0%). After initial examination and diagnosis, 4.2% patients refused treatment. Conclusions The epidemiological statistics of TDI in Xi'an, China show consistency with other studies from around the world, but they also vary in diagnosis proportion and the choice of treatments. This information may further instruct treatment, prevention and emergency resources distribution to target the high-risk groups.Background Preventive and early diagnostic methods such as health promotion and disease screening are increasingly advocated to improve detection and survival rates for oral cancer.  https://www.selleckchem.com/products/myk-461.html  These strategies are most effective when targeted at "high-risk" individuals and populations. Bayesian disease-mapping modelling is a statistical method to quantify and explain spatial and temporal patterns for risk and covariate factor influence, thereby identifying "high-risk" sub-regions or "case clustering" for targeted intervention. Rarely applied to oral cancer epidemiology, this paper highlights the efficacy of disease mapping for the Hong Kong population. Methods Following ethical approval, anonymized individual-level data for oral cancer diagnoses were obtained retrospectively from the Clinical Data Analysis and Reporting System (CDARS) of the Hong Kong Hospital Authority (HA) database for a 7-year period (January 2013 to December 2019). Data facilitated disease mapping and estimation of relative risks of oral cancer incidence and mortality. Results A total of 3,341 new oral cancer cases and 1,506 oral cancer-related deaths were recorded during the 7-year study period. Five districts, located in Hong Kong Island and Kowloon, exhibited considerably higher relative incidence risks with 1 significant "case cluster" hotspot. Six districts displayed higher mortality risks than expected from territory-wide values, with highest risk identified for two districts of Hong Kong Island. Conclusion Bayesian disease mapping is successful in identifying and characterizing "high-risk" areas for oral cancer incidence and mortality within a community. This should facilitate targeted preventive and interventional strategies. Further work is encouraged to enhance global-level data and comprehensive mapping of oral cancer incidence, mortality and survival.Objective To evaluate the impact of the coronavirus pandemic and the quarantine in orthodontic appointments, and patients' anxiety and concerns about their ongoing orthodontic treatment. Settings and sample population Patients from private dental clinics of two orthodontists that were undergoing active orthodontic treatment. Material and methods An online anonymous questionnaire regarding their anxiety about the coronavirus situation, availability/acceptance to attend an appointment, among others, was answered by orthodontic patients. Descriptive statistics with percentages was performed and responses were compared between sexes, cities, and association of the feelings/level of anxiety of patients and willingness to attend an appointment were performed with chi-square, independent t test, one-way ANOVA and Tukey's tests. Results The questionnaire was answered by 354 patients (231 female; 123 male) with mean age of 35.49 years. Most patients are respecting the quarantine, 44.7% related to be calm and 46.3% afraid or anxious. The level of anxiety was greater for females than males. There was significant association of the level of anxiety and the willingness to attend an appointment. The greatest concern of patients was delay in the end of treatment. Conclusion The quarantine and coronavirus pandemic showed to have impact on orthodontic appointments and patients' anxiety. Patients willing to attend an orthodontic appointment presented significantly lower level of anxiety than patients that would not go or would go only in urgency/emergency. Females were more anxious than males about coronavirus pandemic, quarantine and impact on their orthodontic treatments. Delay in treatment was the greatest concern of patients undergoing orthodontic treatment.With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy-haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies The accident-risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario-risk-accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short-term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context-aware risk knowledge reasoning framework.Background The number of invasive Candida infections has significantly increased in recent decades. For the successful treatment of fungal infections, rapid identification at the species level, particularly in polyfungal infections, is a key factor. In this study, four commercially available chromogenic media, CandiSelect™ 4 (CS4), chromID™ Candida Agar (CCA), BBL™ CHROMagar™ Candida Medium (BBL) and Brilliance™ Candida Agar (BCA) were evaluated for Candida identification. Material/methods Overall, 181 bronchial secretion samples from intensive care patients were analysed prospectively. In addition, 18 primarily sterile materials, previously tested positive for Candida, were investigated retrospectively. All samples were cultured as recommended by the manufacturer and visually inspected after 24 and 48 hours by three independent investigators. As a control, colonies were identified by MALDI-TOF MS. Specificity and sensitivity were determined for C albicans identification prospectively. Results CS4 and BCA showed the best overall consensus with the identification results reached by MALDI-TOF MS for Candida albicans and species.