SPLUNC1 in sputum of children with cystic fibrosis pulmonary exacerbations
Bibliographic record
Abstract
Background: SPLUNC1 is an innate defense protein that acts as anti-microbial agent and regulates airway surface liquid volume. We aimed to investigate the role of sputum SPLUNC1 as a biomarker for pulmonary exacerbations (PEx) in children with cystic fibrosis (CF). Methods: Sputum samples from CF children with PEx treated with i) intravenous antibiotic therapy for severe PEx, or ii) oral antibiotics for mild PEx, as well as from the TRACK study (Perrem, L et al., AJRCCM 2021; 203(8):977-86), in which CF school-age children were followed prospectively for two years. SPLUNC1, interleukin-8 (IL-8) and neutrophil elastase (NE) were measured by ELISA. Paired analyses were performed by Wilcoxon-test. Results: Eleven CF children with a median (range) age of 13.5 (9.6-17.0) years were treated for severe PEx. Median SPLUNC1 levels were 529.3 (224.6-1698) before and 2705 (642.1-3975) ng/mL after treatment (p=0.003). Seven children, 11.38 (8.4-16.8) years of age, were treated for mild PEx. SPLUNC1 increased from 189.5 (97.3-314.9) before to 362.5 (220.5-1015.0) ng/mL after treatment (p=0.031). In TRACK, there were 17 participants with samples from a stable visit followed by a PEx visit after 147 (24-378) days and 21 with samples from a PEx followed by a stable visit after 176 (50-430) days. SPLUNC1 decreased from 3101 (623.7-4913) at stable visit to 1515 (512.8-4424) ng/mL at PEx (p=0.035) and increased from 1390 (505.4-1520) at PEx to 3482 (1820-6179) ng/mL at stable follow-up (p=0.001). There were no correlations between SPLUNC1 and IL-8 or NE levels in sputum, FEV1 or the lung clearance index (LCI). Conclusion: SPLUNC1 sputum levels are decreased in PEx and have promising clinimetric properties as a biomarker in CF children.
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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 | 0.002 |
| 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 | 0.000 | 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".