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The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.The maturity of the computational argumentation field, demonstrated with the first live debate between a machine and a human,1 triggers a demanding question how can we build argumentation technologies that bring people together? We believe that an important part of the answer is to include the audience's beliefs into the process.Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.Microglia are important immune cells in the central nervous system. Replacement of mutated microglia by wild-type cells through microglia replacement by bone marrow transplantation can correct gene deficiencies. However, the limited availability of bone marrow cells may restrict its potential of becoming a widely used clinical treatment. Here, we introduce a potentially clinical-feasible strategy achieving efficient microglia replacement by peripheral blood cells in mice, boosting the donor cell availability. We named it microglia replacement by peripheral blood (Mr PB). For complete details on the use and execution of this protocol, please refer to Xu et al. (2020). The original abbreviation of this microglia replacement strategy is mrPB. We hereby change the name to Mr PB.Reproducible in vivo models are necessary to address functional aspects of the gut microbiome in various diseases. Here, we present a gnotobiotic mouse model that allows for the investigation of specific microbial functions within the microbiome. We describe how to culture 14 different well-characterized human gut species and how to verify their proper colonization in germ-free mice. This protocol can be modified to add or remove certain species of interest to investigate microbial mechanistic details in various disease models. For complete details on the use and execution of this protocol, please refer to Desai et al. (2016).One of the key public health strategies in coronavirus 2019 disease (COVID-19) management is the early detection of infected individuals to limit the transmission. As a result, serological assays have been developed to complement PCR-based assays. Here, we report the development of a flow cytometry-based assay to detect antibodies against full-length SARS-CoV-2 spike protein (S protein) in patients with COVID-19. The assay is time-efficient and sensitive, being able to capture the wider repertoire of antibodies against the S protein. For complete details on the use and execution of this protocol, please refer to Goh et al. (2021).In patients with acute myocardial injury secondary to coronavirus disease-2019 (COVID-19), cardiovascular magnetic resonance imaging can identify the underlying pathology. We highlight a case of acute myocardial injury secondary to COVID-19, which demonstrated both epicardial vessel thrombosis and the recently described phenomenon of microvascular thrombosis. (Level of Difficulty Advanced.).<br /><br /> Real-world evidence on the association between autoimmune inflammatory rheumatic diseases, therapies related to these diseases, and COVID-19 outcomes are inconsistent. We aimed to investigate the potential association between autoimmune inflammatory rheumatic diseases and COVID-19 early in the COVID-19 pandemic.<br /><br /> We did an exposure-driven, propensity score-matched study using a South Korean nationwide cohort linked to general health examination records. We analysed all South Korean patients aged older than 20 years who underwent SARS-CoV-2 RT-PCR testing between Jan 1 and May 30, 2020, and received general health examination results from the Korean National Health Insurance Service. We defined autoimmune inflammatory rheumatic diseases (inflammatory arthritis and connective tissue diseases) based on the relevant ICD-10 codes, with at least two claims (outpatient or inpatient) within 1 year. [https://www.selleckchem.com/products/ipi-549.html IPI-549 order] The outcomes were positive SARS-CoV-2 RT-PCR test, severe COVID-19 (requirement of oxygen therapy, intensive care unid outcomes, but those receiving high dose (≥10 mg per day) of systemic corticosteroids had an increased likelihood of a positive SARS-CoV-2 test (adjusted OR 1·47, 95% CI 1·05-2·03; p=0·022), severe COVID-19 outcomes (1·76, 1·06-2·96; p=0·031), and COVID-19-related death (3·34, 1·23-8·90; p=0·017).<br /><br /> Early in the COVID-19 pandemic, autoimmune inflammatory rheumatic diseases were associated with an increased likelihood of a positive SARS-CoV-2 PCR test, worse clinical outcomes of COVID-19, and COVID-19-related deaths in South Korea. A high dose of systemic corticosteroid, but not DMARDs, showed an adverse effect on SARS-CoV-2 infection and COVID-19-related clinical outcomes.<br /><br /> National Research Foundation of Korea.<br /><br />National Research Foundation of Korea.