Dimethylarginines in chronic renal failure
Bibliographic record
Abstract
BACKGROUND: Nitric oxide (NO) is a potent chemical mediator involved in many functions. In vivo production of NO is thought to be regulated by endogenous analogues of L-arginine: asymmetric dimethylarginine (ADMA). AIM: To examine the effect of renal function and dialysis on the serum concentrations of ADMA and symmetric dimethylarginine (SDMA). METHODS: Blood samples were obtained from nine healthy subjects, patients with renal failure before (n = 17) and after haemodialysis (n = 9), nine patients on chronic ambulatory peritoneal dialysis (CAPD), and 13 patients with chronic renal failure on conservative treatment. Serum samples were extracted using a solid phase cation exchange column and the extracts were analysed by high performance liquid chromatography (HPLC). RESULTS: Serum concentrations of ADMA in patients with renal failure (mean, 1.04 micromol/litre; SD, 0.17) were significantly higher than those of controls (mean, 0.61 micromol/litre; SD, 0.13). Haemodialysis significantly decreased the serum concentration by 36% (before dialysis: mean 0.99 (SD, 0.25) micromol/litre; after dialysis: mean, 0.63 (SD, 0.15) micromol/litre). Serum SDMA concentrations were higher in patients with renal failure, and haemodialysis decreased the concentration by 60%. There was no difference in serum arginine concentrations between the groups. CONCLUSION: Serum concentrations of ADMA are increased in renal failure and haemodialysis reduces the concentration.
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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) | 0.000 | 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".