Neutrophil myeloperoxidase as a functional biomarker for RSV severity: implications for <i>in vitro</i> therapeutic screening
Bibliographic record
Abstract
Abstract Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in infants, yet effective therapeutics are lacking. The aim of this study is to develop an in vitro model that recapitulates key clinical outcomes in infants with RSV bronchiolitis to help accelerate the discovery of effective therapeutics. Neutrophil activation and influx into the airways are hallmarks of severe RSV infection, but these responses are difficult to quantify in clinical trials. Here we profile peripheral blood-derived neutrophils from infants with RSV admitted to the Paediatric Intensive Care Unit (PICU) and identify myeloperoxidase (MPO) as a key indicator of disease severity when compared to age matched controls. To mechanistically model this response, we established a paediatric airway epithelial air–liquid interface (ALI) system incorporating an endothelial layer and primary neutrophils to recapitulate the tissue microenvironment at the blood-airway barrier. Following RSV infection, neutrophil migration and activation were assessed using flow cytometry. The inclusion of an endothelial layer enhanced physiological relevance and more accurately replicated in vivo MPO responses. We then evaluated two antiviral candidates (remdesivir (RDV) and RSV604) to assess their ability to modulate neutrophil activation. While both compounds reduced viral load at 24 hours post-infection, only RSV604 attenuated MPO expression. These findings establish MPO as both a biomarker of RSV disease severity and a functional readout of therapeutic efficacy and demonstrate that targeting neutrophil⍰driven inflammatory pathways may be critical for reducing pathology in infant RSV infection. Take home message Antiviral drug discovery should include neutrophil MPO reduction as a readout of therapeutic efficacy. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".