Aging Disrupts The Balance Between Positive And Negative Angiogenic Factors In Skeletal Muscle
Bibliographic record
Abstract
Skeletal muscle microvessel density has been found to decrease with age in both rats and humans, but the mechanisms for this process are still poorly understood. Physiologic regulation of angiogenesis is thought to be controlled by a balance between positive and negative proteins, among which vascular endothelial growth factor (VEGF) and thrombospondin‐1 (TSP‐1) are emerging as two important factors. We hypothesized that age‐related reduction in skeletal muscle capillarity would correlate with lower VEGF and/or higher TSP‐1 levels. We measured protein expression of VEGF and TSP‐1 in the hind limb muscles, plantaris (PLT), soleus (SOL), and gastrocnemius (GA), of 12 week‐ (young, N=5–6) and 20 week‐ old (aged, N=3–6) lean Zucker rats. In the GA, aged animals had a 570% increase in TSP‐1 (p=0.014) and no difference in VEGF. In the PLT, aged animals had a no significant difference in TSP‐1 and a 60% decrease in VEGF (p=0.049). In the SOL, aged animals had a 198% increase in TSP‐1 (p=0.003), and no difference in VEGF. These results suggest that an altered balance between VEGF and TSP‐1 may be responsible for capillary rarefaction in aged muscle. Research supported by NIH 5T32‐HL090610 and AHA 10BGIA3630002.
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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) | 0.000 | 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".