Label-free quantitative proteomic analysis of serum exosomes in mice with thoracic aortic aneurysm.

OBJECTIVE: Thoracic aortic aneurysm (TAA) is a cardiovascular disease with high morbidity and mortality. However, the causes and mechanisms of TAA are not fully understood. Serum exosomes from mice with TAA were used to explore the markers associated with this disease. METHODS: C57BL/6 mice were divided into three groups and given ordinary drinking water, ordinary drinking water plus a saline osmotic pump, or drinking water containing ß-aminopropionitrile (BAPN) (1 g/kg/d) plus an angiotensin II (Ang II) (1 µg/kg/min) osmotic pump. Haematoxylin and eosin staining of thoracic aortic tissues was performed. The basic characteristics of exosomes were analysed. Differentially expressed proteins (DEPs) were identified by LC‒MS/MS. Protein‒protein networks and enrichment analysis were used to explore possible molecular mechanisms. RESULTS: The present study elucidated the protein expression profile of serum exosomes in mice with TAA induced by BAPN combined with Ang II. In this work, the expression of a total of 196 proteins was significantly dysregulated in serum exosomes of mice with TAA, with 122 proteins significantly upregulated and 74 proteins markedly downregulated. Notably, Haptoglobin (Hp) and Serum amyloid p-component (Sap) identified based on the PPI network were significantly upregulated and have been strongly linked to cardiovascular disease. Interestingly, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the upregulated and downregulated proteins were involved in the complement and coagulation cascade pathways. CONCLUSIONS: This study showed that the identified DEPs have potential as biomarkers for the diagnosis of TAA and provided a more comprehensive understanding of the pathophysiological mechanisms of TAA.

Unexpected rise of ozone in urban and rural areas, and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou, China: implications for air quality.

The outbreak of coronavirus named COVID-19, initially identified in Wuhan, China in December 2019, has spread rapidly at the global scale. Most countries have rapidly stopped almost all activities including industry, services and transportation of goods and people, thus decreasing air pollution in an unprecedented way, and providing a unique opportunity to study air pollutants. While satellite data have provided visual evidence for the global reduction in air pollution such as nitrogen dioxide (NO2) worldwide, precise and quantitative information is missing at the local scale. Here we studied changes in particulate matter (PM2.5, PM10), carbon monoxide (CO), NO2, sulfur dioxide (SO2) and ozone (O3) at 10 urban sites in Hangzhou, a city of 7.03 million inhabitants, and at 1 rural site, before city lockdown, January 1-23, during city lockdown, January 24-February 15, and during resumption, February 16-28, in 2020. Results show that city lockdown induced a sharp decrease in PM2.5, PM10, CO, and NO2 concentrations at both urban and rural sites. The NO2 decrease is explained by reduction in traffic emissions in the urban areas, and by lower regional transport in rural areas during lockdown, as expected. SO2 concentrations decreased from 6.3 to 5.3 µg m-3 in the city, but increased surprisingly from 4.7 to 5.8 µg m-3 at the rural site: this increase is attributed both to higher coal consumption for heating and emissions from traditional fireworks of the Spring Eve and Lantern Festivals during lockdown. Unexpectedly, O3 concentrations increased by 145% from 24.6 to 60.6 µg m-3 in the urban area, and from 42.0 to 62.9 µg m-3 in the rural area during the lockdown. This finding is explained by the weakening of chemical titration of O3 by NO due to reductions of NOx fresh emissions during the non-photochemical reaction period from 20:00 PM to 9:00 AM (local time). During the lockdown, compared to the same period in 2019, the daily average concentrations in the city decreased by 42.7% for PM2.5, 47.9% for PM10, 28.6% for SO2, 22.3% for CO and 58.4% for NO2, which is obviously explained by the absence of city activities. Overall, we observed not only the expected reduction in some atmospheric pollutants (PM, SO2, CO, NO2), but also unexpected increases in SO2 in the rural areas and of ozone (O3) in both urban and rural areas, the latter being paradoxically due to the reduction in nitrogen oxide levels. In other words, the city lockdown has improved air quality by reducing PM2.5, PM10, CO, and NO2, but has also decreased air quality by augmenting O3 and SO2.

Quantification and characterization of microplastics in farmland soils of Jiangsu Province, East China.

The soil ecosystem plays a significant role in the dynamics of microplastics. Qualitative and quantitative analyses of microplastics in six farmland plots from the cities of Nanjing, Suzhou, and Xuzhou were conducted by using laser infrared imaging system (LDIR). The results showed that the abundance of microplastics in farmland soils in Jiangsu Province ranged from 667-9333 items/kg, and the difference between the abundance of microplastics in soils from open-field cultivation and greenhouse farming was not significant. The vertical distribution characteristics showed that the abundance of microplastic decreased significantly with the increase of soil depth. However, there was no significant difference in particle size between top soil (0-5 cm) and deep soil (10-20 cm). Fragments were the most common microplastic form in soil samples (94.6%), with polyvinyl chloride (PVC) being the main polymer type, followed by polyethylene terephthalate (PET). Compared with previous studies conducted in China, the abundance of microplastics in farmland soils in Jiangsu Province was at moderate level but was notably higher than other places within the Yangtze River Delta region. The conclusions drawn from this paper provided important reference data for future assessment of microplastic pollution in agricultural fields. Furthermore, they establish a fundamental groundwork for understanding the migration patterns of MPs in soil environments.

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning.

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6-32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the 'dirty' (2.33%) or 'clean' (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data.

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7-86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5- 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

[An analysis of pharyngeal respiratory pressure before and after onset of obstruction in respiratory obstructive sleep apnea hypopnea syndrome].

OBJECTIVE: To study the characteristics of pharyngeal respiratory pressure before and after onset of airflow obstruction events in obstructive sleep apnea hypopnea syndrome (OSAHS). METHODS: The respiratory pressure in nasopharynx, oropharynx, and hypopharynx in 8 patients with OSAHS and 9 normal persons were evaluated through catheter manometer. Base on mean respiratory pressure and the ratio of negative respiratory pressure persistent time, the characteristics of pharyngeal respiratory pressure before and after the event onset were analyzed. RESULTS: During sleep, the pharyngeal respiratory pressure in normal persons had similar and periodical wave shape. The ratio of negative respiratory pressure persistent time was less than 0.5. In patients with OSAHS, when the obstruction of airflow happened, the pharyngeal respiratory pressure fluctuated violently, the wave shape became irregular, and the negative expiratory pressure was evident. The mean respiratory pressure was 1 to 2 order of magnitude larger than in normal persons, even reaching -990 Pa. The ratio of persistent negative pressure time was larger than normal. During the intermittent period, the wave shape of pharyngeal respiratory pressure was regular and periodical, the pressure wave shape was different from onset of obstruction. However, in the intermittent period, the mean respiratory pressure, the ratio of negative respiratory pressure persistent time, and other characteristics were still statistically different from normal persons (P < 0.05). CONCLUSIONS: During the intermittent period, the pharyngeal respiratory pressure in patients with OSAHS during sleep is basically different from the pharyngeal respiratory pressure in normal person. The characteristics of pharyngeal respiratory pressure in intermittent period indicates that both structural and functional abnormalities in pharyngeal cavity in patients with OSAHS, which affect the respiratory airflow during sleep are inherent. The effects are more prominent during onset period, suggesting that the characteristics of the pharyngeal pressure of breathing during the onset period will be more important to the mechanism of airflow obstruction.