Virtual Texture Generated using Elastomeric Conductive Block Copolymer in Wireless Multimodal Haptic Glove.

Haptic devices are in general more adept at mimicking the bulk properties of materials than they are at mimicking the surface properties. This paper describes a haptic glove capable of producing sensations reminiscent of three types of near-surface properties: hardness, temperature, and roughness. To accomplish this mixed mode of stimulation, three types of haptic actuators were combined: vibrotactile motors, thermoelectric devices, and electrotactile electrodes made from a stretchable conductive polymer synthesized in our laboratory. This polymer consisted of a stretchable polyanion which served as a scaffold for the polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT). The scaffold was synthesized using controlled radical polymerization to afford material of low dispersity, relatively high conductivity (0.1 S cm-1), and low impedance relative to metals. The glove was equipped with flex sensors to make it possible to control a robotic hand and a hand in virtual reality (VR). In psychophysical experiments, human participants were able to discern combinations of electrotactile, vibrotactile, and thermal stimulation in VR. Participants trained to associate these sensations with roughness, hardness, and temperature had an overall accuracy of 98%, while untrained participants had an accuracy of 85%. Sensations could similarly be conveyed using a robotic hand equipped with sensors for pressure and temperature

Dynamic Density Functional Theory of Multicomponent Cellular Membranes

We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This framework represents a paradigm shift by enabling simulations that can access cellular length-scales ($\mu$m) and time-scales on the order of seconds, all while maintaining near-fidelity to the underlying MD models. Membrane interactions with RAS, a potentially oncogenic protein, are considered as an application. Simulation results are presented and verified with MD simulations, and implications of this new capability are discussed

Estimating minimally important differences for the PROMIS pain interference scales: results from 3 randomized clinical trials

Minimally important difference (MID) refers to the smallest meaningful difference that carries implications for patient care. Minimally important differences are necessary to help interpret patient-reported pain outcomes in research and clinical practice. The PROMIS pain interference scales were validated across diverse samples; however, more information about their MIDs could improve their interpretability. The purpose of this study was to estimate MIDs for 4 fixed-length PROMIS pain interference scales, including the 6-item Pain Short Form and the 4-, 6-, and 8-item pain interference scales used in the PROMIS profile instruments. Data were analyzed from 3 randomized controlled trials (N = 759). The 3 samples, respectively, consisted of patients with chronic low back pain (n = 261), chronic back pain or hip/knee osteoarthritis pain (n = 240), and a history of stroke (n = 258). For each sample, anchor- and distribution-based approaches were used to estimate MIDs. Standard error of measurement and effect sizes were used as distribution-based MID estimates. Anchor-based MID estimates were established by mapping PROMIS pain interference scores onto established anchor measures, including the Brief Pain Inventory, and retrospective and prospective global ratings of change. The distribution- and anchor-based MID estimates showed convergence. For the pain samples, MID estimates ranged from 2 to 3 T-score points. For the nonpain sample, MID estimates ranged from 3.5 to 4.5 T-score points. The MID estimates were comparable across the 4 fixed-length scales. These MIDs can be used to evaluate treatment effects in research and clinical care and to calculate estimates for powering clinical trials

Breast MRI Utilization in Older Patients with Newly Diagnosed Breast Cancer

Recently, use of advanced imaging modalities, such as MRI, has increased dramatically. One novel but still evolving use for MRI is in the diagnosis and clinical staging of newly diagnosed breast cancer patients. Compared with mammography, MRI is more sensitive, but less specific, and far more expensive. The purpose of this study is to examine the prevalence and predictors of MRI use for clinical staging in older women with newly diagnosed breast cancer

Global health education in U.S. medical schools.

Interest in global health (GH) among medical students worldwide is measurably increasing. There is a concomitant emphasis on emphasizing globally-relevant health professions education. Through a structured literature review, expert consensus recommendations, and contact with relevant professional organizations, we review the existing state of GH education in US medical schools for which data were available. Several recommendations from professional societies have been developed, along with a renewed emphasis on competencies in global health. The implementation of these recommendations was not observed as being uniform across medical schools, with variation noted in the presence of global health curricula. Recommendations for including GH in medical education are suggested, as well as ways to formalize GH curricula, while providing flexibility for innovation and adaptation.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Computational Lipidomics of the Neuronal Plasma Membrane

Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades