Risk Factors for Weak Antibody Response of SARS-CoV-2 Vaccine in Adult Solid Organ Transplant Recipients: A Systemic Review and Meta-Analysis.

Objective: This is the first systematic review and meta-analysis to determine the factors that contribute to poor antibody response in organ transplant recipients after receiving the 2-dose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine. Method: Data was obtained from Embase, PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBM). Studies reporting factors associated with antibody responses to the 2-dose SARS-CoV-2 vaccine in solid organ transplant recipients were included in our study based on the inclusion and exclusion criteria. Two researchers completed the literature search, screening, and data extraction. Randomized models were used to obtain results. Egger's test was performed to determine publication bias. Sensitivity analysis was performed to determine the stability of the result. The heterogeneity was determined using the Galbraith plot and subgroup analysis. Results: A total of 29 studies were included in the present study. The factors included living donor, BNT162b2, tacrolimus, cyclosporine, antimetabolite, mycophenolic acid (MPA) or mycophenolate mofetil (MMF), azathioprine, corticosteroids, high-dose corticosteroids, belatacept, mammalian target of rapamycin (mTOR) inhibitor, tritherapy, age, estimated glomerular filtration rate (eGFR), hemoglobin, and tacrolimus level were significantly different. Multivariate analysis showed significant differences in age, diabetes mellitus, MPA or MMF, high-dose corticosteroids, tritherapy, and eGFR. Conclusion: The possible independent risk factors for negative antibody response in patients with organ transplants who received the 2-dose SARS-CoV-2 vaccine include age, diabetes mellitus, low eGFR, MPA or MMF, high-dose corticosteroids, and triple immunosuppression therapy. mTOR inhibitor can be a protective factor against weak antibody response. Systematic Review Registration: PROSPERO, identifier CRD42021257965.

Mechanism of Xuanbai Qingfei Jiedu decoction in the treatment of COVID-19 based on network pharmacology

Objective: To explore the pharmacological mechanism of Xuanbai Qingfei Jiedu Decoction in the treatment of coronavirus disease 2019 (COVID-19) on account of network pharmacology.

Clinical characteristics and chest computed tomography findings of severe and critical coronavirus disease 2019 in Shanghai, China

Objective To sum up the clinical characteristics and chest computed tomography (CT) findings of severe and critical coronavirus disease 2019 (COVID-19) patients, and to explore the factors affecting the outcomes, so as to provide experience for the clinical diagnosis and treatment of severe and critical COVID-19. Methods The data of 25 severe and critical COVID-19 patients, who were treated in our hospital from Jan. 23, 2020 to Mar. 5, 2020, were collected. The clinical characteristics were retrospectively analyzed, and the clinical and laboratory indexes were compared between cured patients and uncured patients. The laboratory indicators of cured patients were further compared between the progressive and recovery stages. The chest CT findings of the patients were observed, and the lesion volume was quantified to assess the evolution of lung lesions using the CT image-based intelligent pneumonia lesion quantitative analysis software. Results There were 19 male and six female COVID-19 patients, and there were three deaths. The median age of 25 patients was 65 (63, 75) years old, and the body mass index (BMI) was 25.60 (23.51, 28.65) kg/m2. Twenty-two patients had a clear epidemiological history. Fever (22 cases) and cough (14 cases) were the most common first symptoms, and 18 patients had underlying diseases. Twelve patients were cured and discharged (median hospital stay was 25.5 d), and 13 patients were not cured, including three deaths and 10 cases with hospital stay>25 d with no remission. Compared with the uncured patients, the cured patients had significantly lower BMI, longer time from onset to progression to severe or critical illness, and higher CD4 +T lymphocyte counts (all P<0.05). Multivariate logistic regression analysis showed that high CD4 +T lymphocyte count was an independent protective factor for the cure and discharge of severe and critical COVID-19 patients (P=0.031). Compared with those in the progressive stage, the lymphocyte count and CD4 +T lymphocyte count of 12 cured patients were significantly higher in the progression stage, and the C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR) and procalcitonin level were significantly lower (all P<0.01). Twenty-one patients received chest CT examination in the progressive stage;and all of them had multiple ground-glass opacities and consolidation shadows of the multiple-lobe lateral band and the dorsal side of bilateral lungs, 20 cases had pleural thickening, 9 cases had a small amount of bilateral pleural effusion, and 8 cases had mediastinal lymphadenopathy. The 12 cured patients received CT examination during the recovery period, and their lesions were all improved to different extents;some patients had irregular fiber grid shadows and stripe shadows;and the pleural thickening and pleural effusion were reduced to different extents. The quantitative analysis curves showed that lesion volume in the 12 cured patients obviously increased in the progressive stage and reduced in the absorption stage, showing an inverted V shape;and lesion volume in the uncured patients (nine cases received CT examination for two or more times) showed a rapid increase in the progressive stage. Conclusion Most severe and critical COVID-19 patients in Shanghai are older, with higher BMI and underlying diseases. Low BMI, slow disease progression, and high CD4 +T lymphocyte count are beneficial to the improvement of COVID-19. The main findings of chest CT include multiple ground-glass opacities and consolidation shadows, mainly distributing in the lateral band and the dorsal side of lungs and mostly involving the pleura. The laboratory indexes, including the lymphocyte, CRP, CD4 +T lymphocyte, ESR and procalcitonin, and chest CT examination play an important role in the diagnosis, disease monitoring and prognosis assessment of COVID-19

COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature (preprint)

The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 6 million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID-19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power www.cord19.aws and is publicly available.

High expression of angiotensin-converting enzyme-2 (ACE2) on tissue macrophages that may be targeted by virus SARS-CoV-2 in COVID-19 patients (preprint)

Angiotensin-converting enzyme-2 (ACE2) has been recognized as the binding receptor for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects host cells, causing the development of the new coronavirus infectious disease (COVID-19). To better understand the pathogenesis of COVID-19 and build up the host anti-viral immunity, we examined the levels of ACE2 expression on different types of immune cells including tissue macrophages. Flow cytometry demonstrated that there was little to no expression of ACE2 on most of the human peripheral blood-derived immune cells including CD4+ T, CD8+ T, activated CD4+ T, activated CD8+ T, CD4+CD25+CD127low/- regulatory T cells (Tregs), Th17 cells, NKT cells, B cells, NK cells, monocytes, dendritic cells (DCs), and granulocytes. Additionally, there was no ACE2 expression (< 1%) found on platelets. Compared with interleukin-4-treated type 2 macrophages (M2), the ACE2 expression was markedly increased on the activated type 1 macrophages (M1) after the stimulation with lipopolysaccharide (LPS). Immunohistochemistry demonstrated that high expressions of ACE2 were colocalized with tissue macrophages, such as alveolar macrophages found within the lungs and Kupffer cells within livers of mice. Flow cytometry confirmed the very low level of ACE2 expression on human primary pulmonary alveolar epithelial cells. These data indicate that alveolar macrophages, as the frontline immune cells, may be directly targeted by the SARS-CoV-2 infection and therefore need to be considered for the prevention and treatment of COVID-19.

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning (preprint)

There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, genes, pathways, and expressions, from a large scientific corpus of 24 million PubMed publications. Using Amazon AWS computing resources, we identified 41 repurposable drugs (including indomethacin, toremifene and niclosamide) whose therapeutic association with COVID-19 were validated by transcriptomic and proteomic data in SARS-CoV-2 infected human cells and data from ongoing clinical trials. While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.