ANG1 treatment reduces muscle pathology and prevents a decline in perfusion in DMD mice
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) and other pro-angiogenic growth factors have been investigated to enhance muscle tissue perfusion and repair in Duchenne muscular dystrophy (DMD). Current understanding is limited by a lack of functional data following in vivo delivery of these growth factors. We previously used dynamic contrast-enhanced computed tomography to monitor disease progression in murine models of DMD, but no study to date has utilized this imaging technique to assess vascular therapy in a preclinical model of DMD. In the current study, we locally delivered VEGF and ANG1 alone or in combination to dystrophic hind limb skeletal muscle. Using functional imaging, we found the combination treatment as well as ANG1 alone prevented decline in muscle perfusion whereas VEGF alone had no effect compared to controls. These findings were validated histologically as demonstrated by increased alpha-smooth muscle actin-positive vessels in muscles that received either VEGF+ANG1 or ANG1 alone compared to the sham group. We further show that ANG1 alone slows progression of fibrosis compared to either sham or VEGF treatment. The findings from this study shed new light on the functional effects of vascular therapy and suggest that ANG1 alone may be a candidate therapy in the treatment of DMD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".