The utility of Magnetoencephalography in multiple sclerosis – A systematic review

Introduction: Magnetoencephalography (MEG), allows for a high degree temporal and spatial accuracy in recording cortical oscillatory activity and evoked fields. To date, no review has been undertaken to synthesise all MEG studies in Multiple Sclerosis (MS). We undertook a Systematic Review of the utility of MEG in MS. / Methods: We identified MEG studies carried out in MS using EMBASE, Medline, Cochrane, TRIP and Psychinfo databases. We included original research articles with a cohort of minimum of five multiple sclerosis patients and quantifying of at least one MEG parameter. We used a modified version of the JBI (mJBI) for case-control studies to assess for risk of bias. / Results: We identified 30 studies from 13 centres involving at least 433 MS patients and 347 controls. We found evidence that MEG shows perturbed activity (most commonly reduced power modulations), reduced connectivity and association with altered clinical function in Multiple Sclerosis. Specific replicated findings were decreased motor induced responses in the beta band, diminished increase of gamma power after visual stimulation, increased latency and reduced connectivity for somatosensory evoked fields. There was an association between upper alpha connectivity and cognitive measures in people with MS. Overall studies were of moderate quality (mean mJBI score 6.7). / Discussion: We find evidence for the utility of MEG in Multiple Sclerosis. Event-related designs are of particular value and show replicability between centres. At this stage, it is not clear whether these changes are specific to Multiple Sclerosis or are also observable in other diseases. Further studies should look to explore cognitive control in more depth using in-task designs and undertake longitudinal studies to determine whether these changes have prognostic value

Potential Drug-Drug Interactions in Psychiatric Ward of a Tertiary Care Hospital: Prevalence, Levels and Association with Risk Factors

Purpose: To identify the prevalence of potential drug-drug interactions (pDDIs) in a psychiatric ward, their levels and association with risk factors.Methods: This study was conducted in the psychiatric ward of Ayub Teaching Hospital, Abbottabad, Pakistan. Medical records of 415 patients were retrospectively reviewed for pDDIs using Micromedex Drug-Reax software. Logistic regression was applied to determine association of pDDIs with age, gender, hospital stay and number of drugs.Results: In our study, we identified total number of 825 pDDIs of 126 types, with median number of 1 pDDIs per patient. Overall 64.8 % of the patients had at least one pDDI; 27.2 % at least one major pDDI; and 58.5 % patients at least one moderate pDDI. Among 825 identified pDDIs, most were of moderate (75.6 %) or major (20.8 %) severity, good (66.4 %) or fair (29 %) type of scientific evidence; and delayed onset (71 %). The most frequent major and moderate pDDIs included haloperidol + procyclidine (127 cases), haloperidol + olanzapine (49), haloperidol + promethazine (47), haloperidol + fluphenazine (41), diazepam + divalproex sodium (40), haloperidol + trihexyphenidyl (37), lorazepam + divalproex sodium (34), fluphenazine + procyclidine (33) and olanzapine + divalproex sodium (32). There was significant association of occurrence of pDDIs with hospital stay of 7 days or longer (p = 0.005) and taking 7 or more drugs (p < 0.001).                                                       Conclusion: A high prevalence of pDDIs in the psychiatric ward was recorded, a majority of which were of moderate severity. Patients with long hospital stay and increased number of drugs were more exposed to pDDIs.Keywords: Drug-drug interactions, Potential drug-drug interaction, Prescriptions screening, Drug-related problems, Clinical pharmacy

High Throughput Screening of a GlaxoSmithKline Protein Kinase Inhibitor Set Identifies an Inhibitor of Human Cytomegalovirus Replication that Prevents CREB and Histone H3 Post-Translational Modification.

To identify new compounds with anti-human cytomegalovirus (HCMV) activity and new anti-HCMV targets, we developed a high throughput strategy to screen a GlaxoSmithKline (GSK) Published Kinase Inhibitor Set (PKIS). This collection contains a range of extensively characterized compounds grouped into chemical families (chemotypes). From our screen we identified compounds within chemotypes that impede HCMV replication and identified kinase proteins associated with inhibition of HCMV replication that are potential novel anti-HCMV targets. We focused our study on a top "hit" in our screen, SB-734117, which we found inhibits productive replication of several HCMV strains. Kinase selectivity data indicated that SB-734117 exhibits polypharmacology and is an inhibitor of several proteins from the AGC and CMCG kinase groups. Using western blotting we found that SB-734711 inhibited accumulation of HCMV immediate-early proteins, phosphorylation of cellular proteins involved in immediate-early protein production (CREB and histone H3) and histone H3 lysine 36 trimethylation (H3K36me3). Therefore, we identify SB-734117 as a novel anti-HCMV compound and find that inhibition of AGC and CMCG kinase proteins during productive HCMV replication is associated with inhibition of viral protein production and prevents post-translational modification of cellular factors associated with viral protein production

Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS: Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY: Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly

In vitro propagation of Stevia rebaudiana Bert in Bangladesh

An experiment was conducted on in vitro culture of Stevia rebaudiana Bert, an important non-caloric sweetening herb to explore its potential for micro-propagation. Leaf, nodal and inter-nodal segments of the selected herb as explant were cultured on MS medium containing 2,4-D at 2, 3, 4 and 5 mg/L for callus induction. Inter-nodal segments initiated callus earlier than node and leaf. The highest amount of callus was found in MS medium with 3.0 mg/L 2,4-D and MS medium with 5.0 mg/L 2,4-D gave the poorest callu

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model

An Adaptive Distributed Averaging Integral Control Scheme for Micro-Grids with Renewable Intermittency and Varying Operating Cost

The increasing penetration of intermittent renewable energy resources in micro-grids poses several issues, such as stochastic power generation, demand and supply miss-match, frequency fluctuation, and economic dispatch problems. To address such critical issues, a distributed secondary control scheme based for micro-grids with varying operating cost and intermittent renewable energy resources is proposed for frequency regulation and economic load dispatch. The paper presents an adaptive distributed averaging integral control scheme with conditional uncertainties, namely varying operating costs, and renewable intermittency. The proposed control scheme adapts to the uncertainties by updating the control law parameters dynamically and can maintain overall network stability. The distributed control scheme employs communication channels for exchange of generation data from the neighboring power units for optimal power sharing and consensus among the power units. An additional controller at tertiary control layer of the hierarchical control architecture is also augmented in the control structure to economically dispatch the load and the consensus-based algorithm guarantees optimal load sharing. The proposed communication based control scheme reveals the best combination of performance and flexibility. A performance-based comparative analysis is also presented, validating the effectiveness of the proposed control scheme compared to the prior works. The robustness and performance of the proposed control scheme is illustrated through computer simulations

Assessment of Resilience in Desalination Infrastructure Using Semi-Markov Models

As the supply of desalinated water becomes significant in many countries, the reliable long-term operation of desalination infrastructure becomes paramount. As it is not realistic to build desalination systems with components that never fail, instead the system should be designed with more resilience. To answer the question how resilient the system should be, we present in this paper a quantitative approach to measure system resilience using semi-Markov models. This approach allows to probabilistically represent the resilience of a desalination system, considering the functional or failed states of its components, as well as the probability of failure and repair rates. As the desalination plants are connected with the end-user through water transportation and distribution networks, this approach also enables an evaluation of various network configurations and resilience strategies. A case study addressing a segment of the water system in Saudi Arabia is given with the results, benefits, and limitations of the technique discussed.Center for Complex Engineering Systems at MIT and KACSTUnited States. National Aeronautics and Space Administration (Space Technology Research Fellowship, grant number NNX14AM42H

Unilateral, trifocal, diaphyseal fracture of the radius with ipsilateral mid-shaft ulna fracture in an adult: a case report

<p>Abstract</p> <p>Introduction</p> <p>To the best of our knowledge, a trifocal, diaphyseal fracture of the radius associated with ipsilateral mid-shaft fracture of the ulna in an adult has not been reported in the literature to date. The AO classification system does not include such a fracture configuration.</p> <p>Case presentation</p> <p>We report a case of trifocal, diaphyseal fracture of the radius with a mid-diaphyseal fracture of the ulna in a 53-year-old Caucasian, British, right-hand dominant woman involved in a head-on collision with another vehicle. The management of this rare fracture configuration is described and alternative treatment options discussed.</p> <p>Conclusions</p> <p>We describe an unusual, complex fracture, which with prompt surgical treatment resulted in a rapid, full and satisfactory functional recovery for our patient.</p