Higher Levels of the Anti-inflammatory Protein CC10 Are Associated with Improvement in Bronchial Dysplasia and Sputum Cytometric Assessment in Individuals at High Risk for Lung Cancer
Bibliographic record
Abstract
PURPOSE: CC10, a 10-kDa anti-inflammatory protein secreted by bronchiolar Clara cells, is infrequently expressed in non-small cell lung cancer and its overexpression in non-small cell lung cancer cell lines results in a less malignant phenotype. Several lines of evidence have shown that bronchial dysplasia and sputum atypia are predictors of lung cancer. We investigated whether changes in CC10 expression correlate with regression of bronchial dysplasia and/or improvement in sputum abnormalities as measured by image cytometry. EXPERIMENTAL DESIGN: High-risk smokers enrolled in a chemoprevention trial underwent serial bronchoscopies with biopsies and bronchoalveolar lavage (BAL) collection, sputum assessment by image cytometry, and blood collection. CC10 was measured by competitive ELISA in BAL and plasma. Logistic regression analyses were done to determine the associations between CC10 levels and the improvement in bronchial dysplasia and sputum cytometric assessment. RESULTS: The net change in the BAL CC10 levels in subjects with improved bronchial lesions or improved sputum cytometry assessment was significantly higher than in those without improvement (P < 0.05). The odds ratio (95% confidence interval) associated with 1-unit increase in CC10 was 2.72 (1.31-5.64) for regression of dysplastic lesions and 2.94 (1.22-7.05) for improvement in sputum cytometry assessment after multivariate adjustment. Plasma CC10 was not significantly associated with either outcome. CONCLUSIONS: Higher BAL CC10 levels are significantly correlated with regression of bronchial dysplasia and improvement in sputum cytometry assessment in smokers with high lung cancer risk. Whether CC10 levels can predict clinical outcomes among high-risk populations warrants further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".