Reconstruction of exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol using computational fluid dynamics, physiologically based toxicokinetics and statistical modeling.

OBJECTIVES: This study employed computational fluid dynamics (CFD), physiologically based toxicokinetics (PBTK), and statistical modeling to reconstruct exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol. By utilizing a validated CFD model, human respiratory deposition of MDI aerosol in different workload conditions was investigated, while a PBTK model was calibrated using experimental rat data. Biomonitoring data and Markov Chain Monte Carlo (MCMC) simulation were utilized for exposure assessment. RESULTS: Deposition fraction of MDI in the respiratory tract at the light, moderate, and heavy activity were 0.038, 0.079, and 0.153, respectively. Converged MCMC results as the posterior means and prior values were obtained for several PBTK model parameters. In our study, we calibrated a rat model to investigate the transport, absorption, and elimination of 4,4'-MDI via inhalation exposure. The calibration process successfully captured experimental data in the lungs, liver, blood, and kidneys, allowing for a reasonable representation of MDI distribution within the rat model. Our calibrated model also represents MDI dynamics in the bloodstream, facilitating the assessment of bioavailability. For human exposure, we validated the model for recent and long-term MDI exposure using data from relevant studies. CONCLUSION: Our computational models provide reasonable insights into MDI exposure, contributing to informed risk assessment and the development of effective exposure reduction strategies.

Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case.

As timely information about a project's state is key for management, we developed a data toolchain to support the monitoring of a project's progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project's progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project's state, 330+ numerical indicators were identified. According to the project's pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors-real or virtual-deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context.

Effects of occupational exposures on respiratory health in steel factory workers.

Background: The steel factory work environment contains various chemical exposures that can affect indoor air quality and have impact on respiratory health of the workers. Aims: The objective of this study was to assess potential effects of occupational exposures in steel factory workers in Iran on the respiratory symptoms, occurrence and the lung function levels. Method: This was a cross-sectional study of 133 men working in a steel factory forming the exposed group and 133 male office workers forming the reference group from a steel company in Iran. The participants filled in a questionnaire and underwent spirometry. Work history was used both as dichotomous (exposed/reference) and a quantitative measure of exposure, the latter measured as duration of exposure in the specified work (in years) for the exposed group and zero for the reference group. Results: Multiple linear regression and Poisson regression were used to adjust for confounding. In Poisson regression analyses, an increased prevalence ratio (PR) of all respiratory symptoms was observed in the exposed group. Lung function parameters were significantly reduced in the exposed group (p < 0.001). There was a dose-response relation between duration of occupational exposures and reduction in the predicted value of FEV1/FVC level (0.177, 95% CI -0.198 to -0.156) in all models. Conclusion: The results of these analyses showed that occupational exposures in steel factory work increase the prevalence of respiratory symptoms and reduce lung function. Safety training and workplace conditions were found to need improvement. In addition, use of proper personal protective equipment is recommended.

Downregulation of GJB2 and SLC26A4 genes induced by noise exposure is associated with cochlear damage.

BACKGROUND: Noise-induced hearing loss (NIHL) is one the major causes of acquired hearing loss in developed countries. Noise can change the pattern of gene expression, inducing sensorineural hearing impairment. There is no investigation on the effects of noise frequency on the expression of GJB2 and SLC26A4 genes involved in congenital hearing impairment in cochlear tissue. Here we investigated the impacts of white and purple noise on gene expression and pathologic changes of cochlear tissue. METHODS: In this study, 32 adult male Westar rats were randomly divided into experimental groups: WN, animals exposed to white noise with a frequency range of 100-20000 Hz; PN, animals exposed to purple noise with a frequency range of 4-20 kHz, and control group, without noise. The experimental groups were exposed to a 118-120 dB sound pressure level for 8 h per 3 days and 6 days. 1 h and 1 week after termination of noise exposure, cochlear tissue was prepared for pathology and gene expression analysis. RESULTS: Both white and purple noises caused permanent damage to the cortical, estrosilica systems of hair cells and ganglion of the hearing nerve. GJB2 and SLC26A4 were downregulated in both groups exposed with white and purple noise by increasing the time of noise exposure. However, differences are notably more significant in purple noise, which is more intensified. Also, 1 weak post noise exposure, the downregulation is remarkably higher than 1 h. CONCLUSIONS: Our findings suggest that downregulation of GJB2 and SLC26A4 genes are associated with pathological injury in response to noise exposure in cochlear tissue. It would be suggested the demand for assessment of RNA and protein expression of genes involved in noise-induced hearing loss and subsequently the practice of hearing protection programs.

Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment.

While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners' intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight the importance of the reference methodology to theoretically inform and coherently manage all the steps of the project and the need for effective and efficient tools, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts.