Endothelial NOX5 Obliterates the Reno-Protective Effect of Nox4 Deletion by Promoting Renal Fibrosis via Activation of EMT and ROS-Sensitive Pathways in Diabetes
Bibliographic record
Abstract
Chronic hyperglycemia induces intrarenal oxidative stress due to the excessive production of reactive oxygen species (ROS), leading to a cascade of events that contribute to the development and progression of diabetic kidney disease (DKD). NOX5, a pro-oxidant NADPH oxidase isoform, has been identified as a significant contributor to renal ROS in humans. Elevated levels of renal ROS contribute to endothelial cell dysfunction and associated inflammation, causing increased endothelial permeability, which can disrupt the renal ecosystem, leading to progressive albuminuria and renal fibrosis in DKD. This study specifically examines the contribution of endothelial cell-specific human NOX5 expression in renal pathology in a transgenic mouse model of DKD. This study additionally compares NOX5 with the previously characterized NADPH oxidase, NOX4, in terms of their relative roles in DKD. Regardless of NOX4 pathway, this study found that endothelial cell-specific expression of NOX5 exacerbates renal injury, albuminuria and fibrosis. This is attributed to the activation of the endothelial mesenchymal transition (EMT) pathway via enhanced ROS formation and the modulation of redox-sensitive factors. These findings underscore the potential therapeutic significance of NOX5 inhibition in human DKD. The study proposes that inhibiting NOX5 could be a promising approach for mitigating the progression of DKD and strengthens the case for the development of NOX5-specific inhibitors as a potential therapeutic intervention.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".