<i>Ager</i> Deletion Enhances Ischemic Muscle Inflammation, Angiogenesis, and Blood Flow Recovery in Diabetic Mice
Bibliographic record
Abstract
Objective— Diabetic subjects are at higher risk of ischemic peripheral vascular disease. We tested the hypothesis that advanced glycation end products (AGEs) and their receptor (RAGE) block angiogenesis and blood flow recovery after hindlimb ischemia induced by femoral artery ligation through modulation of immune/inflammatory mechanisms. Approach and Results— Wild-type mice rendered diabetic with streptozotocin and subjected to unilateral femoral artery ligation displayed increased accumulation and expression of AGEs and RAGE in ischemic muscle. In diabetic wild-type mice, femoral artery ligation attenuated angiogenesis and impaired blood flow recovery, in parallel with reduced macrophage content in ischemic muscle and suppression of early inflammatory gene expression, including Ccl2 (chemokine [C-C motif] ligand-2) and Egr1 (early growth response gene-1) versus nondiabetic mice. Deletion of Ager (gene encoding RAGE) or transgenic expression of Glo1 (reduces AGEs) restored adaptive inflammation, angiogenesis, and blood flow recovery in diabetic mice. In diabetes mellitus, deletion of Ager increased circulating Ly6C hi monocytes and augmented macrophage infiltration into ischemic muscle tissue after femoral artery ligation. In vitro, macrophages grown in high glucose display inflammation that is skewed to expression of tissue damage versus tissue repair gene expression. Further, macrophages grown in high versus low glucose demonstrate blunted macrophage–endothelial cell interactions. In both settings, these adverse effects of high glucose were reversed by Ager deletion in macrophages. Conclusions— These findings indicate that RAGE attenuates adaptive inflammation in hindlimb ischemia; underscore microenvironment-specific functions for RAGE in inflammation in tissue repair versus damage; and illustrate that AGE/RAGE antagonism may fill a critical gap in diabetic peripheral vascular 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".