Plos One
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The incidence of 2019 novel corona virus (SARS-CoV-2) has created a medical emergency throughout the world. Various efforts have been made to develop the vaccine or effective treatments against the disease. The discovery of crystal structure of SARS-CoV-2 main protease has made the in silico identification of its inhibitors possible. ⋯ While compound 3 (molecular bank code AAD146) exhibited highest negative binding energy of -81.92 kcal/mol for 6Y2F. The stability of the compounds- in complex with viral protease was analyzed by Molecular Dynamics simulation studies, and was found to be stable over the course of 20 ns simulation time. Compound 2, and 3 were predicted to be the significant inhibitors of SARS-CoV-2 3CL hydrolase (Mpro) among the nine short listed compounds.
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Cultural competency describes interventions that aim to improve accessibility and effectiveness of health services for people from ethnic minority backgrounds. Interventions include interpreter services, migrant peer educators and health worker training to provide culturally competent care. Very few studies have focussed on cultural competency for migrant service use in Low- and Middle-Income Countries (LMIC). Migrants and refugees in Thailand and Malaysia report difficulties in accessing health systems and discrimination by service providers. In this paper we describe stakeholder perceptions of migrants' and health workers' language and cultural competency, and how this affects migrant workers' health, especially in Malaysia where an interpreter system has not yet been formalised. ⋯ Perceptions of overuse by migrants in a health system acts as a barrier against system or institutional level improvements for cultural competency, in an already stretched health system. At the micro-level, language interventions with migrant workers appear to be the most feasible leverage point but raises the question of who should bear responsibility for cost and provision-employers, the government, or migrants themselves.
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Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. ⋯ A multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.
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Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. ⋯ The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.
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This study aimed to investigate abnormalities in the gray matter and white matter (GM and WM, respectively) that are shared between schizophrenia (SZ) and bipolar disorder (BD). We used 3T-magnetic resonance imaging to examine patients with SZ, BD, or healthy control (HC) subjects (aged 20-50 years, N = 65 in each group). We generated modulated GM maps through voxel-based morphometry (VBM) for T1-weighted images and skeletonized fractional anisotropy, mean diffusion, and radial diffusivity maps through tract-based special statistics (TBSS) methods for diffusion tensor imaging (DTI) data. ⋯ The two disorders had WM alterations in the corpus callosum, superior longitudinal fasciculus, internal capsule, external capsule, posterior thalamic radiation, and fornix. However, we observed no differences in GM volume or WM integrity between SZ and BD. The study results suggest that GM volume deficits in the thalamus and insular lobe along with widespread disruptions of WM integrity might be the common neural mechanisms underlying the pathologies of SZ and BD.