Exercise training enhances insulin-stimulated nerve arterial vasodilation in rats with insulin-treated experimental diabetes
Bibliographic record
Abstract
Insulin stimulates nerve arterial vasodilation through a nitric oxide (NO) synthase (NOS) mechanism. Experimental diabetes reduces vasa nervorum NO reactivity. Studies investigating hyperglycemia and nerve arterial vasodilation typically omit insulin treatment and use sedentary rats resulting in severe hyperglycemia. We tested the hypotheses that 1) insulin-treated experimental diabetes and inactivity (DS rats) will attenuate insulin-mediated nerve arterial vasodilation, and 2) deficits in vasodilation in DS rats will be overcome by concurrent exercise training (DX rats; 75-85% VO2 max, 1 h/day, 5 days/wk, for 10 wk). The baseline index of vascular conductance values (VCi = nerve blood flow velocity/mean arterial blood pressure) were similar (P ≥ 0.68), but peak VCi and the area under the curve (AUCi) for the VCi during a euglycemic hyperinsulinemic clamp (EHC; 10 mU·kg(-1)·min(-1)) were lower in DS rats versus control sedentary (CS) rats and DX rats (P ≤ 0.01). Motor nerve conduction velocity (MNCV) was lower in DS rats versus CS rats and DX rats (P ≤ 0.01). When compared with DS rats, DX rats expressed greater nerve endothelial NOS (eNOS) protein content (P = 0.04). In a separate analysis, we examined the impact of diabetes in exercise-trained rats alone. When compared with exercise-trained control rats (CX), DX rats had a lower AUCi during the EHC, lower MNCV values, and lower sciatic nerve eNOS protein content (P ≤ 0.03). Therefore, vasa nervorum and motor nerve function are impaired in DS rats. Such deficits in rats with diabetes can be overcome by concurrent exercise training. However, in exercise-trained rats (CX and DX groups), moderate hyperglycemia lowers vasa nervorum and nerve function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".