Background World Health Organization expert groups recommended mortality trials of four repurposed antiviral drugs — remdesivir, hydroxychloroquine, lopinavir, and interferon beta-1a — in patients hospitalized with coronavirus disease 2019 (Covid-19). Methods We randomly assigned inpatients with Covid-19 equally between one of the trial drug regimens that was locally available and open control (up to five options, four active and the local standard of care). The intention-to-treat primary analyses examined in-hospital mortality in the four pairwise comparisons of each trial drug and its control (drug available but patient assigned to the same care without that drug). Rate ratios for death were calculated with stratification according to age and status regarding mechanical ventilation at trial entry. Results At 405 hospitals in 30 countries, 11,330 adults underwent randomization; 2750 were assigned to receive remdesivir, 954 to hydroxychloroquine, 1411 to lopinavir (without interferon), 2063 to interferon (including 651 to interferon plus lopinavir), and 4088 to no trial drug. Adherence was 94 to 96% midway through treatment, with 2 to 6% crossover. In total, 1253 deaths were reported (median day of death, day 8; interquartile range, 4 to 14). The Kaplan–Meier 28-day mortality was 11.8% (39.0% if the patient was already receiving ventilation at randomization and 9.5% otherwise). Death occurred in 301 of 2743 patients receiving remdesivir and in 303 of 2708 receiving its control (rate ratio, 0.95; 95% confidence interval [CI], 0.81 to 1.11; P=0.50), in 104 of 947 patients receiving hydroxychloroquine and in 84 of 906 receiving its control (rate ratio, 1.19; 95% CI, 0.89 to 1.59; P=0.23), in 148 of 1399 patients receiving lopinavir and in 146 of 1372 receiving its control (rate ratio, 1.00; 95% CI, 0.79 to 1.25; P=0.97), and in 243 of 2050 patients receiving interferon and in 216 of 2050 receiving its control (rate ratio, 1.16; 95% CI, 0.96 to 1.39; P=0.11). No drug definitely reduced mortality, overall or in any subgroup, or reduced initiation of ventilation or hospitalization duration. Conclusions These remdesivir, hydroxychloroquine, lopinavir, and interferon regimens had little or no effect on hospitalized patients with Covid-19, as indicated by overall mortality, initiation of ventilation, and duration of hospital stay. (Funded by the World Health Organization; ISRCTN Registry number, ISRCTN83971151 ; ClinicalTrials.gov number, NCT04315948 .)

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We identified 5 risk factors for nocardiosis after SOT. Low-dose cotrimoxazole was not found to prevent Nocardia infection. These findings may help improve management of transplant recipients.

NCT00817908.

Community-acquired RVIs are frequently detected in BAL samples from lung transplant patients. In a significant percentage of patients, symptomatic or asymptomatic viral infection is a trigger for acute rejection and obliterative bronchiolitis/BOS.

Late-onset cytomegalovirus (CMV) disease commonly occurs after discontinuation of antiviral prophylaxis. We determined the utility of testing CD8+ T-cell response against CMV as a predictor of late-onset CMV disease after a standard course of antiviral prophylaxis. Transplant patients at high-risk for CMV disease were enrolled. CD8+ T-cell-mediated immunity (CMI) was tested using the QuantiFERON-CMV assay at baseline, 1, 2 and 3 months posttransplant by measurement of interferon-c response to whole blood stimulation with a 21-peptide pool. The primary outcome was the ability of CMI testing to predict CMV disease in the first 6 months posttransplant. There were 108 evaluable patients (D+/R+ n = 39; D-/R+ n = 34; D+/R-n = 35) of whom 18 (16.7%) developed symptomatic CMV disease. At the end of prophylaxis, CMI was detectable in 38/108 (35.2%) patients (cutoff 0.1 IU/mL interferonc ). CMV disease occurred in 2/38 (5.3%) patients with a detectable interferon-c response versus 16/70 (22.9%) patients with a negative response; p = 0.038. In the subgroup of D+/R-patients, CMV disease occurred in 1/10 (10.0%) patients with a detectable interferon-c response (cutoff 0.1 IU/mL) versus 10/25 (40.0%) patients with a negative CMI, p = 0.12. Monitoring of CMI may be useful for predicting late-onset CMV disease.

Software updates typically require stopping and restarting an application, but many systems cannot afford to halt service, or would prefer not to. Dynamic software updating (DSU) addresses this difficulty by permitting programs to be updated while they run. DSU is appealing compared to other approaches for on-line upgrades because it is quite general and requires no redundant hardware. The challenge is in making DSU practical: it should be flexible, and yet safe, efficient, and easy to use.In this paper, we present a DSU implementation for C that aims to meet this challenge. We compile programs specially so that they can be dynamically patched, and generate most of a dynamic patch automatically. Our compiler performs a series of analyses that when combined with some simple runtime support ensure that an update will not violate type-safety while guaranteeing that data is kept up-to-date. We have used our system to construct and dynamically apply patches to three substantial open-source server programs-Very Secure FTP daemon, OpenSSH daemon, and GNU Zebra. In total, we dynamically patched each program with three years' worth of releases. Though the programs changed substantially, the majority of updates were easy to generate. Performance experiments show that all patches could be applied in less than 5 ms, and that the overhead on application throughput due to updating support ranged from 0 to at most 32%.

Progress in testing requires that we evaluate the effectiveness of testing strategies on the basis of hard experimental evidence, not just intuition or a priori arguments. Random testing, the use of randomly generated test data, is an example of a strategy that the literature often deprecates because of such preconceptions. This view is worth revisiting since random testing otherwise offers several attractive properties: simplicity of implementation, speed of execution, absence of human bias.We performed an intensive experimental analysis of the efficiency of random testing on an existing industrial-grade code base. The use of a large-scale cluster of computers, for a total of 1500 hours of CPU time, allowed a fine-grain analysis of the individual effect of the various parameters involved in the random testing strategy, such as the choice of seed for a random number generator. The results provide insights into the effectiveness of random testing and a number of lessons for testing researchers and practitioners.