International Neurourology Journal 

About: International Neurourology Journal is an academic journal published by Korean Continence Society. The journal publishes majorly in the area(s): Medicine & Lower urinary tract symptoms. It has an ISSN identifier of 2093-4777. It is also open access. Over the lifetime, 719 publications have been published receiving 7915 citations. The journal is also known as: INJ.

Development of Implantable Medical Devices: From an Engineering Perspective

Abstract: From the first pacemaker implant in 1958, numerous engineering and medical activities for implantable medical device development have faced challenges in materials, battery power, functionality, electrical power consumption, size shrinkage, system delivery, and wireless communication. With explosive advances in scientific and engineering technology, many implantable medical devices such as the pacemaker, cochlear implant, and real-time blood pressure sensors have been developed and improved. This trend of progress in medical devices will continue because of the coming super-aged society, which will result in more consumers for the devices. The inner body is a special space filled with electrical, chemical, mechanical, and marine-salted reactions. Therefore, electrical connectivity and communication, corrosion, robustness, and hermeticity are key factors to be considered during the development stage. The main participants in the development stage are the user, the medical staff, and the engineer or technician. Thus, there are three different viewpoints in the development of implantable devices. In this review paper, considerations in the development of implantable medical devices will be presented from the viewpoint of an engineering mind.

Pathophysiological Role of Neuroinflammation in Neurodegenerative Diseases and Psychiatric Disorders.

Abstract: Brain diseases and disorders such as Alzheimer disease, Parkinson disease, depression, schizophrenia, autism, and addiction lead to reduced quality of daily life through abnormal thoughts, perceptions, emotional states, and behavior. While the underlying mechanisms remain poorly understood, human and animal studies have supported a role of neuroinflammation in the etiology of these diseases. In the central nervous system, an increased inflammatory response is capable of activating microglial cells, leading to the release of pro-inflammatory cytokines including interleukin (IL)-1β, IL-6, and tumor necrosis factor-α. In turn, the pro-inflammatory cytokines aggravate and propagate neuroinflammation, degenerating healthy neurons and impairing brain functions. Therefore, activated microglia may play a key role in neuroinflammatory processes contributing to the pathogenesis of psychiatric disorders and neurodegeneration.

Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing.

Abstract: This article is a mini-review that provides a general overview for next-generation sequencing (NGS) and introduces one of the most popular NGS applications, whole genome sequencing (WGS), developed from the expansion of human genomics. NGS technology has brought massively high throughput sequencing data to bear on research questions, enabling a new era of genomic research. Development of bioinformatic software for NGS has provided more opportunities for researchers to use various applications in genomic fields. De novo genome assembly and large scale DNA resequencing to understand genomic variations are popular genomic research tools for processing a tremendous amount of data at low cost. Studies on transcriptomes are now available, from previous-hybridization based microarray methods. Epigenetic studies are also available with NGS applications such as whole genome methylation sequencing and chromatin immunoprecipitation followed by sequencing. Human genetics has faced a new paradigm of research and medical genomics by sequencing technologies since the Human Genome Project. The trend of NGS technologies in human genomics has brought a new era of WGS by enabling the building of human genomes databases and providing appropriate human reference genomes, which is a necessary component of personalized medicine and precision medicine.

Quality of life of women with urinary incontinence: a systematic literature review.

Abstract: Purpose: The purpose of this study was to review studies that have examined the quality of life of women with urinary incontinence. Materials and Methods: A review was conducted that used the databases PubMED, Proquest, CINAHL, and Sciencedirect. Articles were included that were published in English between 2005 and 2010 the Results: A total of 18 studies were identified, and the prevalence of urinary incontinence varied depending on the definition of incontinence used and the age of the population studied. The Incontinence Quality of Life (I-QoL), Incontinence Impact Questionnaire-short form (IIQ-7), and King’s Health Questionnaire (KHQ) were the most commonly used instruments. Demographic, medical, physical, psychological, health, and intervention factors were reported as influencing factors on the quality of life of women with incontinence. Age, severity of urinary incontinence, type of urinary incontinence, number of urinary incontinence episodes, body weight, stress, and help-seeking behavior were statistically significant variables influencing quality of life. Conclusion: Future studies are needed to identify factors related to quality of life among women with incontinence and to use validated instruments according to specific subjects. Int Neurourol J 2010;14:133-8.

Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine

Abstract: In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.