurance male athletes (18-35 yr) participated in a 6-wk SIT program (30-s all-out sprint, 4-min 30-s recovery; 4-9 repetitions, 2 sessions per week) at sea level ( n = 8), 2000 m (fraction of inspired oxygen (F io2 ) 0.167, n = 8), 3000 m (F io2 0.145, n = 7), or 4000 m (F io2 0.13, n = 7). Venous blood samples were taken before and after the training period. Plasma ELVs were isolated by size exclusion chromatography, counted by nanoparticle tracking analysis, and characterized according to international standards. Candidate ELV microRNAs (miRNAs) were quantified by real-time polymerase chain reaction. RESULTS: When the three hypoxic groups were analyzed separately, only very minor differences could be detected in the levels of circulating particles, ELV markers, or miRNA. However, the levels of circulating particles increased (+262%) after training when the three hypoxic groups were pooled, and tended to increase at sea level (+65%), with no difference between these two groups. A trend to an increase was observed for the two ELV markers, TSG101 (+65%) and HSP60 (+441%), at sea level, but not in hypoxia. Training also seemed to decrease the abundance of miR-23a-3p and to increase the abundance of miR-21-5p in hypoxia but not at sea level. CONCLUSIONS: A 6-wk SIT program tended to increase the basal levels of circulating ELVs when performed at sea level but not in hypoxia. In contrast, ELV miRNA cargo seemed to be modulated in hypoxic conditions only. Further research should explore the potential differences in the origin of ELVs between normoxic and local and systemic hypoxic conditions.
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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".