There exists a plethora of studies examining the psychological and physical impacts of COVID-19 on infected victims. Fewer studies have been published assessing the different types of impacts that an individual’s COVID-19 infection has on close friends and family members. This is the first scoping review to gauge the reported psychosocial issues and daily hassles that impact the relatives and friends of infected individuals. This study was conducted by inputting key terms/MeSH terms into selected internet databases to locate prospective studies. The frameworks of scoping reviews by Arksey et al. and the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) were utilized in the methodology for identifying and selecting the studies. After data extraction, 37 studies were deemed suitable for analysis. The findings generated from each study was placed into combined categories. A total of 16 combined categories were generated from the amalgamation of the findings. The results show that psychosocial feelings (e.g., anxiety, stress, depression) was the category with the highest prevalence of grouped findings. The results from this study may serve as the impetus for future interventions targeting the alleviation of psychosocial feelings or day to day hassles associated with having a loved one inflicted with a severe illness.

Evolutionary medicine argues that disease can arise because modern conditions do not match those in which we evolved. For example, a decline in exposure to commensal microbes and gastrointestinal helminths in developed countries has been linked to increased prevalence of allergic and autoimmune inflammatory disorders (the hygiene hypothesis). Accordingly, microbial and helminthic probiotic therapies have been explored as Darwinian treatments for these disorders. A further possibility is that loss of old friend commensals also increases the sterile, aging-associated inflammation known as inflammaging, that contributes to a range of age-related diseases, including cardiovascular disease, dementia and cancer. Interestingly, Crowe et al. (2020) recently reported that treatment with a secreted glycoprotein from a parasitic nematode can protect against murine aging by induction of anti-inflammatory mechanisms. Here, we explore the hypothesis that restorative helminth therapy would have anti-inflammaging effects. Could worm infections provide broad-spectrum protection against age-related disease?

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.