ANALYSIS OF AGOMELATINE TREATMENT WITH DEPRESSIVE SYMPTOMS

Objective: Agomelatine is a new mechanism of antidepressants, which is approved by Taiwan Food and Drug Administration and available in Taiwan.Agomelatine behaves both as a potent agonist at melatonin MT1 and MT2 receptors and as a neutral antagonist at 5-HT2C receptors. The structuresof agomelatine are similar to melatonin with not only the effects to maintain depression symptoms but also can help patients who have insomnia.Methods: This is a retrospective study. In a mental hospital in Taoyuan, we analyzed the prescriptions of the outpatients who were prescribedagomelatine to realize the effects of agomelatine and whether the prescriptions were prescribed appropriately.Results: Catastrophic illnesses were found to be associated with significantly used multiple hypnotics (χ2 =22.02, p<0.001). When patients’ ageincreased by 1 year, multiple hypnotics used increased by 1.013 times (Exp(B)=1.013, p<0.01). Catastrophic illnesses were found to be associatedwith significantly used augmentation with other antidepressants (χ2=54.07, p<0.001).Conclusions: Doctors should be evaluating the benefits and risks when they prescribe a medicine to patients, and they should be written in medicalrecord. This study is the hope to provide relevant units as a reference for formulating health policies

The Development of Audit Detection Risk Assessment System: Using the Fuzzy Theory and Audit Risk Model

The result of audit designation is significantly influenced by the audit evidence collected when planning the audit and the degree of detection risk is further depends on the amount of audit evidence. Therefore, when the assessment factors of detection risk are more objective and correct, audit costs and the risk of audit failure can be reduced. Thus, the aim of this paper is to design an audit detection risk assessment system that could more precisely assess detection risk, comparing with the traditional determination method of detection risk in order to increase the audit quality and reduce the possibility of audit failure. First, the grounded theory is used to reorganize 53 factors affecting detection risk mentioned in literatures and then employed the Delphi method to screen the 43 critical risk factors agreed upon by empirical audit experts. In addition, using the fuzzy theory and audit risk model to calculate the degree of detection risk allow the audit staff to further determine the amount of audit evidence collected and set up initial audit strategies and construct the audit detection risk assessment system. Finally, we considered a case study to evaluate the system in terms of its feasibility and validity

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.Comment: 5 pages, 3 figures, to appear in IEEE Wireless Communications Letter

Use of the Chinese (Taiwan) Version of the Social Phobia Inventory (SPIN) Among Early Adolescents in Rural Areas: Reliability and Validity Study

BackgroundTo assess the screening abilities of the Chinese (Taiwan) version of the Social Phobia Inventory (SPIN) for evaluating social phobia in an adolescent community sample.MethodsA total of 3,393 students (1,669 boys, 1,724 girls), aged 13–15, completed the SPIN questionnaire. A total of 144 students were enrolled for validity. The Mini-International-Neuropsychiatric-Interview-Kid (MINI-Kid) was used to establish Diagnostic and Statistical Manual of Mental Disorders–IV diagnosis.ResultsThe mean SPIN total score of all subjects was 14.2 ± 9.4, which was higher in girls than in boys (14.7 ± 9.4 vs. 13.7 ± 9.1; p < 0.01). The 7th graders had the highest SPIN total scores compared with the 8th and 9th graders (15.4 ± 9.7 vs. 13.4 ± 9.1 and 14.0 ± 9.4; p < 0.001). Internal consistency (Cronbach's α = 0.85) and test–retest reliability (r = 0.73) were both good. A cut-off score of 25 resulted in balanced sensitivity (80%) and specificity (77%).ConclusionThe Chinese (Taiwan) SPIN has good screening abilities. The cut-offs are different from those in other countries, and highlight the importance of culturally adapted cut-offs

Automatic Cephalometric Landmark Detection on X-Ray Images Using Object Detection

We propose a new deep convolutional cephalometric landmark detection framework for orthodontic treatment. Our proposed method consists of two major steps: landmark detection using a deep neural network for object detection, and landmark repair to ensure one instance per landmark class. For landmark detection, we modify the loss function of the backbone network YOLOv3 to eliminate the constrains on the bounding box and incorporate attention mechanism to improve the detection accuracy. For landmark repair, a triangle mesh is generated from the average face to eliminate superfluous instances, followed by estimation of missing landmarks from the detected ones using Laplacian Mesh. Trained and evaluated on a public benchmark dataset from IEEE ISBI 2015 grand challenge, our proposed framework obtains comparable results compared to the state-of-the-art methods for cephalometric landmark detection, and demonstrates the efficacy of using a deep CNN model for accurate object detection of landmarks defined by only a single pixel location

Effects of Jia-Wei-Xiao-Yao-San on the Peripheral and Lymphatic Pharmacokinetics of Paclitaxel in Rats

Paclitaxel is effective against breast cancer. The herbal medicine, Jia-Wei-Xiao-Yao-San (JWXYS), is the most frequent prescription used to relieve the symptoms of breast cancer treatments. The aim of the study was to investigate the herb-drug interaction effects of a herbal medicine on the distribution of paclitaxel to lymph. A validated ultraperformance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method was used to determine the paclitaxel levels in rat plasma and lymph after intravenous infusion of paclitaxel alone with or without 7 days of JWXYS pretreatment. The pharmacokinetic results indicate that paclitaxel concentrations in plasma exceeded those in lymph by approximately 3.6-fold. The biodistribution of paclitaxel from plasma to lymph was 39±5%; however, this increased to 45±4% with JWXYS pretreatment. With JWXYS pretreatment, the AUC and Cmax of paclitaxel in plasma were significantly reduced by approximately 1.5-fold, compared to paclitaxel alone. Additionally, JWXYS decreased the AUC and Cmax of paclitaxel in lymph. However, the lymph absorption rate of paclitaxel with or without JWXYS pretreatment was not significantly changed (27±3 and 30±2%, resp.). Our findings demonstrate that when paclitaxel is prescribed concurrently with herbal medicine, monitoring of the blood pharmacokinetics of paclitaxel is recommended

Loneliness and behavioral characteristics of relationally victimized young girl : A naturalistic observation in kindergarten

The purpose of this study is to investigate the behavioral characteristics of relationally victimized young children. Four and 5-year-old preschoolers (girl N=18) were observed relationally victim and loneliness in natural setting for one year. In study 1, the child (girl A) who were more relationally victimized and felt more loneliness, and the child (girl B) who were less relationally victimized and felt less loneliness were served as subjects and were examined their behavior characteristics. Result indicated that girl B did more approach to other child than girl B did. In study 2, the acts that girl A did to peer were examined by event sample. As a result withdrawal behaviors were observed in 3 episodes. This result suggested that girl A have anxious by relationally victimized

Diagnosis of Polypoidal Choroidal Vasculopathy from Fluorescein Angiography Using Deep Learning

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. Results: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image-level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validated with 56 PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. Conclusions: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. Translational Relevance: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis