Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

Purpose: During the coronavirus disease 2019 (COVID-19) pandemic quarantine, university students were under various types of stressors, including the exams period, which might have affected their quality and quantity of sleep, and consequently, their quality of life. This study aimed to investigate the pattern and predictors of nightmares among university students and coinvestigate the presence of other types of sleep disturbances, mental disorders, and quarantine-related stressors. Methods: This cross-sectional study included 368 university students who answered a self-completed questionnaire covering their sociodemographic features, nightmare indicators, and associated quarantine stressors. Additionally, sleep disturbances were measured using the Generalized Sleep Disturbance Scale (GSDS), anxiety using the Generalized Anxiety Disorder 2 scale, and depression using the Patient Health Questionnaire-2. Results: The participants’ mean age was 20.4 ± 1.6 years, and male participants represented 35.9% of the sample. Nightmares were experienced by 117 (31.8%) of the participants, of whom 44.4% had new-onset nightmares. The mean GSDS was 45.0 ± 14.9 (min. = 12, max. = 130). This value is associated with elevated odds of the following outcomes: the presence of nightmares (odds ratio [OR] = 1.8; confidence interval [CI] 95% = 1.1–3.0); new-onset nightmares at the time of pandemic (OR = 2.6; CI 95% = 1.3–5.5); and anxiety (OR = 1.74; CI 95% = 1.0–2.9). The presence of nightmares elevated the score of GSDS by 11.3 points (S.E. = 1.6, p < 0.001), elevated the odds of anxiety by 4.1 (CI 95% = 2.5–6.8), and depression by 2.1 (CI 95% = 1.3–3.4). Conclusions: Stressors resulting from both the exams period and the fact that it was conducted during COVID-19 quarantine increased the rate and affected the pattern of nightmares. These stressors also led to other sleep disturbances and mental disorders that were significantly more prevalent among females.

Objective COVID-19 is a public health emergency of international concern. There is still no definitive cure for this highly transmittable illness. Immunization and breaking the chain of infection is the only successful approach to mitigate its spread. Our study explored the adherence to COVID-19 preventive measures and its associating factors among Health Care Professionals (HCPs) working in Saudi Arabia. Methods For this cross-sectional study, an online survey was conducted from December 01, 2020, to March 31, 2021, among 978 HCPs in Saudi Arabia. The self-administered questionnaire consisted of demographic information, COVID-19 preventive behaviors, knowledge, attitude, fear, and risk. Mann–Whitney U -test, Kruskal–Wallis one-way analysis, Spearman correlation, and binary logistic regression tests were used in data analysis. Results Most of the HCPs were Saudi nationals (86.9%), females (63.1%), age group 20–29 years (42.3%), Middle Eastern ethnicity (82.5%), and working in the government sector (80.8%). A 52.2% of the participants were compliant with COVID-19 preventive behavior. The most and the least compliant preventive behaviors were “wearing masks” (88.8% compliance) and “keeping social distancing” (60.7% compliance). Preventive behavior was significantly higher in HCPs having a) more knowledge of COVID-19 (U=104849; p 0.001); b) positive attitude (U=84402; p 0.001); c) higher fear (U=103138; p less than 0.001) and d) nursing profession (p 0.01). COVID-19 knowledge (p<0.001), attitude (p<0.001), and fear (p<0.001) contributed significantly to the prediction of preventive behavior compliance. A unit increase in COVID-19 knowledge, attitude, and fear scores raised the odds of being compliant with preventive behavior by factors of 2.34, 1.87, and 1.53 respectively. Conclusion About half of the study participants were compliant with COVID-19 preventive behavior. Preventive behavior is significantly higher among HCPs having more knowledge of COVID-19, more fear, a positive attitude, and the “nursing” profession. Having more knowledge, a positive attitude, and more fear of COVID-19 may increase the likelihood of being compliant with preventive behavior.

Background Recently, there has been an increase in the prevalence of action video gaming among adolescents and young adults. This has made video gaming a topic of interest for behavioral and higher brain cognitive function researchers. The present study investigated the impact and consequences of action video gaming on human behavior—specifically, attention, anxiety levels, and sleep patterns. Objective The study aimed to investigate the potential associations between action video gaming and attention, anxiety, and sleep. Methods Recruited participants (N = 97) were asked to independently complete an online questionnaire consisting of 4 sections: demographic data, gaming behavior, 8-item Epworth Sleepiness Scale, and 7-item Generalized Anxiety Disorders Scale. Participants were further divided into 2 groups (expert and non-expert video gamers) based on the number of hours they spent on action video games. After completing the questionnaires, the patients attended an on-site session, where they completed a validated psychological online battery test that assessed their sustained attention. Results The mean age of the participants was 21 years. There was a significant difference in attention between expert and non-expert video gamers; when exposed to stimuli, expert gamers displayed significantly shorter reaction times than the non-expert gamers (p < 0.05). Both groups showed a non-significant decrease in attention span throughout time. The data demonstrated no statistically significant difference in anxiety levels or daytime sleepiness between expert and non-expert video gamers, and minimal to mild anxiety levels were reported in most expert and non-expert gamers. Conclusion Expert video gamers were significantly more attentive compared to non-expert gamers, and most participants showed low levels of generalized anxiety. Accordingly, expanding our knowledge on the effects of action video games on attention span is important for creatively using games in the field of education, especially for those who suffer from attention deficit hyperactivity disorders.

Stem cells are a versatile source for cell therapy. Their use is particularly significant for the treatment of neurological disorders for which no definitive conventional medical treatment is available. Neurological disorders are of diverse etiology and pathogenesis. Alzheimer’s disease (AD) is caused by abnormal protein deposits, leading to progressive dementia. Parkinson’s disease (PD) is due to the specific degeneration of the dopaminergic neurons causing motor and sensory impairment. Huntington’s disease (HD) includes a transmittable gene mutation, and any treatment should involve gene modulation of the transplanted cells. Multiple sclerosis (MS) is an autoimmune disorder affecting multiple neurons sporadically but induces progressive neuronal dysfunction. Amyotrophic lateral sclerosis (ALS) impacts upper and lower motor neurons, leading to progressive muscle degeneration. This shows the need to try to tailor different types of cells to repair the specific defect characteristic of each disease. In recent years, several types of stem cells were used in different animal models, including transgenic animals of various neurologic disorders. Based on some of the successful animal studies, some clinical trials were designed and approved. Some studies were successful, others were terminated and, still, a few are ongoing. In this manuscript, we aim to review the current information on both the experimental and clinical trials of stem cell therapy in neurological disorders of various disease mechanisms. The different types of cells used, their mode of transplantation and the molecular and physiologic effects are discussed. Recommendations for future use and hopes are highlighted.