Role of glomerular nitric oxide in glycerol-induced acute renal failure
Bibliographic record
Abstract
Myoglobinuric acute renal failure remains one of the least understood clinical syndromes and the mediators involved remain obscure. The aim of the present study was to assess the role of nitric oxide in glycerol-induced acute renal failure under normal conditions and after uninephrectomy. Acute renal failure was induced in rats by injection of 50% glycerol (10 mL x kg(-1) body weight). Half of the animals were subjected to uninephrectomy two days before glycerol injection. Two days after the induction of acute renal failure, glomeruli from some animals were isolated and glomerular nitrite production was measured. Another group of animals was used for acute clearance studies. In this case, the effect of infusing either L-NAME or L-arginine was assayed. Glomerular nitrite production was significantly decreased in glycerol-induced acute renal failure. Glomeruli from uninephrectomized animals showed an increase in nitrite production, both in normal conditions and after glycerol injection, as compared with glomeruli from non-nephrectomized animals. L-NAME infusion worsened renal function in all the study groups, but more slowly in animals with glycerol-induced acute renal failure than in control rats. In uninephrectomized animals L-NAME reduced renal function more than in animals with two kidneys. In conclusion, in this model of acute renal failure the decrease in glomerular nitric oxide production plays an important role in the decrease in renal function. After uninephrectomy, an increase in glomerular nitric oxide synthesis plays a protective role against glycerol-induced acute renal failure.
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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) | 0.000 | 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) | 0.000 | 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".