Objective: To develop a method for determination of metabolites of diphenylmethane diisocyanate (MDI) in urine, i.e. methylenedianiline (MDA) by high performance liquid chromatography-tandem mass (LC-MS-MS) . Methods: Urine samples were prepared by hydrolyzation with sulfuric acid and extraction by acetonitrile, and then separated on a Shim-pack XR-ODS column, analyzed with high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) . The external solvent standard calibration were tested. Results: The linearity ranges were 0.05~20.00 μg/L, The related coefficients were 0.999 5. The limit of detection was 0.02 μg/L. The rats of recovery were 91.0%~103.4%. The relative standard deviations were between 2.7%~7.3%. Conclusion The method was sensitive, accurate and suitable for the MDA determination in urine of MDI exposed population.

Objective To analyze the qualified rate of the fiducial markers during 3D-printing co-planar template assisted CT-guided implantation in stereotactic body radiation therapy by CyberKnife,and to explore the cause of the unavailable markers in order to provide the reference for the fiducial implantation,treatment planning and radiotherapy delivery.Methods From March to December 2017,a total of 52 cases were planned to stereotactic body radiation therapy(SBRT) using CyberKnife by fiducial tracking,and the fiducial markers were implanted based on CT-guided 3D-printing co-planar template,including 22 in lung,12 in liver,5 in mediastinal lymph node,8 in retroperitoneal lymph node,3 in pancreas,each in celiac and pelvic lymph nodes,respectively.Except 7 cases not fit for CyberKnife treatment,45 cases finished the treatment of CyberKnife,but there were 3 cases changed to spine tracking due to unqualified fiducial markers.The number of fiducial markers used and the qualified rate of fiducial markers were analyzed,and the cause of unqualified fiducial markers was studied.Results A total of 131 fiducial markers were impanted into 42 cases who finally received the treatment of CyberKnife by fiducial tracking,including 85 fiducial markers qualified (64.89％) and 46 fiducial markers unqualified (35.11％).The main causes of the unqualified fiducial markers varied,including outrange of rigidity error(26.08％),fiducial markers unavailable(41.31％),and other (32.61％).Conclusions The 3D-printing co-planar template assisted CT-guided implantation could reduce the number of puncture needles used,help to decrease the risk of puncture and trauma and the incidence of complications after the fiducial markers implantation.However,the fiducial markers implanted by this way would be abandoned by a variety of causes and should be taken into account before the fiducial markers implantation.

OBJECTIVE: To compare the results of occupational health risk of 2-butoxyethanol(2-BE) by two risk assessment methods. METHODS: Occupational health investigation and detecting 2-BE level in workplace were carried out in a bicycle manufacturing factory in Tianjin City, a printing factory in Shenzhen City and an automobile manufacturing factory in Beijing City. The occupational health risk of 2-BE was assessed by Singapore's semi-quantitative risk assessment model and occupational hazards risk assessment index method. The risk classification results of the 2 risk assessment methods were compared and analyzed. RESULTS: The results of Singapore's semi-quantitative risk assessment method showed that all the 2-BE risk ratios of the decals workshop in the bicycle manufacturing factory, the binding and printing workshops of the printing factory, the spray finishing and the intermediate painting and the electrophoresis workshops of the automobile manufacturing factory were 0.4. The classification of 2-BE risk ratios belongs to low risk level. The results of occupational hazards risk assessment index method showed that the risk ratios of decals workshop in the bicycle manufacturing factory, the binding and printing of the printing factory, the spray finishing and the intermediate painting and the electrophoresis of the automobile manufacturing factory were 0.4, 0.4 and 0.2, respectively, which correspondence to low, low and negligible risk classification, respectively. The two methods were consistent with the appraisal positions of decals post in the bicycle manufacturing factory and the evaluation of binding and printing positions of a printing factory, although the risk assessment results of key positions in the paint shop of an automobile manufacturing industry were inconsistent. CONCLUSION: The occupational hazard risk assessment index method takes into account of the health effects, exposure conditions and operating conditions, and can comprehensively and accurately assess the occupational health risks caused by 2-BE.

Background With the increasing exposure to hazardous chemicals in the workplace and frequency of occupational injuries and occupational safety accidents, the acquisition of occupational exposure limits of hazardous chemicals is imminent. Objective To obtain more unknown immediately dangerous to life or health (IDLH) concentrations of hazardous chemicals in the workplace by exploring the application of quantitative structure-activity relationship (QSAR) prediction method to IDLH concentrations, and to provide a theoretical basis and technical support for the assessment and prevention of occupational injuries. Methods QSAR was used to correlate the IDLH values of 50 benzene and its derivatives with the molecular structures of target compounds. Firstly, affinity propagation algorithm was applied to cluster sample sets. Secondly, Dragon 2.1 software was used to calculate and pre-screen 537 molecular descriptors. Thirdly, the genetic algorithm was used to select six characteristic molecular descriptors as dependent variables and to construct a multiple linear regression model (MLR) and two nonlinear models using support vector machine (SVM) and artificial neural network (ANN) respectively. Finally, model performance was evaluated by internal and external validation and Williams diagram was drawn to determine the scopes of selected models. Results The ANN model results showed that \begin{document}$ {R}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}}^{2} $\end{document}=0.8526 and \begin{document}$ {R}_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}^{2} $\end{document}=0.8505 respectively, root mean square (RMSE) error=0.5243, mean absolute error (MAE)=0.4610, internal and external validation coefficients \begin{document}$ {Q}_{\mathrm{l}00}^{2} $\end{document}=0.8476 and \begin{document}$ {Q}_{\mathrm{e}\mathrm{x}\mathrm{t}}^{2} $\end{document}=0.8905 respectively. By comparison, the performance verification parameters of the ANN model were superior to the MLR and SVM models, and all substances were in the applicable domain. Conclusion At present, the ANN model has the best performance in fitting ability, stability, and prediction, and is suitable for predicting IDLH concentrations of benzene and its derivatives. Predicting the IDLH concentraitons of benzene and its derivatives by QSAR method is an effective method, and provides a theoretical basis and technical support for the development of occupational health and safety.