Integrating lung and plasma expression of pneumo-proteins in developing biomarkers in COPD: a case study of surfactant protein D.
Bibliographic record
Abstract
BACKGROUND: Surfactant protein D (SP-D) is a promising blood biomarker in patients with chronic obstructive pulmonary disease (COPD). Nevertheless, circulating levels of SP-D are not related to pulmonary functions. In the present exploratory study, we created a simple index of plasma to bronchoalveolar lavage (BAL) fluid ratio of SP-D (pSP-D/bSP-D), and determined whether this index would relate to the severity of airflow limitation and hence represent a superior biomarker than pSP-D alone. MATERIAL/METHODS: In 50 ex and current smokers (mean age 57.6±7.8 years, 74% men), SP-D was measured in BAL fluid and plasma samples, and the relationships between spirometric variables and a composite parameter - the pSP-D/bSP-D ratio were determined. RESULTS: There was a significant inverse correlation between the pSP-D/bSP-D ratio and the severity of airflow obstruction, as measured by FEV1/FVC (p=0.012). In contrast, no relationship was observed between FEV1/FVC and pSP-D alone. CONCLUSIONS: We suggest that integrating both lung and plasma expression of pneumo-proteins may be more useful than plasma expression alone in developing pneumo-proteins as potential biomarkers in COPD.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".