miR-193a as a potential mediator of WT-1/synaptopodin in the renoprotective effect of Losartan on diabetic kidney
Bibliographic record
Abstract
Diabetic nephropathy (DN) is the most common complication of diabetic patients, and has become a global healthcare problem. In this study, we used diabetic mice to evaluate the effect of Losartan on DN, in which the experimental animals were divided into three groups: non-diabetic mice (db/m group), untreated-diabetic mice (db/db group), and Losartan-treated diabetic mice (db/db-losartan). Next, immunohistochemistry and immunofluorescence were used to detect Wilms tumor protein 1 (WT-1) and synaptopodin expression, respectively. Protein levels of WT-1, synaptopodin, claudin1, and Pax-2 were assessed by Western blotting and real-time PCR. The miR-193a mRNA levels were quantitated by real-time PCR. The results showed that albuminuria was increased in diabetic mice compared with control animals and was significantly ameliorated by treatment with Losartan. In addition, Losartan significantly upregulated the immunopositive cell numbers of WT-1, the expression of WT-1 and synaptopodin in renal tissue. By contrast, expression of claudin1 and Pax-2 in renal tissue were decreased in db/db-losartan group. Besides, expression of miR-193a was decreased significantly in db/db-losartan group compared with the untreated diabetic group. Thus, Losartan has renoprotective effects on the control of tissue damage possibly by inhibiting the expression of miR-193a, thereby promoting the repair of podocyte injury in mice with DN.
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 | 0.000 |
| 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".