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Bronchoconstriction is a key feature of asthma but the disease drivers and signalling mechanisms involved are not fully understood. TRPM3, a member of the Transient Receptor family of ion channels, has recently been shown to activate airway sensory nerves in both animal and human tissue (Bonvini et al 2018, AJRCCM:197:A7411). However, its role in direct bronchoconstriction is currently untested. We therefore aimed to investigate the role of TRPM3 in contraction of airway smooth muscle. The TRPM3 agonist CIM0216 was shown to cause bronchospasm in vivo in the anaesthetised guinea pig (GP), independent of sensory nerve activation. Tissue bath assays indicated that CIM0216 caused a concentration dependent contraction of GP trachea, which was inhibited by the TRPM3 antagonist Primidone. Further investigation revealed that both removal of the epithelium and addition of the selective NK2 antagonist MEN10376 significantly inhibited the contraction induced by CIM0216. RTPCR studies revealed that the TRPM3 gene was expressed on GP epithelial cells, and the TACR2 gene, which is the receptor for NK2, was expressed on GP ASM. These data suggest that activation of TRPM3 on epithelial cells leads to the release of tachykinins which activate the NK2 receptor on airway smooth muscle to cause bronchoconstriction. Tachykinins such as NKA have been well documented to cause contraction in the asthmatic airway. Although further work is required, this implicates the TRPM3 receptor on epithelial cells as a potential non-neuronal source of tachykinin release and subsequent bronchoconstriction, and also highlights it as a possible novel therapeutic target.

Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure dip from 24-hour blood pressure trends using a wrist-worn Photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 days during which 5111 reference values for blood pressure were obtained with a 24-hour ambulatory blood pressure monitor as ground truth and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Machine learning models (linear regression, random forests, dense neural networks and long- and short-term memory neural networks) were then trained and evaluated in their capability of tracking trends in systolic and diastolic blood pressure, as well as the estimation of the nocturnal systolic blood pressure dip. Main results Best performance was obtained with a deep long- and shortterm memory neural network with a Root Mean Squared Error (RMSE) of 3.12±2.20 ∆mmHg and a correlation of 0.69 (p = 3 ∗ 10−5) with the ground truth Systolic Blood Pressure (SBP) dip. This dip was derived from trend estimates of blood pressure which had an RMSE of 8.22±1.49 mmHg for systolic and 6.55±1.39 mmHg for diastolic blood pressure. The random forest model showed slightly lower average error magnitude for SBP trends (7.86±1.57 mmHg), however Bland-Altmann analysis revealed systematic problems in its predictions that were less present in the long- and short-term memory model. Significance The work provides first evidence for the unobtrusive estimation of the nocturnal blood pressure dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive blood pressure measurement in a large data set of unconstrained 24-hour measurements in free-living individuals and provides evidence for the utility of long- and short-term models in this domain.