Application of Multiple Occupational Health Risk Assessment Models for Metal Fumes in Welding Process.

Welding fumes have an important role to create the adverse health effects. So, the aim of this study was to use of multiple occupational health risk assessment models for metal fumes in welding process. This cross-sectional study was conducted among welding workers. Sampling of heavy metals such as Sn, Zn, Al, Fe, Cd, Pb, Cu, Mn, Ni, Cr, and As was provided based on the National Institute for Occupational Safety and Health (NIOSH) method 7300 and analyzed by inductively coupled plasma mass spectroscopy (ICP-MS). Risk assessment was managed by four methods including Malaysia's method, Control of Substances Hazardous to Health Essentials (COSHH model), Chinese OHRA standard (GBZ/T 298-2017), and EPA method. Also, Monte Carlo simulation was used to examine the uncertainties by using the Crystal Ball tool. To compare the models, the risk levels of each model were converted into the risk ratio and the SPSS 22.0 software was used to the statistical analysis. The consistency of the two occupational health risk assessment models was examined by Cohen's Kappa. Risk ration was the highest level for Cr (VI) fumes in all models. Also, carcinogenic risk was unacceptable for all examined fumes. Moreover, non-carcinogenic risk was the highest (HI > 1) for As fumes. Mont Carlo simulations suggested that exposure time (ET) had a significant effect on the risk. Also, there was a good consistency between Malaysia method/GBZ/T 298-2017 and COSHH model/GBZ/T 298-2017. Therefore, it is recommended that the engineering and administrative controls should be provided to reduce exposure.

Meta-Analysis of Body Concentration of Polychlorinated Biphenyls and Prostate Cancer.

Prostate Cancer (PCa) is the second most common hormone-sensitive neoplasm among men and the fifth cause of death due to malignancy in developed countries. Moreover, studies have shown the links between polychlorinated biphenyls (PCBs) and hormone-related cancers such as prostate cancer. Hence, we conducted a systematic review and meta-analysis to evaluate the potential relationship between the PCBs and developing PCa. In this meta-analysis study, the relevant databases such as Web of Science, PubMed, and Scopus were studied for English research. The Newcastle-Ottawa Scale was applied to evaluate the quality of the selected publications. The GRADE method was used to assess the risk of bias studies. After reviewing the relevant studies, a cohort and seven case-control studies entered the meta-analysis. These articles were published during 2003-2021 with 2989 participants and 1212 PCa cases. The heterogeneity among the studies was significant (p = 0.001, I2 = 70.61). Using a random-effects model, the association between the serum and plasma levels of PCBs and the risk of PCa was not shown to be significant (OR = 1.12; 95% CI: 0.90-1.39). The results of Egger's test showed no trace of publication bias in the studies (P of bias = 0.573). This systematic review and meta-analysis was presented based on relatively strong evidence and has confirmed negatively significant associations between PCa risk and some PCBs congeners (PCB 44, 52, and 101). This study does not provide strong evidence that total PCB exposure is a risk factor for PCa development in humans.

Occupational exposure to wood dust and risk of nasopharyngeal cancer: A systematic review and meta-analysis.

INTRODUCTION: Nasopharyngeal cancer (NPC) is one of the most commonly occurring cancers in some regions. While wood dust is a confirmed human carcinogen, its association with NPC remains uncertain due to inconsistent findings in the related studies. We performed the first systematic review and meta-analysis on the epidemiological evidence to examine the association between occupational exposure to wood dust and the risk of NPC. METHODS: In this meta-analysis study, the PubMed and Scopus databases were searched for English-language publications. seven case-control studies were included in the pooled analysis. RESULTS: These studies were published between 1991 and 2016. The heterogeneity across the studies was significant (P = 0.06, I2 = 50.4%). The results of the random effects model meta-analysis showed that there was a direct relationship between occupational exposure to wood dust and NPC (OR = 1.5 95% CI: 1.09-2.07). Among different histological subtypes of NPC, there was a significantly increased risk for the nonkeratinizing carcinoma following wood dust exposure (OR = 1.68, 95%CI: 1.03-2.74). We found no evidence of publication bias across studies according to the result of the Egger's test (P of bias = 0.073). CONCLUSIONS: This meta-analysis suggests that occupational exposure to wood dust can be associated with an increased risk of the nonkeratinizing carcinoma of the histological subtypes of nasopharyngeal cancer.

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies.

Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibers. Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFMMLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving. According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFMMLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.