QSPR modeling for the prediction of partitioning of VOCs and SVOCs to indoor fabrics: Integrating environmental factors.

Porous fabrics have a significant impact on indoor air quality by adsorbing and emitting chemical substances, such as volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs). Understanding the partition behavior between organic compound molecules and indoor fabrics is crucial for assessing their environmental fate and associated human exposure. The physicochemical properties of fabrics and compounds are fundamental in determining the free energy of partitioning. Moreover, environmental factors like temperature and humidity critically affect the partition process by modifying the thermal and moisture conditions of the fabric. However, existing methods for determining the fabric-air partition coefficient are limited to specific fabric-chemical combinations and lack a comprehensive consideration of indoor environmental factors. In this study, large amounts of experimental data on fabric-air partition coefficients (Kfa) of (S)VOCs were collected for silk, polyester, and cotton fabrics. Key molecular descriptors were identified, integrating the influences of physicochemical properties, temperature, and humidity. Subsequently, two typical quantitative structure-property relationship (QSPR) models were developed to correlate the Kfa values with the molecular descriptors. The fitting performance, robustness, and predictive ability of the two QSPR models were evaluated through statistical analysis and internal/external validation. This research provides insights for the high-throughput prediction of the environmental behaviors of indoor organic compounds.

Physiologically based pharmacokinetic modeling to predict maternal pharmacokinetics and fetal carbamazepine exposure during pregnancy.

Carbamazepine is an antiepileptic drug commonly used in pregnant women, during which the physiological changes may affect its efficacy. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) model of carbamazepine and its active metabolite carbamazepine-10,11-epoxide, and simulate maternal and fetal pharmacokinetic changes of carbamazepine and carbamazepine-10,11-epoxide in different trimesters and propose dose adjustment. We established pregnancy PBPK models for carbamazepine and carbamazepine-10,11-epoxide in PK-Sim® and Mobi® and validated the models with observed data from clinical reports. The placental transfer parameters obtained using different methods were also imported into the model and compared with the observed data to establish and validate fetal pharmacokinetic curves. The simulated results showed that mean steady-state trough plasma concentration of carbamazepine decreased by 27, 43.1, and 52 % during the first, second, and third trimesters, respectively. Therefore, to achieve an optimum therapeutic concentration, administering at least 1.4, 1.8, and 2.1 times the baseline dose of carbamazepine in the first, second, and third trimesters, respectively can be used as a dose reference. In conclusion, this study established and validated a pregnancy PBPK model of carbamazepine and carbamazepine-10,11-epoxide to assess exposure in pregnant women and fetuses, which provided a reference for the dosage adjustment of carbamazepine during pregnancy.

The Impact of Cooperative Behavior between Social Organizations during the COVID-19 Pandemic Outbreak in Shanghai: A Simulation Approach.

In 2022, a new outbreak of the COVID-19 pandemic created considerable challenges for the Shanghai public health system. However, conventional prevention and control strategies, which only rely on formal organizations, inefficiently decrease the number of infections. Thus, a multi-organization management mode is needed for pandemic prevention. In this paper, we applied a stochastic actor-oriented model (SAOM) to analyze how these social organizations cooperate with others and further identify the mechanism that drives them to create a reliable and sustainable cooperative relationship network from the perspective of social network analysis. The model allowed us to assess the effects of the actor's attributes, the network structure, and dynamic cooperative behavior in RSiena with longitudinal data collected from 220 participants in 19 social organizations. The results indicated that the number of cooperative relationships increased during the pandemic, from 44 to 162, which means the network between social organizations became more reliable. Furthermore, all the hypotheses set in four sub-models were significant (t-ratio < 0.1, overall max t-ratio < 0.25, and e/s > 2). Additionally, the estimated values showed that four factors played a positive role in forming the cooperative relationship network, i.e., all except the "same age group effect (−1.02)". The results also indicated that the social organizations tend to build relationships with more active actors in the community in every time period. This paper is of great significance regarding the innovation of public health system management and the improvement of Chinese grassroots governance.