Safety and immunogenicity of the SARS-CoV-2 ARCoV mRNA vaccine in Chinese adults: a randomised, double-blind, placebo-controlled, phase 1 trial.

BACKGROUND: Safe and effective vaccines are urgently needed to end the COVID-19 pandemic caused by SARS-CoV-2 infection. We aimed to assess the preliminary safety, tolerability, and immunogenicity of an mRNA vaccine ARCoV, which encodes the SARS-CoV-2 spike protein receptor-binding domain (RBD). METHODS: This single centre, double-blind, randomised, placebo-controlled, dose-escalation, phase 1 trial of ARCoV was conducted at Shulan (Hangzhou) hospital in Hangzhou, Zhejiang province, China. Healthy adults aged 18-59 years negative for SARS-CoV-2 infection were enrolled and randomly assigned using block randomisation to receive an intramuscular injection of vaccine or placebo. Vaccine doses were 5 µg, 10 µg, 15 µg, 20 µg, and 25 µg. The first six participants in each block were sentinels and along with the remaining 18 participants, were randomly assigned to groups (5:1). In block 1 sentinels were given the lowest vaccine dose and after a 4-day observation with confirmed safety analyses, the remaining 18 participants in the same dose group proceeded and sentinels in block 2 were given their first administration on a two-dose schedule, 28 days apart. All participants, investigators, and staff doing laboratory analyses were masked to treatment allocation. Humoral responses were assessed by measuring anti-SARS-CoV-2 RBD IgG using a standardised ELISA and neutralising antibodies using pseudovirus-based and live SARS-CoV-2 neutralisation assays. SARS-CoV-2 RBD-specific T-cell responses, including IFN-γ and IL-2 production, were assessed using an enzyme-linked immunospot (ELISpot) assay. The primary outcome for safety was incidence of adverse events or adverse reactions within 60 min, and at days 7, 14, and 28 after each vaccine dose. The secondary safety outcome was abnormal changes detected by laboratory tests at days 1, 4, 7, and 28 after each vaccine dose. For immunogenicity, the secondary outcome was humoral immune responses: titres of neutralising antibodies to live SARS-CoV-2, neutralising antibodies to pseudovirus, and RBD-specific IgG at baseline and 28 days after first vaccination and at days 7, 15, and 28 after second vaccination. The exploratory outcome was SARS-CoV-2-specific T-cell responses at 7 days after the first vaccination and at days 7 and 15 after the second vaccination. This trial is registered with www.chictr.org.cn (ChiCTR2000039212). FINDINGS: Between Oct 30 and Dec 2, 2020, 230 individuals were screened and 120 eligible participants were randomly assigned to receive five-dose levels of ARCoV or a placebo (20 per group). All participants received the first vaccination and 118 received the second dose. No serious adverse events were reported within 56 days after vaccination and the majority of adverse events were mild or moderate. Fever was the most common systemic adverse reaction (one [5%] of 20 in the 5 µg group, 13 [65%] of 20 in the 10 µg group, 17 [85%] of 20 in the 15 µg group, 19 [95%] of 20 in the 20 µg group, 16 [100%] of 16 in the 25 µg group; p<0·0001). The incidence of grade 3 systemic adverse events were none (0%) of 20 in the 5 µg group, three (15%) of 20 in the 10 µg group, six (30%) of 20 in the 15 µg group, seven (35%) of 20 in the 20 µg group, five (31%) of 16 in the 25 µg group, and none (0%) of 20 in the placebo group (p=0·0013). As expected, the majority of fever resolved in the first 2 days after vaccination for all groups. The incidence of solicited systemic adverse events was similar after administration of ARCoV as a first or second vaccination. Humoral immune responses including anti-RBD IgG and neutralising antibodies increased significantly 7 days after the second dose and peaked between 14 and 28 days thereafter. Specific T-cell response peaked between 7 and 14 days after full vaccination. 15 µg induced the highest titre of neutralising antibodies, which was about twofold more than the antibody titre of convalescent patients with COVID-19. INTERPRETATION: ARCoV was safe and well tolerated at all five doses. The acceptable safety profile, together with the induction of strong humoral and cellular immune responses, support further clinical testing of ARCoV at a large scale. FUNDING: National Key Research and Development Project of China, Academy of Medical Sciences China, National Natural Science Foundation China, and Chinese Academy of Medical Sciences.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

A prediction model based on machine learning for diagnosing the early COVID-19 patients (preprint)

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.

Fatal outcome in a liver transplant recipient with COVID-19.

Liver injury is common in patients with COVID-19, but little is known about its clinical presentation and severity in the context of liver transplant. We describe a case of COVID-19 in a patient who underwent transplant 3 years ago for hepatocellular carcinoma. The patient came to clinic with symptoms of respiratory disease; pharyngeal swabs for severe acute respiratory syndrome coronavirus 2 were positive. His disease progressed rapidly from mild to critical illness and was complicated by several nosocomial infections and multiorgan failure. Despite multiple invasive procedures and rescue therapies, he died from the disease. The management of COVID-19 in the posttransplant setting presents complex challenges, emphasizing the importance of strict prevention strategies.

Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series.

OBJECTIVE: To study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for coronavirus disease 2019 (covid-2019). DESIGN: Retrospective case series. SETTING: Seven hospitals in Zhejiang province, China. PARTICIPANTS: 62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020. MAIN OUTCOME MEASURES: Clinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification. RESULTS: Of the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days. CONCLUSION: As of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.