Metabolic and Vascular Limb Differences Affected by Exercise, Gender, Age, and Disease
Bibliographic record
Abstract
PURPOSE: We studied previously resistance-trained men and compared the effects of concentric and eccentric training on performance and structural muscle parameters. METHODS: Seventeen trained individuals (age 26.9 +/- 3.4 yr) participated in 12 wk of either maximum concentric (N = 8) or eccentric (N = 9) resistance training of the elbow flexors. The functional performance was measured as the maximum concentric and eccentric strength and angular velocity at standard loads. Muscle cross-sectional area and cross-sectional area of single cells were used as measures of muscular hypertrophy. Fiber-type proportions were assessed by staining cells for myofibrillar ATPase. RESULTS: Both eccentric and concentric training increased concentric strength to a similar extent (14 vs 18%), whereas eccentric training led to greater increases in eccentric strength than concentric training did (26 vs 9%). The maximum angular velocity at all loads was enhanced equally in both training groups. The cross-sectional area of both the elbow flexors (+11%) and of the type I and type IIA fibers increased only after the eccentric training. In addition, the relative cross-sectional area occupied by the type II fibers increased from 64 to 73% after the eccentric training. There were only minor changes in the fiber-type proportions. CONCLUSION: The present data suggest that for resistance-trained men, increases in concentric strength and velocity performance after eccentric training are largely mediated by changes in fiber and muscle cross-sectional area. However, hypertrophy alone could not explain the increase in eccentric strength. Because the increases in strength and velocity performance after concentric training could not be ascribed to muscular adaptations alone, we suggest that they may be attributable to additional neural factors.
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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".