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The cases of stomach cancer (SC) incidence are increasing per year and the SC burden has remained very high in some countries. We aimed to evaluate the global geographical variation in SC incidence and temporal trends from 1978 to 2007, with an emphasis on the effect of birth cohort. Joinpoint regression and age-period-cohort model were applied. From 2003 to 2007, male rate were 1.5- to 3-fold higher than female in all countries. Rates were highest in Eastern Asian and South American countries. Except for Uganda, all countries showed favorable trends. Pronounced cohort-specific increases in risk for recent birth cohorts were seen in Brazil, Colombia, Iceland, New Zealand, Norway, Uganda and US white people for males and in Australia, Brazil, Colombia, Costa Rica, Czech Republic, Ecuador, Iceland, India, Malta, New Zealand, Norway, Switzerland, United Kingdom, Uganda, US black and white people for females. The cohort-specific ratio for male significantly decreased in Japan, Malta and Spain for cohorts born since 1950 and in Austria, China, Croatia, Ecuador, Russia, Switzerland and Thailand for cohorts born since 1960 and for female in Japan for cohorts born since 1950 and in Canada, China, Croatia, Latvia, Russia and Thailand for cohorts born since 1960. Disparities in incidence and carcinogenic risk persist worldwide. The favorable trends may be due to changes in environmental exposure and lifestyle, including decreased Helicobacter pylori prevalence, increased intake of fresh fruits and vegetables, the availability of refrigeration and decreased intake of salted and preserved food and smoking prevalence.

BackgroundIn China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue.Methodology/Principal findingsWeekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China.Conclusion and significanceThe proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG-based signature, is useful in predicting recurrence for patients with E-HCC.

Microplastic pollution is an emerging environmental problem, and little research has focused on its impact on the human body. Based on retrospective case series, the study required participants to fill out a questionnaire and provide sputum samples in order to investigate the presence of microplastics in human sputum and determine whether humans involuntarily inhale them. A total of 22 patients suffering from different respiratory diseases were recruited. We used an Agilent 8700 laser infrared imaging spectrometer and Fourier-transform infrared microscope to analyze sputum samples and evaluate microplastics in the respiratory tract. Remarkably, the size range of the method for detecting microplastics in our study is 20–500 μm. The results showed that 21 types of microplastics were identified, and polyurethane was dominant, followed by polyester, chlorinated polyethylene, and alkyd varnish, accounting for 78.36% of the total microplastics. Most of the aspirated microplastics detected are smaller than 500 μm in size (median: 75.43 μm; interquartile range: 44.67–210.64 μm). Microplastics are ubiquitous in all sputum, indicating that inhalation is a potential way for plastics to enter the human body. Additionally, the quantities of microplastic types in the respiratory tract are related to smoking, invasive examination, etc. (P < 0.05). This study sheds new light on microplastic exposure, which provides basic data for the risk assessment of microplastics to human health.