Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus

The Middle East respiratory syndrome coronavirus (MERS-CoV) was first documented in the Kingdom of Saudi Arabia (KSA) in 2012 and, to date, has been identified in 180 cases with 43% mortality. In this study, we have determined the MERS-CoV evolutionary rate, documented genetic variants of the virus and their distribution throughout the Arabian peninsula, and identified the genome positions under positive selection, important features for monitoring adaptation of MERS-CoV to human transmission and for identifying the source of infections. Respiratory samples from confirmed KSA MERS cases from May to September 2013 were subjected to whole-genome deep sequencing, and 32 complete or partial sequences (20 were ≥99% complete, 7 were 50 to 94% complete, and 5 were 27 to 50% complete) were obtained, bringing the total available MERS-CoV genomic sequences to 65. An evolutionary rate of 1.12 × 10−3 substitutions per site per year (95% credible interval [95% CI], 8.76 × 10−4; 1.37 × 10−3) was estimated, bringing the time to most recent common ancestor to March 2012 (95% CI, December 2011; June 2012). Only one MERS-CoV codon, spike 1020, located in a domain required for cell entry, is under strong positive selection. Four KSA MERS-CoV phylogenetic clades were found, with 3 clades apparently no longer contributing to current cases. The size of the population infected with MERS-CoV showed a gradual increase to June 2013, followed by a decline, possibly due to increased surveillance and infection control measures combined with a basic reproduction number (R0) for the virus that is less than 1

Viral shedding and antibody response in 37 patients with MERS-coronavirus infection

Background. The Middle East respiratory syndrome (MERS) coronavirus causes isolated cases and outbreaks of severe respiratory disease. Essential features of the natural history of disease are poorly understood. Methods. We studied 37 adult patients infected with MERS coronavirus for viral load in the lower and upper respiratory tracts (LRT and URT, respectively), blood, stool, and urine. Antibodies and serum neutralizing activities were determined over the course of disease. Results. One hundred ninety-nine LRT samples collected during the 3 weeks following diagnosis yielded virus RNA in 93% of tests. Average (maximum) viral loads were 5 × 106 (6 × 1010) copies/mL. Viral loads (positive detection frequencies) in 84 URT samples were 1.9 × 104 copies/mL (47.6%). Thirty-three percent of all 108 serum samples tested yielded viral RNA. Only 14.6% of stool and 2.4% of urine samples yielded viral RNA. All seroconversions occurred during the first 2 weeks after diagnosis, which corresponds to the second and third week after symptom onset. Immunoglobulin M detection provided no advantage in sensitivity over immunoglobulin G (IgG) detection. All surviving patients, but only slightly more than half of all fatal cases, produced IgG and neutralizing antibodies. The levels of IgG and neutralizing antibodies were weakly and inversely correlated with LRT viral loads. Presence of antibodies did not lead to the elimination of virus from LRT. Conclusions. The timing and intensity of respiratory viral shedding in patients with MERS closely matches that of those with severe acute respiratory syndrome. Blood viral RNA does not seem to be infectious. Extrapulmonary loci of virus replication seem possible. Neutralizing antibodies do not suffice to clear the infection

Laparoscopic Cholecystectomy for Acute Calcular Cholecystitis in a Patient with Ventriculoperitoneal Shunt: A Case Report and Literature Review

Management of patients who have ventriculoperitoneal shunt presenting with acute calcular cholecystitis has remained a clinical challenge. In this paper, the hospital course and the follow-up of a patient presenting with acute calcular cholecystitis and ventriculoperitoneal shunt managed with laparoscopic cholecystectomy are presented followed by literature review on the management of acute calcular cholecystitis in patients who have ventriculoperitoneal shunts

Influence of the ATP-dependent DNA ligase, Lig E, on Neisseria gonorrhoeae microcolony and biofilm formation

Neisseria gonorrhoeae, the causative agent of the sexually transmitted infection, gonorrhoea, is known to form biofilms rich in extracellular DNA on human cervical cells. Biofilm formation is conducive to increased antimicrobial resistance and evasion of the host immune system, potentially causing asymptomatic infections. Using plate-based assays we have previously shown that disruption of a potential extracellular DNA ligase, Lig E, in N. gonorrhoeae impacts biofilm formation. In this research, we further explored this phenotype using confocal and scanning electron microscopy to directly visualise the morphology of microcolony and biofilm formation. Biofilm growth on artificial surfaces and on 3-dimensional human vaginal epithelial tissue was evaluated for strains where lig E was either disrupted or overexpressed. Results demonstrated that Lig E was important for the formation of robust, compact N. gonorrhoeae microcolonies, as well as extensive biofilms on artificial surfaces. The lig E deletion strain also had the highest tendency to be retained on the surface of epithelial tissues, with decreased invasion and damage to host cell layers. These findings support a role for Lig E to be secreted from N. gonorrhoeae cells for the purpose of inter-cell adhesion and biofilm formation. We suggest that Lig E strengthens the extracellular matrix and hence microcolony and biofilm formation of N. gonorrhoeae by ligation of extracellular DNA

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare

Deep Learning-based Method for Enhancing the Detection of Arabic Authorship Attribution using Acoustic and Textual-based Features

Authorship attribution (AA) is defined as the identification of the original author of an unseen text. It is found that the style of the author’s writing can change from one topic to another, but the author’s habits are still the same in different texts. The authorship attribution has been extensively studied for texts written in different languages such as English. However, few studies investigated the Arabic authorship attribution (AAA) due to the special challenges faced with the Arabic scripts. Additionally, there is a need to identify the authors of texts extracted from livestream broadcasting and the recorded speeches to protect the intellectual property of these authors. This paper aims to enhance the detection of Arabic authorship attribution by extracting different features and fusing the outputs of two deep learning models. The dataset used in this study was collected from the weekly livestream and recorded Arabic sermons that are available publicly on the official website of Al-Haramain in Saudi Arabia. The acoustic, textual and stylometric features were extracted for five authors. Then, the data were pre-processed and fed into the deep learning-based models (CNN architecture and its pre-trained ResNet34). After that the hard and soft voting ensemble methods were applied for combining the outputs of the applied models and improve the overall performance. The experimental results showed that the use of CNN with textual data obtained an acceptable performance using all evaluation metrics. Then, the performance of ResNet34 model with acoustic features outperformed the other models and obtained the accuracy of 90.34%. Finally, the results showed that the soft voting ensemble method enhanced the performance of AAA and outperformed the other method in terms of accuracy and precision, which obtained 93.19% and 0.9311 respectively

An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer’s disease

Alzheimer’s disease (AD) is a common form of dementia that affects the central nervous system, causing progressive cognitive decline, particularly in memory. Early, non-invasive diagnosis is critical for improving patient care and treatment outcomes. This study proposes a robust feature extraction approach combined with three classifiers to achieve optimal classification of AD stages. T1-weighted brain MRI scans were used as input data. Features were extracted using Harris Corner interest points and the Histogram of Oriented Gradients (HOG) method. Classification was performed using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and a Deep Neural Network (DNN)-based pipeline. The proposed system classified three AD stages—Control Normal (CN), Mild Cognitive Impairment (MCI), and AD—with high accuracy: KNN (88%), SVM (91.5%), and DNN (95.6%). The DNN approach outperformed other classifiers and was further compared with state-of-the-art deep learning models, demonstrating competitive performance. These results highlight the potential of the proposed framework for early, accurate AD diagnosis using non-invasive imaging

Biomimetic Whitening Effect of Polyphosphate-Bleaching Agents on Dental Enamel.

This in vitro study investigated the extrinsic tooth-whitening effect of bleaching products containing polyphosphates on the dental enamel surface compared to 10% carbamide peroxide (CP). Eighty human molars were randomly allocated into four whitening-products groups. Group A (control) was treated with 10% CP (Opalescence). The other groups with non-CP over-the-counter (OTC) products were group B = polyphosphates (iWhiteWhitening-Kit); group C = polyphosphates+fluoride (iWhite-toothpaste); and group D = sodium bicarbonate (24K-Whitening-Pen). L*, a*, b* color-parameters were spectrophotometer-recorded at baseline (T0), one day (T1), and one month (T2) post-treatment. Changes in teeth color (ΔEab) were calculated. Data were analyzed using ANOVA and the Bonferroni test (α = 0.05). Groups A, B, and D showed significant differences in ΔL*&Δa* parameters at T1, but not in Δb* at T0. Group C showed no difference for ΔL*, Δa*, Δb* at T0 and T1. Group A showed differences for ΔL*, Δa*, Δb*, at T2, while groups B, C, and D had no difference in any parameters at T0. At T1, ΔEab values = A > D> B > C (ΔEab = 13.4 > 2.4 > 2.1 > 1.2). At T2, ΔEab values increased = A > B > C > D (ΔEab = 12.2 > 10.6 > 9.2 > 2.4). In conclusion, the 10% CP and Biomimetic polyphosphate extrinsic whitening kit demonstrated the highest color change, while simulated brushing with dark stain toothpaste and a whitening pen demonstrated the lowest color change at both measurement intervals

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease