Membrane glucocorticoid receptors are localised in the extracellular matrix and signal through the MAPK pathway in mammalian skeletal muscle fibres

A number of studies have previously proposed the existence of glucocorticoid receptors on the plasma membrane of many cell types including skeletal muscle fibres. However, their exact localisation and the cellular signalling pathway(s) they utilise to communicate with the rest of the cell are still poorly understood. In this study, we investigated the localisation and the mechanism(s) underlying the non-genomic physiological functions of these receptors in mouse skeletal muscle cells. The results show that the receptors were localised in the cytoplasm in myoblasts, in the nucleus in myotubes and in the extracellular matrix, in satellite cells and in the proximity of mitochondria in adult muscle fibres. Also, they bound laminin in a glucocorticoid-dependent manner. Treating small skeletal muscle fibre bundles with the synthetic glucocorticoid, beclomethasone dipropionate, increased the phosphorylation (=activation) of extracellular-signal regulated kinase 1&2, c-Jun N-terminal kinase and p38 mitogen-activated protein kinase. This occurred within 5min and depended on the fibre-type and the duration of the treatment. It was also abolished by the glucocorticoid receptor inhibitor, mifepristone, and a monoclonal antibody against the receptor. From these results we conclude that the non-genomic/non-canonical physiological functions of glucocorticoids, in adult skeletal muscle fibres are mediated by a glucocorticoid receptor localised in the extracellular matrix, in satellite cells and close to mitochondria and involve activation of the MAPK pathway

Metacarpophalangeal pattern profile analysis of a sample drawn from a North Wales population

This is tha author's PDF version of an article published in Annals of human biology© 2001. The definitive version is available at http://www.tandf.co.uk/journalsSexual dimorphism and population differences were investigated using metacarpophalangeal pattern profile (MCPP) analysis. Although it is an anthropmetric technique, MCPP analysis is more frequently used in genetic syndrome analysis and has been under-used in the study of human groups. The present analysis used a series of hand radiographics from Gwynedd, North Wales, to make comparisons, first, between the sexes within the sample and then with previously reported data from Japan. The Welsh sexes showed MCPP analyses that indicated size and shape differences but certain similarities in shape were also evident. Differences with the Japanese data were more marked. MCPP anlysis is a potentially useful anthropmetric technique but requires further statistical development

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (Ambr(TM) 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up

Comment on 'A first map of tropical Africa's above-ground biomass derived from satellite imagery'

Copyright Institute of Physics © 2011We present a critical evaluation of the above-ground biomass (AGB) map of Africa published in this journal by Baccini et al (2008 Environ. Res. Lett. 3 045011). We first test their map against an independent dataset of 1154 scientific inventory plots from 16 African countries, and find only weak correspondence between our field plots and the AGB value given for the surrounding 1 km pixel by Baccini et al. Separating our field data using a continental landcover classification suggests that the Baccini et al map underestimates the AGB of forests and woodlands, while overestimating the AGB of savannas and grasslands. Secondly, we compare their map to 216 000 × 0.25 ha spaceborne LiDAR footprints. A comparison between Lorey's height (basal-area-weighted average height) derived from the LiDAR data for 1 km pixels containing at least five LiDAR footprints again does not support the hypothesis that the Baccini et al map is accurate, and suggests that it significantly underestimates the AGB of higher AGB areas. We conclude that this is due to the unsuitability of some of the field data used by Baccini et al to create their map, and overfitting in their model, resulting in low accuracies outside the small areas from which their field data are drawn

A computational model based on human performance for fluid management in critical care

Computational simulation is one of the most important ways of reproducing the dynamic responses of a Cyber Physical System using a model of the system. The simulation discovers areas of differential system performance and allows linking such performance back to system characteristics. In the medical domain, patient simulators are used to train physicians in patient management. One critical question is how to verify these systems under realistic human (physician) input. This requires the creation of realistic human models that would be able to capture human cognitive and decision abilities and limitations. Verification of such an overall physician-patient model would result in two advantages: (a) since physicians realistically would not give all possible inputs to the system, verification could be more efficient and (b) the verification may uncover areas of poor human performance. In this paper, we describe our methodology and results in creating a computational model of human fluid management in critical care, based on human experiments

Formulation and performance of variational integrators for rotating bodies

Variational integrators are obtained for two mechanical systems whose configuration spaces are, respectively, the rotation group and the unit sphere. In the first case, an integration algorithm is presented for Euler’s equations of the free rigid body, following the ideas of Marsden et al. (Nonlinearity 12:1647–1662, 1999). In the second example, a variational time integrator is formulated for the rigid dumbbell. Both methods are formulated directly on their nonlinear configuration spaces, without using Lagrange multipliers. They are one-step, second order methods which show exact conservation of a discrete angular momentum which is identified in each case. Numerical examples illustrate their properties and compare them with existing integrators of the literature

Long Term outcomes of percutaneous atrial fibrillation ablation in patients with continuous monitoring

INTRODUCTION: There is limited data using continuous monitoring to assess outcomes of atrial fibrillation (AF) ablation. This study assessed long-term outcomes of AF ablation in patients with implantable cardiac devices. METHODS: 207 patients (mean age 68.1 ± 9.5, 50.3% men) undergoing ablation for symptomatic AF were followed up for a mean period of 924.5 ± 636.7 days. Techniques included The Pulmonary Vein Ablation Catheter (PVAC) (59.4%), cryoablation (17.4%), point by point (14.0%) and The Novel Irrigated Multipolar Radiofrequency Ablation Catheter (nMARQ) (9.2%). RESULTS: 130 (62.8%) patients had paroxysmal AF (PAF) and 77 (37.2%) persistent AF. First ablation and repeat ablation reduced AF burden significantly (relative risk 0.91, [95% CI 0.89 to 0.94]; P <0.0001 and 0.90, [95% CI, 0.86-0.94]; P <0.0001). Median AF burden in PAF patients reduced from 1.05% (interquartile range [IQR], 0.1%-8.70%) to 0.10% ([IQR], 0%-2.28%) at one year and this was maintained out to four-years. Persistent AF burden reduced from 99.9% ([IQR], 51.53%-100%) to 0.30% ([IQR], 0%-77.25%) at one year increasing to 87.3% ([IQR], 4.25%-100%) after four years. If a second ablation was required, point-by-point ablation achieved greater reduction in AF burden (relative risk, 0.77 [95% CI, 0.65-0.91]; P <0.01). CONCLUSION: Ablation reduces AF burden both acutely and in the long-term. If a second ablation was required the point-by-point technique achieved greater reductions in AF burden than "single-shot" technologies. Persistent AF burden increased to near pre ablation levels by year 4 suggesting a different mechanism from PAF patients where this increase did not occur

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525