Microbial activity and community structure in PM2 .5 at different heights in ground boundary layer of Beijing atmosphere under various air quality levels.

The outbreak of the COVID-19 epidemic is a reminder that aerosols have important health effects as a potential route for disease transmission. Biological components in aerosols (especially PM2.5 ) may pose potential threats to humans as pathogens and allergens. Research on PM2.5 and biological components currently focuses mainly on polluted conditions, with less emphasis on clean environments. Sampling has also been primarily based on a single point with a lack of data at different positions. In this study, a modified fluorescein diacetate hydrolysis method was used to measure microbial activity in PM2.5 at different altitudes over a year in Beijing, China. A high-throughput sequencing method was used to study the microbial community. Results showed that microbial activity 1.5 m (0.0465 ng m-3 ) above the ground was higher than 31.5 m (0.0348 ng m-3 ). There was higher microbial activity at both heights during spring. Furthermore, a positive correlation was observed between microbial activity and relative abundance of dominant species. Microbial activity increased during autumn and winter increased alongside the pollution level, but in spring higher levels of microbial activity were observed in excellent or good weather conditions. The results from this study are valuable for further research regarding the biological components of atmospheric PM, the prevention of biological pollution, and establishing a comprehensive air quality evaluation system.

The Microbial Activity in PM2.5 in Indoor Air: As an Index of Air Quality Level

Bioaerosols are a major source of pollution in indoor environments, where people spend approximately 90% of their time, and the microorganisms adhered to PM2.5 adversely affect human health. However, most research has focused on the concentration of these aerosols and the factors that influence it rather than the correlation between microbial activity and air quality. Thus, this study used a modified technique of fluorescein diacetate (FDA) hydrolysis to evaluate the activity of microorganisms in the PM2.5 during three seasons (summer, autumn and winter) in Beijing. 0.155–5.388 ng m–3 and 0.091–5.740 ng m–3 of sodium fluorescein, a marker of microbial activity, were measured indoors and outdoors, respectively;thus, no significant difference in concentration between the two environments was detected, but the indoor activity was affected by outdoor conditions to an extent. The most active season was autumn, followed by winter and summer. Furthermore, the highest activity in summer and autumn was observed during conditions of excellent air quality, and in summer, the activity during conditions of good air quality also obviously exceeded that during conditions of slight pollution. Additionally, the microbial activity in a room varied according to the room’s ventilation (or lack thereof), suggesting a strong association between these parameters. In general, when the air quality was excellent, 20 minutes of ventilation achieved the optimal air exchange, but this duration should be reduced during polluted conditions. Our results provide new insights into evaluating the indoor air quality based on the microbial activity.

MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.

Brucellosis prevalence in yaks in China in 1980-2019: A systematic review and meta-analysis.

In the Qinghai-Tibet Plateau of China, the yak is an animal of particular economic interest, which provides protein and income for herders in daily life. Brucellosis is a bacterial disease that can infect humans and animals, including yaks. It can damage the yak reproductive system, causing miscarriage and orchitis. At the same time, brucellosis threatens the health of herders. We performed this meta-analysis using R software to explore the combined prevalence and risk factors of brucellosis in yak in China. Variability was assessed by the I2 statistic and Cochran Q statistic. We identified 52 publications of related research from four databases (Wanfang Data, VIP Chinese Journal Database, China National Knowledge Infrastructure, and of PubMed). The pooled prevalence of yak brucellosis was 8.39 %. Prevalence was highest in Southwestern China (11.1 %). The point estimate of brucellosis in yak from 2012 to 2016 was the highest (11.47 %). The point estimate of age ≤ 12 months (1.44 %) was lower than that of age > 12 months (15.6 %). This study shows that yak brucellosis is serious, and its incidence is higher than before 2012. We recommend carrying out large-scale yak brucellosis investigations in Western China and conducting comprehensive testing planning. The detection of brucellosis in adult animals should be strengthened to reduce the economic loss caused by brucellosis to herders and to improve public health.

Venous thromboembolism in non-COVID-19 population during the pandemic: a nationwide multicenter retrospective survey.

Impact of pandemic on the incidence of venous thromboembolism (VTE) in non-COVID-19 patients is undetermined. Thus, a nationwide multicenter retrospective survey was conducted to evaluate the disease burden in non-COVID-19 population. This multi-center survey involved 94 hospitals from 24 provinces in the mainland of China, and collected data on non-COVID-19 patients admitted to the radiology departments due to VTE between January 24 and April 16, 2020. Baseline characteristics, VTE risk factors, clinical manifestations and the treatments were compared with those in the same period of 2019. 3,358 patients with VTE from 74 hospitals were included in this study (1,458 in 2020, 1,900 in 2019). Most aged ≥ 50 years (80.6% in the pandemic, 81.2% in 2019). The number of patients aged 30-39 years increased from 3.9% in 2019 period to 5.8% in the pandemic (p = 0.009). Among the VTE risk factors, the rate of decreased activity increased significantly in the pandemic, and was much higher than that in 2019 (30.7% vs 22.6%, p < 0.0001). Under the risk of decreased activity, patients with comorbidities chronic diseases, especially diabetes, showed significantly a higher incidence of VTE (30.4% vs 22.0%, p < 0.0001). In the pandemic period, fewer patients were treated with anticoagulation alone (33.5% vs 36.7%, p = 0.05), and more underwent inferior vena cava filter (IVCF) implantation, compared with those in 2019 (66.5% vs 63.2%, p = 0.046). The pandemic increased the VTE risk of decreased activity among the non-COVID-19 population. Patients with comorbidities, especially diabetes, have a significant higher risk of VTE during the pandemic.

Prevalence of coccidia in domestic pigs in China between 1980 and 2019: a systematic review and meta-analysis.

BACKGROUND: Swine coccidiosis, a protozoan disease caused by coccidia, can result in diarrhoea and weight loss in piglets and even economic losses in the pig industry. Here, we report the first systematic review and meta-analysis of the prevalence of coccidia (including Eimeria spp. and Cystoisospora suis) in pigs in China. METHODS: Five databases (PubMed, ScienceDirect, Chinese Web of Knowledge, Wanfang, and Chongqing VIP) were searched and 50 studies (46,926 domestic pigs, 22 provinces) ultimately identified pertaining to the prevalence of coccidia infection from 1980 to 2019. We incorporated the effect size using the random-effects model in the "meta" package in R software and conducted univariate and multivariate meta-regression analyses using a mixed-effects model. RESULTS: The pooled prevalence rate of coccidia in pigs was 21.9%, including the C. suis infection rate of 9.1%. The highest prevalence of coccidia (39.6%) was found in northwest China, and this region also presented the lowest prevalence of C. suis (4.7%). In the subgroup analysis based on sampling year, the highest prevalence of coccidia was detected in 2001 or earlier (32.6%), whereas the lowest rate was found in 2012 or later (14.3%). An opposite trend was observed for C. suis (5.5% in 2000 or earlier vs 14.4% in 2000 or later). The prevalence of coccidia in extensive farming systems (29.5%) was higher than that in intensive farming systems (17.3%). In contrast, the point estimate of C. suis prevalence was lower in the extensive farming systems (5.1%) than in the intensive farming systems (10.0%), but the difference was not significant (P > 0.05). Among the four age categories, the highest total coccidia prevalence (26.2%) was found in finishing pigs, followed by suckling piglets (19.9%), whereas the highest prevalence of C. suis (14.9%) was observed in suckling piglets. CONCLUSIONS: Our findings suggest that coccidia infection in Chinese pigs is common, although the prevalence of C. suis in pigs does not receive sufficient attention. We recommend the rational use of anticoccidial drugs to avoid drug resistance and the development of preventive and control measures for C. suis to reduce the incidence of swine coccidiosis.

Different characteristics of microbial diversity and special functional microbes in rainwater and topsoil before and after 2019 new coronavirus epidemic in Inner Mongolia Grassland.

Grassland ecosystems are vital terrestrial ecosystems. As areas sensitive to climate change, they are critical for assessing the effects of global climate change. In China, grasslands account for over 40% of the land area. There is currently limited information on microbial diversity evolution in different grassland areas, particularly microorganisms with ice nucleation activity (INA) and their potential resources with potential influence to regulate regional precipitation and climate. We used Illumina MiSeq to sequence the 16S rRNA V3-V4 hypervariable region and performed a simple droplet freezing experiment to determine the variation in the grassland microbial community species composition and community structure. Rainwater and topsoil samples from the Hulunbuir Grassland in Inner Mongolia collected over three years were characterized. The dominant bacterial genus in the rainwater was Massilia, and the dominant fungus was Cladosporium. Additionally, the dominant bacteria in the soil were Sphingomonas, and the dominant fungus was Gibberella. There were differences in the microbial communities before and after the coronavirus disease epidemic. Pathogenic microorganisms exhibited inconsistent responses to environmental changes. The low relative abundance of known high-INA microorganisms and the higher freezing temperature indicated that unknown high-efficiency biological ice nucleating particles may be present. We found significant differences in species diversity and richness between the rainwater and soil populations in grassland areas by analyzing the sample community structures. Our research results revealed the species composition and structure of the microbiota in grassland ecosystems in China, indicating that environmental media and human activities may affect the microbiota in the grassland area and indicating underlying microorganisms with high INA.

Hematologic changes predict clinical outcome in recovered patients with COVID-19.

2019 coronavirus disease (COVID-19) presents as a newly recognized pneumonia that has brought about a global pandemic and is increasingly considered as a systemic illness. We investigated the clinical and laboratory features of recovered COVID-19 patients without pre-existing hematologic diseases at Wuhan No. 1 Hospital. Fifty-nine male and 68 female Chinese patients were included with the median age at 64 years in the present study. Eosinopenia (37.80%), monocytosis (51.97%), lymphocytopenia (25.20%), and anemia (51.97%) were the most common hematologic findings in our cohort, particularly in severe or critically ill COVID-19. The levels of changes in leukocytes, neutrophils, lymphocytes, monocytes, eosinophils, basophils, platelets, hemoglobin levels, mean corpuscular volume (MCV), and mean cell hemoglobin concentration (MCHC) are overall associated with lung involvement, oxygen demand, and disease activity. However, changes of eosinophils (end hospitalization-baseline) (coefficients = 10.32; 95% CI = 1.03-19.60, P = 0.03) and basophils (Max - Min) (coefficients = 71.43; 95% CI = 8.55-134.31, P = 0.03) were independent predictors of delayed recovery in the hospital by the multivariate analysis in this recovered population. A variety of hematologic changes are associated with the severity and clinical outcome of recovered COVID-19 patients, which warrants further exploration of their underlying mechanisms.

Survey of background microbial index in inhalable particles in Beijing.

As a potential transmission route for diseases, aerosols have an important impact on human health. At present, research concerning the biological components of atmospheric particulate matter (PM) is of increasing interest. However, previous research has mainly focused on serious pollution conditions, creating a knowledge gap regarding background atmospheric microbes. In this study, we observed the atmosphere of Huairou in Beijing for one year, analyzed the characteristics of the physiological metabolic activity of the microorganisms as an index to determine the air quality, and further explored the microbial communities. From January 2018 to January 2019, a total of 157 days of microbial activity data for PM2.5 and PM10 were obtained through the use of a modified fluorescein diacetate (FDA) hydrolysis method. Our results showed that there was no significant difference between the microbial activity of PM2.5 and PM10, even though there was significant seasonal variation. At increasing pollution levels, the results showed that the microbial activity decreased at first, and then increased as the conditions worsened. The microbial community of PM2.5 was analyzed using the high-throughput sequencing method. There were significant seasonal differences in species richness and community diversity of bacteria in PM2.5, whereas there was variation only in its fungi species richness. Notably, the microbial community dominated by bacteria has a significant influence on microbial activity. From the perspective of microbial community composition, this study uncovered the possible causes of microbial activity variation and identified the key bacteria and fungi. These results will provide a theoretical basis for both improving air biological pollution predictions and ambient air quality evaluations.

T cell immunobiology and cytokine storm of COVID-19.

2019 coronavirus disease (COVID-19) presents as a newly recognized pneumonia and could rapidly progress into acute respiratory distress syndrome which has brought about a global pandemic. Until now, no curative therapy has been strongly recommended for COVID-19 except for personalized supportive care. T cells and virus-specific T cells are essential to protect against virus infection, including COVID-19. Delayed immune reconstitution (IR) and cytokine storm (CS) remain serious obstacles for the cure of COVID-19. Most COVID-19 patients, especially among elderly patients, had marked lymphopenia and increased neutrophils, but T cell counts in severe COVID-19 patients surviving the disease gradually restored later. Elevated pro-inflammatory cytokines, particularly IL-6, IL-10, IL-2 and IL-17, and exhausted T cells are found in peripheral blood and the lungs. It suggests that Thymosin α1 and adoptive COVID-19-specific T cells could improve IR, while convalescent plasma, IL-6 blockade, mesenchymal stem cells and corticosteroids could suppress CS. More clinical studies in this field worldwide are urgently warranted to pave the way for therapy of COVID-19 in the future.