Lack of renoprotective effects of targeting the endothelin A receptor and (or) sodium glucose transporter 2 in a mouse model of Type 2 diabetic kidney disease
Bibliographic record
Abstract
Two recent clinical trials, using sodium glucose cotransporter (SGLT2) or endothelin-A receptor (ET-A) blocker, reported the first efficacious treatments in 18 years to slow progression of diabetic kidney disease (DKD). We hypothesized that combined inhibition of SGLT2 and ET-A receptor may confer greater protection against renal injury than either agent alone. Uninephrectomized male db/db mice were randomized to four groups: vehicle, SGLT2 inhibitor (dapagliflozin (dapa), 1 mg/kg/day), ET-A blocker (atrasentan (atra), 5 mg/kg/day), or dual treatment from 10 weeks until 22 weeks of age. At 10 weeks of age, no differences were observed in body weight, blood glucose or urinary albumin excretion among the four groups. At 16 and 22 weeks of age, body weight was lower and blood glucose levels higher in the vehicle and atra groups compared with dapa- and dual-treated groups. No notable differences were observed among the four groups in urinary albumin excretion at weeks 16 and 22. Histological analysis showed mild glomerulosclerosis and tubular injury (<5%) in all four groups with reduced glomerulosclerosis in the dual treatment group compared with vehicle. Individual or combined treatment with an SGLT2 inhibitor and (or) an ET-A antagonist did not confer renoprotective effects in this model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".