Sleep Fainting: A Neurocardiogenic Entity

Fainting is a common clinical presentation, with vagally mediated (neurocardiogenic) causes being the most common for syncope presentation to the emergency room, and for hospital admissions. Classic teaching is that upright posture is a prerequisite for vagally mediated syncope (VMS) and that syncope in the supine position has more sinister causes. We present five patients, three males and two females, with a mean age of 44.4 (range 29-67) years, who presented with VMS in the supine position (sleep fainting). Four patients also had a history of classic upright syncope. Based on their clinical features and thorough investigations, we excluded other causes of loss of consciousness and diagnosed these patients to be having VMS in the supine position (sleep fainting). We further describe the management and follow-up of these patients. Sleep fainting/syncope is a new entity and has to be recognized for appropriate management. A diagnosis can be established if there is clinical suspicion, preserved left ventricular function without evidence of coronary artery disease, no high-risk electrocardiographic evidence of pre-excitation, long or short QT syndrome, Brugada syndrome or arrhythmogenic right ventricular dysplasia, and normal neurological work-up

Flipped Publishing: A New Paradigm for Medical Textbooks

Medical textbooks remain an integral component of the undergraduate education pathway. These texts are traditionally prepared by senior clinicians or academics, based on their long experience of the subject matter. Medical students and junior doctors are commonly asked to review these books, but often have little role in influencing the content. This article will discuss the opening of a new paradigm in medical publishing, whereby students and junior doctors (juniors) take the lead in planning and producing the content of their textbooks with senior clinicians taking the role of reviewer.

Coinfections in patients hospitalized with COVID-19: a descriptive study from the United Arab Emirates

Purpose: Microbial coinfections in COVID-19 patients carry a risk of poor outcomes. This study aimed to characterize the clinical and microbiological profiles of coinfections in patients with COVID-19. Methods: A retrospective review of the clinical and laboratory records of COVID-19 patients with laboratory-confirmed infections with bacteria, fungi, and viruses was conducted. Only adult COVID-19 patients hospitalized at participating health-care facilities between February 1 and July 31, 2020 were included. Data were collected from the centralized electronic system of Dubai Health Authority hospitals and Sheikh Khalifa General Hospital Umm Al Quwain. Results: Of 29,802 patients hospitalized with COVID-19, 392 (1.3%) had laboratory-confirmed coinfections. The mean age of patients with coinfections was 49.3± 12.5 years, and a majority were male (n=330 of 392, 84.2%). Mean interval to commencement of empirical antibiotics was 1.2± 3.6) days postadmission, with ceftriaxone, azithromycin, and piperacillin–tazobactam the most commonly used. Median interval between admission and first positive culture (mostly from blood, endotracheal aspirates, and urine specimens) was 15 (IQR 8– 25) days. Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli were predominant in first positive cultures, with increased occurrence of Stenotrophomonas maltophilia, methicillin-resistant Staphylococcus aureus, Acinetobacter baumannii, Candida auris, and Candida parapsilosis in subsequent cultures. The top three Gram-positive organisms were Staphylococcus epidermidis, Enterococcus faecalis, and Staphylococcus aureus. There was variability in levels of sensitivity to antibiotics and isolates harboring mecA, ESBL, AmpC, and carbapenemase-resistance genes were prevalent. A total of 130 (33.2%) patients died, predominantly those in the intensive-care unit undergoing mechanical ventilation or extracorporeal membrane oxygenation. Conclusion: Despite the low occurrence of coinfections among patients with COVID-19 in our setting, clinical outcomes remained poor. Predominance of Gram-negative pathogens, emergence of Candida species, and prevalence of isolates harboring drug-resistance genes are of concern

Kinematics of shear deformation of materials under high pressure and shear stress

Ph.D.Ward O. Wine

A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification

Skin cancer is one of the most common human malignancies, which is generally diagnosed by screening and dermoscopic analysis followed by histopathological assessment and biopsy. Deep-learning-based methods have been proposed for skin lesion classification in the last few years. The major drawback of all methods is that they require a considerable amount of training data, which poses a challenge for classifying medical images as limited datasets are available. The problem can be tackled through transfer learning, in which a model pre-trained on a huge dataset is utilized and fine-tuned as per the problem domain. This paper proposes a new Convolution neural network architecture to classify skin lesions into two classes: benign and malignant. The Google Xception model is used as a base model on top of which new layers are added and then fine-tuned. The model is optimized using various optimizers to achieve the maximum possible performance gain for the classifier output. The results on ISIC archive data for the model achieved the highest training accuracy of 99.78% using Adam and LazyAdam optimizers, validation and test accuracy of 97.94% and 96.8% using RMSProp, and on the HAM10000 dataset utilizing the RMSProp optimizer, the model achieved the highest training and prediction accuracy of 98.81% and 91.54% respectively, when compared to other models

Implementing psychosocial interventions within low and middle-income countries to improve community-based care for people with psychosis-A situation analysis.

Background: Globally, a treatment gap exists for individuals with severe mental illness, with 75% of people with psychosis failing to receive appropriate care. This is most pronounced in low and middle-income countries, where there are neither the financial nor human resources to provide high-quality community-based care. Low-cost, evidence-based interventions are urgently needed to address this treatment gap. Aim: To conduct a situation analysis to (i) describe the provision of psychosocial interventions within the context of existing care in two LMICs-India and Pakistan, and (ii) understand the barriers and facilitators of delivering a new psychosocial intervention. Method: A situation analysis including a quantitative survey and individual interviews with clinicians, patients and caregivers was conducted. Quantitative survey data was collected from staff members at 11 sites (private and government run hospitals) to assess organizational readiness to implement a new psychosocial intervention. To obtain in-depth information, 24 stakeholders including clinicians and service managers were interviewed about the typical care they provide and/or receive, and their experience of either accessing or delivering psychosocial interventions. This was triangulated by six interviews with carer and patient representatives. Results and discussion: The results highlight the positive views toward psychosocial interventions within routine care and the enthusiasm for multidisciplinary working. However, barriers to implementation such as clinician time, individual attitudes toward psychosocial interventions and organizational concerns including the lack of space within the facility were highlighted. Such barriers need to be taken into consideration when designing how best to implement and sustain new psychosocial interventions for the community treatment of psychosis within LMICs

Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete

The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2, RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3% and 13.5% higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3% and 9.21% better than DNN1 and GEP1 respectively in terms of R2. DNN2 model performed 20.32% and 31.5% better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1% and 31.5% better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively utilized in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and utilize hybrid models to further enhance the accuracy and reliability of the models