Risk of neuropsychiatric adverse effects of lipid-lowering drugs: a Mendelian randomization study

Background: Recent studies have highlighted the possible risk of neuropsychiatric adverse effects during treatment with lipid-lowering medications. However, there are still controversies that require a novel genetic-based approach to verify whether the impact of lipid-lowering drug treatment results in neuropsychiatric troubles including insomnia, depression, and neuroticism. Thus, we applied Mendelian randomization to assess any potential neuropsychiatric adverse effects of conventional lipid-lowering drugs such as statins, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, and ezetimibe. Methods: A 2-sample Mendelian randomization study was conducted based on summary statistics from genome-wide association studies for lipids, insomnia, depression, and neuroticism. Single-nucleotide polymorphisms located in or near drug target genes of HMGCR, PCSK9, and NPC1L1 were used as proxies for statins, PCSK9 inhibitors, and ezetimibe therapy, respectively. To assess the validity of the genetic risk score, their associations with coronary artery disease were used as a positive control. Results: The Mendelian randomization analysis showed a statistically significant (P <.004) increased risk of depression after correcting for multiple testing with both statins (odds ratio=1.15, 95% CI: 1.04–1.19) and PCSK9 inhibitor treatment (odds ratio =1.19, 95%CI: 1.1–1.29). The risk of neuroticism was slightly reduced with statin therapy (odds ratio=0.9, 95%CI: 0.83–0.97). No significant adverse effects were associated with ezetimibe treatment. As expected, the 3 medications significantly reduced the risk of coronary artery disease. Conclusion: Using a genetic-based approach, this study showed an increased risk of depression during statin and PCSK9 inhibitor therapy while their association with insomnia risk was not significant

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection

Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics

The Immunomodulatory Role of Probiotics

Probiotics are particularly beneficial living microorganisms that help improve human health. Although probiotics have long been used as nutritional supplements in various cultures around the world, new research has investigated their antimicrobial and immune boosting effects in individuals. Lactobacillus and Bifidobacterium are popular probiotics used worldwide that benefit human health by acting as antibacterial, antiviral, and antifungal agents, reducing pathogen binding to the host receptor and thus capturing pathogenic microorganisms. Probiotics have been shown to be beneficial in a variety of bacterial and viral diseases worldwide. The regulation of the host’s immune response is one of the most important mechanisms of probiotic action. Immunomodulatory effects of probiotic-derived compounds have been characterized using genomic and proteomic analysis. These compounds have the ability to regulate and initiate mucosal immunity against various diseases. Probiotics produce many bactericidal compounds, which inhibit the growth of pathogenic microorganisms and their toxins, promoting the sustainability and structural integrity of enterocytes. This chapter focused on recent scientific research findings that help us better understand how probiotics regulate the host immune response and how they can be used to prevent and treat disease and there beneficial role to improve the health status of individuals

A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air

A convolutional neural network-based decision support system for neonatal quiet sleep detection

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health

Knowledge about metabolic dysfunction-associated steatotic liver disease among the medical professionals from countries in the MENA region

Introduction and Objectives: Given the substantial burden of metabolic dysfunction-associated steatotic liver disease (MASLD), there is an urgent need to assess knowledge and awareness levels among physicians. We assessed MASLD knowledge among healthcare providers from Saudi Arabia, Egypt, and Turkiye. Materials and Methods: Two global surveys containing 54-59 items assessed awareness and knowledge of MASLD/NAFLD- one was for hepatologists and gastroenterologists, and the second was for non-specialists (e. g. endocrinologists, primary care providers [PCPs], and other healthcare professionals). Data were collected using an electronic data collection form. Knowledge scores and variables associated with higher knowledge scores were compared across all specialties. Results: A total of 584 physicians completed the survey (126 hepatologists, 178 gastroenterologists (GEs), 38 endocrinologists, 242 PCPs/others). Practice guidelines were the primary source for knowledge across all specialties (43-51%), then conferences (24-31%) except PCPs/others who selected the internet as the second common source (25%). Adherence to societal guidelines varied by specialty (81-84% of specialists vs 38-51% of non-specialists). Hepatologists and GEs showed similar mean knowledge scores (51-72% correct answers across three knowledge domains, p > 0.05); endocrinologists outperformed PCPs/others in knowledge scores in all knowledge domains, including Epidemiology/Pathogenesis (72% vs. 60%), Diagnostics (73% vs. 67%), and Treatment (78% vs. 67%) (all p < 0.01). Hospital-based practice and seeing a greater number of patients with MASLD/NAFLD were identified as independent predictors of higher knowledge scores among specialists (both p < 0.05). Conclusions: A knowledge gap in the identification, diagnosis, and management of MASLD/NAFLD was found despite the growing burden of MASLD/NAFLD in Saudi Arabia, Egypt, and Turkiye. Education to increase awareness is needed

Hepatitis delta virus infection prevalence, diagnosis and treatment in the Middle East: A scoping review

Hepatitis D virus (HDV) infection is a global public health concern, especially because of its unique existence in the presence of hepatitis B virus infection. HDV infection is estimated to affect 12 million people globally. Having a clearer understanding of its prevalence in all regions of the world is essential for helping direct preventive and early interventional treatment. This mini-review assessed the literature over the last 10 years to determine the prevalence, diagnostic means and treatment guidelines available for HDV in the Middle East. The search found limited data available in 21 articles, of which 18 were studies focused on Iran. Prevalence rates ranged dramatically among the countries, and none of the 12 countries included in the search had specific HDV guidelines. This review highlights the urgent need for more precise data for the Middle East region to help establish early diagnosis and treatment options for HDV

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

The role of fMRI in the mind decoding process in adults: a systematic review

Background Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults. Methodology A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms). Results This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others’ mental states). Conclusions This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field

Anesthesia with Respiratory Therapist, Nursing and Radiology Team Participation in Dental Practice: Review

The use of general anesthesia can make it easier for dental professionals to treat patients who, in the absence of this therapy, would be unable to obtain dental care themselves. Furthermore, the readiology team and the respiratory therapist, in conjunction with the nursing staff, will work together to ensure that the dental operation is carried out in a comfortable and risk-free manner. According to the opinions and preferences of dental practitioners, the type of local anesthetic that was most generally desired was lidocaine. On the other hand, the type of topical anesthetic that was most preferred was benzocaine in gel form. Furthermore, among Saudi dentists, the precise body weight was the factor that was utilized the most frequently in the process of determining the dosage of local anesthesia