Effectiveness of Inactivated COVID-19 Vaccines against Delta-Variant COVID-19: Evidence from an Outbreak in Inner Mongolia Autonomous Region, China.

Phase 3 clinical trials and real-world effectiveness studies showed that China's two main inactivated COVID-19 vaccines are very effective against serious illness. In November 2021, an outbreak occurred in the Inner Mongolia Autonomous Region that provided an opportunity to assess the vaccine effectiveness (VE) of these inactivated vaccines against COVID-19 caused by the delta variant. We evaluated VE with a retrospective cohort study of close contacts of infected individuals, using a generalized linear model with binomial distribution and log-link function to estimate risk ratios (RR) and VE. A total of 8842 close contacts were studied. Compared with no vaccination and adjusted for age, presence of comorbidity, and time since last vaccination, full vaccination reduced symptomatic infection by 62%, pneumonia by 64% and severe COVID-19 by 90%; reductions associated with homologous booster doses were 83% for symptomatic infection, 92% for pneumonia and 100% for severe COVID-19. There was no significant decline in two-dose VE for any outcome for up to 325 days following the last dose. There were no differences by vaccine brand. Inactivated vaccines were effective against delta-variant illness, and were highly effective against pneumonia and severe COVID-19; VE was increased by booster doses.

CCT: Lightweight compact convolutional transformer for lung disease CT image classification.

Computed tomography (CT) imaging results are an important criterion for the diagnosis of lung disease. CT images can clearly show the characteristics of lung lesions. Early and accurate detection of lung diseases helps clinicians to improve patient care effectively. Therefore, in this study, we used a lightweight compact convolutional transformer (CCT) to build a prediction model for lung disease classification using chest CT images. We added a position offset term and changed the attention mechanism of the transformer encoder to an axial attention mechanism module. As a result, the classification performance of the model was improved in terms of height and width. We show that the model effectively classifies COVID-19, community pneumonia, and normal conditions on the CC-CCII dataset. The proposed model outperforms other comparable models in the test set, achieving an accuracy of 98.5% and a sensitivity of 98.6%. The results show that our method achieves a larger field of perception on CT images, which positively affects the classification of CT images. Thus, the method can provide adequate assistance to clinicians.

COVID-19 CT image segmentation method based on swin transformer.

Owing to its significant contagion and mutation, the new crown pneumonia epidemic has caused more than 520 million infections worldwide and has brought irreversible effects on the society. Computed tomography (CT) images can clearly demonstrate lung lesions of patients. This study used deep learning techniques to assist doctors in the screening and quantitative analysis of this disease. Consequently, this study will help to improve the diagnostic efficiency and reduce the risk of infection. In this study, we propose a new method to improve U-Net for lesion segmentation in the chest CT images of COVID-19 patients. 750 annotated chest CT images of 150 patients diagnosed with COVID-19 were selected to classify, identify, and segment the background area, lung area, ground glass opacity, and lung parenchyma. First, to address the problem of a loss of lesion detail during down sampling, we replaced part of the convolution operation with atrous convolution in the encoder structure of the segmentation network and employed convolutional block attention module (CBAM) to enhance the weighting of important feature information. Second, the Swin Transformer structure is introduced in the last layer of the encoder to reduce the number of parameters and improve network performance. We used the CC-CCII lesion segmentation dataset for training and validation of the model effectiveness. The results of ablation experiments demonstrate that this method achieved significant performance gain, in which the mean pixel accuracy is 87.62%, mean intersection over union is 80.6%, and dice similarity coefficient is 88.27%. Further, we verified that this model achieved superior performance in comparison to other models. Thus, the method proposed herein can better assist doctors in evaluating and analyzing the condition of COVID-19 patients.

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

Effectiveness of adenovirus type 5 vectored and inactivated COVID-19 vaccines against symptomatic COVID-19, COVID-19 pneumonia, and severe COVID-19 caused by the B.1.617.2 (Delta) variant: Evidence from an outbreak in Yunnan, China, 2021.

BACKGROUND: In partial response to the coronavirus disease 2019 (COVID-19) pandemic, countries around the world are conducting large-scale vaccination campaigns. Real-world estimates of vaccine effectiveness (VE) against the B.1.617.2 (Delta) variant are still limited. An outbreak in Ruili city of Chinaprovided an opportunity to evaluate VE against the Delta variant of two types of COVID-19 vaccines in use in China and globally - inactivated (CoronaVac and BBIBP-CorV) and adenovirus type 5 vectored (Convidecia) vaccines. METHODS: We estimated VE using a retrospective cohort study two months after the Ruili vaccination campaign (median: 63 days). Close contacts of infected people (Chinese nationality, 18 years and above) were included to assess VE against symptomatic Covid-19, COVID-19 pneumonia, and severe COVID-19. We calculated the relative risks (RR) of the outcomes for unvaccinated compared with fully vaccinated individuals. We used logistic regression analyses to estimate adjusted VEs, controlling for gender and age group (18-59 years and 60 years and over).We compared unvaccinated and fully vaccinated individuals on duration of RT-PCR positivity and Ct value. FINDINGS: There were 686 close contacts eligible for VE estimates. Adjusted VE ofad5-vectored vaccine was 61.5% (95% CI, 9.5-83.6) against symptomatic COVID-19, 67.9% (95%CI: 1.7-89.9) against pneumonia, and 100% (95%CI: 36.6-100) against severe/critical illness. For the two inactivated vaccines, combined VE was 74.6% (95% CI, 36.0-90.0) against symptomatic COVID-19, 76.7% (95% CI: 19.3-93.3) against pneumonia, and 100% (95% CI: 47.6-100) against severe/critical COVID-19. There were no statistically significant differences in VE between twoinactivated vaccines for symptomatic COVID-19 and for pneumonia, nor were there statistically significant differences between inactivated and ad5-vectored VE in any of the three outcomes. The median durations of RT-PCR positivity were 17 days for fifteen people vaccinated with an inactivated vaccine, 18 days for forty-four people vaccinated with the Ad5 vectored vaccine, and 26 days for eleven unvaccinated individuals. INTERPRETATION: These results provide reassuring evidence that the three vaccines are effective at preventing Delta-variant COVID-19 in short term following vaccination campaign, and are most effective at preventing more serious illness. The findings of reduced duration of RT-PCR positivity and length of hospital stay associated with full vaccination suggests potential saving of health-care system resources.

Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19.

Humoral responses in coronavirus disease 2019 (COVID-19) are often of limited durability, as seen with other human coronavirus epidemics. To address the underlying etiology, we examined post mortem thoracic lymph nodes and spleens in acute SARS-CoV-2 infection and observed the absence of germinal centers and a striking reduction in Bcl-6+ germinal center B cells but preservation of AID+ B cells. Absence of germinal centers correlated with an early specific block in Bcl-6+ TFH cell differentiation together with an increase in T-bet+ TH1 cells and aberrant extra-follicular TNF-α accumulation. Parallel peripheral blood studies revealed loss of transitional and follicular B cells in severe disease and accumulation of SARS-CoV-2-specific "disease-related" B cell populations. These data identify defective Bcl-6+ TFH cell generation and dysregulated humoral immune induction early in COVID-19 disease, providing a mechanistic explanation for the limited durability of antibody responses in coronavirus infections, and suggest that achieving herd immunity through natural infection may be difficult.

The Loss of Bcl-6 Expressing T Follicular Helper Cells and the Absence of Germinal Centers in COVID-19.

Humoral responses in COVID-19 disease are often of limited durability, as seen with other human coronavirus epidemics. To address the underlying etiology, we examined postmortem thoracic lymph nodes and spleens in acute SARS-CoV-2 infection and observed the absence of germinal centers, a striking reduction in Bcl-6+ germinal center B cells but preservation of AID+ B cells. Absence of germinal centers correlated with an early specific block in Bcl-6+TFH cell differentiation together with an increase in T-bet+TH1 cells and aberrant extra-follicular TNF-a accumulation.  Parallel peripheral blood studies revealed loss of transitional and follicular B cells in severe disease and accumulation of SARS-CoV-2-specific "disease-related" B cell populations. These data identify defective Bcl-6+TFH cell generation and dysregulated humoral immune induction early in COVID-19 disease, providing a mechanistic explanation for the limited durability of antibody responses in coronavirus infections and suggest that achieving herd immunity through natural infection may be difficult. Funding: This work was supported by NIH U19 AI110495 to SP, NIH R01 AI146779 to AGS, NIH R01AI137057 and DP2DA042422 to DL, BMH was supported by NIGMS T32 GM007753, TMC was supported by T32 AI007245. Funding for these studies from the Massachusetts Consortium of Pathogen Readiness, the Mark and Lisa Schwartz Foundation and Enid Schwartz is also acknowledged. Conflict of Interest: None. Ethical Approval: This study was performed with the approval of the Institutional Review Boards at the Massachusetts General Hospital and the Brigham and Women's Hospital.

Online Antenatal Care During the COVID-19 Pandemic: Opportunities and Challenges.

People across the world have been greatly affected by the ongoing coronavirus disease (COVID-19) pandemic. The high infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals is particularly problematic for recently delivered mothers and currently pregnant women who require professional antenatal care. Online antenatal care would be a preferable alternative for these women since it can provide pregnancy-related information and remote clinic consultations. In addition, online antenatal care may help to provide relatively economical medical services and diminish health care inequality due to its convenience and cost-effectiveness, especially in developing countries or regions. However, some pregnant women will doubt the reliability of such online information. Therefore, it is important to ensure the quality and safety of online services and establish a stable, mutual trust between the pregnant women, the obstetric care providers and the technology vis-a-vis the online programs. Here, we report how the COVID-19 pandemic brings not only opportunities for the development and popularization of online antenatal care programs but also challenges.