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Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-μm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

We present a novel algorithm combined with computational modeling to simulate the development of knee osteoarthritis. The degeneration algorithm was based on excessive and cumulatively accumulated stresses within knee joint cartilage during physiological gait loading. In the algorithm, the collagen network stiffness of cartilage was reduced iteratively if excessive maximum principal stresses were observed. The developed algorithm was tested and validated against experimental baseline and 4-year follow-up Kellgren-Lawrence grades, indicating different levels of cartilage degeneration at the tibiofemoral contact region. Test groups consisted of normal weight and obese subjects with the same gender and similar age and height without osteoarthritic changes. The algorithm accurately simulated cartilage degeneration as compared to the Kellgren-Lawrence findings in the subject group with excess weight, while the healthy subject group’s joint remained intact. Furthermore, the developed algorithm followed the experimentally found trend of cartilage degeneration in the obese group (R 2  = 0.95, p < 0.05; experiments vs. model), in which the rapid degeneration immediately after initiation of osteoarthritis (0–2 years, p < 0.001) was followed by a slow or negligible degeneration (2–4 years, p > 0.05). The proposed algorithm revealed a great potential to objectively simulate the progression of knee osteoarthritis.

In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the ‘constrained diffusional variance decomposition’ (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor. •Neuroimaging was performed with linear and spherical tensor encoding (LTE and STE) at 3T and 7T.•Microscopic anisotropy was quantified by two methods: NODDI and CODIVIDE.•NODDI predictions of microscopic anisotropy were not supported by STE data.•Levels of microscopic anisotropy were low in the cortex and high in the white matter.

Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research. [Display omitted]

Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R2=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10–16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical–experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response.

Long noncoding RNAs (lncRNAs) regulate gene expression by association with chromatin, but how they target chromatin remains poorly understood. We have used chromatin RNA immunoprecipitation-coupled high-throughput sequencing to identify 276 lncRNAs enriched in repressive chromatin from breast cancer cells. Using one of the chromatin-interacting lncRNAs, MEG3 , we explore the mechanisms by which lncRNAs target chromatin. Here we show that MEG3 and EZH2 share common target genes, including the TGF-β pathway genes. Genome-wide mapping of MEG3 binding sites reveals that MEG3 modulates the activity of TGF-β genes by binding to distal regulatory elements. MEG3 binding sites have GA-rich sequences, which guide MEG3 to the chromatin through RNA–DNA triplex formation. We have found that RNA–DNA triplex structures are widespread and are present over the MEG3 binding sites associated with the TGF-β pathway genes. Our findings suggest that RNA–DNA triplex formation could be a general characteristic of target gene recognition by the chromatin-interacting lncRNAs. Long noncoding RNAs (lncRNAs) regulate gene expression by association with chromatin. Here, the authors show that lncRNA MEG3 regulates the TGF-β pathway by bridging the interactions between polycomb repressive complex 2 and the distal regulatory elements of the TGF-β pathway genes via formation of RNA–DNA triplexes.

Solvents are commonly used in pharmaceutical and cosmetic formulations and sanitary products and cleansers. The uptake of solvent into the skin may change the molecular organization of skin lipids and proteins, which may in turn alter the protective skin barrier function. We herein examine the molecular effects of 10 different solvents on the outermost layer of skin, the stratum corneum (SC), using polarization transfer solid-state NMR on natural abundance 13C in intact SC. With this approach it is possible to characterize the molecular dynamics of solvent molecules when present inside intact SC and to simultaneously monitor the effects caused by the added solvent on SC lipids and protein components. All solvents investigated cause an increased fluidity of SC lipids, with the most prominent effects shown for the apolar hydrocarbon solvents and 2-propanol. However, no solvent other than water shows the ability to fluidize amino acids in the keratin filaments. The solvent molecules themselves show reduced molecular mobility when incorporated in the SC matrix. Changes in the molecular properties of the SC, and in particular alternation in the balance between solid and fluid SC components, may have significant influences on the macroscopic SC barrier properties as well as mechanical properties of the skin. Deepened understanding of molecular effects of foreign compounds in SC fluidity can therefore have strong impact on the development of skin products in pharmaceutical, cosmetic, and sanitary applications.

An improved understanding of the mechanical properties of human femurs is a milestone towards a more accurate assessment of fracture risk. Digital image correlation (DIC) has recently been adopted to provide full-field strain measurements during mechanical testing of femurs. However, it has typically been used to measure strains on the anterior side of the femur, whereas in both single-leg-stance and sideways fall loading conditions, the highest deformations result on the medial and lateral sides of the femoral neck. The goal of this study was to measure full-field deformations simultaneously on the medial and lateral side of the femoral neck in a configuration resembling a fall to the side. Twelve female cadaver femurs were prepared for DIC measurements and tested in sideways fall at 5 mm/s displacement rate. Two pairs of cameras recorded the medial and lateral side of the femoral neck, and deformations were calculated using DIC. The samples exhibited a two-stage failure: first, a compressive collapse on the superolateral side of the femoral neck in conjunction with peak force, followed by complete femoral neck fracture at the force drop following the post-elastic phase. DIC measurements corroborated this observation by reporting no tensile strains above yield limit for the medial side of the neck up to peak force. DIC measurements registered onto the bone micro-architecture showed strain localizations in proximity of cortical pores due to, for instance, blood vessels. This could explain previously reported discrepancies between simulations and experiments in regions rich with large pores, like the superolateral femoral neck.

Study designProspective quasi-experimental study, pre- and post-design.ObjectivesAssess safety, feasibility, training characteristics and changes in gait function for persons with spinal cord injury (SCI) using the robotic exoskeletons from Ekso Bionics.SettingNine European rehabilitation centres.MethodsRobotic exoskeleton gait training, three times weekly over 8 weeks. Time upright, time walking and steps in the device (training characteristics) were recorded longitudinally. Gait and neurological function were measured by 10 Metre Walk Test (10 MWT), Timed Up and Go (TUG), Berg Balance Scale (BBS), Walking Index for Spinal Cord Injury (WISCI) II and Lower Extremity Motor Score (LEMS).ResultsFifty-two participants completed the training protocol. Median age: 35.8 years (IQR 27.5–52.5), men/women: N = 36/16, neurological level of injury: C1-L2 and severity: AIS A–D (American Spinal Injury Association Impairment Scale). Time since injury (TSI) < 1 year, N = 25; > 1 year, N = 27.No serious adverse events occurred. Three participants dropped out following ankle swelling (overuse injury). Four participants sustained a Category II pressure ulcer at contact points with the device but completed the study and skin normalized. Training characteristics increased significantly for all subgroups. The number of participants with TSI < 1 year and gait function increased from 20 to 56% (P = 0.004) and 10MWT, TUG, BBS and LEMS results improved (P < 0.05). The number of participants with TSI > 1 year and gait function, increased from 41 to 44% and TUG and BBS results improved (P < 0.05).ConclusionsExoskeleton training was generally safe and feasible in a heterogeneous sample of persons with SCI. Results indicate potential benefits on gait function and balance.