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Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform “proxy” sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen’s kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Abstract This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically “discover” a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics.

Abstract Background Poor data exist on the influence of psychosocial variables on the female/male ratio of depression because of the small number of cases and the resulting limited numbers of variables available for investigation. For this investigation a large number of first admitted depressed patients (N = 2599) was available which offered the unique opportunity to calculate age specific sex ratios for different marital and employment status categories. Methods Age and sex specific population based depression rates were calculated for first ever admissions for single year intervals. Moving averages with interpolated corrections for marginal values in the age distribution were employed. Results For the total group the female/male ratio of depression showed an inverted U-shape over the life-cycle. This pattern was influenced by the group of married persons, which showed a sex-ratio of 3:1 between the age of 30–50, but ratios of around 1:1 at younger and older ages. For not married persons the female/male ratio was already around 2:1 at the age of 18 and rose to 2.5:1 in mid-life and declined to 1 at around 55. The almost parallel decline of depression rates in employed men and women resulted in a female/male ratio of about 2:1 from age 18 to age 50 and became 1 after the age of 60. The female/male ratio among the not employed was about 1, in mid-life it became negative. Conclusions Our analyses show that the gender-gap in first admitted depressed patients is age dependent and that psychosocial factors modify the sex ratio.

Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of an oxygen desaturation or an arousal. Ambulatory polygraphy without neurological signals, often referred to as home sleep apnea testing (HSAT), can potentially underestimate the severity of sleep disordered breathing (SDB) as sleep and arousals are not assessed. We aim to improve the diagnostic accuracy of HSATs by extracting surrogate sleep and arousal information derived from autonomic nervous system activity with artificial intelligence.Methods: We used polysomnographic (PSG) recordings from 245 subjects (148 with simultaneously recorded HSATs) to develop and validate a new algorithm to detect autonomic arousals using artificial intelligence. A clinically validated auto-scoring algorithm (Somnolyzer) scored respiratory events, cortical arousals, and sleep stages in PSGs, and provided respiratory events and sleep stages from cardio-respiratory signals in HSATs. In a four-fold cross validation of the newly developed algorithm, we evaluated the accuracy of the estimated arousal index and HSAT-derived surrogates for the AHI.Results: The agreement between the autonomic and cortical arousal index was moderate to good with an intraclass correlation coefficient of 0.73. When using thresholds of 5, 15, and 30 to categorize SDB into none, mild, moderate, and severe, the addition of sleep and arousal information significantly improved the classification accuracy from 70.2% (Cohen’s κ = 0.58) to 80.4% (κ = 0.72), with a significant reduction of patients where the severity category was underestimated from 18.8% to 7.3%.Discussion: Extracting sleep and arousal information from autonomic nervous system activity can improve the diagnostic accuracy of HSATs by significantly reducing the probability of underestimating SDB severity without compromising specificity.

The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they suffer from hallucinations and misinformation. In this paper, we investigate how a very limited amount of domain-specific data, from lecture slides and transcripts, can be used to build knowledge-based and generative educational chatbots. We found that knowledge-based chatbots allow full control over the system’s response but lack the verbosity and flexibility of GLMs. The answers provided by GLMs are more trustworthy and offer greater flexibility, but their correctness cannot be guaranteed. Adapting GLMs to domain-specific data trades flexibility for correctness.

Abstract Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

BackgroundNational quality indicators show little change in the overuse of antibiotics for uncomplicated acute bronchitis. We compared the effect of 2 decision support strategies on antibiotic treatment of uncomplicated acute bronchitis.MethodsWe conducted a 3-arm cluster randomized trial among 33 primary care practices belonging to an integrated health care system in central Pennsylvania. The printed decision support intervention sites (11 practices) received decision support for acute cough illness through a print-based strategy, the computer-assisted decision support intervention sites (11 practices) received decision support through an electronic medical record-based strategy, and the control sites (11 practices) served as a control arm. Both intervention sites also received clinician education and feedback on prescribing practices, as well as patient education brochures at check-in. Antibiotic prescription rates for uncomplicated acute bronchitis in the winter period (October 1, 2009, through March 31, 2010) following introduction of the intervention were compared with the previous 3 winter periods in an intent-to-treat analysis.ResultsCompared with the baseline period, the percentage of adolescents and adults prescribed antibiotics during the intervention period decreased at the printed decision support intervention sites (from 80.0% to 68.3%) and at the computer-assisted decision support intervention sites (from 74.0% to 60.7%) but increased slightly at the control sites (from 72.5% to 74.3%). After controlling for patient and clinician characteristics, as well as clustering of observations by clinician and practice site, the differences for the intervention sites were statistically significant from the control sites (P = .003 for control sites vs printed decision support intervention sites and P = .01 for control sites vs computer-assisted decision support intervention sites) but not between themselves (P = .67 for printed decision support intervention sites vs computer-assisted decision support intervention sites). Changes in total visits, 30-day return visit rates, and proportion diagnosed as having uncomplicated acute bronchitis were similar among the study sites.ConclusionsImplementation of a decision support strategy for acute bronchitis can help reduce the overuse of antibiotics in primary care settings. The effect of printed vs computer-assisted decision support strategies for providing decision support was equivalent.Trial registrationclinicaltrials.gov Identifier: NCT00981994.

A new stereoscopic method for collecting particle image velocimetry (PIV) measurements within ~1 mm of a wavy air–water interface with simultaneous measurements of the morphology of the interface is described. The method, termed three-dimensional interfacial particle image velocimetry (3D-IPIV), is tested in a wind wave channel with a wind speed of 5.8 m s−1, water depth of 10 cm and a fetch of ~9 m. Microscale breaking waves populate the interface and their flow patterns are clearly visible in the velocimetry results. The associated capillary waves and surface divergence patterns are observed. Several statistical measurements of the flow are compared with independent measurements and good agreement is found. The method shows great promise for investigating the transfer of momentum, heat and gases across an air–water interface, both in the laboratory and in field settings. Additional methods are described for manufacturing the flow tracers needed for the 3D-IPIV method. These tracers are likely to be useful for other researchers, and have the characteristics of being fluorescent, neutrally buoyant, non-toxic, monodisperse, inexpensive and easy to manufacture.

Therapeutic options to cure osteoarthritis (OA) are not yet available, although cell-based therapies for the treatment of traumatic defects of cartilage have already been developed using, e.g., articular chondrocytes. In order to adapt cell-based therapies to treat OA, appropriate cell culture conditions are necessary. Chondrocytes require a 3-dimensional (3D) environment for redifferentiation after 2-dimensional (2D) expansion. Fetal bovine serum (FBS) is commonly used as a medium supplement, although the usage of a xenogeneic serum could mask the intrinsic behavior of human cells in vitro. The aim of this study was to compare human articular chondrocytes cultivated as monolayers (2D) and the development of microtissues (3D) in the presence of FBS with those cultivated with human serum (HS). Evaluation of the expression of various markers via immunocytochemistry on monolayer cells revealed a higher dedifferentiation degree of chondrocytes cultivated with HS. Scaffold-free microtissues were generated using the agar overlay technique, and their differentiation level was evaluated via histochemistry and immunohistochemistry. Microtissues cultivated in the medium with FBS showed a higher redifferentiation level. This was evidenced by bigger microtissues and a more cartilage-like composition of the matrix with not any/less positivity for cartilage-specific markers in HS versus moderate-to-high positivity in FBS-cultured microtissues. The present study showed that the differentiation degree of chondrocytes depends both on the microenvironment of the cells and the serum type with FBS achieving the best results. However, HS should be preferred for the engineering of cartilage-like microtissues, as it rather enables a "human-based" situation in vitro. Hence, cultivation conditions might be further optimized to gain an even more adequate and donor-independent redifferentiation of chondrocytes in microtissues, e.g., designing a suitable chemically-defined serum supplement.

Antibody assessment is an essential part in the serological diagnosis of autoimmune diseases. However, different diagnostic strategies have been proposed for the work up of sera in particular from patients with systemic autoimmune rheumatic disease (SARD). In general, screening for SARD-associated antibodies by indirect immunofluorescence (IIF) is followed by confirmatory testing covering different assay techniques. Due to lacking automation, standardization, modern data management, and human bias in IIF screening, this two-stage approach has recently been challenged by multiplex techniques particularly in laboratories with high workload. However, detection of antinuclear antibodies by IIF is still recommended to be the gold standard method for antibody screening in sera from patients with suspected SARD. To address the limitations of IIF and to meet the demand for cost-efficient autoantibody screening, automated IIF methods employing novel pattern recognition algorithms for image analysis have been introduced recently. In this respect, the AKLIDES technology has been the first commercially available platform for automated interpretation of cell-based IIF testing and provides multiplexing by addressable microbead immunoassays for confirmatory testing. This paper gives an overview of recently published studies demonstrating the advantages of this new technology for SARD serology.