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Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain–Computer Interface (BCI), to provide better human–machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.

The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

The Internet of Things (IoT) has been widely used in every aspect of life. The rapid development of IoT technologies raises concerns regarding security and privacy. IoT security is a critical concern in the preservation of the privacy and reliability of users’ private information. The privacy concern becomes the biggest barrier to further usage of IoT technology. This paper presents a review of IoT application areas in smart cities, smart homes, and smart healthcare that leverage such techniques from the point of view of security and privacy and present relevant challenges. In addition, we present potential tools to ensure the security and preservation of privacy for IoT applications. Furthermore, a review of relevant research studies has been carried out and discusses the security of IoT infrastructure, the protocols, the challenges, and the solutions. Finally, we provide insight into challenges in the current research and recommendations for future works. The reviewed IoT applications have made life easier, but IoT devices that use unencrypted networks are increasingly coming under attack by malicious hackers. This leads to access to sensitive personal data. There is still time to protect devices better by pursuing security solutions with this technology. The results illustrate several technological and security challenges, such as malware, secure privacy management, and non-security infrastructure for cloud storage that still require effective solutions.

Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain–Computer Interface (BCI), to provide better human–machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.

Epidemiological studies have shown that the consumption of a high-fat diet (HFD) is positively related to the development of obesity. Lycopene (LYC) can potentially combat HFD-induced obesity and metabolic disorders in rats. This study aimed to investigate the effect of LYC on metabolic syndrome and assess its anti-inflammatory and antioxidant effects on the liver and adipose tissue in rats fed an HFD. Thirty-six male Wistar albino rats were divided into three groups. Group Ι (the control group) was fed a normal diet, group ΙΙ (HFD) received an HFD for 16 weeks, and group ΙΙΙ (HFD + LYC) received an HFD for 12 weeks and then LYC (25 mg/kg b.wt) was administered for four weeks. Lipid peroxidation, antioxidants, lipid profile, liver function biomarkers, and inflammatory markers were determined. The results showed that long-term consumption of an HFD significantly increased weight gain, liver weight, and cholesterol and triglyceride levels. Rats on an HFD displayed higher levels of lipid peroxidation and inflammatory markers. Moreover, liver and white adipose tissue histopathological investigations showed that LYC treatment mended the damaged tissue. Overall, LYC supplementation successfully reversed HFD-induced changes and shifts through its antioxidant and anti-inflammatory activity. Therefore, LYC displayed a therapeutic potential to manage obesity and its associated pathologies.

Background The purpose was to measure the prevalence and related risk factors of low back pain (LBP) among health care workers (HCWs) at different levels of health care in southwestern Saudi Arabia. Methods A cross-sectional study using a self-administered questionnaire was conducted among HCWs providing primary, secondary and tertiary health care services in the Aseer region, southwestern Saudi Arabia. The questionnaire collected data regarding having LBP in the past 12 months, socio-demographics, work conditions and history of chronic diseases, regular physical exercise and overexertional back trauma. Univariate and multivariable logistic regression analyses were performed. Results Out of 740 participants, the overall prevalence of LBP in the past 12 months amounted to73.9% (95% CI: 70.7–77.0). The prevalence of LBP with neurological symptoms reached 50.0%. The prevalence of LBP necessitating medications and or physiotherapy was 40.5%, while the prevalence of LBP requiring medical consultation was 20%. Using multivariable logistic regression, the following risk factors were identified: working in secondary and tertiary hospitals (aOR = 1.32, 95% CI:1.01–1.76), increased BMI (aOR = 1.10, 95% CI:1.01–3.65), and positive history of overexertional back trauma (aOR = 11.52, 95% CI:4.14–32.08). On the other hand, practising regular physical exercise was a significant protective factor (aOR = 0.61, 95% CI: 0.42–0.89). Conclusions LBP is a common problem among HCWs. Many preventable risk factors have been identified, including exertional back trauma, increased BMI and lack of regular physical exercise. Occupational health and safety programmes to build ergonomically safe working conditions and encourage regular physical exercise are needed.

Obesity is a worldwide epidemic associated with many health problems. One of the new trends in health care is the emphasis on regular exercise and a healthy diet. Zeaxanthin (Zea) is a carotenoid with many beneficial effects on human health. The aim of this study was to investigate whether the combination of Zea and exercise had therapeutic effects on obesity induced by an HFD in rats. Sixty male Wistar rats were randomly divided into five groups of twelve: rats fed a standard diet; rats fed a high-fat diet (HFD); rats fed an HFD with Zea; rats fed an HFD with Exc; and rats fed an HFD with both Zea and Exc. To induce obesity, rats were fed an HFD for twelve weeks. Then, Zea and exercise were introduced with the HFD for five weeks. The results showed that the HFD significantly increased visceral adipose tissue, oxidative stress, and inflammation biomarkers and reduced insulin, high-density lipoprotein, and antioxidant parameters. Treatments with Zea, Exc, and Zea plus Exc reduced body weight gain, triacylglycerol, glucose, total cholesterol, and nitric oxide levels and significantly increased catalase and insulin compared with the HFD group. This study demonstrated that Zea administration and Exc performance appeared to effectively alleviate the metabolic alterations induced by an HFD. Furthermore, Zea and Exc together had a better effect than either intervention alone.

In the last decade, research has shown that most diseases are associated with organelle dysfunction in which metabolites play a crucial role or indicate specific processes. Peroxisomes are cellular organelles attracting an increasing amount of attention and are now recognized as essential players in physiological conditions and diseases. However, a limited amount of research focuses on isolating the organelles and studying their properties and the diseases resulting from organelle dysfunction. All methods for isolating peroxisomes are based on fresh tissue samples. To the best of our knowledge, this is the first work in which peroxisomes have been isolated from frozen rat liver. In our work, we isolated peroxisomes from frozen rat liver at −80 °C and evaluated the separation success and degree of purification of isolated peroxisomes by measuring the relative specific activity, purification fold, and percentage yield (Y%) of organelle marker enzymes in the isolated fractions. The percentage of protein distribution and density was also estimated. Our results showed that the purified peroxisome fraction (F3-peroxisome) had significantly higher relative specific activity, as well as the highest purification fold and percentage yield of catalase compared with the enzyme markers of other organelles in the postnuclear supernatant (PNS), postmitochondrial supernatant (PMS), and light mitochondria–peroxisome (LM-P) fractions. In addition, the percentage of protein distribution was significantly lower in the F3-peroxisome fraction compared with PNS, PMS, and LM-P fractions while the percentage of protein distribution and density of the F3-peroxisome fraction after iodixanol centrifugation were significantly higher than those of the F1 and F2 fractions. The present work demonstrates the possibility of isolating peroxisomes from frozen liver samples efficiently, which could pave the way for further research in the future on other subcellular organelles from frozen samples.

Background: In response to COVID-19, many big pharmacy chains in Saudi Arabia have started to provide home drug delivery services. This study aims to understand home drug delivery service from the perspective of community pharmacists in Saudi Arabia. Also, the study investigates the obstacles that may limit the use of home drug delivery service. Methods: A cross-sectional self-reported survey was distributed from February 2021 to May 2021. Descriptive analysis of sociodemographic characteristics was conducted and presented. Frequencies and percentages were calculated for all variables. Results: A total of 965 community pharmacists were surveyed. Most of the pharmacists, (73.5%) were young, aged 23 to 34 years old. The vast majority of the participants, (93.6%), said that the service will improve drug adherence. The lack of required knowledge and skills among pharmacists could be the main obstacle to implement home drug delivery service (34%). A shortage in the number of community pharmacists was the second main obstacle (24%). Conclusion: Home delivery services in the future may largely replace the tradition of going in person to the pharmacy. There are obstacles that may limit the full use of the service like shortage in number of pharmacists and the lack of required training.

Continuing professional development (CPD) is a cornerstone of modern nursing practice, enabling professionals to maintain competence and deliver high-quality, evidence-based care. This review explores the importance of CPD in nursing, highlighting its role in adapting to developments in healthcare, promoting critical thinking, and fostering professional growth. Approaches such as workshops and online platforms enhance nurses’ professional development. However, challenges remain. This review explores strategies to address these barriers, including institutional support, enhancing relevance, and cultivating a culture of lifelong learning. Integrating CPD into nursing practice improves individual and organizational outcomes, ensuring that nurses remain equipped to meet evolving patient needs.