Club Cell-16 and RelB as Novel Determinants of Arterial Stiffness in Exacerbating COPD Patients
Bibliographic record
Abstract
BACKGROUND: Exacerbations of chronic obstructive pulmonary disease (COPD) are acute events of worsened respiratory symptoms that may increase the risk of cardiovascular disease (CVD), a leading cause of mortality amongst COPD patients. The utility of lung-specific inflammatory mediators such as club cell protein-16 (CC-16) and surfactant protein D (SPD) and that of a novel marker of CV outcomes in COPD- RelB- in predicting adverse cardiovascular events during exacerbation is not known. METHODS: Thirty-eight subjects with COPD admitted to the hospital for severe exacerbation were included in this analysis. Clinical, physiological and arterial stiffness measurements were performed within 72 hours of admission; this was followed by measurements taken every 3 days until hospital discharge, then once a week until 30 days after discharge, and then again at 90 and 180 days. Plasma concentrations of inflammatory mediators were measured from peripheral venous blood taken at admission, and at days 15, 30, 90 and 180. RESULTS: CC-16 and RelB concentrations were increased at day 15 of exacerbations whereas SPD concentrations were decreased. The course of change in CC-16 and RelB levels over time was inversely associated with that of carotid-femoral pulse wave velocity, the gold-standard measure of arterial stiffness. Increases in CC-16 could predict a decreased number of subsequent exacerbations during follow-up. CONCLUSIONS: Lung-specific (CC-16) and novel (RelB) biomarkers are associated with systemic cardiovascular changes over time. CC-16 can predict subsequent exacerbations in subjects with severe COPD and may be an important biomarker of pulmonary and systemic stress 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".