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If a COVID-19 patient develops a so-called severe course, he or she must be taken to hospital as soon as possible. This proves difficult in domestic isolation, as patients are not continuously monitored. The aim of our study was to establish a telemonitoring system in this setting. Oxygen saturation, respiratory rate, heart rate and temperature were measured every 15 minutes using an in-ear device. The data was transmitted to the Telecovid Centre via mobile network or internet and monitored 24/7 by a trained team. The data were supplemented by daily telephone calls. The patients' individual risk was assessed using a modified National Early Warning Score. In case of a deterioration, a physician initiated the appropriate measures. Covid-19 Patients were included if they were older than 60 years or fulfilled at least one of the following conditions: pre-existing disease (cardiovascular, pulmonary, immunologic), obesity (BMI >35), diabetes mellitus, hypertension, active malignancy, or pregnancy. 153 patients (median age 59 years, 77 female) were included. Patients were monitored for 9 days (median, IQR 6-13 days) with a daily monitoring time of 13.3 hours (median, IQR 9.4-17.0 hours). 20 patients were referred to the clinic by the Telecovid team. 3 of these required intensive care without invasive ventilation, 4 with invasive ventilation, 1 of the latter died. All patients agreed that the device was easy to use. About 90% of hospitalised patients indicated that they would have delayed hospitalisation further if they had not been part of the study.

Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.

In contemporary healthcare, effective risk stratification in the general population is vital amidst rising chronic disease rates and an ageing demographic. Deceleration Capacity of the heart rate (DC), derived from 24-hour Holter electrocardiograms, holds promise in risk stratification for cardiac patients. However, the potential of short-term electrocardiograms of five minutes duration for population screening has not been fully explored. Our study aims to investigate the utility of Deceleration Capacity derived from short-term electrocardiograms as a scalable, fully-automated screening tool for predicting long-term mortality risk in the general population. Within a cohort of a representative population-based survey in Germany (KORA-KMC-study), 823 participants with sinus rhythm aged 27 to 76 years at enrollment (females 47.4%) were followed for a median of 13.4 years (IQR 13.1–13.6). All-cause mortality was defined as the primary endpoint and observed in 159 participants. Deceleration Capacity was calculated from 5-minute 12-lead electrocardiograms by a fully automated approach. Participants were divided into three predefined risk categories: DC category0 – low-risk (> 4.5ms); DC category1 – intermediate-risk (2.5-4.5ms); and DC category2 – high-risk (≤ 2.5ms). More than two-thirds of the participants ( n  = 564, 68.5%) fell into DC category0 , about one-fifth ( n  = 168, 20.4%) into DC category1 , and about one-tenth ( n  = 91, 11.1%) into DC category2 . Estimated 13-years mortality in the risk groups was 16.7%, 23.5%, and 49.1%, respectively ( p  < 0.001). Adjusting for age, life-style-related risk factors, and comorbidities, increased mortality risk was observed for DC category2 (HR 2.34, 95%-CI 1.56–3.50). Deceleration Capacity, derived automatically from brief 5-minute electrocardiogram recordings, emerges as a robust, feasible, and independent predictor of long-term mortality risk in the general population.

In a cohort including individuals with suspected high alcohol consumption, the concentrations of the indirect alcohol biomarkers carbohydrate-deficient transferrin (CDT) and mean corpuscular volume (MCV) and the direct alcohol biomarker phosphatidylethanol (PEth) were investigated. Blood alcohol concentration (BAC) was analysed as a marker for acute alcohol ingestion. In addition to questions about subjective alcohol consumption behaviour, 147 homeless persons underwent a physical examination with blood sampling. BAC, PEth, CDT and MCV were determined in the blood samples. Special focus was on the comparison of PEth and CDT for indicating excessive alcohol consumption. BAC was measured above 0.1‰ in 39 blood samples (0.1–2.5‰, median 0.75‰). PEth was detected in all of them. Overall, PEth was positive (≥10 ng/ml) in 104 samples (71%) (11–5687 ng/ml, median 650 ng/ml) with 68 (46%) being above the cut-off for excessive alcohol consumption (210 ng/ml). In 26 subjects PEth was the only positive alcohol biomarker. CDT was ≥ 1.7% in 66 cases (47%) (1.8–22.2%, median 4.4%) and ≥ 2.5% in 52 (35%) cases. MCV was elevated (≥95 fl) in 58 subjects (39%). CDT and PEth concentrations showed a significant positive correlation (spearman's correlation coefficient ρ = 0.77, p < 0.001). PEth concentrations were significantly higher in samples that were also CDT positive than solely PEth positive (p = 0.004). PEth did not indicate excessive alcohol consumption (< 210 ng/ml) in eight and two cases in which CDT was ≥ 1.7% and ≥ 2.5%, respectively. On the other hand, CDT was< 1.7% and< 2.5% in ten and 18 cases, respectively, in which PEth was above cut-off for excessive alcohol consumption. Taking the self-reports of the participants into consideration, PEth’s sensitivity for detecting excessive alcohol consumption was 100% (10 ng/ml) and 94% (210 ng/ml) and CDT’s was 88% (1.7%) and 75% (2.5%). In individuals of the investigated cohort unusually high concentrations of the alcohol consumption markers PEth and CDT were quantified, which proves the assumption of chronic excessive alcohol consumption in parts of the cohort. PEth was the marker that was positive most often and was more sensitive for excessive alcohol consumption than CDT. •in the cohort of homeless persons very high concentrations of PEth and CDT were found.•PEth and CDT correlated well.•But some discrepancies occurred considering excessive alcohol consumption.•50% stating no/occasional consumption, had PEth/CDT>cut-off for excessive drinking.•PEth’s sensitivity for excessive alcohol consumption was higher than CDT’s.

The introduction of lockdowns and other containment measures during the COVID-19 pandemic substantially altered people’s lifestyle and dietary behavior. Several studies evaluated the short-term effects of these measures; yet reports on long-term consequences are scarce. We sought to address this gap in the literature by analyzing dietary and lifestyle data collected at an obesity center in Rome, Italy. The Italian region of Lazio was hit hard by the pandemic. To evaluate the potential health impacts, we compared the pre- and post-lockdown data of 118 individuals. Contrary to the common belief that lockdown had adverse effects solely on people’s dietary habits, we observed a significantly increased consumption of raw vegetables, whole grains, and water in our study sample. Favorable effects, however, were also accompanied by adverse trends, such as a higher prevalence of sleeping difficulties. Our data emphasize that the lockdowns associated with the pandemic also influenced participants’ social behavior, with less individuals reporting eating out or in company. Our study highlights the substantial impact of lockdowns on many dimensions of life. As such, it is of utmost importance in the critical evaluation of such stringent containment measures.

Heart rate variability (HRV) reflects the autonomous nervous system modulation on heart rate and is associated with several pathologies, including cardiac mortality. While mechanistic studies show that smoking is associated with lower HRV, population-based studies present conflicting results. We assessed the mutual effects of active smoking status, cumulative smoking history, and current smoking intensity, on HRV among 4751 adults from the Cooperative Health Research In South Tyrol (CHRIS) study. The HRV metrics standard deviation of normal-to-normal (NN) inter-beat intervals (SDNN), square root of the mean squared differences of consecutive NN intervals (RMSSD), total power (TP), low (LF) and high frequency (HF) power, and their ratio (LF/HF), were derived from 20-minute electrocardiograms. Smoking status, pack-years (PY), and tobacco grams/day from standardized questionnaires were the main exposures. We fitted linear mixed models to account for relatedness, non-linearity, and moderating effects, and including fractional polynomials. Past smokers had higher HRV levels than never smokers, independently of PY. The association of HRV with current smoking became apparent when accounting for the interaction between smoking status and PY. In current smokers, but not in past smokers, we observed HRV reductions between 2.0% (SDNN) and 4.9% (TP) every 5 PY increase. Furthermore, current smokers were characterized by dose-response reductions of 9.8% (SDNN), 8.9% (RMSSD), 20.1% (TP), 17.7% (LF), and 19.1% (HF), respectively, every 10 grams/day of smoked tobacco, independently of common cardiometabolic conditions and HRV-modifying drugs. The LF/HF ratio was not associated with smoking status, history, or intensity. Smoking cessation was associated with higher HRV levels. In current smokers, heavier smoking intensity appears gradually detrimental on HRV, corroborating previous evidence. By affecting both the sympathetic and parasympathetic nervous system indexes, but not the LF/HF balance, smoking intensity seems to exert a systemic dysautonomic effect.

Due to their rarity and radioactive nature, comparatively little is known about the actinides, particularly those with atomic numbers higher than that of plutonium, and their compounds. In this work, we describe how transmission electron microscopy can provide comprehensive, safe, and cost-effective characterization using only single nanogram amounts of highly-radioactive, solid compounds. Chlorides of the rare elements berkelium and californium are dropcast and then converted in situ to oxides using the electron beam. The f-band occupancies are probed using electron energy loss spectroscopy and an unexpectedly weak spin-orbit-coupling is identified for berkelium. In contrast, californium follows a jj coupling scheme. These results have important implications for the chemistries of these elements and solidify the status of californium as a transitional element in the actinide series. The obtention and study of actinide elements is challenging due to various factors including their radioactivity and scarcity. Herein, the authors characterize the atomic and electronic structure of Am, Cm, Bk, and Cf compounds using a transmission electron microscopy-based workflow that only requires nanogram amounts of the actinide element.

We have investigated the unidirectional spin wave heat conveyer effect in sub-micron thick yttrium iron garnet (YIG) films using lock-in thermography (LIT). Although the effect is small in thin layers this technique allows us to observe asymmetric heat transport by magnons which leads to asymmetric temperature profiles differing by several mK on both sides of the exciting antenna, respectively. Comparison of Damon-Eshbach and backward volume modes shows that the unidirectional heat flow is indeed due to non-reciprocal spin-waves. Because of the finite linewidth, small asymmetries can still be observed when only the uniform mode of ferromagnetic resonance is excited. The latter is of extreme importance for example when measuring the inverse spin-Hall effect because the temperature differences can result in thermovoltages at the contacts. Because of the non-reciprocity these thermovoltages reverse their sign with a reversal of the magnetic field which is typically deemed the signature of the inverse spin-Hall voltage.