04-0.9) p = 0.027, NNT 9]. Intervention group infants also showed lower oxygenation requirements during the first 72 h post surfactant administration and a reduced incidence of pneumothorax. They were less frequently intubated [31 infants (28.4%) vs. 100 [100%]; P less then 0.001] and required fewer days of mechanical ventilation. However, no significant difference in bronchopulmonary dysplasia incidence was observed between both groups. Conclusions LISA approach effectively reduces severe IVH in very low and low birth weight (BW) preterms with RDS. In addition we observe a significant trend towards reduction in both need and duration of MV support, air leak, and overall mortality in the intervention group.Today, engineered nanomaterials are frequently used. Nanosized titanium dioxide (TiO2) has been extensively used for many years and graphene is one type of emerging nanomaterial. Occupational airborne exposures to engineered nanomaterials are important to ensure safe workplaces and to extend the information needed for complete risk assessments. The main aim of this study was to characterize workplace emissions and exposure of graphene nanoplatelets, graphene oxide, TiO2 nanofibers (NFs) and nanoparticles (NPs) during down-stream industrial handling. Surface contaminations were also investigated to assess the potential for secondary inhalation exposures. In addition, a range of different sampling and aerosol monitoring methods were used and evaluated. The results showed that powder handling, regardless of handling graphene nanoplatelets, graphene oxide, TiO2 NFs, or NPs, contributes to the highest particle emissions and exposures. However, the exposure levels were below suggested occupational exposure limits. It was also shown that a range of different methods can be used to selectively detect and quantify nanomaterials both in the air and as surface contaminations. However, to be able to make an accurate determination of which nanomaterial that has been emitted a combination of different methods, both offline and online, must be used.Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically based models from Jongeneelen and Berge (Ann Occup Hyg 55841-864, 2011) and Clewell et al. (Toxicol Sci 63160-172, 2001), while calculated physicochemical data was obtained from EPA's CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed blood and exhaled breath concentrations was 0.46 with an r2 = 0.45 and a root mean square error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11. Approximately 5.1% (n = 108) of the data points analyzed were >2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.Predictive models are used to estimate exposures from consumer products to support risk management decision-making. These model predictions may be used alone in the absence of measured data or integrated with available exposure data. When different models are used, the resulting estimates of exposure and conclusions of risk may be disparate and the origin of these differences may not be obvious. This Perspectives Paper provides recommendations that could promote more systematic evaluation and a wider range of applicability of consumer product exposure models and their predictions, improve confidence in model predictions, and result in more accurate communication of consumer exposure model estimates. Key insights for the exposure science community to consider include consistency in product descriptions, exposure routes, and scenarios; consistent and explicit definitions of exposure metrics; situation-dependent benefits from using one or multiple models; distinguishing between model algorithms and exposure factors; and corroboration of model predictions with measured data.The COVID-19 pandemic has shown a markedly low proportion of cases among children1-4. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval 12-31%) of infections in 10- to 19-year-olds, rising to 69% (57-82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures-such as many low-income countries-the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Ciforadenant Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic.