Chronic wheel running selectively augments insulin‐stimulated vasodilation in arterioles from the white gastrocnemius
Bibliographic record
Abstract
Insulin-stimulated vasodilation (ISV) typically accounts for ~40% of insulin-stimulated glucose uptake but is impaired in obesity and insulin resistance (IR) due to an imbalance in endothelium-derived nitric oxide and endothelin-1 (ET-1). To determine whether voluntary wheel running (RUN): 1) restores ISV after the development of IR and 2) differentially impacts ISV in second order arterioles (2A) from white (Gw) and red gastrocnemius (Gr), we randomized sedentary 12 wk OLETF rats (SED12) prone to hyperphagia-induced obesity/IR to: 1) SED12; 2) SED, 20 wk (SED20); 3) RUN 12–20 wk (RUN20); or 4) caloric restriction 12–20 wk, weight-matched to RUN (CR20). Glucose and insulin responses to an ip glucose tolerance test were reduced in RUN20, maintained in CR20, and elevated in SED20 (P<0.05 vs. SED12). Cytochrome c declined in heart, Gr and Gw of SED20 and CR20 and was maintained in heart and Gr but not Gw of RUN20. ISV was greater in Gw 2As but not Gr 2As of RUN20 (P<0.05 vs. SED12, SED20 and CR20). ET-1 receptor inhibition improved ISV in Gw 2As from SED20 and CR20 (P<0.05). Insulin-stimulated phosphorylation of Akt and eNOS in Gr and Gw 2As did not differ among groups. Thus, despite evidence of training adaptations in Gr but not Gw, RUN improved ISV in Gw 2As, an effect which may be mediated by attenuated sensitivity to ET-1 and may contribute to greater insulin-mediated glucose disposal. Support: NIH HL36088, AHA, VHA, MU iCATS.
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 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 | 0.001 |
| 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".