Renal Side Effects of COVID-19 Vaccination.

Background: The COVID-19 pandemic has imposed a challenge on global healthcare and has tremendously impacted everyone's lives. Vaccination is one of the most effective and vital strategies to halt the pandemic. However, new-onset and relapsed kidney diseases have been reported after COVID-19 vaccination. This narrative review was conducted to collect published data and generalize some hypotheses for the pathogenesis of renal side effects of COVID-19 vaccines. Methods: A systematic literature search of articles reporting renal adverse reactions, including in adults and children, in the PubMed and Web of Science databases until August 2022 was performed. Results: A total of 130 cases reporting a renal adverse reaction following COVID-19 vaccination from 90 articles were included in this review, of which 90 (69%) were new-onset kidney diseases, while 40 (31%) were relapsed kidney diseases. The most frequent renal side effects of COVID-19 vaccination were minimal change disease (52 cases), IgA nephropathy (48 cases), antineutrophil cytoplasmic autoantibody vasculitis (16 cases), and acute interstitial nephritis (12 cases). Other renal side effects occurred at a much lower frequency. Follow-up data were available for 105 patients, and 100 patients (95%) responded to the treatments. Conclusions: The number of reported cases is far less than the hundreds of millions of vaccinations, and the benefit of COVID-19 vaccination far outweighs its risks. This review will assist healthcare professionals, particularly nephrologists, who should be aware of these side effects and recognize them early and treat them efficiently.

Airport capacity constraints and air traffic demand in China

This paper analyzes the degree of airport capacity utilization of 239 China's civil airports in 2019. Our database is unique and very detailed in that we combine actual operation and scheduled flight data. We are particularly interested in the list of capacity constrained airports, their characteristics, and future development. To this end, we develop a classification matrix to investigate capacity constrained airports. We then forecast the demand and capacity of these airports under three scenarios and use the data as inputs for Monte Carlo simulation, to predict the capacity utilization trends of these airports in 2025 and 2035. Our research shows that: (1) There are significant differences between airports in terms of airport congestion status, and over 85% of airports in China experience no overload;(2) Big spatial differences are observed in the distribution of airports with less spare capacity and can be summarized as “more in eastern China and less in western China”;(3) Ten airports stand out as seriously capacity constrained and all of them serves as international or regional hubs;(4) Airport location, airport orientation, and linkage to the local economy are closely related to airport capacity constraints;(5) As most constrained airports are going to increase capacity in the future, as well as the outbreak of COVID-19 negatively affected aviation demand, most airports will have sufficient capacity reserves in the future. However, Shenzhen Bao'an and Xi'an Xianyang International Airport will face heavy capacity pressure. We also provide several recommendations to improve airport capacity utilization and relieve capacity shortages.

Nonpharmaceutical interventions against the COVID-19 pandemic significantly decreased the spread of enterovirus in children.

Precise prevention and control measures have been adopted to impede the transmission of coronavirus disease 2019 (COVID-19) in China. This study was performed to investigate the effect of protective measures on gastrointestinal infection in children during the COVID-19 pandemic. The data on the rotavirus and adenovirus antigen tests were collected in outpatient children due to gastroenteritis from January 1, 2019 to December 31, 2020, at the Children's Hospital of Zhejiang University School of Medicine. According to age and month distribution, the positive number and rate of rotavirus and adenovirus in 2020 were compared with 2019. A 3.8-fold and 4-fold reduction in the number of rotavirus- and adenovirus-positive patients in 2020 were found, respectively. The overall positive rate of rotavirus and adenovirus infection was drastically decreased in 2020 (rotavirus 2020: 18.18% vs. 2019: 9.75%, p < 0.001; adenovirus 2020: 3.13% vs. 2019: 1.58%, p < 0.001). The proportions of rotavirus and adenovirus in all age groups in 2020 decreased compared with those in 2019. The highest frequency of rotavirus infection occurred among children aged 1-3 years both in 2019 and 2020 (2019: 27.95% vs. 2020: 17.19%, p < 0.001), while adenovirus infection was detected in children aged 3-5 years, which had the highest percent positivity (2019: 8.19% vs. 2020: 4.46%; p < 0.001). An obvious peak prevalence of rotavirus incidence was found during December-April, and the percent positivity of rotavirus significantly decreased in 2020 (December 2019: 24.26% vs. 2020: 8.44%, p < 0.001; January 2019: 40.67% vs. 2020: 38.18%, p < 0.05; February 2019: 40.73% vs. 2020: 15.04%, p < 0.001; March 2019: 31.47% vs. 2020: 7.88%, p < 0.001; April 2019: 15.52% vs. 2020: 4.78%, p < 0.001). The positive rate of adenovirus distributed throughout 2019 was 1.91%-4.86%, while the percent positivity during 2020 in the same period was much lower (0.00%-3.58%). Our results confirmed that the preventive and control measures adopted during the COVID-19 pandemic and the collateral benefit of these interventions have significantly decreased the transmission of rotavirus or adenovirus.

Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M 2 UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M 2 UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.