Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Molen, S.

Redox-active dithiolated tetrathiafulvalene derivatives (TTFdT) were inserted in two-dimensional nanoparticle arrays to build interlinked networks of molecular junctions. Upon oxidation of the TTFdT to the dication state, we observed a conductance increase of the networks by up to 1 order of magnitude. Successive oxidation and reduction cycles demonstrated a clear switching behavior of the molecular junction conductance. These results show the potential of interlinked nanoparticle arrays as chemical sensors.

Summary The objective of this study was to compare to each other the methods currently recommended by the American Academy of Sleep Medicine (AASM) to measure snoring: an acoustic sensor, a piezoelectric sensor and a nasal pressure transducer (cannula). Ten subjects reporting habitual snoring were included in the study, performed at Landspitali—University Hospital, Iceland. Snoring was assessed by listening to the air medium microphone located on a patient's chest, compared to listening to two overhead air medium microphones (stereo) and manual scoring of a piezoelectric sensor and nasal cannula vibrations. The chest audio picked up the highest number of snore events of the different snore sensors. The sensitivity and positive predictive value of scoring snore events from the different sensors was compared to the chest audio: overhead audio (0.78, 0.98), cannula (0.55, 0.67) and piezoelectric sensor (0.78, 0.92), respectively. The chest audio was capable of detecting snore events with lower volume and higher fundamental frequency than the other sensors. The 200 Hz sampling rate of the cannula and piezoelectric sensor was one of their limitations for detecting snore events. The different snore sensors do not measure snore events in the same manner. This lack of consistency will affect future research on the clinical significance of snoring. Standardization of objective snore measurements is therefore needed. Based on this paper, snore measurements should be audio‐based and the use of the cannula as a snore sensor be discontinued, but the piezoelectric sensor could possibly be modified for improvement.

Summary Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea–hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.

Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently the methods Sands et al. [1] developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors’ code, which is computationally expensive and requires technological expertise to run. We present a re-implementation and validation of the integrity of the original authors’ code by reproducing the endo-Phenotype Using Polysomnography (PUP) method of Sands et al. [1, 2] The original MATLAB methods were reprogrammed in Python; efficient methods were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p<10 -6 for all): ventilation at eupnea V̇passive (ICC=0.97), ventilation at arousal onset V̇active (ICC=0.97), loop-gain (ICC=0.96), and arousal threshold (ICC=0.90). We successfully implemented the original method by Sands et.al. [1, 2] providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.