Towards Newer Molecular Targets for Chronic Diabetic Complications
Bibliographic record
Abstract
Prior to the discovery of insulin, the major cause of death in the diabetic population was ketoacidosis. Although insulin and improved glycemic control have improved the longevity of diabetic patients, they still suffer from significant morbidity and mortality due to chronic secondary complications. Long standing diabetes leads to structural and functional alterations in both the micro- and macrovasculature. These complications, involving the retina, kidney, and peripheral nerves, as well as cardiovascular system, severely compromise the quality and expectancy of life. Large scale clinical trials have identified hyperglycemia as the key determinant for the development of such complications. Therapeutic modalities have been developed to target glucose-induced alterations, such as protein kinase C activation, augmented polyol pathway activity, non-enzymatic glycation and oxidative stress to ameliorate chronic complications. However, clinical trials targeting these biochemical alterations have failed to show significant beneficial effects. The plethora of biochemical anomalies that govern the development of chronic diabetic complications may therefore be subject to cross-interaction and complex interplays. Studies in both animal and human diabetes have, however, showed alteration of several vasoactive effector molecules such as endothelins. These molecules may be instrumental in mediating diabetes-induced structural and functional deficits at both the early and late stages of the disease. This review will discuss the current mechanistic understanding of chronic diabetic complications and will explore the potential novel therapeutic interventions.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".