A randomized controlled study in healthy participants to explore the exposure continuum when smokers switch to a tobacco heating product or an E-cigarette relative to cessation.

BACKGROUND: Cigarette smoking is associated with a number of diseases, such as cancer and cardiovascular diseases. Recently, there has been an increase in the use of electronic cigarettes (ECs) and tobacco-heating products (THPs) as an alternative to cigarettes, which may reduce the health burden associated with smoking. However, an exposure continuum when smokers switch to ECs or THPs compared to complete smoking cessation is not well established. METHODS: 148 healthy smokers were randomized to either continue smoking cigarettes, switch to using the glo THP or a prototype EC, or completely quit any nicotine or tobacco product use for 5 days, after a 2-day baseline period. During this study breath and 24-h urine samples were collected for Biomarker of Exposure (BoE) analysis. RESULTS: After a 5-day switching period BoE levels showed a substantial significant decrease in levels from baseline in the groups using the glo THP, the prototype EC, and having quit all nicotine and tobacco use. On an exposure continuum, smokers who completely quit nicotine had the lowest levels of assessed BoEs, followed by those who switched to the EC and then those who switched to glo THP use. Participants who continued to smoke had the highest levels of BoEs. CONCLUSIONS: THP or EC use over a 5-day period resulted in significant reductions in exposure to smoke toxicants, in some cases to levels similar to those for nicotine cessation. These results show that on an exposure continuum, nicotine cessation gives the greatest reduction in exposure to tobacco smoke toxicants, closely followed by the EC and the glo THP. These significant reductions in exposure to toxicants suggest that the glo THP and EC have the potential to be Reduced Risk Products. STUDY REGISTRATION: ISRCTN80651909.

A randomised controlled single-centre open-label pharmacokinetic study to examine various approaches of nicotine delivery using electronic cigarettes.

Smokers who switch completely to e-cigarettes may reduce their relative risk of tobacco-related disease. Effective nicotine delivery from e-cigarettes is important in consumer acceptance. We assessed whether protonated nicotine and e-cigarette devices delivering greater aerosol mass increase nicotine delivery and product liking. A randomised controlled non-blinded eight-arm crossover study was used to assess plasma nicotine pharmacokinetics and product liking for two e-cigarettes (Vype ePen3 and Vype ePen) with various nicotine e-liquid formulations and a conventional cigarette among 24 healthy dual-users of cigarettes and e-cigarettes. Product use and puff count were also assessed. Results show that nicotine bioavailability was greater for Vype ePen3 with greater aerosol mass delivery than for Vype ePen (Cmax, p = 0.0073; AUC0-120 min, p = 0.0102). Protonated nicotine (18 mg/mL, medium protonation) e-liquid yielded higher nicotine bioavailability than unprotonated nicotine (18 mg/mL) e-liquid (Cmax, p = 0.0001; AUC0-120 min, p = 0.0026). There was no significant difference in Tmax between e-liquids. Nicotine bioavailability did not differ between nicotine benzoate formulation (30 mg/mL nicotine, high protonation) and combustible cigarettes (Cmax, p = 0.79; AUC0-120 min, p = 0.13). Vype ePen3 with protonated nicotine delivers nicotine more efficiently with the potential to increase product liking relative to earlier devices using unprotonated e-liquid.

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.

Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study.

Electroencephalography (EEG) recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants, and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array, which holds great potential for sleep-wake monitoring research. It is comfortable to wear, simple to apply, and minimally intrusive during sleep. Moreover, it can be combined with a smartphone-controlled miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive research. The present study aimed to explore the suitability of cEEGrid for sleep research, by testing whether cEEGrid data affords the signal quality and characteristics necessary for sleep stage scoring. In an accredited sleep laboratory, sleep data from cEEGrid and a standard PSG system were acquired simultaneously. Twenty participants were recorded for one extended nocturnal sleep opportunity. Fifteen data sets were scored manually. Sleep parameters relating to sleep maintenance and sleep architecture were then extracted and statistically assessed for signal quality and concordance. The findings suggest that the cEEGrid system is a viable and robust recording tool to capture sleep and wake EEG. Further research is needed to fully determine the suitability of cEEGrid for basic and applied research as well as sleep medicine.

Application of Transcranial Direct Current Stimulation in Neurorehabilitation: The Modulatory Effect of Sleep.

The relationship between sleep disorders and neurological disorders is often reciprocal, such that sleep disorders are worsened by neurological symptoms and that neurological disorders are aggravated by poor sleep. Animal and human studies further suggest that sleep disruption not only worsens single neurological symptoms but may also lead to long-term negative outcomes. This suggests that sleep may play a fundamental role in neurorehabilitation and recovery. We further propose that sleep may not only alter the efficacy of behavioral treatments but also plasticity-enhancing adjunctive neurostimulation methods, such as transcranial direct current stimulation (tDCS). At present, sleep receives little attention in the fields of neurorehabilitation and neurostimulation. In this review, we draw together the strands of evidence from both fields of research to highlight the proposition that sleep is an important parameter to consider in the application of tDCS as a primary or adjunct rehabilitation intervention.