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BackgroundMillions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.MethodsThis paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.ResultsAlthough we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.ConclusionsThe promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Conventional anticancer therapies, such as chemo- and/or radio-therapy are often unable to completely eradicate cancers due to abnormal tumor microenvironment, as well as increased drug/radiation resistance. More effective therapeutic strategies for overcoming these obstacles are urgently in demand. Aptamers, as chemical antibodies that bind to targets with high affinity and specificity, are a promising new and novel agent for both cancer diagnostic and therapeutic applications. Aptamer-based cancer cell targeting facilitates the development of active targeting in which aptamer-mediated drug delivery could provide promising anticancer outcomes. This review is to update the current progress of aptamer-based cancer diagnosis and aptamer-mediated active targeting for cancer therapy in vivo, exploring the potential of this novel form of targeted cancer therapy.

Insufficient penetration of therapeutic agents into tumor tissues results in inadequate drug distribution and lower intracellular concentration of drugs, leading to the increase of drug resistance and resultant failure of cancer treatment. Targeted drug delivery to solid tumors followed by complete drug penetration and durable retention will significantly improve clinical outcomes of cancer therapy. Monoclonal antibodies have been commonly used in clinic for cancer treatment, but their limitation of penetrating into tumor tissues still remains because of their large size. Aptamers, as “chemical antibodies”, are 15-20 times smaller than antibodies. To explore whether aptamers are superior to antibodies in terms of tumor penetration, we carried out the first comprehensive study to compare the performance of an EpCAM aptamer with an EpCAM antibody in theranostic applications. Penetration and retention were studied in in vitro three-dimensional tumorspheres, in vivo live animal imaging and mouse colorectal cancer xenograft model. We found that the EpCAM aptamer can not only effectively penetrate into the tumorsphere cores but can also be retained by tumor sphere cells for at least 24 h, while limited tumor penetration by EpCAM antibody was observed after 4 h incubation. As observed from in vivo live animal imaging, EpCAM aptamers displayed a maximum tumor uptake at around 10 min followed by a rapid clearance after 80 min, while the signal of peak uptake and disappearance of antibody appeared at 3 h and 6 h after intravenous injection, respectively. The signal of PEGylated EpCAM aptamers in xenograft tumors was sustained for 26 h, which was 4.3-fold longer than that of the EpCAM antibody. Consistently, there were 1.67-fold and 6.6-fold higher accumulation of PEGylated aptamer in xenograft tumors than that of antibody, at 3 h and 24 h after intravenous administration, respectively. In addition, the aptamer achieved at least a 4-time better tumor penetration in xenograft tumors than that of the antibody at a 200 μm distances from the blood vessels 3 h after intravenous injection. Taken together, these data indicate that aptmers are superior to antibodies in cancer theranostics due to their better tumor penetration, more homogeneous distribution and longer retention in tumor sites. Thus, aptamers are promising agents for targeted tumor therapeutics and molecular imaging.

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.

Rapid advancements in neurostimulation technologies are providing relief to an unprecedented number of patients affected by debilitating neurologic and psychiatric disorders. Neurostimulation therapies include invasive and noninvasive approaches that involve the application of electrical stimulation to drive neural function within a circuit. This review focuses on established invasive electrical stimulation systems used clinically to induce therapeutic neuromodulation of dysfunctional neural circuitry. These implantable neurostimulation systems target specific deep subcortical, cortical, spinal, cranial, and peripheral nerve structures to modulate neuronal activity, providing therapeutic effects for a myriad of neuropsychiatric disorders. Recent advances in neurotechnologies and neuroimaging, along with an increased understanding of neurocircuitry, are factors contributing to the rapid rise in the use of neurostimulation therapies to treat an increasingly wide range of neurologic and psychiatric disorders. Electrical stimulation technologies are evolving after remaining fairly stagnant for the past 30 years, moving toward potential closed-loop therapeutic control systems with the ability to deliver stimulation with higher spatial resolution to provide continuous customized neuromodulation for optimal clinical outcomes. Even so, there is still much to be learned about disease pathogenesis of these neurodegenerative and psychiatric disorders and the latent mechanisms of neurostimulation that provide therapeutic relief. This review provides an overview of the increasingly common stimulation systems, their clinical indications, and enabling technologies.