The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https//clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.
  The objective of this study was to evaluate the haemodynamic performance of transcatheter mitral valve replacement (TMVR) Implant with a focus on turbulence and washout adjacent to the ventricular surface of the leaflets. TMVR holds the promise of treating a large spectrum of mitral valve diseases.  https://www.selleckchem.com/products/glpg3970.html  However, the haemodynamic performance and flow dynamics of such replacements are not fully understood.
 
  A tri-leaflet biopsrosthetic TMVR represented by Caisson implant of size 36A was implanted in the mitral position of a left heart simulator pulse duplicating system under physiological conditions. The 36A implant covers an anterior-posterior range of 26-32 mm and a commissure-to-commissure range of 30-36 mm. Transmitral pressure gradient, effective orifice area and regurgitant fraction were calculated. Particle image velocimetry was performed to evaluate turbulence in 2 perpendicular planes (Reynolds and viscous shear stresses, respectively). Additionally, dye experiments were performed to visualize washout.
 
  Transmitral pressure gradient was 1.29 ± 0.27 mmHg and effective orifice area was 2.96 ± 0.28 cm2. Regurgitant fraction was 14.13 ± 0.08%. Total washout was 4.27 cardiac cycles. Largest viscous shear stress reaches 3.7 Pa and 2.4 Pa in ventricle and atrium, respectively. Reynolds shear stress in the atrial side was <10 Pa. In the ventricular side, the largest Reynolds shear stress reached ∼35 Pa.
 
  TMVR leads to favourable haemodynamics with low degree of turbulence combined with fast washout around the leaflets indicating promising potential for freedom from blood damage potential and thrombosis corroborated by initial clinical studies as part of the valves's Early Feasibility Study.
 TMVR leads to favourable haemodynamics with low degree of turbulence combined with fast washout around the leaflets indicating promising potential for freedom from blood damage potential and thrombosis corroborated by initial clinical studies as part of the valves's Early Feasibility Study.In clinic, perioperative neurocognitive disorder is becoming a common complication of surgery in old patients. Neuroinflammation and blood-brain barrier (BBB) disruption are important contributors for cognitive impairment. Atorvastatin, as a strong HMG-CoA reductase inhibitor, has been widely used in clinic. However, it remains unclear whether atorvastatin could prevent anesthesia and surgery-induced BBB disruption and cognitive injury by its anti-inflammatory property. In this study, aged C57BL/6J mice were used to address this question. Initially, the mice were subject to atorvastatin treatment for 7 days (10 mg/kg). After a simple laparotomy under 1.5% isoflurane anesthesia, Morris water maze was performed to assess spatial learning and memory. Western blot analysis, immunohistochemistry, and enzyme-linked immunosorbent assay were used to examine the inflammatory response, BBB integrity, and cell apoptosis. Terminal-deoxynucleotidyl transferase mediated nick end labeling assay was used to assess cell apoptprotective effects on cognition in aged mice undergoing surgery.
  Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference.
 
  We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis.
 
  R function and instruction are available from the authors' website https//www4.stat.ncsu.edu/∼jytzeng/Software/TR.omics/TRinstruction.pdf.
 
  Supplementary materials are available at Bioinformatics online.
 Supplementary materials are available at Bioinformatics online.Radical cyclizations are essential reactions in the biosynthesis of secondary metabolites and the chemical synthesis of societally valuable molecules. In this review, we highlight the general mechanisms utilized in biocatalytic radical cyclizations. We specifically highlight cytochrome P450 monooxygenases (P450s) involved in the biosynthesis of mycocyclosin and vancomycin, nonheme iron- and α-ketoglutarate-dependent dioxygenases (Fe/αKGDs) used in the biosynthesis of kainic acid, scopolamine, and isopenicillin N, and radical S-adenosylmethionine (SAM) enzymes that facilitate the biosynthesis of oxetanocin A, menaquinone, and F420. Beyond natural mechanisms, we also examine repurposed flavin-dependent "ene"-reductases (ERED) for non-natural radical cyclization. Overall, these general mechanisms underscore the opportunity for enzymes to augment and enhance the synthesis of complex molecules using radical mechanisms.