Efficacy and safety of hydroxychloroquine for treatment of mild SARS-CoV-2 infection and prevention of COVID-19 severity in pregnant and postpartum women: A randomized, double-blind, placebo-controlled trial.

INTRODUCTION: Pregnant women have an increased risk of severe COVID-19. Evaluation of drugs with a safety reproductive toxicity profile is a priority. At the beginning of the pandemic, hydroxychloroquine (HCQ) was recommended for COVID-19 treatment. MATERIAL AND METHODS: A randomized, double-blind, placebo-controlled clinical trial was conducted in eight teaching hospitals in Spain to evaluate the safety and efficacy of HCQ in reducing viral shedding and preventing COVID-19 progression. Pregnant and postpartum women with a positive SARS-CoV-2 PCR (with or without mild COVID-19 signs/symptoms) and a normal electrocardiogram were randomized to receive either HCQ orally (400 mg/day for 3 days and 200 mg/day for 11 days) or placebo. PCR and electrocardiogram were repeated at day 21 after treatment start. Enrollment was stopped before reaching the target sample due to low recruitment rate. Trial registration EudraCT #: 2020-001587-29, on April 2, 2020. CLINICAL TRIALS: gov # NCT04410562, registered on June 1, 2020. RESULTS: A total of 116 women (75 pregnant and 41 post-partum) were enrolled from May 2020 to June 2021. The proportion of women with a positive SARS-CoV-2 PCR at day 21 was lower in the HCQ group (21.8%, 12/55) than in the placebo group (31.6%, 18/57), although the difference was not statistically significant (P = 0.499). No differences were observed in COVID-19 progression, adverse events, median change in QTc, hospital admissions, preeclampsia or poor pregnancy and perinatal outcomes between groups. CONCLUSIONS: HCQ was found to be safe in pregnant and postpartum women with asymptomatic or mild SARS-CoV-2 infection. Although the prevalence of infection was decreased in the HCQ group, the statistical power was insufficient to confirm the potential beneficial effect of HCQ for COVID-19 treatment.

Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI.

Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.

Synthetic MRI improves radiomics-based glioblastoma survival prediction.

Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and ≤ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.

A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data.

BACKGROUND AND OBJECTIVE: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as a bridge between qualitative and quantitative MRI. However, the proposed methods require very specific sequences or private protocols which have scarcely found integration in clinical scanners. We propose a learning-based approach to compute T1, T2, and PD parametric maps from only a pair of T1- and T2-weighted images customarily acquired in the clinical routine. METHODS: Our approach is based on a convolutional neural network (CNN) trained with synthetic data; specifically, a synthetic dataset with 120 volumes was constructed from the anatomical brain model of the BrainWeb tool and served as the training set. The CNN learns an end-to-end mapping function to transform the input T1- and T2-weighted images to their underlying T1, T2, and PD parametric maps. Then, conventional weighted images unseen by the network are analytically synthesized from the parametric maps. The network can be fine tuned with a small database of actual weighted images and maps for better performance. RESULTS: This approach is able to accurately compute parametric maps from synthetic brain data achieving normalized squared error values predominantly below 1%. It also yields realistic parametric maps from actual MR brain acquisitions with T1, T2, and PD values in the range of the literature and with correlation values above 0.95 compared to the T1 and T2 maps obtained from relaxometry sequences. Further, the synthesized weighted images are visually realistic; the mean square error values are always below 9% and the structural similarity index is usually above 0.90. Network fine tuning with actual maps improves performance, while training exclusively with a small database of actual maps shows a performance degradation. CONCLUSIONS: These results show that our approach is able to provide realistic parametric maps and weighted images out of a CNN that (a) is trained with a synthetic dataset and (b) needs only two inputs, which are in turn obtained from a common full-brain acquisition that takes less than 8 min of scan time. Although a fine tuning with actual maps improves performance, synthetic data is crucial to reach acceptable performance levels. Hence, we show the utility of our approach for both quantitative MRI in clinical viable times and for the synthesis of additional weighted images to those actually acquired.

A clinically viable vendor-independent and device-agnostic solution for accelerated cardiac MRI reconstruction.

BACKGROUND AND OBJECTIVE: Recent research has reported methods that reconstruct cardiac MR images acquired with acceleration factors as high as 15 in Cartesian coordinates. However, the computational cost of these techniques is quite high, taking about 40 min of CPU time in a typical current machine. This delay between acquisition and final result can completely rule out the use of MRI in clinical environments in favor of other techniques, such as CT. In spite of this, reconstruction methods reported elsewhere can be parallelized to a high degree, a fact that makes them suitable for GPU-type computing devices. This paper contributes a vendor-independent, device-agnostic implementation of such a method to reconstruct 2D motion-compensated, compressed-sensing MRI sequences in clinically viable times. METHODS: By leveraging our OpenCLIPER framework, the proposed system works in any computing device (CPU, GPU, DSP, FPGA, etc.), as long as an OpenCL implementation is available, and development is significantly simplified versus a pure OpenCL implementation. In OpenCLIPER, the problem is partitioned in independent black boxes which may be connected as needed, while device initialization and maintenance is handled automatically. Parallel implementations of both a groupwise FFD-based registration method, as well as a multicoil extension of the NESTA algorithm have been carried out as processes of OpenCLIPER. Our platform also includes significant development and debugging aids. HIP code and precompiled libraries can be integrated seamlessly as well since OpenCLIPER makes data objects shareable between OpenCL and HIP. This also opens an opportunity to include CUDA source code (via HIP) in prospective developments. RESULTS: The proposed solution can reconstruct a whole 12-14 slice CINE volume acquired in 19-32 coils and 20 phases, with an acceleration factor of ranging 4-8, in a few seconds, with results comparable to another popular platform (BART). If motion compensation is included, reconstruction time is in the order of one minute. CONCLUSIONS: We have obtained clinically-viable times in GPUs from different vendors, with delays in some platforms that do not have correspondence with its price in the market. We also contribute a parallel groupwise registration subsystem for motion estimation/compensation and a parallel multicoil NESTA subsystem for l1-l2-norm problem solving.

OpenCLIPER: An OpenCL-Based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices.

Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU, and others), which may hinder developers from using them. This paper describes an OpenCL-based framework that is capable of handling dedicated computing devices seamlessly and that allows the developer to concentrate on image processing tasks. The framework handles automatically device discovery and initialization, data transfers to and from the device and the file system and kernel loading and compiling. Data structures need to be defined only once independently of the computing device; code is unique, consequently, for every device, including the host CPU. Pinned memory/buffer mapping is used to achieve maximum performance in data transfers. Code fragments included in the paper show how the computing device is almost immediately and effortlessly available to the users algorithms, so they can focus on productive work. Code required for device selection and initialization, data loading and streaming and kernel compilation is minimal and systematic. Algorithms can be thought of as mathematical operators (called processes), with input, output and parameters, and they may be chained one after another easily and efficiently. Also for efficiency, processes can have their initialization work split from their core workload, so process chains and loops do not incur in performance penalties. Algorithm code is independent of the device type targeted.

Global transcriptional response of Escherichia coli MG1655 cells exposed to the oxygenated monoterpenes citral and carvacrol.

DNA microarrays were used to study the mechanism of bacterial inactivation by carvacrol and citral. After 10-min treatments of Escherichia coli MG1655 cells with 100 and 50ppm of carvacrol and citral, 76 and 156 genes demonstrated significant transcriptional differences (p≤0.05), respectively. Among the up-regulated genes after carvacrol treatment, we found gene coding for multidrug efflux pumps (acrA, mdtM), genes related to phage shock response (pspA, pspB, pspC, pspD, pspF and pspG), biosynthesis of arginine (argC, argG, artJ), and purine nucleotides (purC, purM). In citral-treated cells, transcription of purH and pyrB and pyrI was 2 times higher. Deletion of several differentially expressed genes confirmed the role of ygaV, yjbO, pspC, sdhA, yejG and ygaV in the mechanisms of E. coli inactivation by carvacrol and citral. These results would indicate that citral and carvacrol treatments cause membrane damage and activate metabolism through the production of nucleotides required for DNA and RNA synthesis and metabolic processes. Comparative transcriptomics of the response of E. coli to a heat treatment, which caused a significant change of the transcription of 1422 genes, revealed a much weaker response to both individual constituents of essential oils (ICs).·Thus, inactivation by citral or carvacrol was not multitarget in nature.

Rapid genetic diversification and high fitness penalties associated with pathogenicity evolution in a plant virus.

Under the gene-for-gene model of host-pathogen coevolution, recognition of pathogen avirulence factors by host resistance factors triggers host defenses and limits infection. Theory predicts that the evolution of higher levels of pathogenicity will be associated with fitness penalties and that the cost of higher pathogenicity must be much smaller than that of not infecting the host. The analysis of pathogenicity costs is of academic and applied relevance, as these are determinants for the success of resistance genes bred into crops for disease control. However, most previous attempts of addressing this issue in plant pathogens yielded conflicting and inconclusive results. We have analyzed the costs of pathogenicity in pepper-infecting tobamoviruses defined by their ability to infect pepper plants with different alleles at the resistance locus L. We provide conclusive evidence of pathogenicity-associated costs by comparison of pathotype frequency with the fraction of the crop carrying the various resistance alleles, by timescaled phylogenies, and by temporal analyses of population dynamics and selection pressures using nucleotide sequences. In addition, experimental estimates of relative fitness under controlled conditions also provided evidence of high pathogenicity costs. These high pathogenicity costs may reflect intrinsic properties of plant virus genomes and should be considered in future models of host-parasite coevolution.

Liquid-vapor equilibrium of the systems butylmethylimidazolium nitrate-CO2 and hydroxypropylmethylimidazolium nitrate-CO2 at high pressure: influence of water on the phase behavior.

Ionic liquids (IL) are receiving increasing attention due to their potential as "green" solvents, especially when used in combination with SC-CO2. In this work liquid-vapor equilibria of binary mixtures of CO2 with two imidazolium-based ionic liquids (IL) with a nitrate anion have been experimentally determined: butylmethylimidazolium nitrate (BMImNO3) and hydroxypropylmethylimidazolium nitrate (HOPMImNO3), using a Cailletet apparatus that operates according to the synthetic method. CO2 concentrations from 5 up to 30 mol % were investigated. It was found that CO2 is substantially less soluble in HOPMImNO3 than in BMImNO3. Since these ILs are very hygroscopic, water easily can be a major contaminant, causing changes in the phase behavior. In case these Ils are to be used in practical applications, for instance, together with CO2 as a medium in supercritical enzymatic reactions, it is very important to have quantitative information on how the water content will affect the phase behavior. This work presents the first systematic study on the influence of water on the solubility of carbon dioxide in hygroscopic ILs. It was observed that the presence of water reduces the absolute solubility of CO2. However, at fixed ratios of CO2/IL, the bubble point pressure remains almost unchanged with increasing water content. In order to explain the experimental results, the densities of aqueous mixtures of both ILs were determined experimentally and the excess molar volumes calculated.

Tabaquismo: una adicción que nos concierne / Smoking: an addiction that concerns all of us

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