SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity.

The devastating COVID-19 pandemic caused by SARS-CoV-2 and multiple variants or subvariants remains an ongoing global challenge. SARS-CoV-2-specific T cell responses play a critical role in early virus clearance, disease severity control, limiting the viral transmission and underpinning COVID-19 vaccine efficacy. Studies estimated broad and robust T cell responses in each individual recognized at least 30 to 40 SARS-CoV-2 antigen epitopes and associated with COVID-19 clinical outcome. Several key immunodominant viral proteome epitopes, including S protein- and non-S protein-derived epitopes, may primarily induce potent and long-lasting antiviral protective effects. In this review, we summarized the immune response features of immunodominant epitope-specific T cells targeting different SRAS-CoV-2 proteome structures after infection and vaccination, including abundance, magnitude, frequency, phenotypic features and response kinetics. Further, we analyzed the epitopes immunodominance hierarchy in combination with multiple epitope-specific T cell attributes and TCR repertoires characteristics, and discussed the significant implications of cross-reactive T cells toward HCoVs, SRAS-CoV-2 and variants of concern, especially Omicron. This review may be essential for mapping the landscape of T cell responses toward SARS-CoV-2 and optimizing the current vaccine strategy.

Combinational benefit of antihistamines and remdesivir for reducing SARS-CoV-2 replication and alleviating inflammation-induced lung injury in mice.

COVID-19 is an immune-mediated inflammatory disease caused by SARS-CoV-2 infection, the combination of anti-inflammatory and antiviral therapy is predicted to provide clinical benefits. We recently demonstrated that mast cells (MCs) are an essential mediator of SARS-CoV-2-initiated hyperinflammation. We also showed that spike protein-induced MC degranulation initiates alveolar epithelial inflammation for barrier disruption and suggested an off-label use of antihistamines as MC stabilizers to block degranulation and consequently suppress inflammation and prevent lung injury. In this study, we emphasized the essential role of MCs in SARS-CoV-2-induced lung lesions in vivo, and demonstrated the benefits of co-administration of antihistamines and antiviral drug remdesivir in SARS-CoV-2-infected mice. Specifically, SARS-CoV-2 spike protein-induced MC degranulation resulted in alveolar-capillary injury, while pretreatment of pulmonary microvascular endothelial cells with antihistamines prevented adhesion junction disruption; predictably, the combination of antiviral drug remdesivir with the antihistamine loratadine, a histamine receptor 1 (HR1) antagonist, dampened viral replication and inflammation, thereby greatly reducing lung injury. Our findings emphasize the crucial role of MCs in SARS-CoV-2-induced inflammation and lung injury and provide a feasible combination antiviral and anti-inflammatory therapy for COVID-19 treatment.

Estrogen and Estrogen Receptor Modulators: Potential Therapeutic Strategies for COVID-19 and Breast Cancer.

Owing to the ongoing coronavirus disease 2019 (COVID-19) pandemic, we need to pay a particular focus on the impact of coronavirus infection on breast cancer patients. Approximately 70% of breast cancer patients express estrogen receptor (ER), and intervention therapy for ER has been the primary treatment strategy to prevent the development and metastasis of breast cancer. Recent studies have suggested that selective estrogen receptor modulators (SERMs) are a potential therapeutic strategy for COVID-19. With its anti-ER and anti-viral combined functions, SERMs may be an effective treatment for COVID-19 in patients with breast cancer. In this review, we explore the latent effect of SERMs, especially tamoxifen, and the mechanism between ER and virus susceptibility.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

An analysis of spatiotemporal pattern for COIVD-19 in China based on space-time cube.

This study seeks to examine and analyze the spatial and temporal patterns of 2019 novel coronavirus disease (COVID-19) outbreaks and identify the spatiotemporal distribution characteristics and changing trends of cases. Hence, local outlier analysis and emerging spatiotemporal hot spot analysis were performed to analyze the spatiotemporal clustering pattern and cold/hot spot trends of COVID-19 cases based on space-time cube during the period from 23 January 2020 to 24 February 2020. The main findings are as follows: (1) The outbreak had spread rapidly throughout the country within a short time and the current totality incidence rate has decreased. (2) The spatiotemporal distribution of cases was uneven. In terms of the spatiotemporal clustering pattern, Wuhan and Shiyan city were the center as both cities had high-high clustering pattern with a surrounding unstable multiple-type pattern in partial areas of Henan, Anhui, Jiangxi, and Hunan provinces, and Chongqing city. Those regions are continuously in the hot spot on the spatiotemporal tendency. (3) The spatiotemporal analysis technology based on the space-time cube can analyze comprehensively the spatiotemporal pattern of epidemiological data and produce a visual output of the consequences, which can reflect intuitively the distribution and trend of data in space-time. Therefore, the Chinese government should strengthen the prevention and control efforts in a targeted manner to cope with a highly changeable situation.