The Role of Infection in Acute Exacerbation of Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Background . Acute exacerbation of IPF (AE-IPF) is associated with high mortality. We studied changes in pathogen involvement during AE-IPF and explored a possible role of infection in AE-IPF. Objectives . Our purpose is to investigate the role of infection in AE-IPF. Methods . Overall, we recruited 170 IPF patients (48 AE-IPF, 122 stable) and 70 controls at Shanghai Pulmonary Hospital. Specific IgM against microbial pathogens and pathogens in sputum were assessed. RNA sequences of pathogens in nasopharyngeal swab of IPF patients were detected by PathChip. A panel of serum parameters reflecting immune function were assessed. Results . Antiviral/bacterial IgM was higher in IPF vs. controls and in AE-IPF vs. stable IPF. Thirty-eight different bacterial strains were detected in IPF patient sputum. Bacteria-positive results were found in 9/48 (18.8%) of AE-IPF and in 26/122 (21.3%) stable IPF. Fifty-seven different viruses were detected in nasopharyngeal swabs of IPF patients. Virus-positive nasopharyngeal swabs were found in 18/30 (60%) of tested AE-IPF and in 13/30 (43.3%) of stable IPF. AE-IPF showed increased inflammatory cytokines (IL-6, IFN- γ , MIG, IL-17, and IL-9) vs. stable IPF and controls. Mortality of AE-IPF in one year (39.5%) was higher compared to stable IPF (28.7%) .Conclusions . IPF patients had different colonization with pathogens in sputum and nasopharyngeal swabs; they also displayed abnormally activated immune response, which was exacerbated during AE-IPF.
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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 | 0.000 |
| 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".