Positive correlation of airway resistance and serum asymmetric dimethylarginine (ADMA) in bronchial asthma patients lacking evidence for systemic inflammation
Bibliographic record
Abstract
Contribution of nitric-oxide (NO) pathway to the pathogenesis of bronchial asthma (asthma) is ambiguous as NO may confer both protective and detrimental effects depending on the NO synthase (NOS) isoforms, tissue compartments and underlying pathological conditions (e.g. systemic inflammation). Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor and uncoupler of NOS with distinct selectivity for NOS isoforms. In a cross-sectional study, we assessed whether ADMA is an independent predictor of airway resistance (R aw ) in therapy-controlled asthma. 154 therapy-controlled asthma patients were recruited. ADMA, symmetric dimethylarginine and arginine were quantitated by HPLC with fluorescent detection. Pulmonary function test was done using whole-body plethysmography, quality of life via St. George’s Respiratory questionnaire (SGRQ). Multiple linear regression was used to identify independent determinants of R aw . The final model was stratified based on therapy control. Evidence for systemic inflammation indicated by CRP and procalcitonin was lacking in our sample. Log R aw showed significant positive correlation with log ADMA in the whole data set and well-controlled but not in the not well-controlled stratum (Spearman correlation coefficients: 0.27, p < 0.001; 0.30, p < 0.001; 0.12, p = 0.51 respectively). This relationship remained significant after adjusting for confounders by multiple linear regression (β = 0.22, CI 0.054, 0.383 p = 0.01). FEF 25–75% % predicted and SGRQ Total score showed significant negative while SGRQ Activity score showed significant positive correlation with R aw in the final model. Positive correlation between R aw and ADMA in the absence of systemic inflammation implies that higher ADMA has detrimental effect on NO homeostasis and can contribute to a poor outcome in asthma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".