Promotion of cell proliferation by clusterin in the renal tissue repair phase after ischemia-reperfusion injury
Bibliographic record
Abstract
Renal repair begins soon after the kidney suffers ischemia-reperfusion injury (IRI); however, its molecular pathways are not fully understood. Clusterin (Clu) is a chaperone protein with cytoprotective functions in renal IRI. The aim of this study was to investigate the role of Clu in renal repair after IRI. IRI was induced in the left kidneys of wild-type (WT) C57BL/6J (B6) vs. Clu knockout (KO) B6 mice by clamping the renal pedicles for 28-45 min at the body temperature of 32°C. The renal repair was assessed by histology and confirmed by renal function. Gene expression was examined using PCR array. Here, we show that following IRI, renal tubular damage and Clu expression in WT kidneys were induced at day 1, reached the maximum at day 3, and significantly diminished at day 7 along with normal function, whereas the tubular damage in Clu KO kidneys steadily increased from initiation of insult to the end of the experiment, when renal failure occurred. Renal repair in WT kidneys was positively correlated with an increase in Ki67(+) proliferative tubular cells and survival from IRI. The functions of Clu in renal repair and renal tubular cell proliferation in cultures were associated with upregulation of a panel of genes that could positively regulate cell cycle progression and DNA damage repair, which might promote cell proliferation but not involve cell migration. In conclusion, these data suggest that Clu is required for renal tissue regeneration in the kidney repair phase after IRI, which is associated with promotion of tubular cell proliferation.
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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".