ABSTRACT
Barrett's esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC), and is caused by chronic gastroesophageal reflux. BE can progress over time from metaplasia to dysplasia, and eventually to EAC. EAC is associated with a poor prognosis, often due to advanced disease at the time of diagnosis. However, if BE is diagnosed early, pharmacologic and endoscopic treatments can prevent progression to EAC. The current standard of care for BE surveillance utilizes the Seattle protocol. Unfortunately, a sizable proportion of early EAC and BE-related high-grade dysplasia (HGD) are missed due to poor adherence to the Seattle protocol and sampling errors. New modalities using artificial intelligence (AI) have been proposed to improve the detection of early EAC and BE-related HGD. This review will focus on AI technology and its application to various endoscopic modalities such as high-definition white light endoscopy, narrow-band imaging, and volumetric laser endomicroscopy.
ABSTRACT
BACKGROUND: People experiencing homelessness are at increased risk of coronavirus disease 2019 (COVID-19), but little is known about specific risk factors for infection within homeless shelters. METHODS: We performed widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction testing and collected risk factor information at all homeless shelters in Chicago with at least 1 reported case of COVID-19 (n = 21). Multivariable, mixed-effects log-binomial models were built to estimate adjusted prevalence ratios (aPRs) for SARS-CoV-2 infection for both individual- and facility-level risk factors. RESULTS: During March 1 to May 1, 2020, 1717 shelter residents and staff were tested for SARS-CoV-2; 472 (27%) persons tested positive. Prevalence of infection was higher for residents (431 of 1435, 30%) than for staff (41 of 282, 15%) (prevalence ratio = 2.52; 95% confidence interval [CI], 1.78-3.58). The majority of residents with SARS-CoV-2 infection (293 of 406 with available information about symptoms, 72%) reported no symptoms at the time of specimen collection or within the following 2 weeks. Among residents, sharing a room with a large number of people was associated with increased likelihood of infection (aPR for sharing with >20 people compared with single rooms = 1.76; 95% CI, 1.11-2.80), and current smoking was associated with reduced likelihood of infection (aPR = 0.71; 95% CI, 0.60-0.85). At the facility level, a higher proportion of residents leaving and returning each day was associated with increased prevalence (aPR = 1.08; 95% CI, 1.01-1.16), whereas an increase in the number of private bathrooms was associated with reduced prevalence (aPR for 1 additional private bathroom per 100 people = 0.92; 95% CI, 0.87-0.98). CONCLUSIONS: We identified a high prevalence of SARS-CoV-2 infections in homeless shelters. Reducing the number of residents sharing dormitories might reduce the likelihood of SARS-CoV-2 infection. When community transmission is high, limiting movement of persons experiencing homelessness into and out of shelters might also be beneficial.
