Crosstalk of Hyperglycaemia and Cellular Mechanisms in the Pathogenesis of Diabetic Kidney Disease
Bibliographic record
Abstract
Among all nephropathies, diabetic kidney disease (DKD) is the most common cause of kidney impairment advancement to end-stage renal disease (ESRD). Although DKD has no cure, the disease is commonly managed by strict control of blood glucose and blood pressure, and in most of these cases, kidney function often deteriorates, resulting in dialysis, kidney replacement therapy, and high mortality. The difficulties in finding a cure for DKD are mainly due to a poor understanding of the underpinning complex cellular mechanisms that could be identified as druggable targets for the treatment of this disease. The review is thus aimed at giving insight into the interconnection between chronic hyperglycaemia and cellular mechanistic perturbations of nephropathy in diabetes. A comprehensive literature review of observational studies on DKD published within the past ten years, with 57 percent published within the past three years was carried out. The article search focused on original research studies and reviews published in English. The articles were explored using Google Scholar, Medline, Web of Science, and PubMed databases based on keywords, titles, and abstracts related to the topic. This article provides a detailed relationship between hyperglycaemia, oxidative stress, and various cellular mechanisms that underlie the onset and progression of the disease. Moreover, it also shows how these mechanisms affect organelle dysfunction, resulting in fibrosis and podocyte impairment. The advances in understanding the complexity of DKD mechanisms discussed in this review will expedite opportunities to develop new interventions for treating the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".