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BACKGROUND As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. OBJECTIVE We examine the factors that affect participants’ responsiveness to ecological momentary assessments (EMA) in a 10-day wearable and EMA-based affect sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the two constructs. METHODS We collected the data from 45 healthy participants wearing two devices measuring electrodermal activity, acceleration, electrocardiography, and skin temperature while answering 10 EMAs a day containing questions related to perceived mood. Due to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during a response. Therefore, we utilized unsupervised and supervised learning methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to find the relationship between physiological relationships and responsiveness then inferred the emotional state during non-responses. For the supervised learning method, we primarily used Random Forest (RF) and Neural Networks (NN) to predict affect of unlabeled data points. RESULTS Based on our findings we showed that using a receptivity model to trigger EMAs will decrease the reported negative affect by more than 3 points or 0.29 standard deviation using our psychological instrument scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during non-responses. CONCLUSIONS Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of a mHealth study, particularly those studies that employ a learning algorithm to trigger EMAs. Therefore, we propose a smart trigger that promotes EMA and JITI receptivity without influencing affect during sampled time points as future work.

Engineering microbes to synthesize molecules of societal value has historically been a time consuming and artisanal process, with the synthesis of each new non-native molecule typically warranting its own separate publication. Because most microbial strain engineering efforts leverage a finite number of common metabolic engineering design tactics, we reasoned that automating these design steps would help create a pipeline that can quickly, cheaply, and reliably generate so-called microbial factories. In this work we describe the design and implementation of a computational system, an Automated Scientist we call Lila, which handles all metabolic engineering design and optimization through the design-build-test-learn (DBTL) paradigm. Lila generates metabolic routes, identifies relevant genetic elements for perturbation, and specifies the design and re-design of microbial strains in a matter of seconds to minutes. Strains specified by Lila are then built and subsequently phenotyped as part of a largely automated in-house pipeline. Humans remain in-the-loop to curate choices made by the system, helping for example to refine the metabolic model or suggest custom protein modifications. Lila attempted to build strains that could produce 454 biochemically diverse molecules with precursors located broadly throughout the metabolism of two microbial hosts,Saccharomyces cerevisiaeandEscherichia coli. Notably, we observed the highest published titers for the molecule naringenin, the metabolic precursor to flavonoids. In total we created hundreds of thousands of microbial strains capable of overproducing 242 molecules, of which 180 are not native toS. cerevisiaeorE. coli.

SummaryImmCellFie is a user-friendly, web-based platform for comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. It enables researchers to leverage the powerful mechanistic insight provided by complex genome-scale metabolic models with little to no bioinformatics training required. The platform has been integrated with a series of useful tools and richly annotated scientific visualizations for interactive exploration by the user. ImmCellFie pushes beyond simple statistical enrichment and incorporates complex biological mechanisms to quantify cell activity.Graphical abstract

ObjectiveTo determine whether race is associated with the development of epilepsy after subdural hematoma (SDH), we identified adult survivors of SDH in a statewide administrative dataset and followed them up for at least 1 year for revisits associated with epilepsy.MethodsWe performed a retrospective cohort study using claims data on all discharges from emergency departments (EDs) and hospitals in California. We identified adults (age ≥18 years) admitted from 2005 to 2011 with first-time traumatic and nontraumatic SDH. We used validated diagnosis codes to identify a primary outcome of ED or inpatient revisit for epilepsy. We used multivariable Cox regression for survival analysis to identify demographic and medical risk factors for epilepsy.ResultsWe identified 29,342 survivors of SDH (mean age 71.2 [SD 16.4] years, female sex 11,954 [41.1%]). Three thousand two hundred thirty (11.0%) patients had revisits to EDs or hospitals with a diagnosis of epilepsy during the study period. Black patients (n = 1,684 [5.7%]) had significantly increased risk compared to White patients (n = 16,945 [57.7%]; hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.28–1.64, p < 0.001). Status epilepticus during the index SDH admission, although infrequent (n = 94 [0.3%]), was associated with a nearly 4-fold risk of epilepsy (HR 3.75, 95% CI 2.80–5.03, p < 0.001). Alcohol use, drug use, smoking, renal disease, and markers of injury severity (i.e., intubation, surgical intervention, length of stay, disposition other than home) were also associated with epilepsy (all p < 0.05).ConclusionsWe found an association between Black race and ED and hospital revisits for epilepsy after SDH, establishing the presence of a racial subgroup that is particularly vulnerable to post-SDH epileptogenesis.

Background Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. Results Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. Conclusions The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

ObjectiveTo estimate the risk of intracerebral hemorrhage (ICH) recurrence in a large, diverse, US-based population and to identify racial/ethnic and socioeconomic subgroups at higher risk.MethodsWe performed a longitudinal analysis of prospectively collected claims data from all hospitalizations in nonfederal California hospitals between 2005 and 2011. We used validated diagnosis codes to identify nontraumatic ICH and our primary outcome of recurrent ICH. California residents who survived to discharge were included. We used log-rank tests for unadjusted analyses of survival across racial/ethnic groups and multivariable Cox proportional hazards regression to determine factors associated with risk of recurrence after adjusting for potential confounders.ResultsWe identified 31,355 California residents with first-recorded ICH who survived to discharge, of whom 15,548 (50%) were white, 6,174 (20%) were Hispanic, 4,205 (14%) were Asian, and 2,772 (9%) were black. There were 1,330 recurrences (4.1%) over a median follow-up of 2.9 years (interquartile range 3.8). The 1-year recurrence rate was 3.0% (95% confidence interval [CI] 2.8%–3.2%). In multivariable analysis, black participants (hazard ratio [HR] 1.22; 95% CI 1.01–1.48; p = 0.04) and Asian participants (HR 1.29; 95% CI 1.10–1.50; p = 0.001) had a higher risk of recurrence than white participants. Private insurance was associated with a significant reduction in risk compared to patients with Medicare (HR 0.60; 95% CI 0.50–0.73; p < 0.001), with consistent estimates across racial/ethnic groups.ConclusionsBlack and Asian patients had a higher risk of ICH recurrence than white patients, whereas private insurance was associated with reduced risk compared to those with Medicare. Further research is needed to determine the drivers of these disparities.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome., Mechanistic insight into the regulation of transcriptional modules remains scarce. Here, the authors identify statistically independent gene sets by applying independent component analysis to a high-quality E. coli RNA-seq data compendium and find that most gene sets represent the effects of specific transcriptional regulators.

INTRODUCTION Short message service (SMS) is a widely accepted telecommunications approach used to support health informatics, including behavioral interventions, data collection, and patient-provider communication. However, SMS delivery platforms are not standardized and platforms are typically commercial "off-the-shelf" or developed "in-house." As a consequence of platform variability, implementing SMS-based interventions may be challenging for both providers and patients. Off-the-shelf SMS delivery platforms may require minimal development or technical resources from providers, but users are often limited in their functionality. Conversely, platforms that are developed in-house are often specified for individual projects, requiring specialized development and technical expertise. Patients are on the receiving end of programming and technical specification challenges; message delays or lagged data affect quality of SMS communications. To date, little work has been done to develop a generalizable SMS platform that can be scaled across health initiatives. OBJECTIVE We propose the Configurable Assessment Messaging Platform for Interventions (CAMPI) to mitigate challenges associated with SMS intervention implementation (e.g., programming, data collection, message delivery). METHOD CAMPI aims to optimize health data captured from a multitude of sources and enhance patient-provider communication through a technology that is simple and familiar to patients. Using representative examples from three behavioral intervention case studies implemented among diverse populations (pregnant women, young sexual minority men, and parents with young children), we describe CAMPI capabilities and feasibility. CONCLUSION As a generalizable SMS platform, CAMPI can be scaled to meet the priorities of various health initiatives, while reducing unnecessary resource utilization and burden on providers and patients. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Gene replacement and pre-mRNA splicing modifier therapies represent breakthrough gene targeting treatments for the neuromuscular disease spinal muscular atrophy (SMA), but mechanisms underlying variable efficacy of treatment are incompletely understood. Our examination of severe infantile onset human SMA tissues obtained at expedited autopsy revealed persistence of developmentally immature motor neuron axons, many of which are actively degenerating. We identified similar features in a mouse model of severe SMA, in which impaired radial growth and Schwann cell ensheathment of motor axons began during embryogenesis and resulted in reduced acquisition of myelinated axons that impeded motor axon function neonatally. Axons that failed to ensheath degenerated rapidly postnatally, specifically releasing neurofilament light chain protein into the blood. Genetic restoration of survival motor neuron protein (SMN) expression in mouse motor neurons, but not in Schwann cells or muscle, improved SMA motor axon development and maintenance. Treatment with small-molecule SMN2 splice modifiers beginning immediately after birth in mice increased radial growth of the already myelinated axons, but in utero treatment was required to restore axonal growth and associated maturation, prevent subsequent neonatal axon degeneration, and enhance motor axon function. Together, these data reveal a cellular basis for the fulminant neonatal worsening of patients with infantile onset SMA and identify a temporal window for more effective treatment. These findings suggest that minimizing treatment delay is critical to achieve optimal therapeutic efficacy.

Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering., The first genome‐scale metabolic model for the near‐minimal bacterium Mesoplasma florum is reported. Comparing the model‐driven prediction of a M. florum genome reduction scenario to a closely related minimal cell reveals key features of a minimal gene set.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

Background Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks. Results We built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform. Conclusions Escher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/.

High levels of stress during pregnancy increase the chances of having a premature or low-birthweight baby. Perceived self-reported stress does not often capture or align with the physiological and behavioral response. But what if there was a self-report measure that could better capture the physiological response? Current perceived stress self-report assessments require users to answer multi-item scales at different time points of the day. Reducing it to one question, using microinteraction-based ecological momentary assessment (micro-EMA, collecting a single in situ self-report to assess behaviors) allows us to identify smaller or more subtle changes in physiology. It also allows for more frequent responses to capture perceived stress while at the same time reducing burden on the participant. We propose a framework for selecting the optimal micro-EMA that combines unbiased feature selection and unsupervised Agglomerative clustering. We test our framework in 18 women performing 16 activities in-lab wearing a Biostamp, a NeuLog, and a Polar chest strap. We validated our results in 17 pregnant women in real-world settings. Our framework shows that the question "How worried were you?" results in the highest accuracy when using a physiological model. Our results provide further in-depth exposure to the challenges of evaluating stress models in real-world situations.

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BackgroundSwitchgrass (Panicum virgatumL.), a North American prairie grassland species, is a potential lignocellulosic biofuel feedstock owing to its wide adaptability and biomass production. Production and genetic manipulation of switchgrass should be useful to improve its biomass composition and production for bioenergy applications. The goal of this project was to develop a high-throughput stable switchgrass transformation method usingAgrobacterium tumefacienswith subsequent plant regeneration.ResultsRegenerable embryogenic cell suspension cultures were established from friable type II callus-derived inflorescences using two genotypes selected from the synthetic switchgrass variety ‘Performer’ tissue culture lines 32 and 605. The cell suspension cultures were composed of a heterogeneous fine mixture culture of single cells and aggregates.Agrobacterium tumefaciensstrain GV3101 was optimum to transfer into cells the pANIC-10A vector with a hygromycin-selectable marker gene and apporRFPorange fluorescent protein marker gene at an 85% transformation efficiency. Liquid cultures gave rise to embryogenic callus and then shoots, of which up to 94% formed roots. The resulting transgenic plants were phenotypically indistinguishable from the non-transgenic parent lines.ConclusionThe new cell suspension-based protocol enables high-throughputAgrobacterium-mediated transformation and regeneration of switchgrass in which plants are recovered within 6–7 months from culture establishment.

Glyburide (also known as glibenclamide) is a second-generation sulfonylurea drug that inhibits sulfonylurea receptor 1 (Sur1) at nanomolar concentrations. Long used to target KATP (Sur1–Kir6.2) channels for the treatment of diabetes mellitus type 2, glyburide was recently repurposed to target Sur1–transient receptor potential melastatin 4 (Trpm4) channels in acute central nervous system injury. Discovered nearly two decades ago, SUR1–TRPM4 has emerged as a critical target in stroke, specifically in large hemispheric infarction, which is characterized by edema formation and life-threatening brain swelling. Following ischemia, SUR1–TRPM4 channels are transcriptionally upregulated in all cells of the neurovascular unit, including neurons, astrocytes, microglia, oligodendrocytes and microvascular endothelial cells. Work by several independent laboratories has linked SUR1–TRPM4 to edema formation, with blockade by glyburide reducing brain swelling and death in preclinical models. Recent work showed that, following ischemia, SUR1–TRPM4 co-assembles with aquaporin-4 to mediate cellular swelling of astrocytes, which contributes to brain swelling. Additionally, recent work linked SUR1–TRPM4 to secretion of matrix metalloproteinase-9 (MMP-9) induced by recombinant tissue plasminogen activator in activated brain endothelial cells, with blockade of SUR1–TRPM4 by glyburide reducing MMP-9 and hemorrhagic transformation in preclinical models with recombinant tissue plasminogen activator. The recently completed GAMES (Glyburide Advantage in Malignant Edema and Stroke) clinical trials on patients with large hemispheric infarctions treated with intravenous glyburide (RP-1127) revealed promising findings with regard to brain swelling (midline shift), MMP-9, functional outcomes and mortality. Here, we review key elements of the basic science, preclinical experiments and clinical studies, both retrospective and prospective, on glyburide in focal cerebral ischemia and stroke.