Chronic Inflammation in Chronic Kidney Disease Progression: Role of Nrf2
Bibliographic record
Abstract
Despite recent advances in the management of chronic kidney disease (CKD), morbidity and mortality rates in these patients remain high. Although pressure-mediated injury is a well-recognized mechanism of disease progression in CKD, emerging data indicate that an intermediate phenotype involving chronic inflammation, oxidative stress, hypoxia, senescence, and mitochondrial dysfunction plays a key role in the etiology, progression, and pathophysiology of CKD. A variety of factors promote chronic inflammation in CKD, including oxidative stress and the adoption of a proinflammatory phenotype by resident kidney cells. Regulation of proinflammatory and anti-inflammatory factors through NF-κB- and nuclear factor, erythroid 2 like 2 (Nrf2)-mediated gene transcription, respectively, plays a critical role in the glomerular and tubular cell response to kidney injury. Chronic inflammation contributes to the decline in glomerular filtration rate (GFR) in CKD. Whereas the role of chronic inflammation in diabetic kidney disease (DKD) has been well-elucidated, there is now substantial evidence indicating unresolved inflammatory processes lead to fibrosis and eventual end-stage kidney disease (ESKD) in several other diseases, such as Alport syndrome, autosomal-dominant polycystic kidney disease (ADPKD), IgA nephropathy (IgAN), and focal segmental glomerulosclerosis (FSGS). In this review, we aim to clarify the mechanisms of chronic inflammation in the pathophysiology and disease progression across the spectrum of kidney diseases, with a focus on Nrf2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".