White blood cells, FeNO, glutathione, 8-oxodG and 8-isoprostane in respiratory diseases
Bibliographic record
Abstract
Inflammation and oxidative stress (OS) play an important role in pathogenesis of respiratory diseases. Such biomarkers as fractional exhaled nitric oxide (FeNO), white blood cells, glutathione, urinary 8-oxodG and 8-isoprostane can serve in evaluating clinical course of the disease. We aimed at estimating the association of biomarkers of OS and inflammation with current asthma (CA), past asthma (PA) and chronic bronchitis (CB). The data from GEIRD survey (www.geird.org) have been used in this study. The hierarchic outcome variable was built to achieve mutually exclusive diseases, i.e. Controls (no respiratory disorders, n=549), CA (no PA, n=404), PA (no CA, n=185), CB (no CA, PA, n=92). Multinomial logistic regressions were applied to analyze associations, adjusting for age, BMI, sex, cohort, centre, smoke, comorbidities and alcohol. Relative Risk Ratios (RRR) for one standard deviation increase were adduced for all biomarkers. Glutathione (mg/ml) was higher in subjects with CB (RRR=1.77, CI(1.18-3.07)). FeNO (ppm) was higher in CA (RRR=1.47, CI(1.19-1.82)). Basophils (e+06/ml) had higher levels in CA (RRR=1.48, CI(1.20-1.84)) and CB(RRR=1.51, CI(1.01-2.25)); eosinophils (e+06/ml) were higher in CA (RRR=2.37, CI(1.79-3.13)), PA (RRR=1.79, CI(1.30-2.47)) and CB (RRR=2.14, CI(1.42-3.22)); leucocytes (e+06/ml) were increased in CA (RRR=1.34, CI(1.07-1.67)); lymphocytes (e+06/ml) had higher levels in CA (RRR=1.27, CI(1.03-1.55)) and CB (RRR=1.53, CI(1.05-2.25)). Our results showed that biomarkers of inflammation and OS were differently associated with asthma and chronic bronchitis, suggesting that they might be useful in phenotyping respiratory diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".