Normoxic low‐altitude simulation (at 714 mmHg) improves limb blood perfusion in mice with hindlimb ischemia
Bibliographic record
Abstract
Humans have fewer cardiovascular events and improved outcomes after cardiovascular events when living at low and moderate altitudes (<3000 m) above sea level. We have previously shown that low-altitude simulation using reductions in barometric pressure enhances vasodilation ex vivo in arterial segments and reduces systemic vascular resistance in vivo and can also improve left ventricular function after a myocardial infarction. We hypothesize that low-altitude simulation could also improve hindlimb ischemia, a model of peripheral artery disease in humans. We performed femoral artery ligation to generate hindlimb ischemia in 3-month-old C57BL6 mice. Control group mice (n = 10) recovered at 754 mmHg (control) for 14 days. Treatment group mice (n = 15) were placed in a low-altitude simulation chamber (at 714 mmHg) to recover from surgery for 3-hours daily for 14 days. Hindlimb perfusion imaging using a laser Doppler line scanner was performed for all mice prior to the surgery, and then on days 1, 3, 7, and 14 post-surgery. At 2 weeks, ischemic reserve was significantly higher in the treatment group mice (0.50 ± 0.13 vs. 0.20 ± 0.06; p = 0.01). Treatment mice had higher functional scores and were able to walk better at two weeks. There was approximately three times less HIF1α found via western blotting and a small but statistically significant improvement of lectin perfusion in calf tissue of treatment mice. We conclude that low-altitude simulation improves blood perfusion in murine hindlimb ischemia. This approach may have therapeutic implications for humans with peripheral artery disease.
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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) | 0.001 | 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) | 0.003 | 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".