CONCLUSION This study showed that CE on the visual sustained attention task seems to influence suicidal ideation as a result of interaction with depressive symptoms.Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. https://www.selleckchem.com/products/scutellarin.html With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion�-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.BACKGROUND/AIMS We retrospectively analyzed Crohn's disease (CD) patients with small intestinal strictures who underwent single-balloon enteroscopy (SBE) to ascertain whether prototype SBEs with a passive bending mechanism and high force transmission insertion tube had better insertability in the small intestine than a conventional SBE. METHODS Among 253 CD patients who underwent SBE, we identified 94 CD patients who had undergone attempted endoscopic balloon dilatation (EBD) for small intestinal stenosis for inclusion in this study. We analyzed whether the type of scope used for their initial procedure affected the cumulative surgery-free rate. For the insertability analysis, patients who underwent SBE at least twice were divided into 3 groups according to the type of scope used conventional SBE only, prototype SBE only, and both conventional and prototype SBEs. For each group, depth of insertion, procedure time, and number of EBDs were compared in the same patient at different time points. RESULTS The success rate of EBD was 88.3%. The 5- and 10-year cumulative surgery-free rate was 75.7% and 72.8%, respectively. Cox regression analysis indicated that the factors contributing to surgery were long stricture (≥2 cm), EBD failure, and elevated Crohn's Disease Activity Index, but not the type of scope used for EBD. The prototype SBEs significantly improved the depth of insertion (P=0.03, Wilcoxon's signed-rank test). CONCLUSIONS In CD patients with small intestinal stenosis, the prototype SBEs with a passive bending mechanism and high force transmission insertion tube did not improve long-term EBD outcome but did improve deep insertability. (Clinical Trial Registration No. UMIN000037102).

Background In Canada, rates of hospital admission from opioid overdose are higher for older adults (≥ 65) than younger adults, and opioid use disorder (OUD) is a growing concern. In response, Health Canada commissioned the Canadian Coalition of Seniors' Mental Health to create guidelines for the prevention, screening, assessment, and treatment of OUD in older adults. Methods A systematic review of English language literature from 2008-2018 regarding OUD in adults was conducted. Previously published guidelines were evaluated using AGREE II, and key guidelines updated using ADAPTE method, by drawing on current literature. Recommendations were created and assessed using the GRADE method. Results Thirty-two recommendations were created. Prevention recommendations it is key to prioritize non-pharmacological and non-opioid strategies to treat acute and chronic noncancer pain. Assessment recommendations a comprehensive assessment is important to help discern contributions of other medical conditions. Treatment recommendations buprenorphine is first line for both withdrawal management and maintenance therapy, while methadone, slow-release oral morphine, or naltrexone can be used as alternatives under certain circumstances; non-pharmacological treatments should be offered as an integrated part of care. Conclusion These guidelines provide practical and timely clinical recommendations on the prevention, assessment, and treatment of OUD in older adults within the Canadian context. © 2020 Author(s). Published by the Canadian Geriatrics Society.Background Benzodiazepine receptor agonist (BZRA) use disorder among older adults is a relatively common and challenging clinical condition. Method The Canadian Coalition for Seniors' Mental Health, with financial support from Health Canada, has produced evidence-based guidelines on the prevention, identification, assessment, and management of this form of substance use disorder. Results Inappropriate use of BZRAs should be avoided by considering non-pharmacological approaches to the management of late life insomnia, anxiety, and other common indications for the use of BZRA. Older persons should only be prescribed BZRAs after they are fully informed of alternatives, benefits, and risks associated with their use. Clinicians should have a high index of suspicion for the presence of BZRA use disorders. The full version of these guidelines can be accessed at www.ccsmh.ca. Conclusions A person-centred, stepped care approach utilizing gradual dose reductions should be used in the management of BZRA use disorder. © 2020 Author(s). Published by the Canadian Geriatrics Society.Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p  less then  0.05) and MI (p  less then  0.01). Moreover, the training data size can be reduced through fusion of these two modalities. © Springer Nature B.V. 2019.Mental imagery has long been of interest to the cognitive and neurosciences, but how it manifests itself in the mind and brain still remains unresolved. In pursuit of this, we built a spiking neural model that can perform mental rotation and mental map scanning using strategies informed by the psychology and neuroscience literature. Results When performing mental map scanning, reaction times (RTs) for our model closely match behavioural studies (approx. 50 ms/cm), and replicate the cognitive penetrability of the task. When performing mental rotation, our model's RTs once again closely match behavioural studies (model 55-65°/s; studies 60°/s), and performed the task using the same task strategy (whole unit rotation of simple and familiar objects through intermediary points). Overall, our model suggests (1) vector-based approaches to neuro-cognitive modelling are well equipped to re-produce behavioural findings, and (2) the cognitive (in)penetrability of imagery tasks may depend on whether or not the task makes use of (non)symbolic processing. © Springer Nature B.V. 2019.When a bistable visual image, such as the Necker cube, is continuously viewed, the percept of the image endogenously alternates between one possible percept and the other. However, perceptual alternation can also be induced by an exogenous perturbation. For example, a typical external perturbation is the flashlight, which is expected to pervasively activate many brain regions. https://www.selleckchem.com/products/AZD5438.html Therefore, the neural mechanism related to exogenous perceptual alternation remains to be clarified. As a cue to solving this problem, our recent psychophysiological experiment reported a positive correlation between the enhancement of visual mismatch negativity evoked by breaks in the sequential regularity of the visual stimuli and the proportion of perceptual alternation. To elucidate the mechanism underlying exogenous perceptual alternation induced by visual mismatch negativity, the present study attempted to construct a neural network model for bistable perception of the Necker cube, whose perceptual alternation is facilitated by an increase in visual mismatch negativity.