No abstract

Since occupational hearing loss is frequently non-curable, results of accurate prediction can be used by occupational health experts to modify and improve noise exposure conditions.

Exposure to bioaerosols in the air of hospitals is associated with a wide range of adverse health effects due to the presence of airborne microorganisms. Intensity and type of health effects depend on many factors such as the type, density, and diversity of bioaerosols in hospital environments. Therefore, identifying and determining their distribution in hospital environment contribute to reduce their adverse effects and maintain the physical health of patients and staff, as well as find the source of infections and possible allergies due to the presence of bioaerosols. Therefore, the present study was conducted to determine the type and concentration of the bacterial and fungal bioaerosols, and their distribution in the indoor and outdoor air of a teaching hospital to establish a reference for future studies or measures. The air samples were collected with a one-stage Anderson sampler and particle mass counter for a period of four months in the fall and winter of 2019. In total, 262 bacterial and fungal samples were collected from the air of the wards of Tohid Hospital, Sanandaj, Iran. Antibiotic resistance test, bacterial identification by PCR method, and modeling the dispersion of concentrations of bio-aerosols were also conducted. In order to identify bacteria and fungi, some biochemical and molecular tests and microscopic and macroscopic characteristic methods were applied, respectively. The results showed that the highest and lowest densities of the bioaerosols were observed in lung and operating wards (336.67 and 15.25 CFU/m 3 ). Moreover, the highest and least concentrations of particles were seen in the emergency and operating wards, respectively. The most common fungi isolated from the hospital air were Penicillium (24.7%), Cladosporium (23. 4%), Aspergillus niger (13.3%), and Aspergillus Flavus (11.4%). Furthermore, the highest concentration of the isolated bacterium was Staphylococcus hemolyticus (31.84%). Most bacteria showed the highest resistance to gentamicin. The overall average hospital air pollution to bioaerosols was slightly higher than the standards proposed by international organizations. Due to the high concentration of bioaerosols and particles in the studied hospital, providing suitable conditions such as temperature, humidity, proper ventilation, and intelligent air conditioning system using efficient ventilation systems, and restricting the entrance of wards can reduce airborne particles in hospital environment.

No abstract

Background:Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers.Materials and Methods:A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software.Results:Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively.Conclusion:Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.