Effects of losartan on urinary secretion of extracellular matrix and their modulators in type 2 diabetes mellitus patients with microalbuminuria.
Bibliographic record
Abstract
PURPOSE: Angiotensin II receptor Type 1 antagonists postpone the development of nephropathy in type 2 diabetes mellitus (DM). We hypothesize that Losartan may ameliorate renal function in diabetic patients through the regulation on the generation of transforming growth factor (TGF)-beta and fibrinolytic regulators. METHODS: Twenty-two type 2 DM patients with microalbuminuria were treated with 50-100 mg/day of Losartan for 6 months. Urinary secretion of TGF-, plasminogen activator inhibitor-1 (PAI-1), tissue and urokinase plasminogen activators (tPA and uPA) fibronectin, collagen IV and plasma levels of TGF-beta, PAI-1, tPA and uPA of the patients before and after the treatment were analyzed using enzyme-linked immunoabosorbance assay. RESULTS: Losartan effectively reduced arterial blood pressure and urinary albumin excretion. The levels of TGF-beta in urine, but not in plasma, were reduced after 2, 4 and 6 months of the treatment (-32% to -48%, P < 0.05 or 0.01). Urinary or plasma levels of PAI-1, tPA or uPA, and urinary secretion of fibronectin or collagen IV were not significantly altered by Losartan treatment. Urinary levels of collagen IV positively correlated with uPA, and that of fibronectin negatively correlated with PAI-1 in the patients (P < 0.01). Urinary TGF-beta negatively correlated uPA in urine of the patients (P < 0.01). CONCLUSION: Losartan reduced urinary excretion of TGF-beta and albumin in type 2 DM patients with microalbuminuria. Fibrinolytic regulators and TGF-beta are implicated in the regulation of ECM turnover in kidneys of the patients with diabetic nephropathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".