Muellerterrell2685

ely reduce the mortality rate of severe/critical COVID-19 and improve the cure rate.The suprachiasmatic nucleus (SCN) is the master circadian pacemaker that drives body temperature rhythm. Time-restricted feeding (TRF) has potential as a preventative or therapeutic approach against many diseases. The potential side effects of TRF remain unknown. Here we show that a 4-hour TRF stimulus in mice can severely impair body temperature homeostasis and can result in lethality. Nearly half of the mice died at 21 °C, and all mice died at 18 °C during 4-hour TRF. Moreover, this effect was modulated by the circadian clock and was associated with severe hypothermia due to loss of body temperature homeostasis, which is different from "torpor", an adaptive response under food deprivation. Disrupting the circadian clock by the SCN lesions or a non-invasive method (constant light) which disrupts circadian clock rescued lethality during TRF. Analysis of circadian gene expression in the dorsomedial hypothalamus (DMH) demonstrated that TRF reprograms rhythmic transcriptome in DMH and suppresses expression of genes, such as Ccr5 and Calcrl, which are involved in thermoregulation. We demonstrate a side effect of 4-hour TRF on the homeostasis of body temperature and a rescue function by impairing the SCN function. Altogether, our results suggested that constructing a circadian arrhythmicity may have a beneficial effect on the host response to an acute stress.Automated recognition of daily human tasks is a novel method for continuous monitoring of the health of elderly people. Nowadays mobile devices (i.e. smartphone and smartwatch) are equipped with a variety of sensors, therefore activity classification algorithms have become as useful, low-cost, and non-invasive diagnostic modality to implement as mobile software. The aim of this article is to introduce a new deep learning structure for recognizing challenging (i.e. similar) human activities based on signals which have been recorded by sensors mounted on mobile devices. In the proposed structure, the residual network concept is engaged as a new substructure inside the main proposed structure. This part is responsible to address the problem of accuracy saturation in convolutional neural networks, thanks to its ability in jump over some layers which leads to reducing vanishing gradients effect. Therefore the accuracy of the classification of several activities is increased by using the proposed structure. Performsuperiorities even reach to at least 28% when the capability of the proposed method in recognizing downstairs and upstairs is compared to its non-family methods for the first dataset. Increasing the recognition rate of the proposed method for challenging activities (i.e. downstairs and upstairs, eating sandwich and eating chips) in parallel with its acceptable performance for other non-challenging activities shows its effectiveness in mobile sensor-based health monitoring systems.Mathematical modelling in biomechanics of infarcted left ventricle (LV) serves as an indispensable tool for remodelling mechanism exploration, LV biomechanical property estimation and therapy assessment after myocardial infarction (MI). However, a review of mathematical modelling after MI has not been seen in the literature so far. In the paper, a systematic review of mathematical models in biomechanics of infarcted LV was established. The models include comprehensive cardiovascular system model, essential LV pressure-volume and stress-stretch models, constitutive laws for passive myocardium and scars, tension models for active myocardium, collagen fibre orientation optimization models, fibroblast and collagen fibre growth/degradation models and integrated growth-electro-mechanical model after MI. The primary idea, unique characteristics and key equations of each model were identified and extracted. Discussions on the models were provided and followed research issues on them were addressed. Considerable improvements in the cardiovascular system model, LV aneurysm model, coupled agent-based models and integrated electro-mechanical-growth LV model are encouraged. Substantial attention should be paid to new constitutive laws with respect to stress-stretch curve and strain energy function for infarcted passive myocardium, collagen fibre orientation optimization in scar, cardiac rupture and tissue damage and viscoelastic effect post-MI in the future.This paper reviews the current status of soft robots in biomedical field. Soft robots are made of materials that have comparable modulus of elasticity to that of biological systems. Several advantages of soft robots over rigid robots are safe human interaction, ease of adaptation with wearable electronics and simpler gripping. We review design factors of soft robots including modeling, controls, actuation, fabrication and application, as well as their limitations and future work. For modeling, we survey kinematic, multibody and numerical finite element methods. Atglistatin Finite element methods are better suited for the analysis of soft robots, since they can accurately model nonlinearities in geometry and materials. However, their real-time integration with controls is challenging. We categorize the controls of soft robots as model-based and model-free. Model-free controllers do not rely on an explicit analytical or numerical model of the soft robot to perform actuation. Actuation is the ability to exert a force using actuators such as shape memory alloys, fluid gels, elastomers and piezoelectrics. Nonlinear geometry and materials of soft robots restrict using conventional rigid body controls. The fabrication techniques used for soft robots differ significantly from that of rigid robots. We survey a wide range of techniques used for fabrication of soft robots from simple molding to more advanced additive manufacturing methods such as 3D printing. We discuss the applications and limitations of biomedical soft robots covering aspects such as functionality, ease of use and cost. The paper concludes with the future discoveries in the emerging field of soft robots.Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.