Over the years, neurotoxicity and cognitive dysfunction have separately been associated with endogenous formaldehyde and reduction of acetylcholine signals. However, a limited number of studies have shown a relationship between cholinergic neurotransmitter and formaldehyde exposure. Therefore, the aim of this study was to assess the neurological effect on workers from melamine-dish preparation workshop, who were exposed to formaldehyde. A total of 35 formaldehyde-exposed workers were compared with 32 control employees from the food industry. Occupational exposure to formaldehyde was conducted using the National Institute of Occupational Safety and Health 3500 methods. Using the Ellman method, acetylcholinesterase (AChE) as a biomarker for neurotoxicity was analyzed in blood erythrocyte. The effects of alcohol dehydrogenase III (ADH3) and Mn-superoxide dismutase (Mn-SOD) polymorphism were used to survey the level of AChE activity. In this study, it was found that exposure to airborne formaldehyde increased from 0.024 to 0.74 ppm and the median personnel exposure was 0.057. Induction of AChE activity was observed in formaldehyde-exposed workers as compared with the control group (p < 0.01), while AChE activity increased in 64 % of the exposed subjects. Spearman's correlation (p < 0.02) was used to evaluate the association between AChE activity and occupational exposure to formaldehyde. Exposed subjects containing ADH3 genotype had higher AChE than others. The findings of this study suggest that the neurotoxic effect of formaldehyde depends on the AChE activity, which is affected by metabolism. It can be concluded that cholinergic signal reduction in cases of cognitive dysfunction could be associated with endogenous formaldehyde.

Epidemiological and clinical characteristics of 99 cases of 2019 novel ... [8] Identification of a novel coronavirus in patients ... [9] A report on the latest situation of the Coronavirus ... [10] Psychological responses, behavioral changes and public perceptions during the early phase ... [11] Factors associated with preventive behaviours of COVID-19 among hospital staff in ... [12] The role of perceived mental stress in the ... [13] MERS transmission and risk ... [14] Timely mental health care for the 2019 novel ... [15] Prediction of preventive behaviors of the needlestick injuries during surgery among ...

This method described herein does not need any sophisticated laboratory devices and deploy local resource.

Establishing an adequate level of reliability in the overhead crane operations is an important and vital principle to avoid undesirable consequences. To do this, it is appropriate to have a comprehensive approach for risk and reliability assessment of the most probable failure scenarios during overhead crane operations. In this study, fault tree analysis (FTA) in combination with fuzzy set theory, Bayesian network (BN), and Markov chain was used to evaluate the probability of top event and reliability of overhead cranes. A total of 47 basic events were identified for ladle fall in overhead cranes. The results showed that the probability of the ladle fall in the FT approach is equal to 0.0523035 and in the BN approach in the prior event is equal to 0.0273394 which is less than the FT method. Based on the values predicted by Markov chain, the reliability of the system decreases over time by 67.9% after 60 months. This study showed that the plan for ladle fall prevention should consider all influencing parameters identified by proper risk assessment methodologies.

BACKGROUND: Many occupational accidents annually occur worldwide. The construction industry injury is greater than the average injury to other industries. The severity of occupational accidents and the resulting injuries in these industries is very high and severe and several factors are involved in their occurrence. OBJECTIVE: Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm. METHODS: In this study, occupational accidents were analyzed and modeled during five years at construction sites of 5 major projects affiliated with a gas turbine manufacturing company based on census sampling. 712 accidents with all the studied variables were selected for the study. The process was implemented in MATLAB software version 2018a using combined artificial neural network and genetic algorithm. Additional information was also collected through checklists and interviews. RESULTS: Mean and standard deviation of accident severity rate (ASR) were obtained 283.08±102.55 days. The structure of the model is 21, 42, 42, 2, indicating that the model consists of 21 inputs (selected feature), 42 neurons in the first hidden layer, 42 neurons in the second hidden layer, and 2 output neurons. The two methods of genetic algorithm and artificial neural network showed that the severity rate of accidents and occupational injuries in this industry follows a systemic flow and has different causes. CONCLUSION: The model created based on the selected parameters is able to predict the accident occurrence based on working conditions, which can help decision makers in developing preventive strategies.