The severe acute respiratory syndrome corona virus-2 (referred to as SARS CoV2) pandemic had a great impact on public life in general as well as on populations with preexisting disease and co-morbidities. Liver transplant and immunosuppressant medication predisposes to more severe disease and is often associated with poor outcome. The clinical features, disease course, treatment and process of modulating the immunosuppression is challenging. Here, we describe the clinical presentation, treatment and outcomes in six liver transplant recipients. Out of those six patients, three had mild, one had moderate and one had severe COVID-19, and one was asymptomatic. The immunosuppression minimization or withdrawal was done based upon the clinical severity. Consideration of tocilizumab and/o convalescent plasma as well as antivirals i.e. remdesvir done in severe cases. The routine practice of prophylactic anticoagulation, consideration of repurposed drugs (i.e. teicoplanin and doxycycline), and watchful monitoring of asymptomatic recipients helped to achieve an uneventful recovery.

Aims:The aim of this study was to correlate serum uric acid (SUA) levels and carotid intima-media thickness (CIMT) in patients with type 2 diabetes mellitus (DM).Settings and Design:This study was a cross-sectional observational study on 103 diabetic patients conducted from September 2015 to May 2017.Subjects and Methods:We screened 103 patients with type 2 DM between the ages of 30–65 years. SUA levels and the CIMT were measured. The patients were divided into quartiles based on uric acid level. The CIMT of the quartiles is compared and analyzed.Statistical Analysis Used:Chi-squared test, Analysis of Variance, and Pearson's correlation.Results:Uric acid levels were positively associated with CIMT (P = 0.001). The association remained significant after further adjustment for potential confounders. Strong correlation was found among them as depicted by correlation coefficient (r = 0.779).Conclusions:Carotid atherosclerosis as measured by IMT is associated with SUA levels in patients with type 2 DM.

Background and Aim: Risk stratification beyond the endoscopic classification of esophageal varices (EVs) to predict first episode of variceal bleeding (VB) is currently limited in patients with compensated advanced chronic liver disease (cACLD). We aimed to assess if machine learning (ML) could be used for predicting future VB more accurately. Methods: In this retrospective analysis, data from patients of cACLD with EVs, laboratory parameters and liver stiffness measurement (LSM) were used to generate an extreme-gradient boosting (XGBoost) algorithm to predict the risk of VB. The performance characteristics of ML and endoscopic classification were compared in internal and external validation cohorts. Bleeding rates were estimated in subgroups identified upon risk stratification with combination of model and endoscopic classification. Results: Eight hundred twenty-eight patients of cACLD with EVs, predominantly related to non-alcoholic fatty liver disease (28.6%), alcohol (23.7%) and hepatitis B (23.1%) were included, with 455 (55%) having the high-risk varices. Over a median follow-up of 24 (12-43) months, 163 patients developed VB. The accuracy of machine learning (ML) based model to predict future VB was 98.7 (97.4-99.5)%, 93.7 (88.8-97.2)%, and 85.7 (82.1-90.5)% in derivation (n = 497), internal validation (n = 149), and external validation (n = 182) cohorts, respectively, which was better than endoscopic classification [58.9 (55.5-62.3)%] alone. Patients stratified high risk on both endoscopy and model had 1-year and 3-year bleeding rates of 31-43% and 64-85%, respectively, whereas those stratified as low risk on both had 1-year and 3-year bleeding rates of 0-1.6% and 0-3.4%, respectively. Endoscopic classification and LSM were the major determinants of model's performance. Conclusion: Application of ML model improved the performance of endoscopic stratification to predict VB in patients with cACLD with EVs.

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