Per- and polyfluoroalkyl substances (PFAS) exposure in plasma and their blood-brain barrier transmission efficiency-A pilot study.

Per- and polyfluoroalkyl substances (PFAS) have been shown to penetrate the blood-brain barrier (BBB) and accumulate in human brain. The BBB transmission and accumulation efficiency of PFAS, as well as the potential health risks from human co-exposure to legacy and emerging PFAS due to differences in transport efficiency, need to be further elucidated. In the present pilot study, 23 plasma samples from glioma patients were analyzed for 17 PFAS. The concentrations of PFAS in six paired brain tissue and plasma samples were used to calculate the BBB transmission efficiency of PFAS (RPFAS). This RPFAS analysis was conducted with utmost care and consideration amid the limited availability of valuable paired samples. The results indicated that low molecular weight PFAS, including short-chain and emerging PFAS, may have a greater potential for accumulation in brain tissue than long-chain PFAS. As an alternative to perfluorooctane sulfonic acid (PFOS), 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA) exhibited brain accumulation potential similar to that of PFOS, suggesting it may not be a suitable substitute concerning health risk in brain. The BBB transmission efficiencies of perfluorooctanoic acid, PFOS, and 6:2 Cl-PFESA showed similar trends with age, which may be an important factor influencing the entry of exogenous compounds into the brain. A favorable link between perfluorooctane sulfonamide (FOSA) and the development and/or progression of glioma may be implicated by a strong positive correlation (r2 = 0.94; p < 0.01) between RFOSA and Ki-67 (a molecular marker of glioma). However, a causal relationship between RFOSA and glioma incidence were not established in the present study. The present pilot study conducted the first examination of BBB transmission efficiency of PFAS from plasma to brain tissue and highlighted the importance of reducing and/or controlling exposure to PFAS.

Identification of Prognostic Markers of Glioblastoma through Bioinformatics Analysis.

Objective: Glioblastoma is the most common and aggressive type of the central nervous system cancers. Although radiotherapy and chemotherapy are used in the treatment of glioblastoma, survival rates remain unsatisfactory. This study aimed to explore differentially expressed genes (DEGs) based on the survival prognosis of patients with glioblastoma and to establish a model for classifying patients into different risk groups for overall survival. Methods: DEGs from 160 tumor samples from patients with glioblastoma and 5 nontumor samples from other patients in The Cancer Genome Atlas database were identified. Functional enrichment analysis and a protein-protein interaction network were used to analyze the DEGs. The prognostic DEGs were identified by univariate Cox regression analysis. We split patient data from The Cancer Genome Atlas database into a high-risk group and a low-risk group as the training data set. Least absolute shrinkage and selection operator and multiple Cox regression were used to construct a prognostic risk model, which was validated in a test data set from The Cancer Genome Atlas database and was analyzed using external data sets from the Chinese Glioma Genome Atlas database and the GSE74187 and GSE83300 data sets. Furthermore, we constructed and validated a nomogram to predict survival of patients with glioblastoma. Results: A total of 3572 prognostic DEGs were identified. Functional analysis indicated that these DEGs were mainly involved in the cell cycle and focal adhesion. Least absolute shrinkage and selection operator regression identified 3 prognostic DEGs (EFEMP2, PTPRN, and POM121L9P), and we constructed a prognostic risk model. The receiver operating characteristic curve analysis showed that the areas under the curve were 0.83 for the training data set and 0.756 for the test data set. The predictive performance of the prognostic risk model was validated in the 3 external data sets. The nomogram showed that the prognostic risk model was reliable and that the accuracy of predicting survival in each patient was high. Conclusion: The prognostic risk model can effectively classify patients with glioblastoma into high-risk and low-risk groups in terms of overall survival rate, which may help select high-risk patients with glioblastoma for more intensive treatment.

Simulation and Optimization Study on the Ventilation Performance of High-Rise Buildings Inspired by the White Termite Mound Chamber Structure.

High-rise buildings often use mechanical systems to assist ventilation to maintain the stability of their internal environments, and the energy consumption of mechanical ventilation poses a great challenge to urban environments and energy systems. The ventilation system of termite mounds with a combination of internal main and attached chambers is one of the classic examples of nature's bionic approach to maintaining a stable internal ventilation environment for large-volume structures. In this study, based on the inspiration of the internal ventilation chamber structure of bionic termite mounds, we constructed seven high-rise building chamber ventilation models based on the chamber structure of termite mounds with main chambers, main chambers plus single-attached chambers (three types), and main chambers plus double-attached chambers (three types) under natural ventilation conditions, aiming at obtaining the optimal low-energy and high-efficiency chamber ventilation model for bionic termite mounds in high-rise buildings. (1) The wind speed and wind pressure of the high-rise building with the addition of the bionic termite mound chamber structure is higher than that of the traditional chamber-free high-rise building in the sample floors, the maximal difference of the wind speed between the two models is 0.05 m/s, and the maximal difference of the wind speed of the single building is 0.14 m/s, with the maximal difference of the wind speed of the single building being 0.14 m/s; and the natural ventilation environment can be satisfied by a high-rise building with a chamber. (2) After increasing the single-attached chamber structure of the bionic termite mound, the difference in wind speed of different floors is 0.15 m/s, which is 0.10 m/s higher than that of the high-rise building model with the main chamber only. (3) Under the bionic termite mound chamber high-rise building double-attached chamber model, the maximum difference in wind speed of each floor sampling point can reach 0.19 m/s, while the wind pressure cloud map shows a stable wind environment system. (4) Two attached chambers are added at A and B of the high-rise building to form the a4 model of the chamber of the high-rise building with a double-chamber bionic termite mound. According to the results, it can be seen that the model of the nine floor sampling points of the maximum wind speed difference has six places for the highest value, and the single building wind speed difference for the minimum value of 0.10 m/s. The study aims to optimize the connectivity and ventilation performance of high-rise buildings under natural ventilation conditions and to promote the green and sustainable design of high-rise buildings.