A Review of the Potential Benefits of Pentoxifylline in Diabetic and Non-Diabetic Proteinuria
Bibliographic record
Abstract
Chronic kidney disease (CKD) as a considerable health problem may have proteinuria as the main complication and strong risk factor to reach end-stage renal disease (ESRD). Decreasing proteinuria is the mainstay of therapy in order to delay the progression of CKD. Current therapeutic regimens provide only partial renoprotection, and a substantial number of patients who have proteinuria progress to ESRD. Pentoxifylline (PTF) is known for its potent inhibitory effects against cell proliferation and inflammation which play important roles in CKD progression. Data derived from both human studies and animal models demonstrated that PTF has broad-spectrum renoprotective effects and therefore, provide a scientific basis for the use of PTF as an anti-proteinuric agent. Conclusion of this review is that short-term use of PTF may produce a significant reduction of proteinuria in subjects with diabetic and also non-diabetic kidney diseases but the reports of long-term use of PTF also show that urinary protein excretion exhibits a progressive and sustained reduction in patients treated with PTF. Whether the long-term use of PTF could be a pharmacological alternative for delaying or preventing the development of end stage renal disease, is among the questions that remained to be appropriately answered in large-scale clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".