The effects of low-dose morphine on sleep and breathlessness in COPD: A randomized trial.

Background: Low-dose morphine may be prescribed to reduce chronic breathlessness in chronic obstructive pulmonary disease (COPD). Recent subjective findings suggest morphine may influence breathlessness through sleep-related mechanisms. However, concerns exist regarding opioid safety in COPD.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity.

Regular snoring is associated with uncontrolled hypertension.

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence.

A novel TASK channel antagonist nasal spray reduces sleep apnea severity in physiological responders: a randomized, blinded, trial.

Preclinical and human physiological studies indicate that topical, selective TASK 1/3 K channel antagonism increases upper airway dilator muscle activity and reduces pharyngeal collapsibility during anesthesia and nasal breathing during sleep. The primary aim of this study was to determine the effects of BAY2586116 nasal spray on obstructive sleep apnea (OSA) severity and whether individual responses vary according to differences in physiological responses and route of breathing. Ten people (5 females) with OSA [apnea-hypopnea index (AHI) = 47 ± 26 events/h (means ± SD)] who completed previous sleep physiology studies with BAY2586116 were invited to return for three polysomnography studies to quantify OSA severity.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data.

Objectives: Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range.

Methods: Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week.

Associations of baseline obstructive sleep apnea and sleep macroarchitecture with cognitive function after 8 years in middle-aged and older men from a community-based cohort study.

Previous prospective studies examining associations of obstructive sleep apnea and sleep macroarchitecture with future cognitive function recruited older participants, many demonstrating baseline cognitive impairment. This study examined obstructive sleep apnea and sleep macroarchitecture predictors of visual attention, processing speed, and executive function after 8 years among younger community-dwelling men. Florey Adelaide Male Ageing Study participants (n = 477) underwent home-based polysomnography, with 157 completing Trail-Making Tests A and B and the Mini-Mental State Examination.

Unraveling the complexities of pathological voice through saliency analysis.

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly.

Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database.

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography.

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze.

High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension.

Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood.

Methanol as a Potential Hydrogen Source for Reduction Reactions Enabled by a Commercial Pt/C Catalyst.

Catalytic reduction reactions using methanol as a transfer hydrogenating agent is gaining significant attention because this simple alcohol is inexpensive and produced on a bulk scale. Herein, we report the catalytic utilization of methanol as a hydrogen source for the reduction of different functional organic compounds such as nitroarenes, olefins, and carbonyl compounds. The key to the success of this transformation is the use of a commercially available Pt/C catalyst, which enabled the transfer hydrogenation of a series of simple and functionalized nitroarenes-to-anilines, alkenes-to-alkanes, and aldehydes-to-alcohols using methanol as both the solvent and hydrogen donor.

Seizure Detection: A Low Computational Effective Approach without Classification Methods.

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task.

Lignin Residue-Derived Carbon-Supported Nanoscale Iron Catalyst for the Selective Hydrogenation of Nitroarenes and Aromatic Aldehydes.

Heterogeneous iron-based catalysts governing selectivity for the reduction of nitroarenes and aldehydes have received tremendous attention in the arena of catalysis, but relatively less success has been achieved. Herein, we report a green strategy for the facile synthesis of a lignin residue-derived carbon-supported magnetic iron (γ-FeO/LRC-700) nanocatalyst. This active nanocatalyst exhibits excellent activity and selectivity for the hydrogenation of nitroarenes to anilines, including pharmaceuticals (e.

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.

Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night, which may have important implications for diagnosis and management. This study aimed to assess OSA prevalence from multinight in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, nonrandomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. A total of 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of approximately 170 nights per participant between July 2020 and March 2021.

Single Channel Surface Electromyogram Deconvolution is a Useful Pre-Processing for Myoelectric Control.

Objective: Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system.

Methods: We consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects.

Wearable Bluetooth Triage Healthcare Monitoring System.

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients' vitals, including respiratory rate, temperature, and pulse rate.

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.

As a definition, Human-Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences.

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches.

Morpho-molecular diversity and avirulence genes distribution among the diverse isolates of Magnaporthe oryzae from Southern India.

Aims: To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence and molecular diversity and relative distribution of five Avr genes.

Methods And Results: Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India.

Progress in Brain Computer Interface: Challenges and Opportunities.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges.

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.

The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing.