Fluorofenidone Offers Improved Renoprotection at Early Interventions during the Course of Diabetic Nephropathy in db/db Mice via Multiple Pathways
Bibliographic record
Abstract
Diabetic nephropathy (DN) remains the leading cause of end-stage renal disease (ESRD), a situation that is in part attributable to the lack of effective treatments. Fluorofenidone is a newly developed reagent with anti-fibrotic activity. While fluorofenidone was previously demonstrated to possess renoprotection from DN pathogenesis in db/db mice, the protective process and its underlying mechanisms have not been well studied. To characterize fluorofenidone-derived renoprotection, we treated 5, 8, or 12-week old db/db mice with daily doses of placebo, fluorofenidone, or losartan until 24 weeks of age; the time at which diabetes and DN were fully developed in placebo-treated animals. In comparison to db/db mice receiving fluorofenidone at 12-weeks old, those treated at 5-weeks had less glomerular expansion and better preservation of renal functions, judged by serum creatinine levels, albumin to creatinine ratio, and urinary albumin excretion (mg/24 hours). These benefits of early treatment were associated with significant reductions of multiple DN-promoting events, such as decreased expression of TGF-β1 and the p22phox subunit of NADPH oxidase as well as downregulated activation of protein kinase C-zeta (ζ), ERK and AKT. This improvement in renoprotection following early interventions is not a unique property of DN pathogenesis, as losartan does not apparently offer the same benefits and is not more renoprotective than fluorofenidone. Additionally, the enhanced renoprotection provided by fluorofenidone did not affect the diabetic process, as it did not alter serum levels of glycated serum proteins, glucose, triglyceride or cholesterol. Collectively, we provide evidence that fluorofenidone offers improved renoprotection at early stages of DN pathogenesis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".