Potential Applications of Nitric Oxide Donors in Type 2 Diabetes
Bibliographic record
Abstract
Nitric oxide (NO) donors are chemical agents that produce NO-related activity in biological systems, mimic endogenous NO-related responses, or compensate for NO deficiency. NO donors have been increasingly studied as promising therapeutic agents for insulin resistance and type 2 diabetes (T2D). Here, we provide evidence, which investigated the effects of the most frequently studied and implemented NOreleasing compounds, including sodium nitroprusside (SNP), S-nitrosothiols [RSNOs, i.e., S-nitrosoglutathione (GSNO), S-nitroso-N-acetyl-penicillamine, (SNAP)], and NDiazeniumdiolates (NONOates, i.e., spermine NONOate, diethylamine NONOate) on glucose and insulin homeostasis. Available evidence could not draw a clear conclusion regarding therapeutic applications of NO donors in T2D due to different methodological approaches (i.e., in vitro vs. in vivo) and different doses and formulations used to assess the potential effects of NO donors on carbohydrate metabolism. Considering key properties and different kinetic behaviors between various classes of NO donors, targeted compound selection, defining optimum doses, and appropriate use of NO-releasing platforms (topical vs. systemic delivery mode) seem to be critical issues that can accelerate the bench-to-beside translation of NO donors in T2D.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".