A comprehensive walkability evaluation system for promoting environmental benefits.

Pedestrian-oriented urban strategies such as the Paris 15-minute City are needed to respond to the global boiling. Quantitative evaluation of pedestrian-oriented urban objectives is important for various cities, and in this paper, a walkability evaluation system for the advanced model is developed considering the characteristics of a large city. The system calculates the walkability of Seoul. The evaluation system uses the Betweenness index as a weight in the urban network analysis. Considering stations with a high betweenness in urban traffic is essential for evaluating a pedestrian-oriented metropolis. Our findings in this study are that the UNA index in WES is critical for transit-oriented, walkable cities. The large city needs to find the location for mobility hubs or stations to observe the last mile. Installing a mobility hub or station at a high-value location in the city center is functionally important. In a pedestrian-oriented city, citizens can walk and bike the last mile in a busy city center. Walkable cities can encourage active transport and ultimately create more sustainable and environmentally friendly transportation systems. This study offers valuable insights into pedestrian infrastructure, urban systems, and policies that promote green transportation.

Immunopathogenesis of ANCA-Associated Vasculitis.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis is an autoimmune disorder which affects small- and, to a lesser degree, medium-sized vessels. ANCA-associated vasculitis encompasses three disease phenotypes: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). This classification is largely based on clinical presentations and has several limitations. Recent research provided evidence that genetic background, risk of relapse, prognosis, and co-morbidities are more closely related to the ANCA serotype, proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA, compared to the disease phenotypes GPA or MPA. This finding has been extended to the investigation of biomarkers predicting disease activity, which again more closely relate to the ANCA serotype. Discoveries related to the immunopathogenesis translated into clinical practice as targeted therapies are on the rise. This review will summarize the current understanding of the immunopathogenesis of ANCA-associated vasculitis and the interplay between ANCA serotype and proposed disease biomarkers and illustrate how the extending knowledge of the immunopathogenesis will likely translate into development of a personalized medicine approach in the management of ANCA-associated vasculitis.

Correction to: Development of a column-switching LC-MS/MS method of tramadol and its metabolites in hair and application to a pharmacogenetic study.

In the original version of the article titled "Development of a column-switching LC-MS/MS method of tramadol and its metabolites in hair and application to a pharmacogenetic study", published in 41(5):554-563 (https://doi.org/10.1007/s12272-018-1013-7), unfortunately an important statement was missing in the Clinical study section by the author.

Influence of Oxidative Etching Solution Temperatures on the Surface Roughness and Wettability of a Titanium Alloy.

Recently, a simple surface modification treatment of titanium (Ti) was developed to produce nano-and micro-scale features on the surfaces via simple immersion in an oxidative aqueous solution (30% hydrogen peroxide/5% sodium bicarbonate). However, this treatment method of Ti surfaces requires a relatively long immersion time (4 h) in the oxidative solution. In this study, we investigated whether an increase in the temperature of the oxidative etching solution can shorten the immersion time of Ti effectively. Polished grade 5 dental Ti (Ti-6Al-4V) discs were immersed in the oxidative aqueous solution either for 30 or 60 min. The temperature of the etching solution was maintained at 25 (similar to room temperature), 35, or 45 °C during etching. The etched surfaces were studied in terms of micro- and nano-structures, surface roughness, and wettability (surface energy). The increase in the temperature of the solution accelerated the etching effect of Ti and created both micro- and nano-structures on the surfaces more effectively. In particular, immersion for 60 min at the solution temperature of 35 °C significantly increased the surface roughness and wettability, although the etching effect was enhanced further at the solution temperature of 45 °C.

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records.

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

Development of a column-switching LC-MS/MS method of tramadol and its metabolites in hair and application to a pharmacogenetic study.

Hair is a valuable specimen for monitoring long-term drug use. Tramadol is an effective opioid analgesic but is associated with risks such as drug dependence and unexpected toxicity arising from genetic differences in metabolism. However, few studies have been performed on the distribution of tramadol and its metabolites in hair. In the present study, a column-switching LC-MS/MS method was developed and fully validated for the simultaneous determination of tramadol and its phase I and II metabolites in hair. Furthermore, the distribution of tramadol and its metabolites in hair was investigated in a pharmacogenetic study. Tramadol and its metabolites were extracted from hair using methanol and injected onto LC-MS/MS. The validation results of selectivity, matrix effect, linearity, precision and accuracy were satisfactory. The (mean) concentrations of O-desmethyltramadol (ODMT) and N,O-didesmethyltramadol (NODMT) in the CYP2D6*10/*10 and CYP2D6*5/*5 groups were lower than those in the CYP2D6*wt/*wt group, while the (mean) concentrations of N-desmethyltramadol (NDMT) were higher. Moreover, the ratios of ODMT/tramadol, NDMT/tramadol and NODMT/NDMT were well correlated with the CYP2D6 genotypes. The developed method was successfully applied to the clinical study, which demonstrated that the concentrations of a drug and its metabolites in hair were dependent on the polymorphism of its metabolizing enzyme.

Toward Predicting Social Support Needs in Online Health Social Networks.

BACKGROUND: While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. OBJECTIVE: The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. METHODS: We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. RESULTS: The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. CONCLUSIONS: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Personas in online health communities.

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tailored support for each OHC user, we developed personas in OHCs illustrating users' needs and requirements in OHC use. To develop OHC personas, we first interviewed 16 OHC users and administrators to qualitatively understand varying user needs in OHC. Based on their responses, we developed an online survey to systematically investigate OHC personas. We received 184 survey responses from OHC users, which informed their values and their OHC use patterns. We performed open coding analysis with the interview data and cluster analysis with the survey data and consolidated the analyses of the two datasets. Four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers. The results inform users' interaction behavior and attitude patterns with OHCs. We discuss implications for how these personas inform OHCs in delivering personalized informational and emotional support.