Abstract 680: Specific Deletion of SHP-1 in Smooth Muscle Cells Restores PDGF Action in Diabetes
Bibliographic record
Abstract
Introduction: Ischemia due to narrowing of the femoral artery and distal vessels is also a major cause of peripheral arterial disease and morbidity affecting patients with diabetes. Our laboratory has previously shown that hyperglycemia reduced platelet-derived growth factor (PDGF) activity in ischemic muscle of diabetic mice, which was associated with increased SHP-1 expression, a protein tyrosine phosphatase. The objective of this study is to evaluate the impact of SHP-1 deletion in smooth muscle cells both in vitro and in vivo. Methods: Non-diabetic (NDM) and 3 months diabetic (DM) mice with deletion of SHP-1 specifically in smooth muscle cells (SMC) were used. Ligation of the femoral artery was performed and blood flow reperfusion was measured by laser Doppler for 4 weeks. Primary SMC were exposed to normal (5.6mM; NG) or high glucose concentrations (25mM; HG) for 48h, in normoxia (20% oxygen) or hypoxia (1%) for the last 24h in presence of PDGF, a pro-angiogenic factor. Results: Blood flow was recovered to 47% in DM mice compared to 80% in NDM mice. Specific SMC deletion of SHP-1 enhanced reperfusion in NDM and DM mice up to 78% and 67%, respectively. In culture, PDGF-induced proliferation, migration, and Akt phosphorylation were reduced by 69%, 50% and 40%, respectively in SMC exposed to HG+hypoxia. Inhibition of PDGF actions was associated with increased SHP-1 phosphatase activity (40%) and enhanced interaction of SHP-1 with the PDGF receptor-β (5.6-fold). Overexpression of the dominant negative form of SHP-1 restored PDGF-induced proliferation and migration as well as Akt and ERK phosphorylation in SMC exposed to HG+hypoxia. Conclusion: High glucose level induced SHP-1 activity and caused inhibition of PDGF pro-angiogenic actions in SMC, whereas the deletion of SHP-1 specifically in SMC restored blood flow reperfusion in diabetes.
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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) | 0.000 | 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 | 0.001 |
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".