Selective tubular activation of hypoxia-inducible factor-2α has dual effects on renal fibrosis
Bibliographic record
Abstract
Hypoxia-inducible factor (HIF) is a key transcriptional factor in the response to hypoxia. Although the effect of HIF activation in chronic kidney disease (CKD) has been widely evaluated, the results have been inconsistent until now. This study aimed to investigate the effects of HIF-2α activation on renal fibrosis according to the activation timing in inducible tubule-specific transgenic mice with non-diabetic CKD. HIF-2α activation in renal tubular cells upregulated mRNA and protein expressions of fibronectin and type 1 collagen associated with the activation of p38 mitogen-activated protein kinase. In CKD mice, activation of HIF-2α at the beginning of CKD significantly aggravated renal fibrosis, whereas it did not lead to renal dysfunction. However, activation at a late-stage of CKD abrogated both renal dysfunction and fibrosis, which was associated with restoration of renal vasculature and amelioration of hypoxia through increased renal tubular expression of VEGF and its isoforms. As with tubular cells with HIF-2α activation, those under hypoxia also upregulated VEGF, fibronectin, and type 1 collagen expressions associated with HIF-1α activation. In conclusion, late-stage renal tubular HIF-2α activation has protective effects on renal fibrosis and the resultant renal dysfunction, thus it could represent a therapeutic target in late stage of CKD.
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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 | 0.000 |
| 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".