Bibliographic record
Abstract
Prolonged hyperglycemia, dyslipidemia and oxidative stress in diabetes result in the increased production and accumulation of advanced glycation end products (AGEs) in the kidney. Covalent AGE modifications significantly influence the structure and function of key protein targets. In addition, activation of AGE receptors, alone or in combination with other ligands, is able to promote renal damage, fibrosis and inflammation associated with diabetic nephropathy. The actions of AGEs synergize and potentiate the activity of other pathogenic mediators in the diabetic kidney, including oxidative stress, protein kinase C and renin-angiotensin system activation, which subsequently promote the development and progression of kidney disease in a vicious and progressive cycle. Their importance as downstream mediators of hyperglycemia in diabetes has been amply demonstrated in studies using mechanistically different inhibitors of advanced glycation to retard the development of kidney disease without directly influencing plasma glucose levels. Furthermore, direct exposure to AGEs is able to generate lesions similar to those seen in diabetic nephropathy. The human body has a number of natural defenses against AGE accumulation, which are reduced in diabetic individuals, and in particular those with nephropathy, while the receptor for AGEs and its ligands are significantly increased. Given such data, a number of different pharmacological agents have been developed to reduce AGEs and with it prevent diabetic kidney disease. Although many have proved effective in experimental models of diabetes, their clinical utility remains unproven.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.020 |
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".