Feasibility and usability of three consecutive nights with self-applied, home polysomnography.

In-laboratory polysomnography, the gold-standard for diagnosing sleep disorders, is resource-demanding and not conducive to multiple night evaluations. Ambulatory polysomnography, especially when self-applied, could be a viable alternative. This study aimed to assess the feasibility and reliability of self-applied polysomnography over three consecutive nights in untrained participants, assessing: technical success rate; comparing sleep diagnostic variables from single and multiple nights; and evaluating participants' subjective experience. Data were collected from 78 participants (55.1% females) invited to test a self-applicable polysomnography device for three consecutive nights at home. The technical success rate for valid sleep recordings was 82.5% out of 234 planned study nights, with 87.2% of participants obtaining at least two valid nights. Misclassification of obstructive sleep apnea severity was higher in participants with mild OSA (21.4%) compared with those with moderate-to-severe obstructive sleep apnea or no obstructive sleep apnea. Sleep efficiency and wake after sleep onset showed improvement from Night 1 to Night 3 (p < 0.001), and the mean polysomnography set-up time decreased significantly over this period. Participants reported moderate-to-high satisfaction with the device (System Usability Scale score 71.2 ± 12.4). The findings suggest that self-applied polysomnography is a feasible diagnostic method for untrained individuals at risk for sleep disorders, and that multiple night assessments can improve diagnostic precision for mild obstructive sleep apnea cases.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

Prevalence and early-life risk factors of school-age allergic multimorbidity: The EuroPrevall-iFAAM birth cohort.

BACKGROUND: Coexistence of childhood asthma, eczema and allergic rhinitis is higher than can be expected by chance, suggesting a common mechanism. Data on allergic multimorbidity from a pan-European, population-based birth cohort study have been lacking. This study compares the prevalence and early-life risk factors of these diseases in European primary school children. METHODS: In the prospective multicentre observational EuroPrevall-iFAAM birth cohort study, we used standardized questionnaires on sociodemographics, medical history, parental allergies and lifestyle, and environmental exposures at birth, 12 and 24 months. At primary school age, parents answered ISAAC-based questions on current asthma, rhinitis and eczema. Allergic multimorbidity was defined as the coexistence of at least two of these. RESULTS: From 10,563 children recruited at birth in 8 study centres, we included data from 5,572 children (mean age 8.2 years; 51.8% boys). Prevalence estimates were as follows: asthma, 8.1%; allergic rhinitis, 13.3%; and eczema, 12.0%. Allergic multimorbidity was seen in 7.0% of the whole cohort, ranging from 1.2% (Athens, Greece) to 10.9% (Madrid, Spain). Risk factors for allergic multimorbidity, identified with AICc, included family-allergy-score, odds ratio (OR) 1.50 (95% CI 1.32-1.70) per standard deviation; early-life allergy symptoms, OR 2.72 (2.34-3.16) for each symptom; and caesarean birth, OR 1.35 (1.04-1.76). Female gender, OR 0.72 (0.58-0.90); older siblings, OR 0.79 (0.63-0.99); and day care, OR 0.81 (0.63-1.06) were protective factors. CONCLUSION: Allergic multimorbidity should be regarded as an important chronic childhood disease in Europe. Some of the associated early-life factors are modifiable and may be considered for prevention strategies.

Assessing the impact of policy changes in the Icelandic cod fishery using a hybrid simulation model.

Most of the Icelandic cod is caught in bottom trawlers or longliners. These two fishing methods are fundamentally different and have different economic, environmental, and even social effects. In this paper we present a hybrid-simulation framework to assess the impact of changing the ratio between cod quota allocated to vessels with longlines and vessels with bottom trawls. It makes use of conventional bioeconomic models and discrete event modelling and provides a framework for simulating life cycle assessment (LCA) for a cod fishery. The model consists of two submodels, a system dynamics model describing the biological aspect of the fishery and a discrete event model for fishing activities. The model was run multiple times for different quota allocation scenarios and results are presented where different scenarios are presented in the three dimensions of sustainability: environmental, social, and economic. The optimal allocation strategy depends on weighing the three different factors. The results were encouraging first-steps towards a useful modelling method but the study would benefit greatly from better data on fishing activities.

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight.

Introduction: Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. Methods: A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. Results: We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ. Discussion: We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.

Metabolic engineering of Thermoanaerobacterium AK17 for increased ethanol production in seaweed hydrolysate.

Sustainably produced renewable biomass has the potential to replace fossil-based feedstocks, for generation of biobased fuels and chemicals of industrial interest, in biorefineries. In this context, seaweeds contain a large fraction of carbohydrates that are a promising source for enzymatic and/or microbial biorefinery conversions. The thermoanaerobe Thermoanaerobacterium AK17 is a versatile fermentative bacterium producing ethanol, acetate and lactate from various sugars. In this study, strain AK17 was engineered for more efficient production of ethanol by knocking out the lactate and acetate side-product pathways. This was successfully achieved, but the strain reverted to acetate production by recruiting enzymes from the butyrate pathway. Subsequently this pathway was knocked out and the resultant strain AK17_M6 could produce ethanol close to the maximum theoretical yield (90%), leading to a 1.5-fold increase in production compared to the wild-type strain. Strain AK17 was also shown to successfully ferment brown seaweed hydrolysate from Laminaria digitata to ethanol in a comparatively high yield of 0.45 g/g substrate, with the primary carbon sources for the fermentations being mannitol, laminarin-derived glucose and short laminari-oligosaccharides. As strain AK17 was successfully engineered and has a wide carbohydrate utilization range that includes mannitol from brown seaweed, as well as hexoses and pentoses found in both seaweeds and lignocellulose, the new strain AK17_M6 obtained in this study is an interesting candidate for production of ethanol from both second and third generations biomass.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Respiratory function and CT abnormalities among survivors of COVID-19 pneumonia: a nationwide follow-up study.

INTRODUCTION: Considering the pulmonary burden caused by acute COVID-19, questions remain of respiratory consequences after recovery. The aim of the study was to describe respiratory function of COVID-19 pneumonia survivors at mid-term follow-up (median 68 days) and assess whether impairments were predicted by acute illness severity or residual CT abnormalities. METHODS: Residents of Iceland that had COVID-19 and oxygen saturation ≤94% from 28 February 2020 to 30 April 2021 were offered a clinical follow-up visit with an interview, a 6 min walk test (6MWT), spirometry with gas exchange measurement and chest CT. The results of these examinations were described, grouped by the level of care during acute illness. The associations of disease severity and CT abnormalities at follow-up with subjective dyspnoea, 6MWT results and lung function test results were estimated with regression analyses. RESULTS: Of 190 eligible patients, 164 (86%) participated in the study. Of those, 32 had never been admitted to hospital, 103 were admitted to hospital without intensive care and 29 had required intensive care. At a follow-up, need for intensive care during acute illness was associated with shorter walking distance on 6MWT, lower oxygen saturation and lower DLCO. Imaging abnormalities at follow-up were observed for most participants (74%) and the magnitude of these changes was associated with decrements in 6MWT distance, oxygen saturation, forced vital capacity and DLCO. CONCLUSIONS: The findings show that impaired exercise capacity and lung physiology at follow-up were primarily observed for patients with COVID-19 pneumonia that required intensive care treatment and/or had persistent imaging abnormalities.