Discovery of new dibenzodiazepine derivatives as antibacterials against intracellular bacteria.

The emergence and spread of multidrug-resistant bacteria underscore the critical need for novel antibacterial interventions. In our screening of 12 synthesized thienobenzodiazepines, pyridobenzodiazepines, and dibenzodiazepines, we successfully identified a small molecule compound SW33. Notably, SW33 demonstrated potent inhibitory activity against intracellular multidrug-resistant and fluoroquinolone-resistant strains of S. typhimurium in both macrophages and epithelial cells. Furthermore, SW33 was also effective against intramacrophagic Salmonella typhi, Yersinia enterocolitica, and Listeria monocytogenes. These significant findings suggest that SW33 possesses broad-spectrum activity against intracellular bacteria.

Repurposing the tyrosine kinase inhibitor nilotinib for use against intracellular multidrug-resistant Salmonella Typhimurium.

BACKGROUND/PURPOSE: The increasing incidence of infections caused by multidrug-resistant Salmonella enterica has become a serious threat to global public health. Here, we found that the tyrosine kinase inhibitor nilotinib exhibits antibacterial activity against intracellular S. enterica serovar Typhimurium in RAW264.7 macrophages. Thus, we aimed to pharmacologically exploit the anti-intracellular Salmonella activity of nilotinib and to elucidate its mechanism of action. METHODS: The antibacterial activity of the compounds was assessed by high-content analysis (HCA) and intracellular CFU, minimum inhibitory concentration (MIC), and bacterial growth assays. The cytotoxicity of the compounds was evaluated by HCA and a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) cell viability assays. The levels of cellular AMPK, phospho-AMPK, Atg7 and ß-actin were determined by immunoblotting. RESULTS: The screen identified two small molecule compounds (SCT1101 and SCT1104) with potent activity against intracellular S. Typhimurium. Moreover, SCT1101 and SCT1104 enhanced the efficacy of ciprofloxacin and cefixime against intracellular S. Typhimurium. However, only SCT1101 exhibited activity against intracellular MDR and fluoroquinolone-resistant S. Typhimurium isolates. Subsequent mechanistic studies showed that neither of these nilotinib derivatives increased the phospho-AMPK level in RAW264.7 cells. Neither the AMPK inhibitor compound C nor SBI-0206965 reversed the inhibitory effects of SCT1101 and SCT1104 on intracellular Salmonella. Furthermore, neither blockade of autophagy by 3-MA nor shRNA-mediated knockdown of Atg7 protein expression in RAW264.7 cells affected the antibacterial activity of SCT1101 and SCT1104. CONCLUSION: The structure of nilotinib could be used to develop novel therapeutics for controlling MDR S. Typhimurium infections.

Discovery of antipsychotic loxapine derivatives against intracellular multidrug-resistant bacteria.

The emergence and spread of multidrug-resistant bacteria highlight the need for new antibacterial interventions. A screening of 24 newly synthesized dibenzoxazepines identified a small molecule compound, SW14, with potent inhibitory activity against intracellular multidrug-resistant and fluoroquinolone-resistant strains of S. typhimurium in macrophages and epithelial cells. Moreover, intra-macrophagic Salmonella typhi, Yersinia enterocolitica, and Listeria monocytogenes and methicillin-resistant Staphylococcus aureus are also susceptible to SW14. Overall, our findings suggest that SW14 has a broad-spectrum activity against intracellular bacteria.

Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model.

Background: Fuzzy inference systems (FISs) based on fuzzy theory in mathematics were previously applied to infer supplementary points for the limited number of monitoring sites and improve the uncertainty of spatial data. Therefore we adopted the FIS method to simulate spatiotemporal levels of air pollutants [particulate matter <2.5 µm (PM2.5), sulfur dioxide (SO2) and (NO2)] and investigated the association of levels of air pollutants with the community-based prevalence of chronic kidney disease (CKD). Methods: A Complex Health Screening program was launched during 2012-2013 and a total of 8284 community residents in Chiayi County, which is located in southwestern Taiwan, received a series of standard physical examinations, including measurement of estimated glomerular filtration rate (eGFR). CKD cases were defined as eGFR <60 mL/min/1.73 m2 and were matched for age and gender in a 1:4 ratio of cases:controls. Data on air pollutants were collected from air quality monitoring stations during 2006-2016. The longitude, latitude and recruitment month of the individual case were entered into the trained FIS. The defuzzification process was performed based on the proper membership functions and fuzzy logic rules to infer the concentrations of air pollutants. In addition, we used conditional logistic regression and the distributed lag nonlinear model to calculate the prevalence ratios of CKD and the 95% confidence interval. Confounders including Framingham Risk Score (FRS), diabetes, gout, arthritis, heart disease, metabolic syndrome and vegetables consumption were adjusted in the models. Results: Participants with a high FRS (>10%), diabetes, heart disease, gout, arthritis or metabolic syndrome had significantly increased CKD prevalence. After adjustment for confounders, PM2.5 levels were significantly increased in CKD cases in both single- and two-pollutant models (prevalence ratio 1.31-1.34). There was a positive association with CKD in the two-pollutant models for NO2. However, similar results were not observed for SO2. Conclusions: FIS may be helpful to reduce uncertainty with better interpolation for limited monitoring stations. Meanwhile, long-term exposure to ambient PM2.5 appears to be associated with an increased prevalence of CKD, based on a FIS model.

Effectiveness of eAsthmaCare on Symptoms, Childhood Asthma Control Test, and Lung Function among Asthmatic Children.

A web-based self-health management system-eAsthmaCare, was developed as an intervention for asthmatic children. A randomized controlled trial was performed. Consent was obtained for 98 children with asthma to participate in the study and the pre- and post-test data collection process. The experimental group was given access to eAsthmaCare online management, the control group was subjected to general asthma management. The experimental and control groups' asthma symptoms, Childhood Asthma Control Test (C-ACT) scores, and lung function were evaluated, and their pre- and 3-month post-test results were compared. The following records were maintained: (1) medication record (2) daily asthma symptoms log (3) monthly C-ACT and lung function records. The C-ACT results indicated a p-value of < .01 for: overall improvements to childhood asthma symptoms, time effect, group and time interaction effects, and group and time interaction effects in relation to sleeping condition on the previous day; cough symptom time effect, and group and time interaction effects; the two groups' time effect in relation to cough symptoms; the two groups' time effect in relation to monthly activity restrictions (number of days); and the two groups' time effect in relation to nasal symptoms; the two groups' time effect; and group and time interaction effects (p < .01). In terms of the predictive values for lung function (FVC, FEV1, PEFR), the improvements in both groups were not statistically significant. The implementation of the eAsthmaCare intervention might have a positive impact on pediatric patients, making it an effective management tool for monitoring asthmatic children's physical function and discomfort.

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management.

We established a web-based ubiquitous health management (UHM) system, "ECG4UHM", for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert-Huang transform (HHT) with empirical mode decomposition to calculate ECGs' intrinsic mode functions (IMFs). The area centroids of the IMFs' marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC-VPC) and the fibril-rapid pattern (i.e., AFib-VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC-VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.

A Novel Dibenzoxazepine Attenuates Intracellular Salmonella Typhimurium Oxidative Stress Resistance.

Salmonella enterica serovar Typhimurium is the leading cause of invasive nontyphoidal salmonellosis. Additionally, the emergence of multidrug-resistant S. Typhimurium has further increased the difficulty of controlling its infection. Previously, we showed that an antipsychotic drug, loxapine, suppressed intracellular Salmonella in macrophages. To exploit loxapine's antibacterial activity, we simultaneously evaluated the anti-intracellular Salmonella activity and cytotoxicity of newly synthesized loxapine derivatives using an image-based high-content assay. We identified that SW14 exhibits potent suppressive effects on intramacrophagic S. Typhimurium with an 50% effective concentration (EC50) of 0.5 µM. SW14 also sensitized intracellular Salmonella to ciprofloxacin and cefixime and effectively controlled intracellular multidrug- and fluoroquinolone-resistant S. Typhimurium strains. However, SW14 did not affect bacterial growth in standard microbiological broth or minimal medium that mimics the phagosomal environment. Cellular autophagy blockade by 3-methyladenine (3-MA) or shATG7 elevated the susceptibility of intracellular Salmonella to SW14. Finally, reactive oxygen species (ROS) scavengers reduced the antibacterial efficacy of SW14, but the ROS levels in SW14-treated macrophages were not elevated. SW14 decreased the resistance of outer membrane-compromised S. Typhimurium to H2O2. Collectively, our data indicated that the structure of loxapine can be further optimized to develop new antibacterial agents by targeting bacterial resistance to host oxidative-stress defense. IMPORTANCE The incidence of diseases caused by pathogenic bacteria with resistance to common antibiotics is consistently increasing. In addition, Gram-negative bacteria are particularly difficult to treat with antibiotics, especially those that can invade and proliferate intracellularly. In order to find a new antibacterial compound against intracellular Salmonella, we established a cell-based high-content assay and identified SW14 from the derivatives of the antipsychotic drug loxapine. Our data indicate that SW14 has no effect on free bacteria in the medium but can suppress the intracellular proliferation of multidrug-resistant (MDR) S. Typhimurium in macrophages. We also found that SW14 can suppress the resistance of outer membrane compromised Salmonella to H2O2, and its anti-intracellular Salmonella activity can be reversed by reactive oxygen species (ROS) scavengers. Together, the findings suggest that SW14 might act via a virulence-targeted mechanism and that its structure has the potential to be further developed as a new therapeutic against MDR Salmonella.

AIoT-Enabled Rehabilitation Recognition System-Exemplified by Hybrid Lower-Limb Exercises.

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.

A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases.

BACKGROUND AND OBJECTIVE: Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This study aims to establish a health informatics transformation model (HITM) based upon intelligent cloud computing with the self-developed analytics modules by open source technique. The model was exemplified by the open data of type 2 diabetes mellitus (DM2) with related cardiovascular diseases. METHODS: The Apache-SPARK framework was employed to generate the infrastructure of the HITM, which enables the machine learning (ML) algorithms including random forest, multi-layer perceptron classifier, support vector machine, and naïve Bayes classifier as well as the regression analysis for intelligent cloud computing. The modeling applied the MIMIC-III open database as an example to design the health informatics data warehouse, which embeds the PL/SQL-based modules to extract the analytical data for the training processes. A coupling analysis flow can drive the ML modules to train the sample data and validate the results. RESULTS: The four modes of cloud computation were compared to evaluate the feasibility of the cloud platform in accordance with its system performance for more than 11,500 datasets. Then, the modeling adaptability was validated by simulating the featured datasets of obesity and cardiovascular-related diseases for patients with DM2 and its complications. The results showed that the run-time efficiency of the platform performed in around one minute and the prediction accuracy of the featured datasets reached 90%. CONCLUSIONS: This study helped contribute the modeling for efficient transformation of health informatics. The HITM can be customized for the actual clinical database, which provides big data for training, with the proper ML modules for a predictable process in the cloud platform. The feedback of intelligent computing can be referred to risk assessment in health promotion.

Fuzzy inference system for modeling the environmental risk map of air pollutants in Taiwan.

This study aimed to improve the uncertainty in spatial data of risk assessment through a Fuzzy inference system (FIS) as a way to conduct an environmental risk map of air pollution in Taiwan. In modeling, the feature inputs of FIS included the geographic coordinates and time, while the outputs are the pollutant concentrations. The outputs are supplements to the concentration contour on the map in comparison with Kriging interpolation. In our model, the FIS was designed using the official open data of air pollutants, including Pb and PM2.5 that were collected from the monitoring stations in mid-southern Taiwan. The model involved data filtration and imputation in the preliminary scheme to extract the historical data for analysis. We used the data of Pb (2001-2013) and PM2.5 (2006-2013) for the training process, and then used the data from 2014 to 2015 for validation. Our model was able to compute the smaller errors of inferred and measured values of Pb and PM2.5 than the conventional method. The approach was applied to deduce the exposure of PM2.5 distributed over the Taiwan Island in accordance with the governmental open data of seventy-three stations during 2006-2016 in order to produce our risk map. The designed model upon Fuzzy inference accesses potential risks of spatiotemporal exposures in the unmeasured locations with feasibility and adaptability for environmental management.

Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements.

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.

Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics.

PURPOSE: This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. METHODS: The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. RESULTS: The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230K sets. Both setups reached efficiency of about 105 sets per second. CONCLUSIONS: The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health.

Developing a ubiquitous health management system with healthy diet control for metabolic syndrome healthcare in Taiwan.

BACKGROUND AND OBJECTIVE: Self-management in healthcare can allow patients managing their health data anytime and everywhere for prevention of chronic diseases. This study established a prototype of ubiquitous health management system (UHMS) with healthy diet control (HDC) for people who need services of metabolic syndrome healthcare in Taiwan. METHODS: System infrastructure comprises of three portals and a database tier with mutually supportive components to achieve functionality of diet diaries, nutrition guides, and health risk assessments for self-health management. With the diet, nutrition, and personal health database, the design enables the analytical diagrams on the interactive interface to support a mobile application for diet diary, a Web-based platform for health management, and the modules of research and development for medical care. For database integrity, dietary data can be stored at offline mode prior to transformation between mobile device and server site at online mode. RESULTS: The UHMS-HDC was developed by open source technology for ubiquitous health management with personalized dietary criteria. The system integrates mobile, internet, and electronic healthcare services with the diet diary functions to manage healthy diet behaviors of users. The virtual patients were involved to simulate the self-health management procedure. The assessment functions were approved by capturing the screen snapshots in the procedure. The proposed system development was capable for practical intervention. CONCLUSION: This approach details the expandable framework with collaborative components regarding the self-developed UHMS-HDC. The multi-disciplinary applications for self-health management can support the healthcare professionals to reduce medical resources and improve healthcare effects for the patient who requires monitoring personal health condition with diet control. The proposed system can be practiced for intervention in the hospital.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

An activity recognition model using inertial sensor nodes in a wireless sensor network for frozen shoulder rehabilitation exercises.

This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%-95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises.

Development of online diary and self-management system on e-Healthcare for asthmatic children in Taiwan.

OBJECTIVE: Many regional programs of the countries educate asthmatic children and their families to manage healthcare data. This study aims to establish a Web-based self-management system, eAsthmaCare, to promote the electronic healthcare (e-Healthcare) services for the asthmatic children in Taiwan. The platform can perform real time online functionality based upon a five-tier infrastructure with mutually supportive components to acquire asthma diaries, quality of life assessments and health educations. METHODS: We have designed five multi-disciplinary portions on the interactive interface functioned with the analytical diagrams: (1) online asthma diary, (2) remote asthma assessment, (3) instantaneous asthma alert, (4) diagrammatical clinic support, and (5) asthma health education. The Internet-based asthma diary and assessment program was developed for patients to process self-management healthcare at home. In addition, the online analytical charts can help healthcare professionals to evaluate multi-domain health information of patients immediately. RESULTS: eAsthmaCare was developed by Java™ Servlet/JSP technology upon Apache Tomcat™ web server and Oracle™ database. Forty-one voluntary asthmatic children (and their parents) were intervened to examine the proposed system. Seven domains of satisfiability assessment by using the system were applied for approving the development. The average scores were scaled in the acceptable range for each domain to ensure feasibility of the proposed system. CONCLUSION: The study revealed the details of system infrastructure and developed functions that can help asthmatic children in self-management for healthcare to enhance communications between patients and hospital professionals.

Measurement equivalence and feasibility of the EORTC QLQ-PR25: paper-and-pencil versus touch-screen administration.

OBJECTIVE: We assessed the measurement equivalence and feasibility of the paper-and-pencil and touch-screen modes of administration of the Taiwan Chinese version of the EORTC QLQ-PR25, a commonly used questionnaire to evaluate the health-related quality of life (HRQOL) in patients with prostate cancer in Taiwan. METHODS: A cross-over design study was conducted in 99 prostate cancer patients at an urology outpatient clinic. Descriptive exact and global agreement percentages, intraclass correlation, and equivalence test based on minimal clinically important difference (MCID) approach were used to examine the equity of HRQOL scores between these two modes of administration. We also evaluated the feasibility of computerized assessment based on patients' acceptability and preference. Additionally, we used Rasch rating scale model to assess differential item functioning (DIF) between the two modes of administration. RESULTS: The percentages of global agreement in all domains were greater than 85% in the EORTC QLQ-PR25. All results from equivalence tests were significant, except for Sexual functioning, indicating good equivalence. Only one item exhibited DIF between the two modes. Although nearly 80% of the study patients had no prior computer-use experience, the overall proportion of acceptance and preference for the touch-screen mode were quite high and there was no significant difference across age groups or between computer-use experience groups. CONCLUSIONS: The study results showed that the data obtained from the modes of administration were equivalent. The touch-screen mode of administration can be a feasible and suitable alternative to the paper-and-pencil mode for assessment of patient-reported outcomes in patients with prostate cancer.

Psychometric evaluation of the Taiwan Chinese version of the EORTC QLQ-PR25 for HRQOL assessment in prostate cancer patients.

OBJECTIVE: To evaluate the psychometric properties of the Taiwan Chinese Version of the EORTC QLQ-PR25 health-related quality of life (HRQOL) questionnaire for patients with prostate cancer. METHODS: 135 prostate cancer patients were recruited in the urology outpatient clinic of a university teaching hospital. Each patient completed the EORTC QLQ-PR25 at every clinic visit between 2004 and 2008, totaling 633 assessments. Confirmatory factor analysis and Rasch analysis were used to evaluate the domain- and item-level psychometric properties. RESULTS: The results supported the unidimensionality of each of the four EORTC QLQ-PR25 domains (urinary, bowel, and hormonal-treatment-related symptoms, and sexual functioning). Item calibrations for each domain were found invariant across the three assessment time periods. The item-person maps showed 71.3% of item coverage for the urinary symptoms domain and 13-42.7% for the other three domains. CONCLUSIONS: The Taiwan Chinese Version of the EORTC QLQ-PR25 questionnaire is reliable and can be used to measure HRQOL over time. Adding new items to each domain may improve its clinical content coverage and measurement precision.

A real time online assessment system with modelized architecture on clinical infometrics for patient reported outcomes of prostate cancer.

OBJECTIVE: The aim of this study was to establish a real time online health and decision support system with the novel information technology integrating modelized architecture and Web services for clinical infometrics on patient reported outcome (PRO) and quality of life (QOL) for prostate cancer patients. METHODS: The patient-oriented interface was practiced with object relation mapping (ORM) and clinical data warehouse to collaborate QOL measurement and medical informatics through internet by incorporating a variety of hospital information systems. The conceptual infrastructure was designed by five primary layers to organize the data flow of online assessment and clinical data for real-time decision support. RESULTS: A preliminary knowledge bank was formed by feedback of expert opinions to provide online guidance for decision references. Observation and assessment of prostate cancer patients' QOL and clinical markers were immediately tracked with automatic computation algorithm to improve health care quality in the treatment cycle. CONCLUSIONS: The established Web-based system can help clinicians concurrently collect and analyze real-time PROs and QOL for enhancing communication with patients and improving the quality of care.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.