Introduction: While COVID-19 pandemic continues to spread worldwide, researchers have linked patterns of traits to poor disease outcomes. Risk factors for COVID-19 include asthma, elderly age, being pregnant, having any underlying diseases such as cardiovascular disease, diabetes, obesity, and experiencing lifelong systemic racism. Recently, connections to certain genes have also been found, although the susceptibility has not yet been established. We aimed to investigate the available evidence for the genetic susceptibility to COVID-19. Methods: This study was a systematic review of current evidence to investigate the genetic susceptibility of COVID-19. By systematic search and utilizing the keywords in the online databases including Scopus, PubMed, Web of Science, and Science Direct, we retrieved all the related papers and reports published in English from December 2019 to September 2020. Results: According to the findings, COVID-19 uses the angiotensin-converting enzyme 2 (ACE2) receptor for cell entry. Previous studies have shown that people with ACE2 polymorphism who have type 2 transmembrane serine proteases (TMPRSS2) are at high risk of SARS-CoV-2 infection. Also, two studies have shown that males are more likely to become infected with SARS-CoV-2 than females. Besides, research has also shown that patients possessing HLA-B*15:03 genotype may become immune to the infection. Conclusion: Combing through the genome, several genes related to immune system’s response were related to the severity and susceptibility to the COVID-19. In conclusion, a correlation was found between the ACE2 levels and the susceptibility to SARS-CoV-2 infection.

Objectives: Stigmatization was reported throughout the COVID pandemic for COVID-19 patients and close contacts. The aim of this systematic review was to comprehensively examine the prevalence and impact of stigmatization during COVID-19 pandemic. Methods: English articles were searched using online databases that included PubMed, Scopus, Embase, and Web of Science up to 24 August 2022. A two-step screening and selection process was followed utilizing an inclusion and exclusion criteria and then data was extracted from eligible articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed, and the risk of bias was assessed using the Newcastle-Ottawa Scale. Results: Seventy-six studies were eligible for inclusion. Twenty-two studies reported the prevalence of social stigma due to COVID-19 infection with social isolation being the most commonly reported stigma. There were 20 studies that reported the majority of participants experienced stigma due to COVID-19 infection, which was as high as 100% of participants in two studies. Participants in 16 studies reported blaming from others as the second most common type of stigma, with various other types reported such as psychological pressure, verbal violence, avoidance, and labeling. The most common effect of the stigma was anxiety followed by depression, and then reduction of socialization. Conclusion: Findings from the present review have identified that COVID-19-related stigma studies have generally focused on its prevalence, type, and outcome. Greater awareness of this topic may assist with improving public education during pandemics such as COVID-19 as well as access to support services for individuals impacted by stigmatization.

Introduction: Chatbots, computer programs emulating natural language conversations, have gained attention in healthcare. Recent advances address issues like obesity, dementia, oncology, and insomnia. A comprehensive assessment of their utility is essential for widespread adoption. This study aims to summarize chatbots' role in healthcare.Material and Methods: The methodology involved a systematic review of English-language literature up to May 8, 2023, from databases of Embase, PubMed, Web of Science, and Scopus. Selection followed a two-step process based on inclusion/exclusion criteria. The PRISMA checklist and AMSTAR-2 tool ensured quality.Results: The review encompassed 38 articles. Findings reveal chatbots primarily promote healthy lifestyles, improving mental well-being. They are widely used for treatment, education, and screening due to their accessibility.Conclusion: Chatbots hold transformative potential in healthcare, especially in mental health, cancer management, and public health. They are poised to revolutionize the industry, offering innovative solutions and improving patient outcomes.

In this study, the feasibility of accelerated quantitative Ultrashort Echo Time Cones (qUTE-Cones) imaging with compressed sensing (CS) reconstruction is investigated. qUTE-Cones sequences for variable flip angle-based UTE T₁ mapping, UTE adiabatic T_1ρ mapping, and UTE quantitative magnetization transfer modeling of macromolecular fraction (MMF) were implemented on a clinical 3T MR system. Twenty healthy volunteers were recruited and underwent whole-knee MRI using qUTE-Cones sequences. The k-space data were retrospectively undersampled with different undersampling rates. The undersampled qUTE-Cones data were reconstructed using both zero-filling and CS reconstruction. Using CS-reconstructed UTE images, various parameters were estimated in 10 different regions of interests (ROIs) in tendons, ligaments, menisci, and cartilage. Structural similarity, percentage error, and Pearson’s correlation were calculated to assess the performance. Dramatically reduced streaking artifacts and improved SSIM were observed in UTE images from CS reconstruction. A mean SSIM of ~0.90 was achieved for all CS-reconstructed images. Percentage errors between fully sampled and undersampled CS-reconstructed images were below 5% for up to 50% undersampling (i.e., 2× acceleration). High linear correlation was observed (>0.95) for all qUTE parameters estimated in all subjects. CS-based reconstruction combined with efficient Cones trajectory is expected to achieve a clinically feasible scan time for qUTE imaging.