Faculty Opinions recommendation of Anti-proline-glycine-proline or antielastin autoantibodies are not evident in chronic inflammatory lung disease.
Bibliographic record
Abstract
RATIONALE: In patients with chronic inflammatory lung disease, pulmonary proteases can generate neoantigens from elastin and collagen with the potential to fuel autoreactive immune responses. Antielastin peptide antibodies have been implicated in the pathogenesis of tobacco-smoke-induced emphysema. Collagen-derived peptides may also play a role.OBJECTIVES: To determine whether autoantibodies directed against elastin- and collagen-derived peptides are present in plasma from three groups of patients with chronic inflammatory lung disease compared with a nonsmoking healthy control group and to identify whether autoimmune responses to these peptides may be an important component of the disease process in these patients.METHODS: A total of 124 patients or healthy control subjects were recruited for the study (Z-A1AT deficiency, n = 20; cystic fibrosis, n = 40; chronic obstructive pulmonary disease, n = 31; healthy control, n = 33). C-reactive protein, IL-32, and antinuclear antibodies were quantified. Antielastin and anti-N-acetylated-proline-glycine-proline autoantibodies were measured by reverse ELISA.MEASUREMENTS AND MAIN RESULTS: All patients were deemed stable and noninfective on the basis of the absence of clinical or radiographic evidence of recent infection. There were no significant differences in the levels of autoantibodies or IL-32 in the patients groups compared with the healthy control subjects.CONCLUSIONS: Antielastin or anti-N-acetylated proline-glycine-proline autoantibodies are not evident in chronic inflammatory lung disease. PMID: 19762563
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.250 | 0.146 |
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".