Lifestyle Factors in Hypertension Drug Research: Systematic Analysis of Articles in a Leading Cochrane Report

Established standards for first-line hypertension management include lifestyle modification and behavior change. The degree to which and how lifestyle modification is systematically integrated into studies of first-line drug management for hypertension is of methodological and clinical relevance. This study systematically reviewed the methodology of articles from a recent Cochrane review that had been designed to inform first-line medical treatment of hypertension and was representative of high quality established clinical trials in the field. Source articles (n=34) were systematically reviewed for lifestyle interventions including smoking cessation, diet, weight loss, physical activity and exercise, stress reduction, and moderate alcohol consumption. 54% of articles did not mention lifestyle modification; 46% contained nonspecific descriptions of interventions. We contend that hypertension management research trials (including drug studies) need to elucidate the benefits and risks of drug-lifestyle interaction, to support the priority of lifestyle modification, and that lifestyle modification, rather than drugs, is seen by patients and the public as a priority for health professionals. The inclusion of lifestyle modification strategies in research designs for hypertension drug trials could enhance current research, from trial efficacy to clinical outcome effectiveness, and align hypertension best practices of a range of health professionals with evidence-based knowledge translation

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5‑HT₃A, Histamine H₁, and Histamine H₄ Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have <i>no</i> affinity for these proteins (<i>true inactives</i>). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H₁ receptor (H₁R) and the histamine H₄ receptor (H₄R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT₃AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different <i>combinations</i> of <i>group</i> fusion and <i>similarity</i> fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening