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Background Pathogenic variants in SPTBN4 have been linked to autosomal recessive “neurodevelopmental disorder with hypotonia, neuropathy, and deafness” (MIM# 617519) known as NEDHND. The disorder is highlighted with neuropathy, muscle weakness, and infrequent appearance of seizures in the affected individuals. This study aims to investigate the natural history of the disease, present genetic and clinical appearance of the syndrome in a highly consanguineous population, Saudi Arabia, and finally provide an overview of the reported cases, their clinical features, and disease-causing variants. Methods The study started with a search through neurology clinics and local databases and utilized genetic testing records after diagnosing a patient with NEDHND at our hospital (King Faisal Specialist Hospital and Research Centre, KFSHRC). Based on the search we have identified additional patients (in total, n  = 10) with the disease and performed genetic testing using whole exome sequencing and confirmatory Sanger sequencing. We performed RT-PCR on RNA extracted from lymphoblastoid cell line from a patient who found to have an aberrant splicing variant. Finally, we comprehensively reviewed current literature and available data related to the disease. Results We present natural history of SPTBN4-associated neurodevelopmental disorder with hypotonia, neuropathy, and deafness in addition to four Saudi families with ten affected individuals who share clinical features of NEDHND. We report three known mutations and one novel nonsense variant, highlight atypical clinical features related to cerebellar involvement, confirm the pathogenicity of a splicing variant by RT-PCR, and review the findings of previously reported patients. Conclusion Our study defines the clinical phenotype of a cohort of NEDHND in detail including the evolution of patients’ clinical features, compares them to previously reported cases, and utilizes the existing data on the disease to direct development of a better prevention plan by means of genetic and preimplantation counseling. Our study may help and enable future clinical trials focusing on NEDHND in our country.

Many studies have documented a strong association between poor sleep quality and physical inactivity. This study evaluates the association between poor sleep quality and physical inactivity among Jazan University students in Saudi Arabia. An observational cross-sectional study was conducted among undergraduate students at Jazan University. Cluster random sampling was used to select 440 students. The Pittsburgh Sleep Quality Index (PSQI), International Physical Activity Questionnaire (IPAQ), and Depression Anxiety Stress Scales (DASS) were utilized for data collection. According to the PSQI results, the majority of respondents (63.9%; n = 281) reported having poor sleep quality. The majority of students (62.7%; n = 276) were also physically inactive. Based on DASS-21 criteria, the majority of students (53.4%) were stressed. Sleep quality differed significantly according to participants' physical activity status ( = 0.0090). Among physically active participants, 43.9% reported having good sleep quality. Sleep duration, daytime dysfunctions, and global PSQI differed significantly according to levels of physical activity ( < 0.05 for all). Univariate analysis revealed that being physically active was significantly associated with good sleep quality (OR = 1.70, 95% CI 1.14-2.54, < 0.001). Multivariate logistic regression models also demonstrated a significant association between physical activity and good sleep quality (OR = 1.72, 95% CI 1.15-2.56, = 0.008). The majority of Jazan University students had poor sleep quality and were physically inactive. Evidence-based prevention and therapeutic strategies are needed to promote physical activity among university students.

Background: Many studies have documented a strong association between poor sleep quality and physical inactivity. This study evaluates the association between poor sleep quality and physical inactivity among Jazan University students in Saudi Arabia. Methods: An observational cross-sectional study was conducted among undergraduate students at Jazan University. Cluster random sampling was used to select 440 students. The Pittsburgh Sleep Quality Index (PSQI), International Physical Activity Questionnaire (IPAQ), and Depression Anxiety Stress Scales (DASS) were utilized for data collection. Results: According to the PSQI results, the majority of respondents (63.9%; n = 281) reported having poor sleep quality. The majority of students (62.7%; n = 276) were also physically inactive. Based on DASS-21 criteria, the majority of students (53.4%) were stressed. Sleep quality differed significantly according to participants' physical activity status (p = 0.0090). Among physically active participants, 43.9% reported having good sleep quality. Sleep duration, daytime dysfunctions, and global PSQI differed significantly according to levels of physical activity (p < 0.05 for all). Univariate analysis revealed that being physically active was significantly associated with good sleep quality (OR = 1.70, 95% CI 1.14-2.54,p < 0.001). Multivariate logistic regression models also demonstrated a significant association between physical activity and good sleep quality (OR = 1.72, 95% CI 1.15-2.56, P = 0.008). Conclusion: The majority of Jazan University students had poor sleep quality and were physically inactive. Evidence-based prevention and therapeutic strategies are needed to promote physical activity among university students. Keywords: sleep quality, PSQI, physical inactivity, Jazan

Objective Depression is Jordan’s most ravaging mental illness. Despite the growth of antidepressant use, only a handful of studies examine the factors affecting antidepressant knowledge among healthcare workers or medical students. Therefore, we aimed to explore the knowledge and attitudes towards antidepressants across Jordan’s six medical schools. Methods Using a cross-sectional design, we investigated the knowledge and attitudes towards antidepressants through the Drug Attitude Inventory and a literature-validated knowledge domain. Clinical students from Jordan’s six medical schools were recruited. Differences in knowledge and attitudes scores were examined by year of study, medical school among other factors. A multivariate linear regression model was utilized to assess predictors of knowledge. Results We included a total of 1,234 participants representing Jordan’s six major schools of medicine. About 14.9% of participants had a personal history of antidepressant use while 20.5% reported family history of psychiatric disease. The majority of students demonstrated favorable attitudes towards antidepressants (74.1%). Students demonstrated an average understanding of antidepressants’ mechanism of action, side effects, but not indications in special populations. Senior medical students, higher GPA, higher family income, personal history of antidepressants, and family history of psychiatric illnesses were associated with significantly higher knowledge scores (all p-values < 0.001). In addition to attitudes scores, the aforementioned were positive predictors of knowledge scores in the multivariate model. Conclusion Medical students’ knowledge towards antidepressants leaves room for significant improvement. Yet, it is evident that significant differences for both attitudes and knowledge exist across medical schools which may indicate a gap in either training or teaching methodology.

The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.

Background Heart failure (HF) is considered one of the main causes of morbidity and death among chronic diseases worldwide. Patients have increasingly reported chronic pain in long-standing heart failure as a disturbing symptom. Its unknown etiology and mechanism, in addition to its insidious progressive nature, made both the doctor and the patient not notice it until it affects the quality of life (QoL) and general health status. The primary objective of this study is to find the prevalence of pain in chronic heart failure patients and its impact on their QoL. The secondary objective is to determine the predictors of QoL in HF patients. Methods A multicenter cross-sectional design was used. The European Quality of Life scale five dimensions scale and the Brief Pain Inventory were adopted to evaluate QoL and pain, respectively. The Statistical Package for the Social Sciences version 25 was applied to present the data. The Mann-Whitney U, Kruskal-Wallis, and Cronbach alpha tests were used. Results The final study had a total of 142 individuals. The prevalence of pain among HF patients was 84.5%. Knee pain was the main complaint among patients. Our patients' median pain severity score was 18 [5.00–25.00], while the median pain interference score was 39 [24.75–53.00]. They had a median EQ-5D score of 0.34 [0.0–0.6] and an EQ-VAS score of 50 [30–70]. Pain severity ( p  = 0.004 and p  < 0.001, respectively) and pain interference ( p  < 0.001 and p  = 0.001, respectively) were found to significantly associated with both QoL scores; the visual analogue scale (EQ-VAS) and EQ-5D-5L. In multivariate analysis, monthly income was the only variable significantly correlated with EQ-VAS and EQ-5D-5L, along with pain variables. Conclusions Pain is a common symptom among patients with HF and is significantly associated with their QoL. Low income is also highly associated with poor QoL. Definitive guidelines should be achieved to increase awareness and understanding of the importance of pain management, reaching a higher QoL level, less pain, and good adherence to HF medications.

With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment.

Having a good awareness of ovarian cancer (OC) risk and protective factors could facilitate early diagnosis. This study aimed to assess Palestinian women's awareness about OC risk and protective factors and to identify the factors associated with having good awareness. A cross-sectional study was conducted from July 2019 to March 2020 in the two main areas of Palestine: the West Bank and Jerusalem (WBJ) and the Gaza Strip. A translated-into-Arabic version of the validated OC awareness measure was utilized. Adult women attending hospitals, primary healthcare centers, and public spaces at 11 governorates were invited to participate. The awareness level was categorized based on the number of factors recognized: poor (0 to 5), fair (6 to 10) and good (11 to 15). Of the 6095 women approached, 5618 agreed and completed the questionnaire (response rate = 92.1%). The final analysis included 5411 questionnaires. The most identified modifiable OC risk factor was 'being a smoker' (n = 4024, 74.4%), whereas the least identified was 'having in vitro fertilization treatment' (n = 1652, 30.5%). The most identified non-modifiable OC risk factor was 'having ovarian cysts' (n = 3136, 58.0%), whereas the least identified was 'having endometriosis' (n = 1880, 34.7%). The most identified OC protective factor was 'breastfeeding' (n = 4770, 88.2%), whereas the least identified was 'using the pill for a long time' (n = 930, 17.2%). Only 820 women (15.2%) displayed good awareness of OC risk and protective factors. Women from the Gaza Strip were slightly more likely than women from the WBJ to have good awareness (16.4% vs. 14.2%). In contrast, post-secondary education, higher monthly income, being married, and knowing someone with cancer were associated with an increase in the likelihood of displaying good awareness. The overall awareness of OC risk and protective factors in this study was low. Educational interventions are needed to improve Palestinian women's awareness.

COVID-19 (coronavirus disease 2019) vaccines induce immunity through different mechanisms. The aim of this study is to compare the titers of specific antibodies in subjects vaccinated with either the Pfizer-BioNTech COVID-19 vaccine or the Sinopharm vaccine. This prospective observational cohort included Jordanian adults vaccinated with two doses, 21 days apart, of either of the two aforementioned vaccines. Titers were collected 6 weeks after the administration of the second dose. Overall, 288 participants were included, of which 141 were administered the Pfizer-BioNTech vaccine, while 147 were administered the Sinopharm vaccine. Remarkably, 140 (99.3%) of the Pfizer-BioNTech vaccine recipients had positive IgG titers, while 126 (85.7%) of Sinopharm recipients had positive IgG (p < 0.001). The mean titer for IgG among Pfizer-BioNTech recipients was 515.5 ± 1143.5 BAU/mL, compared to 170.0 ± 230.0 BAU/mL among Sinopharm subjects (p < 0.001). Multivariable regression analysis showed that the Pfizer-BioNTech vaccine positively correlated with positive IgG titers (OR: 25.25; 95% CI: 3.25–196.15; p = 0.002), compared with a negative effect of cardiovascular diseases (OR: 0.33; 95% CI: 0.11–0.99; p = 0.48) on IgG titers. In conclusion, fully vaccinated recipients of the Pfizer-BioNTech vaccine had superior quantitative efficiency compared to Sinopharm recipients. A booster dose is supported for Sinopharm recipients, or those with chronic immunosuppressive diseases.