Systematic Interrogation of Angiogenesis in the Ischemic Mouse Hind Limb
Bibliographic record
Abstract
OBJECTIVE: There has been little success in translating preclinical studies of mouse hind limb ischemia into benefit for patients with peripheral artery disease. Using systematic strategies, we sought to define the injury and angiogenesis landscapes in mice subjected to hind limb ischemia and ascertain whether published studies to date have used an analysis strategy concordant with these data. Approach and Results: Maps of ischemic injury were generated from 22 different hind limb muscles and 33 muscle territories in 12-week-old C57BL/6 mice, based on loss or centralization of myofiber nuclei. Angiogenesis was similarly mapped based on CD (cluster of differentiation) 31-positive capillary content. Only 10 of 33 muscle territories displayed consistent muscle injury, with the distal anterior hind limb muscles most reliably injured. Angiogenesis was patchy and exclusively associated with zones of regenerated muscle (central nuclei). Angiogenesis was not observed in normal appearing muscle, necrotic muscle, or injury border zones. Systematic review of mouse hind limb angiogenesis studies identified 5147 unique publications, of which 509 met eligibility criteria for analysis. Only 7% of these analyzed manuscripts evaluated angiogenesis in distal anterior hind limb muscles and only 15% consistently examined for angiogenesis in zones of muscle regeneration. CONCLUSIONS: In 12-week C57BL/6 mice, angiogenesis postfemoral artery excision proceeds exclusively in zones of muscle regeneration. Only a minority of studies to date have analyzed angiogenesis in regions of demonstrably regenerating muscle or in high-likelihood territories. Quality assurance standards, informed by the atlas and mapping data herein, could augment data reliability and potentially help translate mouse hind limb ischemia studies to patient care.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".