Respiratory Viral Detection and Small Airway Inflammation in Lung Tissue of Patients with Stable, Mild COPD
Bibliographic record
Abstract
BACKGROUND: Viral respiratory tract infections are implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). In lung tissue specimens from patients with stable, mild COPD and from control smokers without airflow obstruction, we determined the prevalence and load of nucleic acid from common respiratory viruses and concomitant inflammation of small airways measuring less than 2-mm in diameter. METHODS: Frozen lung tissue obtained from patients with stable, mild COPD (n = 20) and control subjects (n = 20) underwent real-time quantitative PCR (qPCR) for 13 respiratory viruses, and quantitative histology for inflammation of small airways. The two groups were compared for viral prevalence and load, and airway inflammation. The relationship between viral load and airway inflammatory cells was also analyzed. RESULTS: Viral nucleic acid were detected in lung tissue of 18/40 (45.0%) of the individuals studied and included seven co-infections that were characterized by a "dominant virus" contributing to most of the total measured viral load. Lung tissue of COPD patients had a significantly higher prevalence of viral nucleic acid (particularly influenza A virus), and increased inflammation of small airways by macrophages and neutrophils versus controls. In qPCR-positive individuals, linear regression analysis showed a direct correlation between viral load and airway neutrophils, and between influenza A virus load and airway macrophages. CONCLUSION: The lung tissue of patients with stable, mild COPD has a higher prevalence and load of respiratory viruses versus non-obstructed control subjects, and increased inflammation of small airways. Respiratory viruses may represent potential targets in COPD patient management.
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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 | 0.001 | 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".