No difference in renal injury and fibrosis between wild-type and NOD1/NOD2 double knockout mice with chronic kidney disease induced by ureteral obstruction
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is characterized by sustained tissue damage and ongoing tubulo-interstitial inflammation and fibrosis. Pattern recognition receptors (PRRs) including Toll-like receptors (TLRs) and NOD-like receptors (NLRs) can sense endogenous ligands released upon tissue damage, leading to sterile inflammation and eventually irreversible kidney disease. It is known that NOD1 and NOD2 contribute to the pathogenesis of various inflammatory diseases, including acute kidney injury. However their role in chronic kidney disease is largely unknown. The aim of this study was therefore to investigate the contribution of NOD1 and NOD2 in renal interstitial fibrosis and obstructive nephropathy. METHODS: To do so, we performed unilateral ureteral obstruction (UUO) in wild type (WT) and NOD1/NOD2 double deficient (DKO) mice and analysed renal damage, fibrosis and inflammation. Data were analysed using the non-parametric Mann-Whitney U-test. RESULTS: Minor changes in inflammatory response were observed in NOD1/2 DKO mice, while no effects were observed on renal injury and the development of fibrosis. CONCLUSION: No difference in renal injury and fibrosis between WT and NOD1/NOD2 DKO mice following obstructive nephropathy induced by ureteral obstruction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".