Abstract 18569: Deletion of AT2 Receptor Prevents SHP-1 Expression and Improves Blood Flow in Diabetic Ischemic Hindlimb
Bibliographic record
Abstract
Introduction: Ischemia due to narrowing of femoral artery and distal vessels is also a major cause of peripheral arterial disease and morbidity affecting patients with diabetes. We have previously reported that the inhibition of the angiogenic response to PDGF and VEGF in diabetic mice (DM) is associated with the increase expression of SHP-1, a protein that can be activated by the AT2 receptors. It has been shown that the deletion of AT2 receptor in mice promotes angiogenesis within the ischemic muscle, but its role in diabetic condition remains unknown. Hypothesis: Our hypothesis is that AT2 receptor induced SHP-1 which contributed to inhibition of pro-angiogenic factor actions in DM mice during ischemia. Methods: Non-DM and DM AT2 null mice underwent femoral artery ligation after two months of diabetes. Blood perfusion was measured every week up to 4 weeks post-surgery. Expression of AT1, AT2, angiotensin-converting enzymes (ACE1/2), SHP-1 and angiogenic factors was evaluated. Results: Blood flow in the ischemic muscle of DM-AT2KO mice recovered faster and up to 80% four weeks following the surgery, compared to a 51% recovery in DM mice. After four weeks, the expression of pro-angiogenic factors (HIF-1α and VEGF) was diminished in the DM and remained at a basal state in the DM-AT2KO suggesting a faster recovery process in these mice. Interestingly, two weeks after ligation, the expression of VEGF and HIF-1α are elevated in DM-AT2DM compared to DM mice and correlated with a reduction of SHP-1 expression in the ischemic muscles. Conclusion: Our results suggest that the deletion of AT2 receptor prevented SHP-1 expression induced by diabetes and restored pro-angiogenic factors causing blood flow reperfusion following ischemia.
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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) | 0.001 | 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) | 0.006 | 0.001 |
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".