Relationship of clusterin with renal inflammation and fibrosis after the recovery phase of ischemia-reperfusion injury
Bibliographic record
Abstract
BACKGROUND: Long-term outcomes after acute kidney injury (AKI) include incremental loss of function and progression towards chronic kidney disease (CKD); however, the pathogenesis of AKI to CKD remains largely unknown. Clusterin (CLU) is a chaperone-like protein that reduces ischemia-reperfusion injury (IRI) and enhances tissue repair after IRI in the kidney. This study investigated the role of CLU in the transition of IRI to renal fibrosis. METHODS: IRI was induced in the left kidneys of wild type (WT) C57BL/6J (B6) versus CLU knockout (KO) B6 mice by clamping the renal pedicles for 28 min at the body temperature of 32 °C. Tissue damage was examined by histology, infiltrate phenotypes by flow cytometry analysis, and fibrosis-related gene expression by PCR array. RESULTS: Reduction of kidney weight was induced by IRI, but was not affected by CLU KO. Both WT and KO kidneys had similar function with minimal cellular infiltration and fibrosis at day 14 of reperfusion. After 30 days, KO kidneys had greater loss in function than WT, indicated by the higher levels of both serum creatinine and BUN in KO mice, and exhibited more cellular infiltration (CD8 cells and macrophages), more tubular damage and more severe tissue fibrosis (glomerulopathy, interstitial fibrosis and vascular fibrosis). PCR array showed the association of CLU deficiency with up-regulation of CCL12, Col3a1, MMP9 and TIMP1 and down-regulation of EGF in these kidneys. CONCLUSION: Our data suggest that CLU deficiency worsens renal inflammation and tissue fibrosis after IRI in the kidney, which may be mediated through multiple pathways.
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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) | 0.000 | 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".