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Chronic glucocorticoid (GC) therapy is common in patients with rheumatic medical diseases (RMD). However, long-term use of GCs can be associated with significant adverse effects. This study aims to determine the prevalence of GC use among patients with RMD and understand their perceptions of GC use and safety. This cross-sectional survey was conducted at a tertiary academic university hospital in Amman, Jordan. RMD patients were anonymously approached while awaiting rheumatology clinic appointments. Convenience sampling was employed, with enrollment taking place between January and September 2021. Of 500 participants, 315 (63%) reported current (171/315, 54.3%) or past (144/315, 45.7%) use of GCs, primarily prescribed for RMD (267/315, 84.7%). Most (270/315, 85.7%) used GCs orally, with the majority taking them daily (266/315, 84.4%). A small percentage (33/315, 10.5%) used GCs only as needed. Long-term use was common, with 57.8% (182/315) reporting use for years and 84.1% (265/315) adhering to doctor recommendations. Perception-wise, 69.5% (219/315) of GC users believed in its efficacy, whereas 78.7% (248/315) considered it unsafe, compared to 51.9% (96/185) of non-users. Awareness of side effects was higher among GC users (219/315, 69.5%) than non-users (79/185, 42.7%), with weight gain being the most reported side effect. Media and personal research were the primary information sources for GC users (193/315, 61.3%). Side effects were reported by 110/315 (34.9%) GC users, with weight gain as the most common (47%). Both perceived efficacy and safety of GC were positively and significantly correlated with educational level (p = .005, p = .000, respectively) and monthly income (p = .044, p = .000, respectively). GC use is prevalent among Jordanian patients with RMDs, with perceptions of efficacy and safety strongly influenced by education and income. Enhanced patient education on the side effects of GC is crucial for improving treatment adherence and outcomes.

The approach of drug delivery systems emphasizes the use of nanoparticles as a vehicle, offering the optional property of delivering drugs as a single dose rather than in multiple doses. The current study aims to improve antioxidant and drug release properties of curcumin loaded gum Arabic-sodium alginate nanoparticles (Cur/ALG-GANPs). The Cur/ALG-GANPs were prepared using the ionotropic gelation technique and further subjected to physico-chemical characterization using attenuated total reflectance–Fourier transform infrared (ATR-FTIR), X-ray diffractometry (XRD), differential scanning calorimetry (DSC), size distribution, and transmission electron microscopy (TEM). The size of Cur/ALG-GANPs ranged between 10 ± 0.3 nm and 190 ± 0.1 nm and the zeta potential was –15 ± 0.2 mV. The antioxidant study of Cur/ALG-GANPs exhibited effective radical scavenging capacity for 1,1-diphenyl-2-picrylhydrazyl (DPPH) at concentrations that ranged between 30 and 500µg/mL. Cytotoxicity was performed using MTT assay to measure their potential in inhibiting the cell growth and the result demonstrated a significant anticancer activity of Cur/ALG-GANPs against human liver cancer cells (HepG2) than in colon cancer (HT29), lung cancer (A549) and breast cancer (MCF7) cells. Thus, this study indicates that Cur/ALG-GANPs have promising anticancer properties that might aid in future cancer therapy.

COVID-19 is a disease caused by the coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2), known as a highly contagious disease, currently affecting more than 200 countries worldwide. The main feature of SARS-CoV-2 that distinguishes it from other viruses is the speed of transmission combined with higher risk of mortality from acute respiratory distress syndrome (ARDS). People with diabetes mellitus (DM), severe obesity, cardiovascular disease, and hypertension are more likely to get infected and are at a higher risk of mortality from COVID-19. Among elderly patients who are at higher risk of death from COVID-19, 26.8% have DM. Although the reasons for this increased risk are yet to be determined, several factors may contribute to type-2 DM patients’ increased susceptibility to infections. A possible factor that may play a role in increasing the risk in people affected by diabetes and/or obesity is the impaired innate and adaptive immune response, characterized by a state of chronic and low-grade inflammation that can lead to abrupt systemic metabolic alteration. SARS patients previously diagnosed with diabetes or hyperglycemia had higher mortality and morbidity rates when compared with patients who were under metabolic control. Similarly, obese individuals are at higher risk of developing complications from SARS-CoV-2. In this review, we will explore the current and evolving insights pertinent to the metabolic impact of coronavirus infections with special attention to the main pathways and mechanisms that are linked to the pathophysiology and treatment of diabetes.

An integrated roadmap toward clinical interpretation of the complete human genome is in dire need. We discuss approaches to meet this goal, including integrating data from diverse, well-phenotyped populations with enhanced long-read genome assemblies, variant calling as well as improved predictive models and scalable functional assays.

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

This is a retrospective single-center study of 417 consecutive patients with coronavirus disease 2019 (COVID-19) admitted to Jaber Al-Ahmad Hospital in Kuwait between February 24, 2020 and May 24, 2020. In total, 39.3% of patients were asymptomatic, 41% were symptomatic with mild/moderate symptoms, 19.7% were admitted to the intensive care unit (ICU). Most common symptoms in cohort patients were fever (34.3%) and dry cough (32.6%) while shortness in breath was reported in (75.6%) of ICU admissions. Reported complications requiring ICU admission included Sepsis (68.3%), acute respiratory distress syndrome (95.1%) and heart failure (63.4%). ICU patients were more likely to have comorbidities, in comparison to non-ICU patients, including diabetes (35.4% vs 20.3%) and hypertension (40.2% vs 26.9%). Mortality rate of cohort was 14.4% and mean age of death was 54.20 years (± 11.09) and 90% of death cases were males. Chest high-resolution computed tomography for ICU cases reveled multifocal large patchy areas of ground glass opacification mixed with dense consolidation. Cases admitted to ICU showed abnormal levels of markers associated with infection, inflammation, abnormal blood clotting, heart problems and kidney problems. Mean hospital stay for asymptomatic cases was 20.69 days ±8.57 and for mild/moderate cases was 21.4 days ±8.28. Mean stay in ICU to outcome for survivors was 11.95 days ±8.96 and for death cases 13.15 days ±10.02. In this single-center case series of 417 hospitalized COVID-19 patients in Kuwait 39.3% were asymptomatic cases, 41% showed mild/moderate symptoms and 18.7% were admitted to ICU with a mortality rate of 14.4%.

The emergence of effective vaccines for COVID-19 has been welcomed by the world with great optimism. Given their increased susceptibility to COVID-19, the question arises whether individuals with type-2 diabetes mellitus (T2DM) and other metabolic conditions can respond effectively to the mRNA-based vaccine. We aimed to evaluate the levels of anti-SARS-CoV-2 IgG and neutralizing antibodies in people with T2DM and/or other metabolic risk factors (hypertension and obesity) compared to those without. This study included 262 people (81 diabetic and 181 non-diabetic persons) that took two doses of BNT162b2 (Pfizer–BioNTech) mRNA vaccine. Both T2DM and non-diabetic individuals had a robust response to vaccination as demonstrated by their high antibody titers. However, both SARS-CoV-2 IgG and neutralizing antibodies titers were lower in people with T2DM. The mean ( ± 1 standard deviation) levels were 154 ± 49.1 vs. 138 ± 59.4 BAU/ml for IgG and 87.1 ± 11.6 vs. 79.7 ± 19.5% for neutralizing antibodies in individuals without diabetes compared to those with T2DM, respectively. In a multiple linear regression adjusted for individual characteristics, comorbidities, previous COVID-19 infection, and duration since second vaccine dose, diabetics had 13.86 BAU/ml (95% CI: 27.08 to 0.64 BAU/ml, p=0.041) less IgG antibodies and 4.42% (95% CI: 8.53 to 0.32%, p=0.036) fewer neutralizing antibodies than non-diabetics. Hypertension and obesity did not show significant changes in antibody titers. Taken together, both type-2 diabetic and non-diabetic individuals elicited strong immune responses to SARS-CoV-2 BNT162b2 mRNA vaccine; nonetheless, lower levels were seen in people with diabetes. Continuous monitoring of the antibody levels might be a good indicator to guide personalized needs for further booster shots to maintain adaptive immunity. Nonetheless, it is important that people get their COVID-19 vaccination especially people with diabetes.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited monogenic renal disease characterised by the accumulation of clusters of fluid-filled cysts in the kidneys and is caused by mutations in PKD1 or PKD2 genes. ADPKD genetic diagnosis is complicated by PKD1 pseudogenes located proximal to the original gene with a high degree of homology. The next generation sequencing (NGS) technology including whole exome sequencing (WES) and whole genome sequencing (WGS), is becoming more affordable and its use in the detection of ADPKD mutations for diagnostic and research purposes more widespread. However, how well does NGS technology compare with the Gold standard (Sanger sequencing) in the detection of ADPKD mutations? Is a question that remains to be answered. We have evaluated the efficacy of WES, WGS and targeted enrichment methodologies in detecting ADPKD mutations in the PKD1 and PKD2 genes in patients who were clinically evaluated by ultrasonography and renal function tests. Our results showed that WES detected PKD1 mutations in ADPKD patients with 50% sensitivity, as the reading depth and sequencing quality were low in the duplicated regions of PKD1 (exons 1–32) compared with those of WGS and target enrichment arrays. Our investigation highlights major limitations of WES in ADPKD genetic diagnosis. Enhancing reading depth, quality and sensitivity of WES in the PKD1 duplicated regions (exons 1–32) is crucial for its potential diagnostic or research applications.

COVID-19 is challenging healthcare preparedness, world economies, and livelihoods. The infection and death rates associated with this pandemic are strikingly variable in different countries. To elucidate this discrepancy, we analyzed 2431 early spread SARS-CoV-2 sequences from GISAID. We estimated continental-wise admixture proportions, assessed haplotype block estimation, and tested for the presence or absence of strains’ recombination. Herein, we identified 1010 unique missense mutations and seven different SARS-CoV-2 clusters. In samples from Asia, a small haplotype block was identified, whereas samples from Europe and North America harbored large and different haplotype blocks with nonsynonymous variants. Variant frequency and linkage disequilibrium varied among continents, especially in North America. Recombination between different strains was only observed in North American and European sequences. In addition, we structurally modelled the two most common mutations, Spike_D614G and Nsp12_P314L, which suggested that these linked mutations may enhance viral entry and replication, respectively. Overall, we propose that genomic recombination between different strains may contribute to SARS-CoV-2 virulence and COVID-19 severity and may present additional challenges for current treatment regimens and countermeasures. Furthermore, our study provides a possible explanation for the substantial second wave of COVID-19 presented with higher infection and death rates in many countries.

Due to an insufficient labeled dataset, class-level variation emotion recognition becomes a challenging task in computer vision. Deep learning (DL) makes it possible to automatically learn meaningful patterns from facial expressions. It captures simple details such as edges, textures at low layers, and gradually builds up to more complex information, including Facial components and the overall meaning of the expression. Despite progress made via end-to-end learning, partial occlusions, inconsistent lighting, and biases within datasets are a few challenges that still remain. In this work, a DL based model is presented to classify two emotional states of human expression. The pipeline depends on several components, including the preparation of data, preprocessing and analysis, and the use of pretrained networks, dimensionality-reduction techniques, and region-based explanation via Grad-CAM. More than 2,000 images of happy and sad faces were derived from Kaggle. These images were used to test a custom-designed CNN and two widely adopted architectures, such as VGG16 and MobileNetV. The custom model attained an accuracy rate of 66% and 67% F1, while the VGG16 performed notably better with 78% accuracy and 77% F1, and the MobileNetV architecture, which achieved 77% accuracy and 73% F1. The statistical comparisons using paired t-tests and Wilcoxon signed-rank tests further confirmed these findings, showing that pre-trained models outperformed a custom CNN with a meaningful effect size. Although deeper networks are more susceptible to overfitting and the hand-crafted CNN suffered exhibited underfitting, the results indicate that pretained architecture provides a clear advantage for facial emotion recognition. This study makes a major contribution to existing computer vision research in removing the trade-off between accuracy and generalization, and opens doors to the application of lightweight yet interpretable models in practical affective computing systems.