Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure

The power of data mining in diagnosis of childhood pneumonia

Childhood pneumonia is the leading cause of death of children under the age of five globally. Diagnostic information on presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia, and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (Creactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (Lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians

Operative treatment of anterior thoracic spinal cord herniation:three new cases and an individual patient data meta-analysis of 126 case reports

OBJECTIVE: Anterior thoracic spinal cord herniation is a rare cause of progressive myelopathy. Much has been speculated about the best operative treatment. However, no evidence in favor of any of the promoted techniques is available to date. Therefore, we decided to analyze treatment procedures and treatment outcomes of anterior thoracic spinal cord herniation to identify those factors that determine postoperative outcome. METHODS: An individual patient data meta-analysis was conducted, focusing on age, gender, vertebral segment of herniation, preoperative neurological status, operative interval, operative findings, operative techniques, intraoperative neurophysiological monitoring, postoperative imaging, neurological outcome and follow-up. Three cases from our own institution were added to the material collected. Bivariate analysis tests and multivariate logistic regression tests were used so as to define which variables were associated with outcome after surgical treatment of anterior thoracic spinal cord herniation. RESULTS: Brown-Séquard syndrome and release of the herniated spinal cord appeared to be strong independent factors, associated with favorable postoperative outcome. Widening of the dura defect is associated with the highest prevalence of postoperative motor function improvement when compared with the application of an anterior dura patch (P < 0.036). CONCLUSION: Most patients with anterior thoracic spinal cord herniation require operative treatment because of progressive myelopathy. Patients with Brown-Séquard syndrome have a better prognosis with respect to postoperative motor function improvement. In this review, spinal cord release and subsequent widening of the dura defect were associated with the highest prevalence of motor function improvement. D-wave recording can be a very useful tool for the surgeon during operative treatment of this disorder

Assessing a digital technology-supported community child health programme in India using the Social Return on Investment framework.

An estimated 5.0 million children aged under 5 years died in 2020, with 82% of these deaths occurring in sub-Saharan Africa and southern Asia. Over one-third of Mumbai's population has limited access to healthcare, and child health outcomes are particularly grave among the urban poor. We describe the implementation of a digital technology-based child health programme in Mumbai and evaluate its holistic impact. Using an artificial intelligence (AI)-powered mobile health platform, we developed a programme for community-based management of child health. Leveraging an existing workforce, community health workers (CHW), the programme was designed to strengthen triage and referral, improve access to healthcare in the community, and reduce dependence on hospitals. A Social Return on Investment (SROI) framework is used to evaluate holistic impact. The programme increased the proportion of illness episodes treated in the community from 4% to 76%, subsequently reducing hospitalisations and out-of-pocket expenditure on private healthcare providers. For the total investment of Indian Rupee (INR) 2,632,271, the social return was INR 34,435,827, delivering an SROI ratio of 13. The annual cost of the programme per child was INR 625. Upskilling an existing workforce such as CHWs, with the help of AI-driven decision- support tools, has the potential to extend capacity for critical health services into community settings. This study provides a blueprint for evaluating the holistic impact of health technologies using evidence-based tools like SROI. These findings have applicability across income settings, offering clear rationale for the promotion of technology-supported interventions that strengthen healthcare delivery

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method

Reduction of blood culture contamination rate by an educational intervention

Background: Although mechanical dyssynchrony parameters derived by speckle tracking echocardiography (STE) may predict response to cardiac resynchronization therapy (CRT), comparability of parameters derived with different STE vendors is unknown. Methods: In the MARC study, echocardiographic images of heart failure patients obtained before CRT implantation were prospectively analysed with vendor specific STE software (GE EchoPac and Philips QLAB) and vendor-independent software (TomTec 2DCPA). Response was defined as change in left ventricular (LV) end-systolic volume between examination before and six-months after CRT implantation. Basic longitudinal strain and mechanical dyssynchrony parameters (septal to lateral wall delay (SL-delay), septal systolic rebound stretch (SRSsept), and systolic stretch index (SSI)) were obtained from either separate septal and lateral walls, or total LV apical four chamber. Septal strain patterns were categorized in three types. The coefficient of variation and intra-class correlation coefficient (ICC) were analysed. Dyssynchrony parameters were associated with CRT response using univariate regression analysis and C-statistics. Results: Two-hundred eleven patients were analysed. GE-cohort (n = 123): age 68 years (interquartile range (IQR): 61-73), 67% male, QRS-duration 177ms (IQR: 160-192), LV ejection fraction: 26 +/- 7%. Philips-cohort (n = 88): age 67 years (IQR: 59-74), 60% male, QRS-duration: 179 ms (IQR: 166-193), LV ejection fraction: 27 +/- 8. LV derived peak strain was comparable in the GE-(GE: -7.3 +/- 3.1%, TomTec: -6.4 +/- 2.8%, ICC: 0.723) and Philips-cohort (Philips: -7.7 +/- 2.7%, TomTec: -7.7 +/- 3.3%, ICC: 0.749). SL-delay showed low ICC values (GE vs. TomTec: 0.078 and Philips vs. TomTec: 0.025). ICC's of SRSsept and SSI were higher but only weak (GE vs. TomTec: SRSsept: 0.470, SSI: 0.467) (Philips vs. QLAB: SRSsept: 0.419, SSI: 0.421). Comparability of septal strain patterns was low (Cohen's kappa, GE vs. TomTec: 0.221 and Philips vs. TomTec: 0.279). Septal strain patterns, SRSsept and SSI were associated with changes in LV end-systolic volume for all vendors. SRSsept and SSI had relative varying C-statistic values (range: 0.530-0.705) and different cut-off values between vendors. Conclusions: Although global longitudinal strain analysis showed fair comparability, assessment of dyssynchrony parameters was vendor specific and not applicable outside the context of the implemented platform. While the standardization taskforce took an important step for global peak strain, further standardization of STE is still warranted

Stopping and straggling of 60-250-keV backscattered protons on nanometric Pt films

The stopping power and straggling of backscattered protons on nanometric Pt films were measured at low to medium energies (60-250 keV) by using the medium-energy ion scattering technique. The stopping power results are in good agreement with the most recent measurements by Primetzhofer Phys. Rev. B 86, 094102 (2012) and are well described by the free electron gas model at low projectile energies. Nevertheless, the straggling results are strongly underestimated by well-established formulas up to a factor of two. Alternatively, we propose a model for the energy-loss straggling that takes into account the inhomogeneous electron-gas response, based on the electron-loss function of the material, along with bunching effects. This approach yields remarkable agreement with the experimental data, indicating that the observed enhancement in energy-loss straggling is due to bunching effects in an inhomogeneous electron system. Nonlinear effects are of minor importance for the energy-loss straggling.This study was financed in part by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001; Conselho Nacional de Desenvolvimento Cientfico e Tecnológico - Brazil (CNPq) Projects No. 141833/2017-3, No. 160018/2019-6, No. 406750/2016- 5 and No. 404301/2016-9; and PRONEX-FAPERGS. J.M., P.L.G., and F.F.S. acknowledge support by RADIATE project under the Grant Agreement 824096 from the EU Research and Innovation program HORIZON 2020. M.V. acknowledges the Australian Research Council - Australia (ARC) funding program for financial support. P.L.G. and F.F.S. acknowledge D. Primetzhofer for the enlightening discussion

Aortic microcalcification is associated with elastin fragmentation in Marfan syndrome

Marfan syndrome (MFS) is a connective tissue disorder in which aortic rupture is the major cause of death. MFS patients with an aortic diameter below the advised limit for prophylactic surgery (<5 cm) may unexpectedly experience an aortic dissection or rupture, despite yearly monitoring. Hence, there is a clear need for improved prognostic markers to predict such aortic events. We hypothesize that elastin fragments play a causal role in aortic calcification in MFS, and that microcalcification serves as a marker for aortic disease severity. To address this hypothesis, we analysed MFS patient and mouse aortas. MFS patient aortic tissue showed enhanced microcalcification in areas with extensive elastic lamina fragmentation in the media. A causal relationship between medial injury and microcalcification was revealed by studies in vascular smooth muscle cells (SMCs); elastin peptides were shown to increase the activity of the calcification marker alkaline phosphatase (ALP) and reduce the expression of the calcification inhibitor matrix GLA protein in human SMCs. In murine Fbn1C1039G/+ MFS aortic SMCs, Alpl mRNA and activity were upregulated as compared with wild-type SMCs. The elastin peptide-induced ALP activity was prevented by incubation with lactose or a neuraminidase inhibitor, which inhibit the elastin receptor complex, and a mitogen-activated protein kinase kinase-1/2 inhibitor, indicating downstream involvement of extracellular signal-regulated kinase-1/2 (ERK1/2) phosphorylation. Histological analyses in MFS mice revealed macrocalcification in the aortic root, whereas the ascending aorta contained microcalcification, as identified with the near-infrared fluorescent bisphosphonate probe OsteoSense-800. Significantly, microcalcification correlated strongly with aortic diameter, distensibility, elastin breaks, and phosphorylated ERK1/2. In conclusion, microcalcification co-localizes with aortic elastin degradation in MFS aortas of humans and mice, where elastin-derived peptides induce a calcification process in SMCs via the elastin receptor complex and ERK1/2 activation. We propose microcalcification as a novel imaging marker to monitor local elastin degradation a