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Factors Related to Treatment Refusal in Taiwanese Cancer Patients
Chiang, Ting-Yu,Wang, Chao-Hui,Lin, Yu-Fen,Chou, Shu-Lan,Wang, Ching-Ting,Juang, Hsiao-Ting,Lin, Yung-Chang,Lin, Mei-Hsiang Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.8
Background: Incidence and mortality rates for cancer have increased dramatically in the recent 30 years in Taiwan. However, not all patients receive treatment. Treatment refusal might impair patient survival and life quality. In order to improve this situation, we proposed this study to evaluate factors that are related to refusal of treatment in cancer patients via a cancer case manager system. Materials and Methods: This study analysed data from a case management system during the period from 2010 to 2012 at a medical center in Northern Taiwan. We enrolled a total of 14,974 patients who were diagnosed with cancer. Using the PRECEDE Model as a framework, we conducted logistic regression analysis to identify independent variables that are significantly associated with refusal of therapy in cancer patients. A multivariate logistic regression model was also applied to estimate adjusted the odds ratios (ORs) with 95% confidence intervals (95%CI). Results: A total of 253 patients (1.69%) refused treatment. The multivariate logistic regression result showed that the high risk factors for refusal of treatment in cancer patient included: concerns about adverse effects (p<0.001), poor performance(p<0.001), changes in medical condition (p<0.001), timing of case manager contact (p=.026), the methods by which case manager contact patients (p<0.001) and the frequency that case managers contact patients (${\geq}10times$) (p=0.016). Conclusions: Cancer patients who refuse treatment have poor survival. The present study provides evidence of factors that are related to refusal of therapy and might be helpful for further application and improvement of cancer care.
Chun-Wei Wu,Kuan-Hung Lin,Ming-Chih Lee,Yung-Liang Peng,Ting-Yi Chou,Yu-Sen Chang 한국원예학회 2015 원예과학기술지 Vol.33 No.6
The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson’s quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, △F/Fm’, and qP in response to t reatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and s trong correlations (R² = 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, △F/Fm’, and above-ground DW. Validation of leaf S PAD, NDVI, and △F/Fm’ revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer i s recommended w hen leaf N concentration, SPAD, NVDI, and △F/Fm’ ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively.
Wu, Chun-Wei,Lin, Kuan-Hung,Lee, Ming-Chih,Peng, Yung-Liang,Chou, Ting-Yi,Chang, Yu-Sen Korean Society of Horticultural Science 2015 원예과학기술지 Vol.33 No.6
The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson's quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, ${\Delta}F/Fm$', and qP in response to treatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and strong correlations ($R^2$= 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, ${\Delta}F/Fm$', and above-ground DW. Validation of leaf S PAD, NDVI, and ${\Delta}F/Fm$' revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer i s recommended w hen leaf N concentration, SPAD, NVDI, and ${\Delta}F/Fm$' ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively.
Fa-Po Chung,Chin-Yu Lin,Yenn-Jiang Lin,Shih-Lin Chang,Li-Wei Lo,Yu-Feng Hu,Ta-Chuan Tuan,Tze-Fan Chao,Jo-Nan Liao,Ting-Yung Chang,Shih-Ann Chen 대한심장학회 2018 Korean Circulation Journal Vol.48 No.10
Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is predominantly an inherited cardiomyopathy with typical histopathological characteristics of fibro-fatty infiltration mainly involving the right ventricular (RV) inflow tract, RV outflow tract, and RV apex in the majority of patients. The above pathologic evolution frequently brings patients with ARVD/C to medical attention owing to the manifestation of syncope, sudden cardiac death (SCD), ventricular arrhythmogenesis, or heart failure. To prevent future or recurrent SCD, an implantable cardiac defibrillator (ICD) is highly desirable in patients with ARVD/C who had experienced unexplained syncope, hemodynamically intolerable ventricular tachycardia (VT), ventricular fibrillation, and/or aborted SCD. Notably, the management of frequent ventricular tachyarrhythmias in ARVD/C is challenging, and the use of antiarrhythmic drugs could be unsatisfactory or limited by the unfavorable side effects. Therefore, radiofrequency catheter ablation (RFCA) has been implemented to treat the drug-refractory VT in ARVD/C for decades. However, the initial understanding of the link between fibro-fatty pathogenesis and ventricular arrhythmogenesis in ARVD/C is scarce, the efficacy and prognosis of endocardial RFCA alone were limited and disappointing. The electrophysiologists had broken through this frontier after better illustration of epicardial substrates and broadly application of epicardial approaches in ARVD/C. In recent works of literature, the application of epicardial ablation also successfully results in higher procedural success and decreases VT recurrences in patients with ARVD/C who are refractory to the endocardial approach during long-term follow-up. In this article, we review the important evolution on the delineation of arrhythmogenic substrates, ablation strategies, and ablation outcome of VT in patients with ARVD/C.
Grey Neural Network-Based Forecasting System for Vision-Guided Robot Trajectory Tracking
Shih-Hung Yang,Chung-Hsien Chou,Chen-Fang Chung,Wen-Pang Pai,Tse-Han Liu,Yung-Sheng Chang,Jung-Che Li,Huan-Chan Ting,Yon-Ping Chen 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
This paper presents a grey neural network-based forecasting system (GNNFS) in solving the prediction problem. GNNFS adopts a grey model to predict the signal and a neural network (NN) to forecast the prediction error of the grey model. A sequential batch learning (SBL) is developed to adjust the weights of the NN. The proposed GNNFS is applied to a binocular robot, called an Eye-Robot, for human-robot interaction which involved predicting the trajectory of a participant’s hand and tracking the hand. By applying the SBL, the GNNFS can gradually learn to predict the trajectory of the hand and track it well. The experimental results show that the GNNFS can carry out the SBL in real-time for vision-guided robot trajectory tracking.