S169 Investigating the effect of baricitinib on neutrophilic inflammation in an ex vivo lung perfusion (EVLP) model of acute respiratory distress syndrome
Bibliographic record
Abstract
Background Acute respiratory distress syndrome (ARDS) is driven by uncontrolled neutrophilic inflammation in the alveolar space with resultant damage to the epithelial and endothelial barrier, resulting in pulmonary oedema. Baricitinib is a JAK1/2 inhibitor which inhibits inflammation in experimental lung injury in animal models and reduces mortality in COVID related respiratory failure. The effect of baricitinib on human lung injury outside of COVID is unknown. Hypothesis JAK1/2 inhibition with baricitinib reduces LPS (lipopolysaccharide)-induced lung injury in ex vivo perfused and ventilated human lungs. Methods Human lungs unsuitable for transplantation, for which there was consent for use in research, were ventilated and perfused ex vivo, using a modified Toronto protocol. Lungs with intact alveolar fluid clearance at baseline were injured by instilling 6 mg LPS (E coli) into a lobe and adding whole blood to the perfusate at a final concentration of 1/10. Lungs were randomised to receive either baricitinib or placebo in the perfusate (final concentration baricitinib=50ng/ml to correspond with Cmax obtained in healthy volunteers receiving 4 mg/day, the standard dose of baricitinib, and that used in the treatment of COVID in RECOVERY). Bronchoalveolar lavage was carried out at 4 hours after LPS instillation. Total cell count in BAL was measured using an Eve Automated cell counter (NanoEntek) and differential white cell count carried out on cytospins prepared from BAL. Ethical approval was obtained from NRES (REC 14 LO 0250) and Queen’s University of Belfast School of Medicine Ethics Committee (SREC14/08). Results 13 pairs of lungs (26 in total) were obtained. Baseline fluid clearance was impaired in 3 lungs and these were excluded from the study. 12 lungs were randomised to receive placebo and 11 baricitinib. Baricitinib reduced BAL neutrophil count at 4 hours from median 5.76 (IQR 2.74–9.16) x104/ml to 2.02 (IQR 1.73–4.73) x104/ml (figure 1), *p=0.0489, Wilcoxon rank-sum test. The effect of baricitinib on markers of permeability, alveolar epithelial and endothelial injury will be measured. Conclusion Baricitinib reduces neutrophil count in the alveolar space in a human EVLP model of LPS-induced lung injury, supporting its potential to inhibit alveolar neutrophilic inflammation in non-COVID related ARDS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".