Asymmetric Dimethylarginine in Chronic Obstructive Pulmonary Disease (ADMA in COPD)
Bibliographic record
Abstract
L-arginine metabolism including the nitric oxide (NO) synthase and arginase pathways is important in the maintenance of airways function. We have previously reported that accumulation of asymmetric dimethylarginine (ADMA) in airways, resulting in changes in L-arginine metabolism, contributes to airways obstruction in asthma and cystic fibrosis. Herein, we assessed L-arginine metabolism in airways of patients with chronic obstructive pulmonary disease (COPD). Lung function testing, measurement of fractional exhaled NO (FeNO) and sputum NO metabolites, as well as quantification of L-arginine metabolites (L-arginine, L-ornithine, L-citrulline, ADMA and symmetric dimethylarginine) using liquid chromatography-mass spectrometry (LC-MS) were performed. Concentrations of L-ornithine, the product of arginase activity, correlated directly with L-arginine and ADMA sputum concentrations. FeNO correlated directly with pre- and post-bronchodilator forced expiratory volume in one second (FEV1). Sputum arginase activity correlated inversely with total NO metabolite (NOx) and nitrite concentrations in sputum, and with pre- and post-bronchodilator FEV1. These findings suggest that ADMA in COPD airways results in a functionally relevant shift of L-arginine breakdown by the NO synthases towards the arginase pathway, which contributes to airway obstruction in these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".