IntroductionRecurrent Aphthous Stomatitis (RAS) is a common oral mucosal disorder which is characterized by recurrent ulcerations mainly confined to non-keratinized oral mucosa. Though the exact etiology is imprecise, stress and anxiety were found to be linked with the onset of RAS. The present study was directed to know the prevalence of RAS among female dental students in King Khalid University, to find out the association between RAS and psychological stress and the effectiveness of Hospital Anxiety and Depression scale (HADS) in finding out the psychological profile of RAS patients.Method122 female dental students of age group 17 to 25 years were selected for the study. Subjects with relevant medical problems and deleterious habits were excluded. A questionnaire comprising general stress related questions and HAD scale were used to assess stress. Those who were giving history of ulcer were diagnosed as RAS based on the clinical features. The questionnaire results were used for statistical analysis and processed.Result14% of the total students were having RAS. 70% could correlate the onset of ulcer with certain specific factors; stress being the major causative agent (91%). In HAD scale, 88% of students were having anxiety and 65% had depression; all patients with depression had anxiety.ConclusionThe prevalence of RAS in female dental students in KKU was around 14%. There is a strong relationship between psychological stress and RAS, as in most of the other studies. HAD scale alone can be used for detecting the psychological factor in RAS.

The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we present a framework to predict the onset of AMI using 713,447 extracted ECG samples and associated auxiliary data from the longitudinal and comprehensive ECG-ViEW II database, previously unexplored in the field of machine learning in healthcare. The framework is realized with two deep learning models, a convolutional neural network (CNN) and a recurrent neural network (RNN), and a decision-tree based model, XGBoost. Synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance. High prediction accuracy of 89.9%, 84.6%, 97.5% and ROC curve areas of 90.7%, 82.9%, 96.5% have been achieved for the best CNN, RNN, and XGBoost models, respectively. Shapley values were utilized to identify the features that contributed most to the classification decision with XGBoost, demonstrating the high impact of auxiliary inputs such as age and sex. This paper demonstrates the promising application of explainable machine learning in the field of cardiovascular disease prediction. INDEX TERMS Machine learning, biomedical informatics, predictive models, acute myocardial infarction.

There is a limitation in the range of effectual antibiotics due to the Pseudomonas aeruginosa (PA) infection due to its innate antimicrobial resistance. Researchers have therefore been concentrating their efforts to discover advanced and cost effective antibacterial agents among the ever-increasing PA bacterial resistance strains. It has been discovered that various nanoparticles can be employed as antimicrobial agents. Here, we evaluated the antibacterial properties of the Zinc Oxide nanoparticles (ZnO NPs), which was biosynthesized, being examined on six hospital strains of PA alongside a reference strain (ATCC 27853). A chemical approach was applied to biosynthesize the ZnO NPs from Olea europaea was performed, and confirmed by using X-ray diffraction and Scanning Electron Microscopes. The nanoparticles then applied their antibacterial properties to examine them against six clinically isolated PA strains alongside the reference strain. This process tested for the results of the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The Growth, biofilm formation and eradication were analyzed. The influence of the differentiating degrees ZnO NPs in regard to Quorom sensing gene expression were further examined. The ZnO NPs exhibited a crystalline size and diameter (Dc) of 40–60 nm and both the MIC and MBC tests revealed positive outcomes of concentrations of 3 and 6 mg/ml for each PA strain, respectively. At sub inhibitory concentration, The ZnO NPs were found to significantly inhibit the growth and biofilm formation of all PA strains and decreases in the biomass and metabolic behavior of PA established biofilms; these decreases varied depending on the dosage. At ZnO NPs concentrations of 900 µg/ml, the expression of majority of quorum sensing genes of all strains were significantly reduced, at ZnO NPs concentrations of 300 µg/ml, few genes were significantly impacted. In conclusion, the treatment of PA and could be other antibiotic resistant bacteria can therefore be approached by using ZnO NPs as it has been uncovered that they withhold advanced antibacterial properties.

Bias can have devastating outcomes on everyday life, and may manifest in subtle preferences for particular attributes (age, gender, ethnicity, profession). Understanding bias is complex, but first requires identifying the variety and interplay of individual preferences. In this study, we deployed a sociotechnical, web-based human-subject experiment to quantify individual preferences in the context of selecting an advisor to successfully pitch a government-expense. We utilized conjoint analysis to rank the preferences of 722 U.S. based subjects, and observed that their ideal advisor was White, middle-aged, and of either a government or STEM-related profession (0.68 AUROC, p < 0.05). The results motivate the simultaneous measurement of preferences as a strategy to offset preferences that may yield negative consequences (e.g. prejudice, disenfranchisement) in contexts where social interests are being represented.

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