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Big Data is a term that refers to tremendously large data sets intended for computational analysis that can be used to advance research through revealing trends and associations. Innovative research that leverages Big Data can dramatically advance the fields of medicine and public health but can also raise new ethical challenges. This paper explores these challenges, and how they might be addressed such that individuals are optimally protected. Key ethical concerns raised by Big Data research include respecting patient's autonomy via provision of adequate consent, ensuring equity, and respecting participants' privacy. Examples of actions that could be taken to address these key concerns on a broader regulatory level, as well as on a case specific level, are presented. Big Data research offers enormous potential, but due to its widespread influence, it also introduces the potential for extensive harm. It is imperative to consider and account for the risks associated with this research.Objective Learning from pilot studies is crucial for the successful implementation of large-scale surveys. In this manuscript, we present the lessons learned for instrumentation and survey methods from a pilot national mental health survey conducted in Nepal. Design We conducted a cross-sectional study among 1,647 participants aged 13 years and older in three districts of Nepal. We used the Nepali translated standard adult and adolescent versions of the Mini International Neuropsychiatric Interview (MINI) 7.0.2 for Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) to do face-to-face structured diagnostic interviews. In addition, we included questionnaires on help-seeking behavior and barriers in accessing care. Results We used a six-step procedure to translate and fit the tools in the context of Nepal. We conducted pretesting to evaluate the Nepali translated tools and adaptations, such as the addition of bridging sentences at the start of different modules. https://www.selleckchem.com/products/at-406.html We identified different challenges during the tools administration and the ways to minimize reporting bias during data collection. Conclusion The pilot survey identified the areas for improvement in survey tools, techniques, and methodology. The lessons learned from the pilot survey and the resulting corrective recommendations helped in more successful implementation of the Nepal national mental health survey.The robot-assisted rehabilitation is a type of technology that has shown great advances in recent years, demonstrating its effectiveness in different neurological disorders; however, the main argument against the introduction of robot technology in rehabilitation is economic considerations. Herein, we discussed the main concerns related to the widespread use of innovation technology and the need for a cost-effectiveness analysis to enter robotics into the framework of the healthcare systems involved in neurorehabilitation.Objective Intellectual disability (ID) is a heterogeneous group of disorders characterized by a congenital limitation in intellectual functioning and adaptive behaviour. Our present work aimed to describe the demographic and clinical characteristics in a series of Moroccan individuals with ID living in Fez city and its regions. Design It was a prospective and descriptive exploratory monocentric study carried out between October 2014 and July 2019. We selected 186 patients diagnosed with ID at three different centers in Fez city. The data were processed and analyzed using the IBM SPSS version 24. Results Our data revealed a high frequency of male patients with ID (67.2% in male patients vs. 32.8% in female patients). The male-to-female ratio was 2.04. The mean age of our patients was 15.52 ±6.59 years (mean±SD), ranging between 2 and 36 years. The mean age of fathers and mothers at the birth of their child with ID was 36 and 28 years, respectively. Several abnormal behaviors were observed 23.1 percent delayed language learning, 17.7 percent anxiety, 12.9 percent aggressiveness, 19.18 percent concentration problems, and 5.4 percent hyperactivity. Epileptic seizures were the most common mental health disorder (21.72%) observed in our patients. Approximately 25 percent of patients with epilepsy took antiepileptic and/or neuroleptics to prevent the occurrence of seizures. Conclusion A significant correlation was observed between ID associated to genetic causes and the increase of consanguinity rate.Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.It is important to monitor the early screening of chronic diseases, predict the risk, and provide the comprehensive management of chronic diseases for the elderly. However, it is difficult to provide the robust and real-time emergency service for elderly chronic disease because of the complex social network and diversity of elderly chronic disease service. To address these issues, we design a new drone assisted robust emergency service system. We formulate the Drone assisted Management (DM) problem to minimize the total time cost of drone subject to all elderly chronic disease services which can be guaranteed exactly once by the drone under its energy constraint. Then, we propose the DRS algorithm to solve the DM problem. To provide the robust and real-time service, we further formulate the Charging driven Drone assisted Management (CDM) problem and present the CDRS algorithm to solve the CDM problem. Through the theoretical analysis and numerical simulation experiments, we demonstrate that DRS and CDRS can decrease the total time cost by 37.