Source tracing of the COVID-19 infection case in Guangxi, China

The study describing the process of discovery and source tracing of a native case of coronavirus disease 2019 (COVID-19) infection on Jan 2021, in Guangxi, China, to provide methodology for source investigation better in the future. Following the Epidemiological Investigation Plan for COVID-19 (version 7), information of the native COVID-19 case and related close contacts were collected. Real time reverse transcription-quantitative polymerase chain reaction was performed to detect the nucleic acids of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in samples collected from the infection case, related close contacts, and the environment, combined with serum specific antibody detection. The positive nucleic acid samples were undergone whole genome sequencing, phylogenetic analysis and analyses of variation of amino acids. The whole genome sequence from the native case and the imported asymptomatic infected case from Indonesia containing 25 nucleotide mutation sites belong to L-Lineage European Branch II. 3. The imported asymptomatic case was the source of infection of this native case. The possible route of infection was that native case was exposed to contaminated environment by imported case, due to improper personal protective equipment. A focus on local outbreaks of COVID-19 caused by SARS-CoV-2-infected people from outside China is needed.

Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence (preprint)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

A Potential Novel COVID-19 Vaccine With RBD-HR1/HR2 Hexamer Structure (preprint)

The COVID-19 pandemic and the continued spreading of the SARS-CoV-2 variants have brought a grave public health consequence and severely devastated the global economy with recessions. Vaccination is considered as one of the most promising and efficient methods to end the COVID-19 pandemic and mitigate the disease conditions if infected. Although a few vaccines have been developed with an unprecedented speed, scientists around the world are continuing pursuing the best possible vaccines with innovations. Comparing to the expensive mRNA vaccines and attenuated/inactivated SARS-CoV-2 vaccines, recombinant protein vaccines have certain advantages, including their safety (non-virus components), potential stronger immunogenicity, broader protection, ease of scaling-up production, reduced cost, etc. In this study, we reported a novel COVID-19 vaccine generated with RBD-HR1/HR2 hexamer that was creatively fused with the RBD domain and heptad repeat 1 (HR1) or heptad repeat 2 (HR2) to form a dumbbell-shaped hexamer to target the spike S1 subunit. The novel hexamer COVID-19 vaccine induced high titers of neutralizing antibody in mouse studies (>100,000), and further experiments also showed that the vaccine also induced an alternative antibody to the HR1 region, which probably alleviated the drop of immunogenicity from the frequent mutations of SARS-CoV-2.

Safety measures for COVID-19 do not compromise the outcomes of patients undergoing primary percutaneous coronary intervention: A single center study (preprint)

Coronavirus disease 2019 (COVID-19) is a global pandemic impacting nearly 170 countries/regions and millions of patients worldwide. Patients with acute myocardial infarction (AMI) still need to be treated at percutaneous coronary intervention (PCI) centers with relevant safety measures. This study was conducted to assess the therapeutic outcomes of PCI performed under the safety measures and normal conditions. AMI patients undergoing PCI between January 24 to April 30, 2020 were performed under safety measures for COVID-19. Patients received pulmonary computed tomography (CT) and underwent PCI in negative pressure ICU. Cardiac catheterization laboratory (CCL) staff and physicians worked with level Ⅲ personal protection. Demographic and clinical data, such as door-to-balloon (DTB) time, operation time, complications for patients in this period (NCP group) and the same period in 2019 (2019 group) were retrieved and analyzed. NCP and 2019 groups had 37 and 96 patients, respectively. There was no significant difference in age, gender, BMI and comorbidity between the two groups. DTB time and operation time were similar between the two groups (60.0 ± 12.39 vs 58.83 ± 12.85 min, p = 0.636; 61.46 ± 9.91 vs 62.55 ± 10.72 min, p = 0.592). Hospital stay time in NCP group was significantly shorter (6.78 ± 2.14 vs 8.85 ± 2.64 days, p < 0.001). The incidences of malignant arrhythmia and Takotsubo Syndrome in NCP group were higher than 2019 group significantly (16.22% vs 5.21%, p = 0.039; 10.81% vs 1.04% p = 0.008). During hospitalization and 3-month follow-up, the incidence of major adverse cardiovascular events and mortality in the two groups were statistically similar (35.13% vs 14.58%, p = 0.094; 16.22% vs 8.33%, p =0.184). Our analysis showed that safety measures undertaken in this hospital, including screening of COVID-19 infection and use of personal protection equipment for conducting PCI did not compromise the surgical outcome as compared with PCI under normal condition, although there were slight increases in incidence of malignant arrhythmia and Takotsubo Syndrome.

COVID-19 patients with hypertension under potential risk of worsened organ injuries (preprint)

COVID -19 has rapidly spread from Wuhan to worldwide, and now has become a global health concern. Hypertension is the most common chronic illness in COVID-19, while the influence on those patients have not been well described. In this retrospective study, 82 confirmed patients with COVID-19 were enrolled, with epidemiological, demographic, clinical, laboratory, radiological, and therapies data analyzed and compared between COVID-19 patients with (29 cases) or without (53 cases) hypertension. Of all 82 patients with COVID-19, the median age of all patients was 60.5 years, including 49 females (59.8%) and 33 (40.2%) males. Hypertension (31[28.2%]) was the most chronic illness, followed by diabetes (16 [19.5%]) and cardiovascular disease (15 [18.3%]). Common symptoms included fatigue (55[67.1%]), dry cough (46 [56.1%]) and fever (≥37.3℃ (46 [56.1%]). The median time from illness onset to positive outcomes of RT-PCR analysis were 13.0 days, ranging from 3-25 days. In hypertension group, 6 (20.7%) patients died compared to 5 (9.4%) died in non-hypertension group. More hypertension patients with COVID-19 (8 [27.6%]) had at least one coexisting disease than those of non-hypertension patients (2 [3.8%]) (P=0.002). Compared with non-hypertension patients, higher levels of neutrophil counts, serum amyloid A, C-reactive protein, and NT-proBNP were observed in hypertension group, whereas levels of lymphocyte count and eGFR were decreased. Dynamic observations displayed more significant and worsened outcomes in hypertension group after hospital admission. COVID-19 patients with hypertension take more risks of severe inflammatory reactions, worsened internal organ injuries, and deteriorated progress. 

SARS-CoV-2 infection induces EMT-like molecular changes, including ZEB1-mediated repression of the viral receptor ACE2, in lung cancer models (preprint)

COVID-19 is an infectious disease caused by SARS-CoV-2, which enters host cells via the cell surface proteins ACE2 and TMPRSS2. Using normal and malignant models and tissues from the aerodigestive and respiratory tracts, we investigated the expression and regulation of ACE2 and TMPRSS2. We find that ACE2 expression is restricted to a select population of highly epithelial cells and is repressed by ZEB1, in concert with ZEB1s established role in promoting epithelial to mesenchymal transition (EMT). Notably, infection of lung cancer cells with SARS-CoV-2 induces metabolic and transcriptional changes consistent with EMT, including upregulation of ZEB1 and AXL, thereby downregulating ACE2 post-infection. This suggests a novel model of SARS-CoV-2 pathogenesis in which infected cells shift toward an increasingly mesenchymal state and lose ACE2 expression, along with its acute respiratory distress syndrome-protective effect, in a ZEB1-dependent manner. AXL-inhibition and ZEB1-reduction, as with bemcentinib, offers a potential strategy to reverse this effect.

A collaborative online AI engine for CT-based COVID-19 diagnosis (preprint)

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.

Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells (preprint)

The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.