JBINS 2017 December

JBINS December 2017 academic articles

Showing 1–8 of 11 results

  • Optogenetics-based Neuromodulation for the Alleviation of hemi-Parkinsonian Motor Asymmetry in Rat


    Abstract: The development of more effective deep brain stimulation (DBS) paradigms for Parkinson’s disease is limited by the non-specific nature of electrical stimulation. Optogenetics, with its spatial and cell-type specificity, is a potential alternative therapeutic approach. In 6-hydroxydopamine-induced hemi-Parkinsonian rats, we investigated the therapeutic values of optogenetic modulation of the subthalamic nucleus (STN) and the motor cortex. Here we report optogenetic inhibition of principal neurons in the STN significantly improved hemi-Parkinsonian motor asymmetry, measured by amphetamine-induced rotations. We also show preliminary results that revealed therapeutic improvement in motor asymmetry by single-site optogenetic excitation of the motor cortex. Although improvement from optogenetic modulations did not exceed the effects of DBS in the STN, our findings suggest that spatially patterned optogenetic stimulation of the cortex, i.e., more precise manipulation of cortical activity over larger area, should be investigated as a therapeutic approach for Parkinson’s disease.

  • The Role of Neuroimaging in Diagnosis of Neurodegenerative Disease


    Abstract: Imaging the human body is one of the most important aspects of medical science in clinics and research. Due to the increasing spread of diseases related to the nervous system, neuroimaging has grown substantially in the last two decades. In this study, neuroimaging techniques that are used to diagnose neurodegenerative diseases have been expressed. Clinical applications of each neuroimaging method have also been reviewed. Some imaging techniques create structural and anatomical images, and some provide physiological and functional images. Recent advances in neuroimaging have led to the creation of hybrid techniques. In these multimodality methods, structural and functional images are combined. This feature leads to increased accuracy in the diagnosis of neurodegenerative diseases.

  • Controlling 3D Object made of CT data in Medical Training System Using Leap Motion


    Abstract: This paper describes a user interface of 3D (three-dimensional) object converted from 2D (two-dimensional) CT data in DICOM format using Leap Motion device that can be used as a medical training system for medical students and interns. The resultant data can be controlled in a 3D development environment of Unity software. The system consists of desktop computer, the displaying software environment and Leap Motion device. The experimental results show that we can have a desirable control over the rendered object in 360 degrees, and that we can check the details of the object using zooming feature in the system.

  • Multi-label classification of brain tumor mass spectrometry data. In pursuit of tumor boundary detection method.


    Abstract: The mass-spectrometry is the promising tool for the fast characterization of brain biopsy samples as a part of the intraoperative identification of tumor boundary. The spray-from-tissue ambient ionization method is a new instrument for mass-spectrometry analysis of soft tissues without sample preparation. In this contribution, we analyze the performance of multi-label classification techniques in detection of the tumor and necrosis fragments within the sample.

  • Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients


    Abstract: For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.

  • Supervised Learning-based Nuclei Segmentation on Cytology Pleural Effusion Images with Artificial Neural Network


    Abstract: Automated segmentation of cell nuclei is the crucial step towards computer-aided diagnosis system because the morphological features of the cell nuclei are highly associated with the cell abnormality and disease. This paper contributes four main stages required for automatic segmentation of the cell nuclei on cytology pleural effusion images. Initially, the image is preprocessed to enhance the image quality by applying contrast limited adaptive histogram equalization (CLAHE). The segmentation process is relied on a supervised Artificial Neural network (ANN) based pixel classification. Then, the boundaries of the extracted cell nuclei regions are refined by utilizing the morphological operation. Finally, the overlapped or touched nuclei are identified and split by using the marker-controlled watershed method. The proposed method is evaluated with the local dataset containing 35 cytology pleural effusion images. It achieves the performance of 0.95%, 0.86 %, 0.90% and 92% in precision, recall, F-measure and Dice Similarity Coefficient respectively. The average computational time for the entire algorithm took 15 mins per image. To our knowledge, this is the first attempt that utilizes ANN as the segmentation on cytology pleural effusion images.

  • A Medical Training System Using Augmented Reality: Trial Development of Environment Platform


    Abstract: A medical training system using augmented reality, AR is presented in this paper. Recognizing an AR marker through a Web camera, computer generated images appear on a real place. We prepared 3D (three-dimensional) anatomical objects to show, and evaluated our system using AR platform. It was found from the experimental result that our system overlaid the digital information of the 3D anatomical objects on the operator’s surrounding real world appropriately, and that feature of visible/invisible objects was verified in AR environment platform.

  • Making protein-protein interaction prediction more reliable with a large-scale dataset at the proteome level


    Abstract: Reliable information about protein-protein interactions (PPIs) enables us to better understand biological processes, pathways and functions. However, there are many experimental problems in identifying complete PPI-networks in a cell or organism. To supplement the limitations of current experimental techniques, we have previously proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. In the PPI prediction, the selection of datasets is a big issue for accurately evaluating the performance of different algorithms [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high-confidence direct physical PPIs obtained from TargetMine [4] (as PPIs being only 0.13% of the total) and 33,098,951 non-PPIs. As a result, the new prediction model achieved a higher AUC of 0.89 (pAUCFPR≤0.5% = 0.24) than the previous model of PSOPIA. Furthermore, it was applied to the problem of filtering out protein pairs incorrectly labeled as interacting from a low-confidence human PPI dataset. Here, we suggest that a diverse set of large-scale examples is key to more reliable PPI prediction, demonstrating the performance of PSOPIA at the proteome level.