Prospective individual patient data meta-analysis of two randomized trials on convalescent plasma for COVID-19 outpatients

Data on convalescent plasma (CP) treatment in COVID-19 outpatients are scarce. We aimed to assess whether CP administered during the first week of symptoms reduced the disease progression or risk of hospitalization of outpatients. Two multicenter, double-blind randomized trials (NCT04621123, NCT04589949) were merged with data pooling starting when = 50 years and symptomatic for <= 7days were included. The intervention consisted of 200-300mL of CP with a predefined minimum level of antibodies. Primary endpoints were a 5-point disease severity scale and a composite of hospitalization or death by 28 days. Amongst the 797 patients included, 390 received CP and 392 placebo; they had a median age of 58 years, 1 comorbidity, 5 days symptoms and 93% had negative IgG antibody-test. Seventy-four patients were hospitalized, 6 required mechanical ventilation and 3 died. The odds ratio (OR) of CP for improved disease severity scale was 0.936 (credible interval (CI) 0.667-1.311); OR for hospitalization or death was 0.919 (CI 0.592-1.416). CP effect on hospital admission or death was largest in patients with <= 5 days of symptoms (OR 0.658, 95%CI 0.394-1.085). CP did not decrease the time to full symptom resolution

FlexHH: A flexible hardware library for Hodgkin-Huxley-based neural simulations

In the field of computational neuroscience, complex mathematical models are used to replicate brain behavior with the goal of understanding the biological processes involved. The simulation of such models are computationally expensive and therefore, in recent years, high-performance computing systems have been identified as a possible solution to accelerate their execution. However, most of those implementations are model-specific and thus non-reusable for other modeling efforts, requiring a completely new development effort per model used. The challenge lies in offering high-performance and scalable libraries (so as to support the construction and simulation of large-scale brain models) while at the same time offering high degrees of modeling flexibility and parameterization. This thesis presents flexHH, a scalable hardware library implementing five accelerated and highly parameterizable instances of the Hodgkin-Huxley neuron model, one of the most widely used biophysically-meaningful neuron representations. As a result, the user is able to instantiate custom models using flexHH and immediately take advantage of the acceleration without the mediation of the engineer. The five flexHH implementations target the Maxeler Data-Flow Engine(DFE), an FPGA-based acceleration solution, and incrementally support a number of features such as custom ion channels, multiple cell compartments and inter-neuron gap-junction connectivity. Furthermore, for each of the five implementations it is possible to select either the forward-Euler, second, or third-order Runge-Kutta numerical method. A speedup between 14x-36x has been achieved compared to a sequential C implementation, when run on a 2.5-GHz Intel Core-i7 CPU, while no practical performance drop is observed when compared to a hard-coded version of a DFE, an Intel Xeon-Phi CPU, and a NVidia Titan X GPU. In this thesis, flexHH kernels are rigorously validated, an evaluation of the influence of the numerical methods is done, and a comprehensive resources usage, performance, and power-consumption evaluation of the various DFE implementations is presented

A novel simulator for extended Hodgkin-Huxley neural networks

Computational neuroscience aims to investigate and explain the behaviour and functions of neural structures, through mathematical models. Due to the models' complexity, they can only be explored through computer simulation. Modern research in this field is increasingly adopting large networks of neurons, and diverse, physiologically-detailed neuron models, based on the extended Hodgkin-Huxley (eHH) formalism. However, existing eHH simulators either support highly specific neuron models, or they provide low computational performance, making model exploration costly in time and effort. This work introduces a simulator for extended Hodgkin-Huxley neural networks, on multiprocessing platforms. This simulator supports a broad range of neuron models, while still providing high performance. Simulator performance is evaluated against varying neuron complexity parameters, network size and density, and thread-level parallelism. Results indicate performance is within existing literature for single-model eHH codes, and scales well for large CPU core counts. Ultimately, this application combines model flexibility with high performance, and can serve as a new tool in computational neuroscience.</p

High-Performance Hardware Accelerators for Solving Ordinary Differential Equations

Ordinary Differential Equations (ODEs) are widely used in many high-performance computing applications. However, contemporary processors generally provide limited throughput for these kinds of calculations. A high-performance hardware accelerator has been developed for speeding-up the solution of ODEs. The hardware accelerator has been developed both for single and double floating-point precision types and a design-space exploration has been performed in terms of performance and hardware resources. The hardware accelerator has been mapped to an FPGA board and connected through PCIe to a typical processor. The performance evaluation shows that the proposed scheme can achieve up to 14x speedup compared to a reference, single-core CPU solution

FlexHH: A Flexible Hardware Library for Hodgkin-Huxley-Based Neural Simulations

The Hodgkin-Huxley (HH) neuron is one of the most biophysically-meaningful models used in computational neuroscience today. Ironically, the model's high experimental value is offset by its disproportional computational complexity. To such an extent that neuroscientists have either resorted to simpler models, losing precious neuron detail, or to using high-performance computing systems, to gain acceleration, for complex models. However, multicore/multinode CPU-based systems have proven too slow while FPGA-based ones have proven too time-consuming to (re)deploy to. Clearly, a solution that bridges user friendliness and high speedups is necessary. This paper presents flexHH, a flexible FPGA library implementing five popular, highly parameterizable variants of the HH neuron model. flexHH is the first crucial step towards making FPGA-based simulations of compute-intensive neural models available to neuroscientists without the debilitating penalty of re-engineering and re-synthesis. Through flexHH, the user can instantiate custom models and immediately take advantage of the acceleration without the mediation of an engineer, which has proven to be a major inhibitor to full adoption of FPGAs in neuroscience labs. In terms of performance, flexHH achieves speedups between 8 × - 20 × compared to sequential-C implementations, while only a small drop in real-time capabilities is observed when compared to hardcoded FPGA-based versions of the models tested. Computer EngineeringNumerical AnalysisBio-Electronic

FlexHH: A Flexible Hardware Library for Hodgkin-Huxley-Based Neural Simulations

The Hodgkin-Huxley (HH) neuron is one of the most biophysically-meaningful models used in computational neuroscience today. Ironically, the model's high experimental value is offset by its disproportional computational complexity. To such an extent that neuroscientists have either resorted to simpler models, losing precious neuron detail, or to using high-performance computing systems, to gain acceleration, for complex models. However, multicore/multinode CPU-based systems have proven too slow while FPGA-based ones have proven too time-consuming to (re)deploy to. Clearly, a solution that bridges user friendliness and high speedups is necessary. This paper presents flexHH, a flexible FPGA library implementing five popular, highly

Chromosome 20 loss is characteristic of breast implant-associated anaplastic large cell lymphoma

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a very rare type of T-cell lymphoma that is uniquely caused by a single environmental stimulus. Here, we present a comprehensive genetic analysis of a relatively large series of BIA-ALCL (n = 29), for which genome-wide chromosomal copy number aberrations (CNAs) and mutational profiles for a subset (n = 7) were determined. For comparison, CNAs for anaplastic lymphoma kinase (ALK)- nodal anaplastic large cell lymphomas (ALCLs; n = 24) were obtained. CNAs were detected in 94% of BIA-ALCLs, with losses at chromosome 20q13.13 in 66% of the samples. Loss of 20q13.13 is characteristic of BIA-ALCL compared with other classes of ALCL, such as primary cutaneous ALCL and systemic type ALK+ and ALK- ALCL. Mutational patterns confirm that the interleukin-6-JAK1-STAT3 pathway is deregulated. Although this is commonly observed across various types of T-cell lymphomas, the extent of deregulation is significantly higher in BIA-ALCL, as indicated by phosphorylated STAT3 immunohistochemistry. The characteristic loss of chromosome 20 in BIA-ALCL provides further justification to recognize BIA-ALCL as a separate disease entity. Moreover, CNA analysis may serve as a parameter for future diagnostic assays for women with breast implants to distinguish seroma caused by BIA-ALCL from other causes of seroma accumulation, such as infection or trauma

Prehospital transdermal glyceryl trinitrate in patients with presumed acute stroke (MR ASAP): an ambulance-based, multicentre, randomised, open-label, blinded endpoint, phase 3 trial

BACKGROUND: Pooled analyses of previous randomised studies have suggested that very early treatment with glyceryl trinitrate (also known as nitroglycerin) improves functional outcome in patients with acute ischaemic stroke or intracerebral haemorrhage, but this finding was not confirmed in a more recent trial (RIGHT-2). We aimed to assess whether patients with presumed acute stroke benefit from glyceryl tr initrate started within 3 h after symptom onset. METHODS: MR ASAP was a phase 3, randomised, open-label, blinded endpoint trial done at six ambulance services serving 18 hospitals in the Netherlands. Eligible participants (aged ≥18 years) had a probable diagnosis of acute stroke (as assessed by a paramedic), a face-arm-speech-time test score of 2 or 3, systolic blood pressure of at least 140 mm Hg, and could start treatment within 3 h of symptom onset. Participants were randomly assigned (1:1) by ambulance personnel, using a secure web-based electronic application with random block sizes stratified by ambulance service, to receive either transdermal glyceryl trinitrate 5 mg/day for 24 h plus standard care (glyceryl trinitrate group) or to standard care alone (control group) in the prehospital setting. Informed consent was deferred until after arrival at the hospital. The primary outcome was functional outcome assessed with the modified Rankin Scale (mRS) at 90 days. Safety outcomes included death within 7 days, death within 90 days, and serious adverse events. Analyses were based on modified intention to treat, and treatment effects were expressed as odds ratios (ORs) or common ORs, with adjustment for baseline prognostic factors. We separately analysed the total population and the target population (ie, patients with intracerebral haemorrhage, ischaemic stroke, or transient ischaemic attack). The target sample size was 1400 patients. The trial is registered as ISRCTN99503308. FINDINGS: On June 24, 2021, the MR ASAP trial was prematurely terminated on the advice of the data and safety monitoring board, with recruitment stopped because of safety concerns in patients with intracerebral haemorrhage. Between April 4, 2018, and Feb 12, 2021, 380 patients were randomly allocated to a study group. 325 provided informed consent or died before consent could be obtained, of whom 170 were assigned to the glyceryl trinitrate group and 155 to the control group. These patients were included in the total population. 201 patients (62%) had ischaemic stroke, 34 (10%) transient ischaemic attack, 56 (17%) intracerebral haemorrhage, and 34 (10%) a stroke-mimicking condition. In the total population (n=325), the median mRS score at 90 days was 2 (IQR 1-4) in both the glyceryl trinitrate and control groups (adjusted common OR 0·97 [95% CI 0·65-1·47]). In the target population (n=291), the 90-day mRS score was 2 (2-4) in the glyceryl trinitrate group and 3 (1-4) in the control group (0·92 [0·59-1·43]). In the total population, there were no differences between the two study groups with respect to death within 90 days (adjusted OR 1·07 [0·53-2·14]) or serious adverse events (unadjusted OR 1·23 [0·76-1·99]). In patients with intracerebral haemorrhage, 12 (34%) of 35 patients allocated to glyceryl trinitrate versus two (10%) of 21 allocated to the control group died within 7 days (adjusted OR 5·91 [0·78-44·81]); death within 90 days occurred in 16 (46%) of 35 in the glyceryl trinitrate group and 11 (55%) of 20 in the control group (adjusted OR 0·87 [0·18-4·17]). INTERPRETATION: We found no sign of benefit of transdermal glyceryl trinitrate started within 3 h of symptom onset in the prehospital setting in patients with presumed acute stroke. The signal of potential early harm of glyceryl trinitrate in patients with intracerebral haemorrhage suggests that glyceryl trinitrate should be avoided in this setting. FUNDING: The Collaboration for New Treatments of Acute Stroke consortium, the Brain Foundation Netherlands, the Ministry of Economic Affairs, Stryker, Medtronic, Cerenovus, and the Dutch Heart Foundation