Surfactant protein D is a biomarker of influenza‐related pediatric lung injury
Bibliographic record
Abstract
BACKGROUND: Biomarkers that can risk-stratify children with influenza virus lower respiratory infection may identify patients for targeted intervention. Early elevation of alveolar-related proteins in the bloodstream in these patients could indicate more severe lung damage portending worse outcomes. METHODS: We used a mouse model of human influenza infection and evaluated relationships between lung pathophysiology and surfactant protein D (SP-D), SP-A, and Club cell protein 16 (CC16). We then measured SP-A, SP-D, and CC16 levels in plasma samples from 94 children with influenza-associated acute respiratory failure (PICFLU cohort), excluding children with underlying conditions explaining disease severity. We tested for associations between levels of circulating proteins and disease severity including the diagnosis of acute respiratory distress syndrome (ARDS), mechanical ventilator, intensive care unit and hospital days, and hospital mortality. RESULTS: Circulating SP-D showed a greater increase than SP-A and CC16 in mice with increased alveolar-vascular permeability following influenza infection. In the PICFLU cohort, SP-D was associated with moderate-severe ARDS diagnosis (p = 0.01) and with mechanical ventilator (r = 0.45, p = 0.002), ICU (r = 0.44, p = 0.002), and hospital days (r = 0.37, p = 0.001) in influenza-infected children without bacterial coinfection. Levels of SP-D were lower in children with secondary bacterial pneumonia (p = 0.01) and not associated with outcomes. CC16 and SP-A levels did not differ with bacterial coinfection and were not consistently associated with severe outcomes. CONCLUSIONS: SP-D has potential as an early circulating biomarker reflecting a degree of lung damage caused directly by influenza virus infection in children. Secondary bacterial pneumonia alters SP-D biomarker performance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".