Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Pharmacokinetics of Intravenous and Oral Phenobarbital Sodium in Healthy Goats.

Phenobarbital is a common drug used to manage epilepsy in goats. However, the recommended dose and dosing frequency are based on studies in dogs and horses. Studies describing the pharmacokinetics of phenobarbital when administered orally and assessing changes in behavior with concurrent electroencephalogram (EEG) readings are warranted in goats. The objectives of this study were to determine the bioavailability of orally administered phenobarbital and determine the effect of phenobarbital on brain activity using EEG in healthy goats. A cross-over design with 8 non-pregnant goats was performed. The goats were administered phenobarbital intravenously at 10 mg/kg, followed by a 2 week wash out period, and then administered phenobarbital, orally, at 10 mg/kg. Plasma sample determination of phenobarbital concentrations were collected at 13 time points. Continuous EEG readings with simultaneous video recording for 12 h was performed to determine the state of vigilance using a behavior scoring system prior to and after phenobarbital administration. Bioavailability of phenobarbital was 24.9%. Mean ± SD for half-life was similar between the oral (3.80 ± 0.826 h) and intravenous (4.0 ± 0.619 h) routes. Time to observed maximum concentration (Tmax), and maximum plasma concentration (Cmax) for the oral administration were 1.75 ± 0.46 h and 4,478.7 ± 962.4 ng/mL, respectively. Clearance was 152.5 ± 102.7 ml/h/kg. Area under the curve from zero to infinity (AUC0→∞) was 155,813 ± 218,448 and 38,763 ± 9,832 h*ng/mL for the intravenous and oral administration routes, respectively. Behavior score at 3 h after phenobarbital administration was different (P = 0.0002) from the score prior to administration for the oral administration route. In contrast, behavior scores before administration of phenobarbital and each time point after administration were not different (P >0.05) for the intravenous administration route or other oral administration route time points. Bioavailability of phenobarbital was poor, and the half-life was very short due to a high clearance. Doses >10 mg/kg should be considered when phenobarbital is administered orally in goats

Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management

American College of Clinical Pharmacy Global Health Practice and Research Network\u27s opinion paper: Pillars for global health engagement and key engagement strategies for pharmacists

The scope of pharmacy practice in global health has expanded over the past decade creating additional education and training opportunities for students, residents and pharmacists. There has also been a shift from short-term educational and clinical experiences to more sustainable bidirectional partnerships between high-income countries (HICs) and low- to middle-income countries (LMICs). As more institutional and individual partnerships between HICs and LMICs begin to form, it is clear that there is a lack of guidance for pharmacists on how to build meaningful, sustainable, and mutually beneficial programs. The aim of this paper is to provide guidance for pharmacists in HICs to make informed decisions on global health partnerships and identify opportunities for engagement in LMICs that yield mutually beneficial collaborations. This paper uses the foundations of global health principles to identify five pillars of global health engagement when developing partnerships: (a) sustainability, (b) shared leadership, (c) mutually beneficial partnerships, (d) local needs-based care and (e) host-driven experiential and didactic education. Finally, this paper highlights ways pharmacists can use the pillars as a framework to engage and support health care systems, collaborate with academic institutions, conduct research, and interface with governments to improve health policy

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months