Bibliographic record
Abstract
Diabetic kidney disease (DKD) is currently the leading cause of end-stage renal disease. It accounts for 40% of morbidity and mortality among the diabetic population despite optimal management. Its clinical hallmark includes persistent hyperglycemia, hypercreatininemia, uremia, sustained albuminuria, renal hemodynamic changes and elevated blood pressure. Histologically, DKD presents with excessive accumulation and deposition of extracellular matrix, leading to expansion of mesangial matrix, thickening of glomerular basement membrane and tubulointerstitial fibrosis. At the molecular level, accumulating evidence suggests that hyperglycemia or high glucose mediates renal injury in DKD via multiple molecular mechanisms such as induction of oxidative stress, upregulation of renal transforming growth factor beta-1 expression, production of pro-inflammatory cytokines, activation of fibroblasts and renin-angiotensin-aldosterone system, and depletion of adenosine triphosphate. Moreover, existing therapies only retard the disease progression but do not prevent or reverse it. Therefore, novel modes of pharmacotherapeutic intervention are in demand to target additional disease mechanisms. A substantial body of experimental evidence demonstrates that hydrogen sulfide (H2S), a gas with a historic notorious label, has recently been established to possess important therapeutic properties that prevent and/or reverses DKD development and progression of DKD by targeting several important molecular pathways, and therefore could be considered a novel pharmacological agent for DKD treatment. The aim of this chapter is to discuss recent experimental findings on the molecular mechanisms underlying the pharmacotherapeutic effects of H2S against DKD development and progression, and its translation from bench to bedside, which could lay the foundation for its future clinical use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".