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Abstract Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Abstract Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the lesions can be challenging and time-consuming, especially in resource-limited settings where laboratory tests may not be available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential in image recognition and classification tasks. To this end, this study proposes an approach using CNNs to classify monkeypox skin lesions. Additionally, the study optimized the CNN model using the Grey Wolf Optimizer (GWO) algorithm, resulting in a significant improvement in accuracy, precision, recall, F1-score, and AUC compared to the non-optimized model. The GWO optimization strategy can enhance the performance of CNN models on similar tasks. The optimized model achieved an impressive accuracy of 95.3%, indicating that the GWO optimizer has improved the model's ability to discriminate between positive and negative classes. The proposed approach has several potential benefits for improving the accuracy and efficiency of monkeypox diagnosis and surveillance. It could enable faster and more accurate diagnosis of monkeypox skin lesions, leading to earlier detection and better patient outcomes. Furthermore, the approach could have crucial public health implications for controlling and preventing monkeypox outbreaks. Overall, this study offers a novel and highly effective approach for diagnosing monkeypox, which could have significant real-world applications.

Abstract Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide. On the other hand, excused confusion and hesitation have widely impacted public health. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient’s medical history. The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The patient’s status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered). In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The results show that the proposed model gives the highest accuracy scores which are 96.031% for the Death target class in the case of PFIEZER vaccination. While in JANSSEN vaccination, the Hospitalized target class has shown the highest performance with an accuracy of 94.7%. Finally, the model has the best performance for the Recovered target class in MODERNA vaccination with an accuracy of 97.794%. Based on the accuracy and the Wilcoxon Signed Rank test, we can conclude that the proposed model is promising for identifying the relationship between the side effects of COVID-19 vaccines and the patient’s status after vaccination. The study displayed that certain side effects were increased in patients according to the type of COVID-19 vaccines. Side effects related to CNS and hemopoietic systems demonstrated high values in all studied COVID-19 vaccines. In the frame of precision medicine, these findings can support the medical staff to select the best COVID-19 vaccine based on the medical history of the patient.

Riverbank filtration (RBF) is an affordable technique to provide drinking water with adequate quality. The ultimate objective of this study is to facilitate the transferability and application of this sustainable technique in Egypt. In this work, a numerical model was constructed using Groundwater Modeling System (GMS) to study the effect of four design parameters on the RBF performance parameters (i.e., river filtrate portion and travel time) with the aid of MODPATH and ZONEBUDGET. The design parameters were: (1) the pumping rates of the RBF wells, (2) number of operating wells, (3) distance between wells and the river, and (4) the spacing between wells. This study was focused on the hydraulic aspects of the technique. The results demonstrated that: (1) the river filtrate portion exceeds 75% regardless of the design conditions, and (2) the hydraulic performance of the RBF technique is highly controlled by the production capacity of the wells and their positions relative to the surface water systems; the spacing between wells has a minimum effect. Two equations were developed to estimate the river filtrate portion and minimum travel time as functions of pumping rate and distance between the pumping well and the river. HIGHLIGHTS Hydraulic aspects of riverbank filtration.; Impact of design parameters on river filtrate portion and travel time.; The relation between over-pumping and river filtrate portion.; Multi-regression analysis for RBF performance.;

Abstract Background Minimal data is available on the prevalence and correlates of hypertension and prehypertension in Dubai. The study aims to measure the prevalence of hypertension and pre-hypertension and the associated socio-demographic characteristics, behavioral risk factors and comorbidities among the adult population of Dubai. Methods This study used data from the Dubai Household Health Survey, 2019. A cross-sectional population survey based on a complex stratified cluster random design. The total eligible sample included 2530 adults (18+). Sociodemographic and behavioral factors were considered as independent covariates. The main study outcome variables, pre-hypertension and hypertension, were ordinal, with normotension as the reference group. Results The overall prevalence of hypertension in adults was 32.5% (38.37% in males and 16.66% in females). Prehypertension was prevalent in 29.8% of adults in Dubai (28.85% in males and 32.31% in females). The multivariate logistic regression analysis revealed that age groups, gender, occupation, and high Body Mass Index were significantly associated with a higher risk of hypertension at the level of P

Objective To study the prevalence of overweight and obesity and determine the associated risk factors among adults in Dubai.Design and setting A cross-sectional survey with a multistage, stratified random sampling design was conducted in the Emirate of Dubai in 2019.Participants The study included 2142 adults aged 18+ years in the Emirate of Dubai.Results The overall prevalence of obesity, which was defined as body mass index (BMI) ≥30 kg/m2, was 17.8%. The highest obesity rates were reported among women (21.6%) and the United Arab Emirates (UAE)-nationals (39.6%). Moreover, 39.8% of the population was overweight (BMI ≥25–29.9 kg/m2). Multivariate logistic regression showed associations between obesity and age, sex, nationality, hypertension and occupation. Obesity increases with age, with the highest risk at age group 50–59 years (OR 4.30; 95% CI 1.57 to 11.78) compared with the reference group (18–24 years). Females had a higher risk of obesity than males (OR 1.62; 95% CI 1.10 to 2.38). Compared with those in the reference group (Western and others), UAE nationals, other Arabs and Asians were more likely to be obese ((OR 2.08; 95% CI 1.18 to 3.67), (OR 3.61; 95% CI 2.41 to 5.44) and (OR 1.98; 95% CI 1.12 to 3.50), respectively). Clerical and service workers (OR 4.50; 95% CI 2.54 to 8.00) and elementary and unskilled occupation categories (OR 2.57; 95% CI 1.56 to 4.25) had higher risks of obesity than the reference group (professionals), p

BackgroundThe world as we know it changed during the COVID-19 pandemic. Hope has emerged with the development of new vaccines against the disease. However, many factors hinder vaccine uptake and lead to vaccine hesitancy. Understanding the factors affecting vaccine hesitancy and how to assess its prevalence have become imperative amid the COVID-19 pandemic. The vaccine hesitancy scale (VHS), developed by the World Health Organization (WHO) Strategic Advisory Group of Experts on Immunization, has been modified to the adult VHS (aVHS) and validated in English and Chinese. To our knowledge, no available aVHS has been designed or validated in Arabic or French. ObjectiveThe aim of this research is to translate the aVHS from its original English language to Arabic and French and validate the translations in the WHO Eastern Mediterranean region. MethodsThe study will follow a cross-sectional design divided into 5 phases. In phase 1, the original aVHS will be forward-translated to Arabic and French, followed by backward translation to English. An expert committee will review and rate all versions of the translations. Expert agreement will then be measured using the Cohen kappa coefficient (k). In phase 2, the translated aVHS will be pilot-tested with 2 samples of participants (n=100): a group that speaks both Arabic and English and another that speaks French and English. Participants’ responses to the English version will also be collected. In phase 3, responses will then be compared. Descriptive statistics and paired t tests or one-way analyses of variance (ANOVA) and Pearson correlation coefficient will be used in the preliminary validation. In phase 4, prefinal versions (Arabic and French) will be tested with larger sample sizes of Arabic speakers (n=1000) and French speakers (n=1000). Sociodemographic information and vaccination status will be collected and used for further analysis. In phase 5, the scale's statistical reliability and internal consistency will be measured using Cronbach alpha. An exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) will be used to examine the model fit resulting from the EFA. ANOVA and regression models will be constructed to control for confounders. All data will be electronically collected. ResultsAs of January 2022, the scale had been translated to Arabic and French and was undergoing the process of back translation. All data collection tools have been prepared (ie, sociodemographics, vaccination status, and open-ended questions) and are ready to go into their electronic formats. We expect to reach the desired sample size in this phase by June 2022. ConclusionsThis study will provide researchers with a validated tool to assess adult vaccine hesitancy within populations that speak Arabic and/or French and provide a road map to scale translation and ensure cross-cultural adaptation. International Registered Report Identifier (IRRID)PRR1-10.2196/36928

OBJECTIVES The aim of this study was to develop a risk score model for predicting hypertension specific to the population of Dubai in the United Arab Emirates (UAE) to facilitate prevention and early intervention. METHODS A retrospective analysis of data from the Dubai Household Health Survey 2019 was conducted. Demographic and physical parameters, as well as blood glucose levels, were included in the data. The risk factors for hypertension were identified using bivariate analysis. A risk score model was developed using the enter method, where all significant predictors of hypertension in bivariate analyses were entered in a single step with the primary outcome of hypertension status (yes/no). The model was validated internally by splitting the data into Emirati and non-Emirati populations. RESULTS A total of 2,533 subjects were studied. The significant risk factors for hypertension identified were male sex, older age (≥40 years), education level, body mass index, diabetes mellitus, and dyslipidaemia. The model showed a high discrimination ability between individuals with and without hypertension, with an area under the curve of 0.77 (95% confidence interval [CI], 0.75 to 0.79), excellent sensitivity (81.0%; 95% CI, 71.9 to 88.2) and moderate specificity (56.0%; 95% CI, 45.7 to 65.9). CONCLUSIONS The model developed by this study is simple, convenient, and based on readily available demographic and medical characteristics. This risk score model could support initial hypertension screening and provide an effective tool for targeted lifestyle counselling and prevention programs.

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens., (© 2024. The Author(s).)