Differential expression of sputum and serum autoantibodies in patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and progressive respiratory disease. Autoimmune processes have been hypothesized to contribute to disease progression; however, the presence of autoantibodies in the serum has been variable. Given that COPD is a lung disease, we sought to investigate whether autoantibodies in sputum supernatant would better define pulmonary autoimmune processes. Matched sputum and serum samples were obtained from the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study and at the Guangzhou Institute of Respiratory Health (GIRH). Samples were collected from patients with varying severity of COPD, asymptomatic smokers, and healthy control subjects. IgG and IgM autoantibodies were detected in sputum and serum of all subjects in both cohorts using a broad-spectrum autoantigen array. No differences were observed in sputum autoantibodies between COPD and asymptomatic smokers in either cohort. In contrast, 16% of detectable sputum IgG autoantibodies were decreased in subjects with COPD compared to healthy controls in the ADEPT cohort. Compared to asymptomatic smokers, approximately 13% of detectable serum IgG and 40% of detectable serum IgM autoantibodies were differentially expressed in GIRH COPD subjects. Of the differentially expressed specificities, anti-nuclear autoantibodies were predominately decreased. A weak correlation between increased serum IgM anti-tissue autoantibodies and a measure of airspace enlargement was observed. The differential expression of specificities varied between the cohorts. In closing, using a comprehensive autoantibody array, we demonstrate that autoantibodies are present in subjects with COPD, asymptomatic smokers, and healthy controls. Cohorts displayed high levels of heterogeneity, precluding the utilization of autoantibodies for diagnostic purposes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".