Zare Sajad, Hemmatjo Rasoul, ElahiShirvan Hossein, Malekabad Ashkan Jafari, Kazemi Reza, Nadri Farshad
Department of Occupational Health Engineering, Faculty of Health, Kerman University of Medical Sciences, Kerman, Iran.
Department of Occupational Health Engineering, Faculty of Health, Urmia University of Medical Sciences, Urmia, Iran.
Heliyon. 2020 Sep 28;6(9):e05044. doi: 10.1016/j.heliyon.2020.e05044. eCollection 2020 Sep.
Noise is one of the most common harmful agents in the workplace. Exposure to excessive noise can lead to complications such as cardiovascular disorders, disturbance of body hormones' rhythm and hearing loss. This study aimed at weighing and modelling factors influencing serum cortisol and melatonin concentrations of workers that are exposed to various sound pressure levels using neural network algorithm.
A case-control design was adopted in the current research. The required data were collected from 75 industrial and mining firm staff members. They were assigned to three groups with equal sample sizes (25 workers). In developing the conceptual model in regard to variables that may affect workers' serum cortisol and melatonin concentration, SPL, age, weight, and height were included. The influence of SPL on serum cortisol concentration as assessed in the three shifts. Moreover, radioimmunoassay (RIA) was utilized to assess serum cortisol and melatonin concentrations. Neural network algorithm was subsequently exploited to weigh and model predictor factors. IBM SPSS Modeler 18.0 was the software program used for data analysis.
The average cortisol concentration values for administrative, condensing, and pelletizing units respectively were 10.24 ± 2.35, 12.15 ± 3.46, and 14.91 ± 4.16 . On the other hand, the average melatonin concentration values for administrative, condensing, and pelletizing units respectively were 37 ± 12.52, 34 ± 13.15, and 27 ± 9.54 . According to the results of the developed model for cortisol, SPL3 (32%) and age (5%) respectively had the highest and lowest impact. On the other hand, considering the model developed for melatonin, height (27%) and SPL1 (10%) were the most and least influential factors in that order. The accuracy rates of the model were also found to be 95% for cortisol and 97% for melatonin.
Comparing cortisol concentrations during various shifts revealed a significant reduction (from the beginning to the end of the shift) in all the three groups. Further, the rise of SPL would result in higher secretion of cortisol. Moreover, in all the three groups, the average serum melatonin concentration went up from the beginning to the middle of the shift and then declined to the end of the shift. Considering the accuracy rates of the models developed to predict hormones, neural network algorithm is a suitable and powerful tool for weighing and modelling factors influencing serum cortisol and melatonin concentrations.
噪声是工作场所最常见的有害因素之一。暴露于过度噪声会导致诸如心血管疾病、身体激素节律紊乱和听力损失等并发症。本研究旨在使用神经网络算法对影响暴露于不同声压水平的工人血清皮质醇和褪黑素浓度的因素进行加权和建模。
本研究采用病例对照设计。所需数据收集自75名工矿企业员工。他们被分为三组，每组样本量相等（25名工人）。在构建可能影响工人血清皮质醇和褪黑素浓度的变量的概念模型时，纳入了声压水平、年龄、体重和身高。评估了三个班次中声压水平对血清皮质醇浓度的影响。此外，采用放射免疫分析法（RIA）评估血清皮质醇和褪黑素浓度。随后利用神经网络算法对预测因素进行加权和建模。IBM SPSS Modeler 18.0是用于数据分析的软件程序。
行政、冷凝和造粒单元的皮质醇平均浓度值分别为10.24±2.35、12.15±3.46和14.91±4.16 。另一方面，行政、冷凝和造粒单元的褪黑素平均浓度值分别为37±12.52、34±13.15和27±9.54 。根据所构建的皮质醇模型的结果，声压水平3（32%）和年龄（5%）分别具有最高和最低的影响。另一方面，考虑到为褪黑素构建的模型，身高（27%）和声压水平1（10%）依次是最有影响和影响最小的因素。还发现皮质醇模型和褪黑素模型的准确率分别为95%和97%。
比较不同班次的皮质醇浓度发现，所有三组的皮质醇浓度均显著降低（从班次开始到结束）。此外，声压水平的升高会导致皮质醇分泌增加。此外，在所有三组中，血清褪黑素平均浓度从班次开始到中间升高，然后在班次结束时下降。考虑到所构建的预测激素模型的准确率，神经网络算法是对影响血清皮质醇和褪黑素浓度的因素进行加权和建模的合适且强大的工具。
