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Abstract Coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Vaccination is a prospective approach to control this pandemic. However, apprehension about the safety of vaccines is a major obstacle to vaccination. Amongst health professionals, one evident concern is the risk of antibody-dependent enhancement (ADE), which may increase the severity of COVID-19. To explore whether ADE occurs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and increase confidence in the safety of vaccination, we conducted a meta-analysis to investigate the relationship between post-immune infection and disease severity from a population perspective. Databases, including PubMed, EMBASE, Chinese National Knowledge Infrastructure, SinoMed, Scopus, Science Direct, and Cochrane Library, were searched for articles on SARS-CoV-2 reinfection published until 25 October 2021. The papers were reviewed for methodological quality, and a random effects model was used to analyse the results. Heterogeneity was assessed using the I 2 statistic. Publication bias was evaluated using a funnel plot and Egger’s test. Eleven studies were included in the final meta-analysis. The pooled results indicated that initial infection and vaccination were protective factors against severe COVID-19 during post-immune infection (OR = 0.55, 95%CI = 0.31–0.98). A subgroup (post-immune infection after natural infection or vaccination) analysis showed similar results. Primary SARS-CoV-2 infection and vaccination provide adequate protection against severe clinical symptoms after post-immune infection. This finding demonstrates that SARS-CoV-2 may not trigger ADE at the population level.

Arbitrarily oriented object detection is one of the most-popular research fields in remote sensing image processing. In this paper, we propose an approach to predict object angles indirectly, thereby avoiding issues related to angular periodicity and boundary discontinuity. Our method involves representing the long edge and angle of an object as a vector, which we then decompose into horizontal and vertical components. By predicting the two components of the vector, we can obtain the angle information of the object indirectly. To facilitate the transformation between angle-based representation and the proposed vector-decomposition-based representation, we introduced two novel techniques: angle-to-vector encode (ATVEncode) and vector-to-angle decode (VTADecode). These techniques not only improve the efficiency of data processing, but also accelerate the training process. Furthermore, we propose an adaptive coarse-to-fine positive–negative-sample-selection (AdaCFPS) method based on the vector-decomposition-based representation of the object. This method utilizes the Kullback–Leibler divergence loss as a matching degree to dynamically select the most-suitable positive samples. Finally, we modified the YOLOX model to transform it into an arbitrarily oriented object detector that aligns with our proposed vector-decomposition-based representation and positive–negative-sample-selection method. We refer to this redesigned model as the vector-decomposition-based object detector (VODet). In our experiments on the HRSC2016, DIOR-R, and DOTA datasets, VODet demonstrated notable advantages, including fewer parameters, faster processing speed, and higher precision. These results highlighted the significant potential of VODet in the context of arbitrarily oriented object detection.

This study aims to explore the relationship between the doses of inactivated COVID-19 vaccines received and SARS-CoV-2 Omicron infection in the real-world setting, so as to preliminarily evaluate the protective effect induced by COVID-19 vaccination. We conducted a test-negative case-control study and recruited the test-positive cases and test-negative controls in the outbreak caused by Omicron BA.2 in April 2022 in Guangzhou, China. All the participants were 3 years and older. The vaccination status between the case group and the control group was compared in the vaccinated and all participants, respectively, to estimate the immune protection of inactivated COVID-19 vaccines. After adjusting for sex and age, compared with a mere single dose, full vaccination of inactivated COVID-19 vaccines (OR = 0.191, 95% CI: 0.050 to 0.727) and booster vaccination (OR = 0.091, 95% CI: 0.011 to 0.727) had a more superior protective effect. Compared with one dose, the second dose was more effective in males (OR = 0.090), as well as two doses (OR = 0.089) and three doses (OR = 0.090) among individuals aged 18–59. Whereas, when compared with the unvaccinated, one dose (OR = 7.715, 95% CI: 1.904 to 31.254) and three doses (OR = 2.055, 95% CI: 1.162 to 3.635) could contribute to the increased risk of Omicron infection after adjusting for sex and age. Meanwhile, by contrast with unvaccinated individuals, the result of increased risk was also manifested in the first dose in males (OR = 12.400) and one dose (OR = 21.500), two doses (OR = 1.890), and a booster dose (OR = 1.945) in people aged 18–59. In conclusion, the protective effect of full and booster vaccination with inactivated COVID-19 vaccines exceeded the incomplete vaccination, of which three doses were more effective. Nevertheless, vaccination may increase the risk of Omicron infection compared with unvaccinated people. This may result from the transmission traits of BA.2, the particularity and stronger protection awareness of the unvaccinated population, as well as the ADE effect induced by the decrease of antibody titers after a long time of vaccination. It is crucial to explore this issue in depth for the formulation of future COVID-19 vaccination strategies.

Mpox (monkeypox) is a zoonotic disease caused by monkeypox virus, which belongs to the orthopoxvirus genus. Concern has recently been expressed over the appearance of the human monkeypox virus and its severe clinical presentation that resembles smallpox. Currently, due to the decrescence of immunity among smallpox-vaccinated residents, and the accumulation of unvaccinated cohorts, people are generally susceptible to mpox. A cumulative number of 79411 laboratory-confirmed cases of mpox and 50 fatalities have been reported to the World Health Organization from 110 countries between 1 January and 13 November 2022. This paper provides an overview of the epidemiology and clinical features of mpox, and control and prevention approaches for mpox to fully understand and prevent human infections.

In April 2022, a COVID-19 outbreak caused by the Omicron variant emerged in Guangzhou. A case–control study was conducted to explore the relationship between vaccination intervals and SARS-CoV-2 infection in the real world. According to the vaccination dose and age information of the cases, a 1:4 matched case–control sample was established, finally including n = 242 for the case group and n = 968 for the control group. The results indicated that among the participants who received three vaccine doses, those with an interval of more than 300 days between the receipt of the first vaccine dose and infection (or the first contact with a confirmed case) were less likely to be infected with SARS-CoV-2 than those with an interval of less than 300 days (OR = 0.67, 95% CI = 0.46–0.99). After age-stratified analysis, among participants aged 18–40 years who received two doses of vaccine, those who received the second dose more than 30 days after the first dose were less likely to be infected with SARS-CoV-2 (OR = 0.53, 95% CI = 0.30–0.96). Our findings suggest that we need to extend the interval between the first dose and the second dose and further explore the optimal interval between the first and second and between the second and third doses in order to improve vaccine efficacy.

Papain-like protease (PLpro) is important for the replication and transcription of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to reveal the PLpro mutations associated with the clinical outcomes of patients. Due to the importance of the S protein in the pathogenicity of SARS-CoV-2, the mutation of the S protein was also analyzed in this study. After downloading the data from the Global Initiative on Sharing Avian Influenza Data (GISAID) database, samples were divided into two groups on the basis of patient status, namely, recovered and dead groups. This study performed a univariate analysis and further explored the association of mutations with patient outcomes through multivariate logistic regression analysis. A total of 138,492 samples were used for analysis. The patients had a mean age of 43.66 ± 21.56 years, and 51.3% of them were female. Multivariate logistic regression results showed that, compared with men, women had a lower risk of dying from coronavirus disease 2019 (COVID-19) (OR = 0.687, 95%CI: 0.638–0.740). Compared with patients aged 17 years and younger, patients aged 18–64 years (OR = 2.864, 95%CI: 1.982–4.139) and patients over 65 years old (OR = 19.135, 95%CI: 13.280–27.572) had a higher risk of death after infection. Compared with the wild type, P78L (OR = 5.185, 95%CI: 2.763–9.730) and K233Q (OR = 5.154, 95%CI: 1.442–18.416) in PLpro were associated with an increased risk of death. A synergistic interaction existed between age and mutations A146D and P78L. The results of the multivariate logistic regression analysis of the data on vaccinated patients demonstrated that, compared with the wild type, the P78L (OR = 3.376, 95%CI: 2.040–5.585) mutation was associated with an increased risk of death. In conclusion, compared with the wild-type PLpro protein, the P78L and K233Q mutations may increase the risk of death in infected individuals. In addition, a synergistic effect existed between age and P78L and K233Q that increased the risk of death in older patients.

Deep learning has attracted increasing attention across a number of disciplines in recent years. In the field of remote sensing, ship detection based on deep learning for synthetic aperture radar (SAR) imagery is replacing traditional methods as a mainstream research method. The multiple scales of ship objects make the detection of ship targets a challenging task in SAR images. This paper proposes a new methodology for better detection of multi-scale ship objects in SAR images, which is based on YOLOv5 with a small model size (YOLOv5s), namely the multi-scale ship detection network (MSSDNet). We construct two modules in MSSDNet: the CSPMRes2 (Cross Stage Partial network with Modified Res2Net) module for improving feature representation capability and the FC-FPN (Feature Pyramid Network with Fusion Coefficients) module for fusing feature maps adaptively. Firstly, the CSPMRes2 module introduces modified Res2Net (MRes2) with a coordinate attention module (CAM) for multi-scale features extraction in scale dimension, then the CSPMRes2 module will be used as a basic module in the depth dimension of the MSSDNet backbone. Thus, our backbone of MSSDNet has the capabilities of features extraction in both depth and scale dimensions. In the FC-FPN module, we set a learnable fusion coefficient for each feature map participating in fusion, which helps the FC-FPN module choose the best features to fuse for multi-scale objects detection tasks. After the feature fusion, we pass the output through the CSPMRes2 module for better feature representation. The performance evaluation for this study is conducted using an RTX2080Ti GPU, and two different datasets: SSDD and SARShip are used. These experiments on SSDD and SARShip datasets confirm that MSSDNet leads to superior multi-scale ship detection compared with the state-of-the-art methods. Moreover, in comparisons of network model size and inference time, our MSSDNet also has huge advantages with related methods.

Summary: Tissue-resident macrophages (TRMs) and dendritic cells (DCs) are highly heterogeneous and essential for immunity, tissue regeneration, and homeostasis maintenance. Here, we comprehensively profile the heterogeneity of TRMs and DCs across adult zebrafish organs via single-cell RNA sequencing. We identify two macrophage subsets: pro-inflammatory macrophages with potent phagocytosis signatures and pro-remodeling macrophages with tissue regeneration signatures in barrier tissues, liver, and heart. In parallel, one conventional dendritic cell (cDC) population with prominent antigen presentation capacity and plasmacytoid dendritic cells (pDCs) featured by anti-virus properties are also observed in these organs. Remarkably, in addition to a single macrophage/microglia population with potent phagocytosis capacity, a pDC population and two distinct cDC populations are identified in the brain. Finally, we generate specific reporter lines for in vivo tracking of macrophage and DC subsets. Our study depicts the landscape of TRMs and DCs and creates valuable tools for in-depth study of these cells in zebrafish.

Summary: Metaphocytes are tissue-resident macrophage (TRM)/dendritic cell (DC)-like cells of non-hematopoietic origin in zebrafish barrier tissues. One remarkable property of metaphocytes is their ability to capture soluble antigens from the external environment via transepithelial protrusions, a unique function manifested by specialized subpopulations of the TRMs/DCs in mammal barrier tissues. Yet, how metaphocytes acquire myeloid-like cell properties from non-hematopoietic precursors and how they regulate barrier immunity remains unknown. Here, we show that metaphocytes are in situ generated from local progenitors guided by the ETS transcription factor Spic, the deficiency of which results in the absence of metaphocytes. We further document that metaphocytes are the major IL-22BP-producing cells, and the depletion of metaphocytes causes dysregulated barrier immunity that resembles the phenotype of IL-22BP-deficient mice. These findings reveal the ontogeny, development, and function of metaphocytes in zebrafish, which facilitates our understanding of the nature and function of the mammalian TRM/DC counterparts.