Objective To evaluate the effects of therapeutic heparin compared with prophylactic heparin among moderately ill patients with covid-19 admitted to hospital wards. Design Randomised controlled, adaptive, open label clinical trial. Setting 28 hospitals in Brazil, Canada, Ireland, Saudi Arabia, United Arab Emirates, and US. Participants 465 adults admitted to hospital wards with covid-19 and increased D-dimer levels were recruited between 29 May 2020 and 12 April 2021 and were randomly assigned to therapeutic dose heparin (n=228) or prophylactic dose heparin (n=237). Interventions Therapeutic dose or prophylactic dose heparin (low molecular weight or unfractionated heparin), to be continued until hospital discharge, day 28, or death. Main outcome measures The primary outcome was a composite of death, invasive mechanical ventilation, non-invasive mechanical ventilation, or admission to an intensive care unit, assessed up to 28 days. The secondary outcomes included all cause death, the composite of all cause death or any mechanical ventilation, and venous thromboembolism. Safety outcomes included major bleeding. Outcomes were blindly adjudicated. Results The mean age of participants was 60 years; 264 (56.8%) were men and the mean body mass index was 30.3 kg/m 2 . At 28 days, the primary composite outcome had occurred in 37/228 patients (16.2%) assigned to therapeutic heparin and 52/237 (21.9%) assigned to prophylactic heparin (odds ratio 0.69, 95% confidence interval 0.43 to 1.10; P=0.12). Deaths occurred in four patients (1.8%) assigned to therapeutic heparin and 18 patients (7.6%) assigned to prophylactic heparin (0.22, 0.07 to 0.65; P=0.006). The composite of all cause death or any mechanical ventilation occurred in 23 patients (10.1%) assigned to therapeutic heparin and 38 (16.0%) assigned to prophylactic heparin (0.59, 0.34 to 1.02; P=0.06). Venous thromboembolism occurred in two patients (0.9%) assigned to therapeutic heparin and six (2.5%) assigned to prophylactic heparin (0.34, 0.07 to 1.71; P=0.19). Major bleeding occurred in two patients (0.9%) assigned to therapeutic heparin and four (1.7%) assigned to prophylactic heparin (0.52, 0.09 to 2.85; P=0.69). Conclusions In moderately ill patients with covid-19 and increased D-dimer levels admitted to hospital wards, therapeutic heparin was not significantly associated with a reduction in the primary outcome but the odds of death at 28 days was decreased. The risk of major bleeding appeared low in this trial. Trial registration ClinicalTrials.gov NCT04362085 .

Background: SGLT2 (sodium-glucose cotransporter 2) inhibitors lower cardiovascular events in type 2 diabetes mellitus but whether they promote direct cardiac effects remains unknown. We sought to determine if empagliflozin causes a decrease in left ventricular (LV) mass in people with type 2 diabetes mellitus and coronary artery disease. Methods: Between November 2016 and April 2018, we recruited 97 individuals ≥40 and ≤80 years old with glycated hemoglobin 6.5% to 10.0%, known coronary artery disease, and estimated glomerular filtration rate ≥60mL/min/1.73m 2 . The participants were randomized to empagliflozin (10 mg/day, n=49) or placebo (n=48) for 6 months, in addition to standard of care. The primary outcome was the 6-month change in LV mass indexed to body surface area from baseline as measured by cardiac magnetic resonance imaging. Other measures included 6-month changes in LV end-diastolic and -systolic volumes indexed to body surface area, ejection fraction, 24-hour ambulatory blood pressure, hematocrit, and NT-proBNP (N-terminal pro b-type natriuretic peptide). Results: Among the 97 participants (90 men [93%], mean [standard deviation] age 62.8 [9.0] years, type 2 diabetes mellitus duration 11.0 [8.2] years, estimated glomerular filtration rate 88.4 [16.9] mL/min/1.73m 2 , LV mass indexed to body surface area 60.7 [11.9] g/m 2 ), 90 had evaluable imaging at follow-up. Mean LV mass indexed to body surface area regression over 6 months was 2.6 g/m 2 and 0.01 g/m 2 for those assigned empagliflozin and placebo, respectively (adjusted difference −3.35 g/m 2 ; 95% CI, −5.9 to −0.81g/m 2 , P =0.01). In the empagliflozin-allocated group, there was significant lowering of overall ambulatory systolic blood pressure (adjusted difference −6.8mmHg, 95% CI −11.2 to −2.3mmHg, P =0.003), diastolic blood pressure (adjusted difference −3.2mmHg; 95% CI, −5.8 to −0.6mmHg, P =0.02) and elevation of hematocrit ( P =0.0003). Conclusions: Among people with type 2 diabetes mellitus and coronary artery disease, SGLT2 inhibition with empagliflozin was associated with significant reduction in LV mass indexed to body surface area after 6 months, which may account in part for the beneficial cardiovascular outcomes observed in the EMPA-REG OUTCOME (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients) trial. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02998970.

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Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a fruitful area focused on processing text of various natural languages. We notice that binary code analysis and NLP share many analogical topics, such as semantics extraction, classification, and code/text comparison. This work thus borrows ideas from NLP to address two important code similarity comparison problems. (I) Given a pair of basic blocks of different instruction set architectures (ISAs), determining whether their semantics is similar; and (II) given a piece of code of interest, determining if it is contained in another piece of code of a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.Despite the evident importance of Problem I, existing solutions are either inefficient or imprecise. Inspired by Neural Machine Translation (NMT), which is a new approach that tackles text across natural languages very well, we regard instructions as words and basic blocks as sentences, and propose a novel cross-(assembly)-lingual deep learning approach to solving Problem I, attaining high efficiency and precision. Many solutions have been proposed to determine whether two pieces of code, e.g., functions, are equivalent (called the equivalence problem), which is different from Problem II (called the containment problem). Resolving the cross-architecture code containment problem is a new and more challenging endeavor. Employing our technique for crossarchitecture basic-block comparison, we propose the first solution to Problem II. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. The case studies applying the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to largescale binary code analysis.