Occupational exposure, carcinogenic and non-carcinogenic risk assessment of formaldehyde in the pathology labs of hospitals in Iran.

Formaldehyde, a known carcinogenic compound, is commonly used in various medical settings. The objective of this study was to assess the carcinogenic and non-carcinogenic risks associated with occupational exposure to formaldehyde. This study was conducted in the pathology labs of four hospitals in Tehran. Cancer and non-cancer risks were evaluated using the quantitative risk assessment method proposed by the United States environmental protection agency (USEPA), along with its provided database known as the integrated risk information system (IRIS). Respiratory symptoms were assessed using the American thoracic society (ATS) questionnaire. The results indicated that 91.23% of exposure levels in occupational groups exceed the NIOSH standard of 0.016 ppm. Regarding carcinogenic risk, 41.03% of all the studied subjects were in the definite carcinogenic risk range (LCR > 10-4), 23.08% were in the possible carcinogenic risk range (10-5 < LCR < 10-4), and 35.90% were in the negligible risk range (LCR < 10-6). The highest index of occupational carcinogenesis was observed in the group of lab technicians with a risk number of 3.7 × 10-4, followed by pathologists with a risk number of 1.7 × 10-4. Furthermore, 23.08% of the studied subjects were within the permitted health risk range (HQ < 1.0), while 76.92% were within the unhealthy risk range (HQ > 1.0). Overall, the findings revealed significantly higher carcinogenic and non-carcinogenic risks among lab technicians and pathologists. Therefore, it is imperative to implement control measures across various hospital departments to mitigate occupational formaldehyde exposure levels proactively. These findings can be valuable for policymakers in the health sector, aiding in the elimination or reduction of airborne formaldehyde exposure in work environments.

Health risk assessment of occupational exposure to heavy metals among green space workers in Iran.

Exposure to heavy metals can result in various adverse health effects. Tehran is rated as one of the world's most polluted cities. Green space workers are continuously exposed to such pollutants in this city. Thus, this study aimed to estimate the health risks caused by exposure to heavy metals among green space workers. Eighty-eight workers and office personnel in two regions with different air quality levels were chosen for sampling. Air samples were collected using the NIOSH-7300 method and analyzed using an Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) instrument. The hazard quotient (HQ) and the lifetime cancer risk (LTCR) were calculated to assess carcinogenic and non-carcinogenic risk levels. The results revealed that the rank order of heavy metals was determined as Zn, Pb, Mn, Ni, Co, and Cd. Workers were subjected to higher concentrations of Ni, Pb, Zn, and Co than office personnel. Furthermore, the Cd, Co, and Zn exposure levels stood significantly higher in region 6 than in region 14. Non-carcinogenic risk levels for all participants fell within the acceptable range. Moreover, no employee had a carcinogenic risk level within the acceptable range when exposed to Cd. Also, 2.3% of individuals demonstrated Ni's acceptable carcinogenic risk level. Owing unacceptable risk levels, proper interventions are required to minimize occupational exposure to heavy metals. These interventions include optimizing shift schedules, using personal protective equipment, and conducting regular health assessments.

Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forcasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran).

Air pollution is one of the major concerns for the population and the environment due to its hazardous effects. PM10 has affected significant scientific and regulatory interest because of its strong correlation with chronic health such as respiratory illnesses, lung cancer, and asthma. Forcasting air quality and assessing the health impacts of the air pollutants like particulate matter is crucial for protecting public health.This study incorporated weather, traffic, green space information, and time parameters, to forcst the AQI and PM10. Traffic data plays a critical role in predicting air pollution, as it significantly influences them. Therefore, including traffic data in the ANN model is necessary and valuable. Green spaces also affect air quality, and their inclusion in neural network models can improve predictive accuracy. The key factors influencing the AQI are the two-day lag time, the proximity of a park to the AQI monitoring station, the average distance between each park and AQI monitoring stations, and the air temperature. In addition, the average distance between each park, the number of parks, seasonal variations, and the total number of vehicles are the primary determinants affecting PM10.The straightforward effective Multilayer Perceptron Artificial Neural Network (MLP-ANN) demonstrated correlation coefficients (R) of 0.82 and 0.93 when forcasting AQI and PM10, respectively. This study also used the forcasted PM10 values from the ANN model to assess the health effects of elevated air pollution. The results indicate that elevated levels of PM10 can increase the likelihood of respiratory symptoms. Among children, there is a higher prevalence of bronchitis, while among adults, the incidence of chronic bronchitis is higher. It was estimated that the attributable proportions for children and adults were 6.87% and 9.72%, respectively. These results underscore the importance of monitoring air quality and taking action to reduce pollution to safeguard public health.

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.

Psychological and Psychosocial Impact and Related Factors during the COVID-19 Pandemic among Iranian Oil Refineries Personnel: A longitudinal study.

BACKGROUND: The COVID-19 outbreak is currently a critical challenge, and proper perception of the mental health situation of individuals in the working environments has become a crucial issue. This study aimed to investigate the psychological and psychosocial impact during the COVID-19 pandemic among Iranian oil refineries personnel. METHODS: This longitudinal study was conducted in two phases from November 2019 to July 2020 among the 850 employees of three refineries in Iran. To study the mental workload, job burnout, lifestyle, occupational stress, general health, and memory quotient of personnel, the NASA-TLX questionnaire, Maslach Burnout Inventory (MBI) questionnaire, Walker's health-promoting lifestyle questionnaire, health and safety executive (HSE) job stress questionnaire, general health questionnaire, and Wechsler memory scale were used, respectively. Data were analyzed using paired sample t test, independent sample t test, and one-way ANOVA in SPSS version 25. RESULTS: The study results revealed that the mean age of studied personnel was 34.62±13.48 years. There was a meaningful correlation between the values of mental workload, job burnout, occupational stress, general health, healthy lifestyle, and memory quotient of personnel before and during the COVID-19 pandemic (P value<0.05). CONCLUSION: The COVID-19 pandemic can significantly affect the psychological and psychosocial parameters of employees in working environments. Therefore, implementing psychological interventions is absolutely necessary to promote personnel's mental health during the COVID-19 pandemic.

Fabrication and characterization of TiO2 and MWCNT coated electrospinning nanofibers for UV protection properties.

This paper aimed to fabricate UV protective nanofibers by the use of specific nanoparticles. The DMF/TiO2 (Titanium dioxide), DMF/MWCNT (Multi-Walled Carbon Nano Tubes), and DMF/MWCNT+TiO2 (MWCNT: TiO2 mass ratio= 1:1) solutions were transferred into a syringe with a stainless steel needle with gauge 21. The electrospinning process was performed for 3 h at the optimized conditions. The surface morphology of nanofibers was characterized by field emission scanning electron microscopy (FESEM). Fourier transform infrared spectroscopy (FTIR) was utilized to characterize functional groups of oxidized MWCNTs and investigate the successful load of nanoparticles at the fiber surface. The UV protection property of nanofibers was investigated by measuring UV rays' transmittance through the composite web. The data of Spectroscopy was used to compute the UV protection factor (UPF). (1) The effect of CNT,TiO2, and CNT+TiO2 nanoparticles on ultraviolet protection property was analyzed separately and simultaneously. (2) The different concentrations of nanoparticles, including 1,5,10, & 15 wt%, were used to fabricate UV protective nanocomposites. (3) The electrospinning condition was optimized as a 15 cm distance between the needle tip and collector, 20 KV voltage, 250 RPM drum rotation, and 1.2 ml/h feeding rate to access the best nanofibers.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air.

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications.

Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers' diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R2 = 0.959) in comparison with MLR results (R2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.

Feasibility of Using Vitamin E-Loaded Poly(ε-caprolactone)/Gelatin Nanofibrous Mat to Prevent Oxidative Stress in Skin.

Some occupational skin exposures lead to the formation of reactive oxygen species (ROS). The occupational exposure of workers to ROS has been found to be associated with an increased risk of developing skin injuries; therefore, it is essential to protect skin against ROS formation. Recently, some studies have been conducted on introducing better alternatives for skin protection. Nanofibers are good candidates for this purpose. The current study was carried out to assess vitamin E-loaded hybrid Poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibres mats as protective layers of skin exposed to occupational exposures. Vitamin E (VE) was successfully incorporated into PCL/Gt nanofibers while they were formed by electrospinning method. Nanofibers mats were characterized using scanning electron microscopy (SEM) and fourier transform infrared spectroscopy (FTIR). Their degradation behavior, in vitro release, biocompatibility, and antioxidant activity were studied. The diameters of the PCL/Gt/VE nanofibers decreased with the addition of vitamin E. The degradation rate of nanofibers was equal to 42.98 and 50.69% during 7 and 14 days, respectively. Nanofibers containing vitamin E showed an initial burst followed by a sustained release. The PCL/Gt/VE nanofibers exhibited good free radical scavenging activities despite being exposed to a high electrical potential during electrospinning. PCL/Gt/VE nanofibers supported a higher level of viability compared to PCL/Gt ones and significantly assisted human skin cells against tert-Butyl hydroperoxide (t-BHP) induced oxidative stress. Overall, PCL/Gt/VE nanofibers can potentially be used to protect skin against oxidative stress as a novel approach for worker's healthcare.

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.

The effects of occupational noise on blood pressure and heart rate of workers in an automotive parts industry.

BACKGROUND: One of the most important impacts of industrial noise is physiological and psychological effects. The increases in workers' blood pressure and heart rate were detected during and after exposure to high levels of noise. The objectives of this research were to determine whether the noise exposures have any effects on blood pressure and heart rate of workers in the automotive parts industry. METHODS: This case study was done in 2011 at different units of an automotive parts manufacturing in Tehran. Sound pressure level was measured at different units of the factory with a calibrated instrument. Demographic features of workers were gathered with an appropriate questionnaire. Heart rate and blood pressure were measured twice in a day in the start time of work day (before exposure to noise) and middle shift hours (during exposure to noise) in the occupational physician office. For analyzing data, chi-square, independent sample t-test, paired t-test, and analysis of covariance (ANCOVA) were used. P < 0.050 was considered statistically significant. RESULTS: The average age of workers in the case and control groups was 35.71 ± 8.10 and 33.40 ± 10.41 years, respectively. There was no difference between the average age of case and control groups (P = 0.436). The results of ANCOVA revealed the significant differences between the mean changes of heart rate F (1, 37) = 26.68, P < 0.001, systolic blood pressure F (1, 37) = 21.70, P < 0.001, and diastolic blood pressure F (1, 37) = 26.20, P < 0.001 of workers in the case and control groups. CONCLUSION: Exposure to industrial noise may increase the heart rate of workers. Although rises in heart rate, systolic, and diastolic blood pressure of workers in the case group were observed after exposure to noise, the values of heart rate, systolic, and diastolic blood pressure were in the normal range. Further experimental investigations are needed to determine the relationships between these variables.

Association of Sick Building Syndrome with Indoor Air Parameters.

BACKGROUND: Energy crisis in 1973 led to smaller residential and office buildings with lower air changes. This resulted in development of Sick Building Syndrome (SBS). The objective of this study was to assess the association of SBS with individual factors and indoor air pollutants among employees in two office buildings of Petroleum Industry Health Organization in Tehran city. MATERIALS AND METHODS: The association between personal and environmental factors and SBS symptoms was examined by a reliable and valid combined questionnaire. Environmental parameters were measured using calibrated instruments. RESULTS: The results suggested that SBS symptoms were more common in women than men. Malaise and headache were the most common symptoms in women and men. Throat dryness, cough, sputum, and wheezing were less prevalent among employees in both offices. Light-intensity was significantly associated with some symptoms such as skin dryness (P = 0.049), eye pain (P = 0.026), and malaise (P = 0.043). There were no significant differences in prevalence of SBS symptoms between female workers of the two offices (P>0.05). CONCLUSION: The main causes of SBS among the employees were recycling of air in rooms using fan coils, traffic noise, poor lighting, and buildings located in a polluted metropolitan area.

Fungal Airborne Contamination as a Serious Threat for Respiratory Infection in the Hematology Ward.

BACKGROUND: Fungi existing in hospital departments may grow and produce micro-colonies. The spores arising from these micro-colonies circulate easily and could be inhaled by patients and cause infections in immune-compromised subjects. Due to the lack of an acceptable method of sampling and evaluation of microbiological quality of air in the isolation units, the purpose of this study was to determine the concentrations of airborne fungi through active and passive sampling and also identify fungi genera in the air of the isolation unit. MATERIALS AND METHODS: The air of the isolation unit was monitored through active and passive sampling. In passive sampling, the plates were placed in the room. The active sampling was performed in the hematology unit by using a slit-to-agar biological air sampler with a flow rate of 10 L/minute. Plates were incubated at 30°C for 10 days and were examined daily for fungal growth. Fungal species were identified on the basis of their macroscopic and microscopic morphological features. RESULTS: In active samples, Penicillium spp. was the predominant genus (66.8%), followed by Aspergillus spp. (23.9%) and Cladosporium spp. (2.5%). Yeast spp. accounted for only 2.2% of the isolated fungi. In passive samples, Penicillium spp. (94.4%) was the most frequently found fungi, followed by Aspergillus spp (2.2%), Cladosporium spp. (1.1%) and Yeast spp. (0.5%). The identified genera included Penicillium, Aspergillus, Alternaria, Mucorales, Cladosporium, Yeasts and other filamentous fungi. CONCLUSION: Active and passive sampling can be used for monitoring the fungal content of air. Assessment of fungal contamination profiles in hospitals may provide important information about the level of fungal concentration in the hospitals and for the control of nosocomial infections. In addition, installation of special ventilation systems equipped with HEPA filters in hematology wards could enhance the quality of air. Also, observing sanitary protocols for disinfection of the surfaces is imperative for infection control.