KCI (Ireland)

RationaleThere is no broadly accessible treatment for patients with refractory chronic cough, a disease characterized by chronic cough that persists despite treatment for other cough-related etiologies or has no identified underlying cause.ObjectivesSOOTHE (NCT04678206), a phase 2b, randomized, placebo-controlled trial, evaluated the efficacy and safety of P2X3 antagonist camlipixant in adults with refractory chronic cough (cough duration, ⩾1 yr; baseline awake cough frequency, ⩾25 coughs/h).MethodsAfter a single-blind, 16-day placebo run-in, patients were randomized (1:1:1:1) to receive camlipixant 12.5, 50, or 200 mg twice daily or placebo for 4 weeks. The primary endpoint was change from baseline to Day 28 in objective 24-hour cough frequency. Secondary endpoints included cough severity and cough-related quality of life.Measurements and Main ResultsOverall, 310 patients were randomized. A statistically significant reduction in placebo-adjusted 24-hour cough frequency was seen in the 50 mg (−34.4%; 95% confidence interval, −50.5 to −13.3; P = 0.0033) and 200 mg (−34.2%; 95% confidence interval, −50.7 to −12.2; P = 0.0047) camlipixant arms. All camlipixant arms showed a trend for greater improvement in cough severity visual analog scale and Leicester Cough Questionnaire scores over placebo. Camlipixant was well tolerated with no serious treatment-emergent adverse events reported. Taste alteration occurred in 4.8–6.5% of patients in camlipixant arms (vs. 0% with placebo); these were usually mild–moderate.ConclusionsCamlipixant treatment reduced cough frequency and improved patient-reported outcomes in patients with refractory chronic cough, with an acceptable safety profile.Clinical trial registered with www.clinicaltrials.gov (NCT04678206).

The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy.