Changing trends in mortality among solid organ transplant recipients hospitalized for COVID-19 during the course of the pandemic

Mortality among patients hospitalized for COVID-19 has declined over the course of the pandemic. Mortality trends specifically in solid organ transplant recipients (SOTR) are unknown. Using data from a multicenter registry of SOTR hospitalized for COVID-19, we compared 28-day mortality between early 2020 (March 1, 2020–June 19, 2020) and late 2020 (June 20, 2020–December 31, 2020). Multivariable logistic regression was used to assess comorbidity-adjusted mortality. Time period of diagnosis was available for 1435/1616 (88.8%) SOTR and 971/1435 (67.7%) were hospitalized: 571/753 (75.8%) in early 2020 and 402/682 (58.9%) in late 2020 (p <.001). Crude 28-day mortality decreased between the early and late periods (112/571 [19.6%] vs. 55/402 [13.7%]) and remained lower in the late period even after adjusting for baseline comorbidities (aOR 0.67, 95% CI 0.46–0.98, p =.016). Between the early and late periods, the use of corticosteroids (≥6 mg dexamethasone/day) and remdesivir increased (62/571 [10.9%] vs. 243/402 [61.5%], p <.001 and 50/571 [8.8%] vs. 213/402 [52.2%], p <.001, respectively), and the use of hydroxychloroquine and IL-6/IL-6 receptor inhibitor decreased (329/571 [60.0%] vs. 4/492 [1.0%], p <.001 and 73/571 [12.8%] vs. 5/402 [1.2%], p <.001, respectively). Mortality among SOTR hospitalized for COVID-19 declined between early and late 2020, consistent with trends reported in the general population. The mechanism(s) underlying improved survival require further study

COVID-19 in hospitalized lung and non-lung solid organ transplant recipients: A comparative analysis from a multicenter study

Lung transplant recipients (LTR) with coronavirus disease 2019 (COVID-19) may have higher mortality than non-lung solid organ transplant recipients (SOTR), but direct comparisons are limited. Risk factors for mortality specifically in LTR have not been explored. We performed a multicenter cohort study of adult SOTR with COVID-19 to compare mortality by 28 days between hospitalized LTR and non-lung SOTR. Multivariable logistic regression models were used to assess comorbidity-adjusted mortality among LTR vs. non-lung SOTR and to determine risk factors for death in LTR. Of 1,616 SOTR with COVID-19, 1,081 (66%) were hospitalized including 120/159 (75%) LTR and 961/1457 (66%) non-lung SOTR (p =.02). Mortality was higher among LTR compared to non-lung SOTR (24% vs. 16%, respectively, p =.032), and lung transplant was independently associated with death after adjusting for age and comorbidities (aOR 1.7, 95% CI 1.0–2.6, p =.04). Among LTR, chronic lung allograft dysfunction (aOR 3.3, 95% CI 1.0–11.3, p =.05) was the only independent risk factor for mortality and age >65 years, heart failure and obesity were not independently associated with death. Among SOTR hospitalized for COVID-19, LTR had higher mortality than non-lung SOTR. In LTR, chronic allograft dysfunction was independently associated with mortality

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Mass transfer effects in fat interesterification reactions catalysed by immobilized lipase

SIGLEAvailable from British Library Document Supply Centre-DSC:DX201552 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.[GRAPHICS]

BioSimulators: A central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line&nbsp;and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML&nbsp;and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations