Biomedical informatics applications for epilepsy management: A systematic review

Epilepsy is a very complex disease which can have a variety of etiologies, co-morbidities, and a long list of psychosocial factors4. Clinical management of epilepsy patients typically includes serological tests, EEG's, and imaging studies to determine the single best antiepileptic drug (AED). Self-management is a vital component of achieving optimal health when living with a chronic disease. For patients with epilepsy self-management includes any necessary actions to control seizures and cope with any subsequent effects of the condition9; including aspects of treatment, seizure, and lifestyle. The use of computer-based applications can allow for more effective use of clinic visits and ultimately enhance the patient-provider relationship through focused discussion of determinants affecting self-management. ^ The purpose of this study is to conduct a systematic literature review on informatics application in epilepsy self-management in an effort to describe current evidence for informatics applications and decision support as an adjunct to successful clinical management of epilepsy. Each publication was analyzed for the type of study design utilized. ^ A total of 68 publications were included and categorized by the study design used, development stage, and clinical domain. Descriptive study designs comprised of three-fourths of the publications and indicate an underwhelming use of prospective studies. The vast majority of prospective studies also focused on clinician use to increase knowledge in treating patients with epilepsy. ^ Due to the chronic nature of epilepsy and the difficulty that both clinicians and patients can experience in managing epilepsy, more prospective studies are needed to evaluate applications that can effectively increase management activities. Within the last two decades of epilepsy research, management studies have employed the use of biomedical informatics applications. While the use of computer applications to manage epilepsy has increased, more progress is needed.^

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Secure tracking for critical applications : communications, GPS and future Galileo services

Tracking/remote monitoring systems using GNSS are a proven method to enhance the safety and security of personnel and vehicles carrying precious or hazardous cargo. While GNSS tracking appears to mitigate some of these threats, if not adequately secured, it can be a double-edged sword allowing adversaries to obtain sensitive shipment and vehicle position data to better coordinate their attacks, and to provide a false sense of security to monitoring centers. Tracking systems must be designed with the ability to perform route-compliance and thwart attacks ranging from low-level attacks such as the cutting of antenna cables to medium and high-level attacks involving radio jamming and signal / data-level simulation, especially where the goods transported have a potentially high value to terrorists. This paper discusses the use of GNSS in critical tracking applications, addressing the mitigation of GNSS security issues, augmentation systems and communication systems in order to provide highly robust and survivable tracking systems.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Mesenchymal stem cells in osteoarthritis therapy : current status of theory, technology, and applications

Osteoarthritis (OA) is a chronic, non-inflammatory type of arthritis, which usually affects the movable and weight bearing joints of the body. It is the most common joint disease in human beings and common in elderly people. Till date, there are no safe and effective diseases modifying OA drugs (DMOADs) to treat the millions of patients suffering from this serious and debilitating disease. However, recent studies provide strong evidence for the use of mesenchymal stem cell (MSC) therapy in curing cartilage related disorders. Due to their natural differentiation properties, MSCs can serve as vehicles for the delivery of effective, targeted treatment to damaged cartilage in OA disease. In vitro, MSCs can readily be tailored with transgenes with anti-catabolic or pro-anabolic effects to create cartilage-friendly therapeutic vehicles. On the other hand, tissue engineering constructs with scaffolds and biomaterials holds promising biological cartilage therapy. Many of these strategies have been validated in a wide range of in vitro and in vivo studies assessing treatment feasibility or efficacy. In this review, we provide an outline of the rationale and status of stem-cell-based treatments for OA cartilage, and we discuss prospects for clinical implementation and the factors crucial for maintaining the drive towards this goal.

Hyaluronic acid : evaluation as a potential delivery vehicle for vitronectin:growth factor complexes in wound healing applications

We have previously reported that novel vitronectin:growth factor (VN:GF) complexes significantly increase re-epithelialization in a porcine deep dermal partial-thickness burn model. However, the potential exists to further enhance the healing response through combination with an appropriate delivery vehicle which facilitates sustained local release and reduced doses of VN:GF complexes. Hyaluronic acid (HA), an abundant constituent of the interstitium, is known to function as a reservoir for growth factors and other bioactive species. The physicochemical properties of HA confer it with an ability to sustain elevated pericellular concentrations of these species. This has been proposed to arise via HA prolonging interactions of the bioactive species with cell surface receptors and/or protecting them from degradation. In view of this, the potential of HA to facilitate the topical delivery of VN:GF complexes was evaluated. Two-dimensional (2D) monolayer cell cultures and 3D de-epidermised dermis (DED) human skin equivalent (HSE) models were used to test skin cell responses to HA and VN:GF complexes. Our 2D studies revealed that VN:GF complexes and HA stimulate the proliferation of human fibroblasts but not keratinocytes. Experiments in our 3D DED-HSE models showed that VN:GF complexes, both alone and in conjunction with HA, led to enhanced development of both the proliferative and differentiating layers in the DED-HSE models. However, there was no significant difference between the thicknesses of the epidermis treated with VN:GF complexes alone and VN:GF complexes together with HA. While the addition of HA did not enhance all the cellular responses to VN:GF complexes examined, it was not inhibitory, and may confer other advantages related to enhanced absorption and transport that could be beneficial in delivery of the VN:GF complexes to wounds.

Applications of medical image processing in the diagnosis and treatment of spinal deformity

In Chapter 10, Adam and Dougherty describe the application of medical image processing to the assessment and treatment of spinal deformity, with a focus on the surgical treatment of idiopathic scoliosis. The natural history of spinal deformity and current approaches to surgical and non-surgical treatment are briefly described, followed by an overview of current clinically used imaging modalities. The key metrics currently used to assess the severity and progression of spinal deformities from medical images are presented, followed by a discussion of the errors and uncertainties involved in manual measurements. This provides the context for an analysis of automated and semi-automated image processing approaches to measure spinal curve shape and severity in two and three dimensions.