Papillary thyroid cancer (PTC) has inflicted huge threats to the health of mankind. Metal pollution could be a potential risk factor of PTC occurrence, but existing relevant epidemiological researches are limited. The current case-control study was designed to evaluate the relationships between exposure to multiple metals and the risk of PTC. A total of 262 histologically confirmed PTC cases were recruited. Age- and gender-matched controls were enrolled at the same time. Urine samples were used as biomarkers to reflect the levels of environmental exposure to 13 metals. Conditional logistic regression models were adopted to assess the potential association. Single-metal and multi-metal models were separately conducted to evaluate the impacts of single and co-exposure to 13 metals. The increased concentration of urinary Cd, Cu, Fe, and Pb quartiles was found significant correlated with PTC risk. We also found the decreased trends of urinary Se, Zn, and Mn quartiles with the ORs for PTC. These dose-response associations between Pb and PTC were observed in the single-metal model and remained significant in the multi-metal model (OR25-50th=1.39, OR50-75th=3.32, OR>75th=7.62, p for trend <0.001). Our study suggested that PTC was positively associated with urinary levels of Cd, Cu, Fe, Pb, and inversely associated with Se, Zn, and Mn. Targeted public health policies should be made to improve the environment and the recognition of potential risk factors. These findings need additional studies to confirm in other population.
Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%. 相似文献