Visualization of Blood-Brain Barrier Disruption in Septic Mice with the New Method Based on in Vivo Imaging Technology.

BACKGROUND: Dynamic monitoring of the blood-brain barrier (BBB) functional status in septic mice can help to explore the pathological mechanisms. Therefore, we proposed a new method for monitoring BBB permeability and applied it to the detection of sepsis models. METHODS: The new method involves the construction of an optical cranial window and in vivo imaging. We performed dynamic monitoring of BBB permeability and cerebral blood flow (CBF) in cecal ligation puncture (CLP) and endotoxemia (lipopolysaccharide [LPS]) mice. RESULTS: The sensitivity and accuracy of this method were higher than those of Evans blue evaluation. The increase of BBB permeability in the group of CLP mice was relatively mild and correlated with overall survival, and the damage was irreversible. Contrarily, BBB damage in the LPS group was more acute and severe, unrelated to overall survival, but recoverable. The CBF decreased significantly in both model mouse groups 24 h after modeling, but only the CBF proportion decrease in the LPS group was significantly correlated with an increase in BBB permeability. Within 24 h after both models were established, the decrease in blood flow in the digestive organs occurred earlier than in the brain and kidneys, and the decrease in small intestine blood flow in the LPS group progressed faster. CONCLUSIONS: We have successfully demonstrated the feasibility of our novel method to detect BBB permeability in mice. Our results revealed a significant difference in the BBB permeability change trend between the CLP and LPS model mice when survival curves were consistent. Notably, the CLP-model mice demonstrated a closer resemblance to clinical patients. Our findings suggest that early-stage brain tissue hypoperfusion has a greater impact on BBB function damage in endotoxemia mice, which is related to the faster progression of blood flow redistribution.

Clinical research text summarization method based on fusion of domain knowledge.

OBJECTIVE: The objective of this study is to integrate PICO knowledge into the clinical research text summarization process, aiming to enhance the model's comprehension of biomedical texts while capturing crucial content from the perspective of summary readers, ultimately improving the quality of summaries. METHODS: We propose a clinical research text summarization method called DKGE-PEGASUS (Domain-Knowledge and Graph Convolutional Enhanced PEGASUS), which is based on integrating domain knowledge. The model mainly consists of three components: a PICO label prediction module, a text information re-mining unit based on Graph Convolutional Neural Networks (GCN), and a pre-trained summarization model. First, the PICO label prediction module is used to identify PICO elements in clinical research texts while obtaining word embeddings enriched with PICO knowledge. Then, we use GCN to reinforce the encoder of the pre-trained summarization model to achieve deeper text information mining while explicitly injecting PICO knowledge. Finally, the outputs of the PICO label prediction module, the GCN text information re-mining unit, and the encoder of the pre-trained model are fused to produce the final coding results, which are then decoded by the decoder to generate summaries. RESULTS: Experiments conducted on two datasets, PubMed and CDSR, demonstrated the effectiveness of our method. The Rouge-1 scores achieved were 42.64 and 38.57, respectively. Furthermore, the quality of our summarization results was found to significantly outperform the baseline model in comparisons of summarization results for a segment of biomedical text. CONCLUSION: The method proposed in this paper is better equipped to identify critical elements in clinical research texts and produce a higher-quality summary.

Governance effects of pollution reduction and carbon mitigation of carbon emission trading policy in China.

China's carbon emission trading policy plays a crucial role in achieving both its "3060" dual carbon objectives and the United Nations Sustainable Development Goal 13 (SDG 13) on climate action. The policy's effectiveness in reducing pollution and mitigating carbon emissions holds significant importance. This paper investigated whether China's carbon emission trading policy affects pollution reduction (PM2.5 and SO2) and carbon mitigation (CO2) in pilot regions, using panel data from 30 provinces and municipalities in China from 2005 to 2019 and employing a multi-period difference-in-differences (DID) model. Furthermore, it analyzed the heterogeneity of carbon market mechanisms and regional variations. Finally, it examined the governance pathways for pollution reduction and carbon mitigation from a holistic perspective. The results indicate that: (1) China's carbon emission trading policy has reduced CO2 emissions by 18% and SO2 emissions by 36% in pilot areas, with an immediate impact on the "carbon mitigation" effect, while the "pollution reduction" effect exhibits a time lag. (2) Higher carbon trading prices lead to stronger "carbon mitigation" effect, and larger carbon market scales are associated with greater "pollution reduction" effects on PM2.5. Governance effects on pollution reduction and carbon mitigation vary among pilot regions: Carbon markets of Beijing, Chongqing, Shanghai, and Tianjin show significant governance effects in both "pollution reduction" and "carbon mitigation", whereas Guangdong's carbon market exhibits only a "pollution reduction" effect, and Hubei's carbon market demonstrates only a "carbon mitigation" effect. (3) Currently, China's carbon emission trading policy achieves pollution reduction and carbon mitigation through "process management" and "end-of-pipe treatment". This study could provide empirical insights and policy implications for pollution reduction and carbon mitigation, as well as for the development of China's carbon emission trading market.

Wettability and Mechanical Properties of Red Mud-Al2O3 Composites.

In 2023, the global production of new red mud is expected to reach nearly 200 million tons, but less than 10% of it is currently being utilized in an environmentally friendly manner. To reduce the sintering temperature of alumina ceramics, a sintering aid method is used, as high-purity alumina ceramics require a solid-phase sintering temperature of over 1700 °C. The metal oxides present in red mud are necessary components for high-performance composite alumina ceramics. Composites were obtained by mixing and sintering red mud and Al2O3. This study focused on the mechanical properties and wettability of these composites. The results indicated that the 10% red mud-Al2O3 composite exhibited the highest hardness (20.12 GPa) and flexural strength (346 MPa). This is attributed to the formation of a mineral phase dominated by CaAl12O19, generated by the red mud during the sintering process, which filled the pores and reduced porosity. The surface energy of the red mud-Al2O3 composite was the highest at room temperature and high temperature, reaching 49.60 mJ·m-2 and 1164.7 mJ·m-2, respectively, indicating that it has better stability at both room and high temperatures. This study provides an important fundamental basis for the application of red mud-alumina composites to replace alumina-based composites in the field of construction materials, molten metal filters, ceramic cleavers, etc.

Relationship between the after-school schedule and bone mineral density in middle-school students in Shanghai / 中国学校卫生

Objective@#To investigate the relationship between after-school schedule and bone mineral density in middle-school students in Shanghai, to provide a reference for taking targeted measures.@*Methods@#From November 2017 to April 2018，eighteen classes of six middle schools in Shanghai (from urban districts, urban-suburb combined districts and suburban districts, respectively) were selected based on cluster random sampling. In each school, 2-4 classes were further randomly chosen in the 6th grade. Questionnaires were completed by 518 students and their parents, as well as physical examination and bone mineral density assessment.@*Results@#The average time spent on after-school academic learning during weekends was 4.0 (IQR: 2.0, 6.0) h. The Z-score of bone density was 0.3 (IQR: -0.7, 1.0). The smooth curve fit model showed a non-linear relationship between after-school academic learning time during weekends and the Z-scores of bone mineral densities. A two-stage multiple linear regression analysis was further applied according to the fit results, and the results showed that when total afterschool academic learning time <4.5 hours during weekends, the learning time was inversely correlated with the bone density Z-scores (β=-0.11,P=0.01), and when the learning time ≥4.5 hours, there was no significant correlation between the learning time and bone density Z-scores (β=0.02, P=0.65). Parent and student questionnaires showed that there was a non-linear relationship between students’ daily time spent on outdoor sports-related activities and bone density Z-scores. When time spent on the outdoor activities ≥45 minutes per day, outdoor activities were positively correlated with bone density Z-scores (P<0.05). However, when the time spent on outdoor activities <45 minutes per day, there was no significant relationship between outdoor activities and bone density Z-scores (P>0.05).@*Conclusion@#Too much time on after-school academic learning during weekends or limited time on outdoor activities are both related to impaired bone mineral densities. Therefore, a reasonable after-school schedule for middle-school students is important for physical development, especially during weekends.