MicroRNA and Mechanisms of Impaired Angiogenesis in Diabetes Mellitus
Bibliographic record
Abstract
D iabetes mellitus impairs physiological angiogenesis, which may be manifested as nonhealing foot ulcers or refractory angina.Multiple molecular mechanisms have been proposed.Hyperglycemia induces the generation of reactive oxygen species that cause endothelial derangements, 1 including the reduced synthesis 2 and accelerated degradation 3 of endothelium-derived nitric oxide (NO).The bioactivity of NO is critical for angiogenic processes such as the survival, proliferation, and migration of endothelial cells.4 The impairment in NO bioactivity may also explain in part the reduced expression of a major angiogenic cytokine, vascular endothelial growth factor (VEGF), in hyperglycemic states, because NO and VEGF have a reinforcing and reciprocal relationship.5 Glucose intolerance also reduces the number and function of bone marrow-derived endothelial progenitor cells, 6 circulating cells that participate in the angiogenic response.In addition to generating reactive oxygen species, hyperglycemia may impair cytoprotective mechanisms against oxidative stress.In particular, the thioredoxins play a key role in angiogenic processes by maintaining endothelial redox homeostasis, with favorable effects on protein folding, activity of reductive and metabolic enzymes, energy utilization, and transcription factor activity.7 Emerging evidence indicates that hyperglycemia upsets this cytoprotective mechanism by increasing the expression of the endogenous inhibitor thioredoxin-interacting protein (TXNIP). Article see p 282Is it possible that these and other disparate mechanisms for the impaired angiogenesis in diabetes mellitus have a common genomic basis?This question logically follows from the work of Caporali and colleagues in the current issue of Circulation.8 They have discovered a novel genomic mechanism for hyperglycemia-induced impairment of angiogenesis, ie, the increased expression of a specific microRNA (miRNA) that appears to orchestrate a pathophysiological response in diabetes mellitus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".