Biomedical Engineering and Computational Biology最新文献

The convolution of the transmembrane current of an excitable cell and a weighting function generates a single fiber action potential (SFAP) model by using the volume conductor theory. Here, we propose an empirical muscle IAP model with multiple Erlang probability density functions (PDFs) based on a modified Newton method. In addition, we generate SFAPs based on our IAP model and referent sources, and use the peak-to-peak ratios (PPRs) of SFAPs for model verification. Through this verification, we find that the relation between an IAP profile and the PPR of its SFAP is consistent with some previous studies, and our IAP model shows close profiles to the referent sources. Moreover, we simulate and discuss some possible ionic activities by using the Erlang PDFs in our IAP model, which might present the underlying activities of ions or their channels during an IAP.

The introduction of endovascular abdominal aortic aneurysm (AAA) repair has revolutionized the therapeutic approach to patients with AAA. Due to an on-going and prolific collaboration between vascular interventionalists and biomedical engineers, the devices used to perform endovascular AAA repair have also changed dramatically. The purpose of this publication is to provide an overview of the currently available and upcoming options for endovascular AAA repair.

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

BACE1, a membrane-bound aspartyl protease that is implicated in Alzheimer's disease, is the first protease to cut the amyloid precursor protein resulting in the generation of amyloid-β and its aggregation to form senile plaques, a hallmark feature of the disease. Few other native BACE1 substrates have been identified despite its relatively loose substrate specificity. We report a bioinformatics approach identifying several putative BACE1 substrates. Using our algorithm, we successfully predicted the cleavage sites for 70% of known BACE1 substrates and further validated our algorithm output against substrates identified in a recent BACE1 proteomics study that also showed a 70% success rate. Having validated our approach with known substrates, we report putative cleavage recognition sequences within 962 proteins, which can be explored using in vivo methods. Approximately 900 of these proteins have not been identified or implicated as BACE1 substrates. Gene ontology cluster analysis of the putative substrates identified enrichment in proteins involved in immune system processes and in cell surface protein-protein interactions.

In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.