Additive Effects of Rebamipide Plus Proton Pump Inhibitors on the Expression of Tight Junction Proteins in a Rat Model of Gastro-Esophageal Reflux Disease.

BACKGROUND/AIMS: The aim of this study was to investigate the effects of rebamipide on tight junction proteins in the esophageal mucosa in a rat model of gastroesophageal reflux disease (GERD). METHODS: GERD was created in rats by tying the proximal stomach. The rats were divided into a control group, a proton pump inhibitor (PPI) group, and a PPI plus rebamipide (PPI+R) group. Pantoprazole (5 mg/kg) was administered intraperitoneally to the PPI and PPI+R groups. An additional dose of rebamipide (100 mg/kg) was administered orally to the PPI+R group. Mucosal erosions, epithelial thickness, and leukocyte infiltration into the esophageal mucosa were measured in isolated esophagi 14 days after the procedure. A Western blot analysis was conducted to measure the expression of claudin-1, -3, and -4. RESULTS: The mean surface area of mucosal erosions, epithelial thickness, and leukocyte infiltration were lower in the PPI group and the PPI+R group than in the control group. Western blot analysis revealed that the expression of claudin-3 and -4 was significantly higher in the PPI+R group than in the control group. CONCLUSIONS: Rebamipide may exert an additive effect in combination with PPI to modify the tight junction proteins of the esophageal mucosa in a rat model of GERD. This treatment might be associated with the relief of GERD symptoms.

Additive Effects of Rebamipide Plus Proton Pump Inhibitors on the Expression of Tight Junction Proteins in a Rat Model of Gastro-Esophageal Reflux Disease

BACKGROUND/AIMS: The aim of this study was to investigate the effects of rebamipide on tight junction proteins in the esophageal mucosa in a rat model of gastroesophageal reflux disease (GERD). METHODS: GERD was created in rats by tying the proximal stomach. The rats were divided into a control group, a proton pump inhibitor (PPI) group, and a PPI plus rebamipide (PPI+R) group. Pantoprazole (5 mg/kg) was administered intraperitoneally to the PPI and PPI+R groups. An additional dose of rebamipide (100 mg/kg) was administered orally to the PPI+R group. Mucosal erosions, epithelial thickness, and leukocyte infiltration into the esophageal mucosa were measured in isolated esophagi 14 days after the procedure. A Western blot analysis was conducted to measure the expression of claudin-1, -3, and -4. RESULTS: The mean surface area of mucosal erosions, epithelial thickness, and leukocyte infiltration were lower in the PPI group and the PPI+R group than in the control group. Western blot analysis revealed that the expression of claudin-3 and -4 was significantly higher in the PPI+R group than in the control group. CONCLUSIONS: Rebamipide may exert an additive effect in combination with PPI to modify the tight junction proteins of the esophageal mucosa in a rat model of GERD. This treatment might be associated with the relief of GERD symptoms.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor.

The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods.

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors.

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

A Survey on Banknote Recognition Methods by Various Sensors.

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network.

Automobile driver information as displayed on marked road signs indicates the state of the road, traffic conditions, proximity to schools, etc. These signs are important to insure the safety of the driver and pedestrians. They are also important input to the automated advanced driver assistance system (ADAS), installed in many automobiles. Over time, the arrow-road markings may be eroded or otherwise damaged by automobile contact, making it difficult for the driver to correctly identify the marking. Failure to properly identify an arrow-road marker creates a dangerous situation that may result in traffic accidents or pedestrian injury. Very little research exists that studies the problem of automated identification of damaged arrow-road marking painted on the road. In this study, we propose a method that uses a convolutional neural network (CNN) to recognize six types of arrow-road markings, possibly damaged, by visible light camera sensor. Experimental results with six databases of Road marking dataset, KITTI dataset, Málaga dataset 2009, Málaga urban dataset, Naver street view dataset, and Road/Lane detection evaluation 2013 dataset, show that our method outperforms conventional methods.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor.

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.

Robust Behavior Recognition in Intelligent Surveillance Environments.

Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR) cameras, thermal cameras (based on medium-wavelength infrared (MWIR), and long-wavelength infrared (LWIR) light) have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance) for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods.

Ten-day empirical sequential or concomitant therapy is more effective than triple therapy for Helicobacter pylori eradication: A multicenter, prospective study.

BACKGROUND: The resistance of Helicobacter pylori to antibiotics has increased the need for new empirical, first-line treatments. However, the efficacy of sequential therapy (ST) and concomitant therapy (CT) compared with triple therapy (TT) has not been adequately evaluated. AIM: In this study, we evaluated the efficacy of these empirical three regimens. METHODS: The 517 patients enrolled in the study were prospectively randomized to receive 10 days of TT (n=171), ST (n=170), and CT (n=176) at 5 university-affiliated hospitals from May 2013 to March 2015. The post-treatment H. pylori status was determined using the (13)C-urea breath test. RESULTS: The baseline characteristics were similar among the three groups. The intention-to-treat eradication rates were 62.6%, 70.6%, and 77.8% in the TT, ST, and CT groups, respectively (p<0.01). The corresponding per-protocol eradication rates were 82.8%, 89.5%, and 94.4%, respectively (p<0.01). There were no significant differences in the compliance, side effects, and follow-up loss rates. CONCLUSION: A higher eradication rate was achieved with empirical 10-day ST, and CT than with the TT regimen, with similar rates of compliance and treatment side effects.

Human Detection Based on the Generation of a Background Image and Fuzzy System by Using a Thermal Camera.

Recently, human detection has been used in various applications. Although visible light cameras are usually employed for this purpose, human detection based on visible light cameras has limitations due to darkness, shadows, sunlight, etc. An approach using a thermal (far infrared light) camera has been studied as an alternative for human detection, however, the performance of human detection by thermal cameras is degraded in case of low temperature differences between humans and background. To overcome these drawbacks, we propose a new method for human detection by using thermal camera images. The main contribution of our research is that the thresholds for creating the binarized difference image between the input and background (reference) images can be adaptively determined based on fuzzy systems by using the information derived from the background image and difference values between background and input image. By using our method, human area can be correctly detected irrespective of the various conditions of input and background (reference) images. For the performance evaluation of the proposed method, experiments were performed with the 15 datasets captured under different weather and light conditions. In addition, the experiments with an open database were also performed. The experimental results confirm that the proposed method can robustly detect human shapes in various environments.

Refractory pseudomembranous colitis that was treated successfully with colonoscopic fecal microbial transplantation.

Pseudomembranous colitis (PMC) is a nosocomial and opportunistic infection caused by Clostridium difficile. PMC is related to the use of antibiotics leading to intestinal dysbiosis and an overgrowth of C. difficile. Metronidazole or vancomycin is considered to be the standard therapy for the management of PMC. However, PMC has a 15%-30% recurrence rate and can be refractory to standard treatments, resulting in morbidity and mortality. Here we describe a patient who experienced refractory PMC who was treated with fecal microbiota transplantation. A 69-year-old woman was admitted to the hospital with consistent abdominal pain and diarrhea, which had been present for 5 months. She was diagnosed with PMC by colonoscopy and tested positive for C. difficile toxin. Even though she took metronidazole for 10 days, followed by vancomycin for 4 weeks, her symptoms did not improve. Because of her recurrent and refractory symptoms, we decided to perform fecal microbiota transplantation. Fifty grams of fresh feces from a donor were obtained on the day of the procedure, mixed with 500 mL of normal saline, and then filtered. The filtered solution was administered to the patient's colon using a colonoscope. After the procedure, her symptoms rapidly improved and a follow-up colonoscopy showed that the PMC had resolved without recurrence.

Refractory pseudomembranous colitis that was treated successfully with colonoscopic fecal microbial transplantation

Pseudomembranous colitis (PMC) is a nosocomial and opportunistic infection caused by Clostridium difficile. PMC is related to the use of antibiotics leading to intestinal dysbiosis and an overgrowth of C. difficile. Metronidazole or vancomycin is considered to be the standard therapy for the management of PMC. However, PMC has a 15%-30% recurrence rate and can be refractory to standard treatments, resulting in morbidity and mortality. Here we describe a patient who experienced refractory PMC who was treated with fecal microbiota transplantation. A 69-year-old woman was admitted to the hospital with consistent abdominal pain and diarrhea, which had been present for 5 months. She was diagnosed with PMC by colonoscopy and tested positive for C. difficile toxin. Even though she took metronidazole for 10 days, followed by vancomycin for 4 weeks, her symptoms did not improve. Because of her recurrent and refractory symptoms, we decided to perform fecal microbiota transplantation. Fifty grams of fresh feces from a donor were obtained on the day of the procedure, mixed with 500 mL of normal saline, and then filtered. The filtered solution was administered to the patient's colon using a colonoscope. After the procedure, her symptoms rapidly improved and a follow-up colonoscopy showed that the PMC had resolved without recurrence.

A Case of Cytomegalovirus-associated Giant Gastric Ulcer in a Patient Who Healed by Discontinuing Immunosuppressive Therapy

Cytomegalovirus (CMV) is not a rare infection and is frequently observed in immuoncompromised patients. CMV infection is usually asymptomatic in immunocompetent patients however it can be a major cause of morbidity and mortality in immunocompromised patients. The diagnosis of CMV gastric ulcer is not easy because of the absence of characteristic endoscopic features and the difficulty in the identification of infection by routine histologic examinations. We experienced a case of CMV-associated giant gastric ulcer in a patient receiving immunosuppressive therapy. She was a 45-year-old woman with dermatomyositis and had received steroid therapy to control her disease. Epigastric pain developed during therapy and upper endoscopy revealed a gastric ulcer. Despite proton pump inhibitor therapy, her epigastric pain aggravated and follow-up endoscopy revealed a huge gastric ulcer approximately 10 cm in diameter. Histologic findings showed intracellular inclusion bodies after immunostaining which confirmed CMV-associated gastric ulcer. Steroid therapy was discontinued and she received proton pump inhibitors without antiviral agents. Her symptoms improved and follow-up endoscopy revealed successful healing of the CMV-associated gastric ulcer. If an unusual gastric ulcer develops in the immunocompromised patients, CMV gastric ulcer should be suspected and examination for inclusion bodies using CMV immunostaining should be considered.

Face recognition system for set-top box-based intelligent TV.

Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user registration stage and multi-level local binary pattern matching. Experimental results indicate that the recall; precision; and genuine acceptance rate were about 95.7%; 96.2%; and 90.2%, respectively.