Stable and durable antibody responses in SARS-recovered donors vaccinated with inactivated SARS-CoV-2 vaccine.

Whether the immune imprinting caused by severe acute respiratory syndrome coronavirus (SARS-CoV) affects the efficiency of SARS-CoV-2 vaccination has attracted global concern. Little is known about the dynamic changes of antibody response in SARS convalescents inoculated with three doses of inactivated SARS-CoV-2 vaccine although lack of cross-neutralizing antibody response to SARS-CoV-2 in SARS survivors has been reported. We longitudinally examined the neutralizing antibodies (nAbs) against SARS-CoV and SARS-CoV-2 as well as spikes binding IgA, IgG, IgM, IgG1, and IgG3 antibodies in 9 SARS-recovered donors and 21 SARS-naïve donors. Stably higher nAbs and spike antigens-specific IgA, IgG antibodies against SARS-CoV-2 were observed in SARS-recovered donors compared with SARS-naïve donors during the period with two doses of BBIBP-CorV vaccination. However, the third-dose BBIBP-CorV stimulated a sharply and shortly higher increase of nAbs in SARS-naïve donors than in SARS-recovered donors. It is worth noting that, regardless of prior SARS infection, the Omicron subvariants were found to subvert immune responses. Moreover, certain subvariants such as BA.2, BA.2.75, or BA.5 exhibited a high degree of immune evasion in SARS survivors. Interestingly, BBIBP-CorV recalled higher nAbs against SARS-CoV compared with SARS-CoV-2 in SARS-recovered donors. In SARS survivors, a single dose of inactivated SARS-CoV-2 vaccine provoked immune imprinting for the SARS antigen, providing protection against wild-type SARS-CoV-2, and the earlier variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta but not against Omicron subvariants. As such, it is important to evaluate the type and dosage of SARS-CoV-2 vaccine for SARS survivors.

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).

A coarse-refine segmentation network for COVID-19 CT images.

The rapid spread of the novel coronavirus disease 2019 (COVID-19) causes a significant impact on public health. It is critical to diagnose COVID-19 patients so that they can receive reasonable treatments quickly. The doctors can obtain a precise estimate of the infection's progression and decide more effective treatment options by segmenting the CT images of COVID-19 patients. However, it is challenging to segment infected regions in CT slices because the infected regions are multi-scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse-refine segmentation network is proposed to address these challenges. The coarse-refine architecture and hybrid loss is used to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. The atrous spatial pyramid pooling module in the network is added to improve the performance in detecting infected regions with different scales. Experimental results show that the model in the segmentation of COVID-19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options.

U-Shaped Relationship of Low-Density Lipoprotein Cholesterol With Risk of Severe COVID-19 From a Multicenter Pooled Analysis.

Low-density lipoprotein cholesterol (LDL-C) is a well-known risk factor for coronary heart disease but protects against infection and sepsis. We aimed to disclose the exact association between LDL-C and severe 2019 novel coronavirus disease (COVID-19). Baseline data were retrospectively collected for 601 non-severe COVID-19 patients from two centers in Guangzhou and one center in Shenzhen, and patients on admission were medically observed for at least 15 days to determine the final outcome, including the non-severe group (n = 460) and the severe group (severe and critical cases) (n = 141). Among 601 cases, 76 (12.65%) received lipid-lowering therapy; the proportion of patients taking lipid-lowering drugs in the severe group was higher than that in the non-severe group (22.7 vs. 9.6%). We found a U-shaped association between LDL-C level and risk of severe COVID-19 using restricted cubic splines. Using univariate logistic regression analysis, odds ratios for severe COVID-19 for patients with LDL-C ≤1.6 mmol/L (61.9 mg/dL) and above 3.4 mmol/L (131.4 mg/dL) were 2.29 (95% confidence interval 1.12-4.68; p = 0.023) and 2.02 (1.04-3.94; p = 0.039), respectively, compared to those with LDL-C of 2.81-3.40 mmol/L (108.6-131.4 mg/dL); following multifactorial adjustment, odds ratios were 2.61 (1.07-6.37; p = 0.035) and 2.36 (1.09-5.14; p = 0.030). Similar results were yielded using 0.3 and 0.5 mmol/L categories of LDL-C and sensitivity analyses. Both low and high LDL-C levels were significantly associated with higher risk of severe COVID-19. Although our findings do not necessarily imply causality, they suggest that clinicians should pay more attention to lipid-lowering therapy in COVID-19 patients to improve clinical prognosis.

Association between the nasopharyngeal microbiome and metabolome in patients with COVID-19.

SARS-CoV-2, the causative agent for COVID-19, infect human mainly via respiratory tract, which is heavily inhabited by local microbiota. However, the interaction between SARS-CoV-2 and nasopharyngeal microbiota, and the association with metabolome has not been well characterized. Here, metabolomic analysis of blood, urine, and nasopharyngeal swabs from a group of COVID-19 and non-COVID-19 patients, and metagenomic analysis of pharyngeal samples were used to identify the key features of COVID-19. Results showed lactic acid, l-proline, and chlorogenic acid methyl ester (CME) were significantly reduced in the sera of COVID-19 patients compared with non-COVID-19 ones. Nasopharyngeal commensal bacteria including Gemella morbillorum, Gemella haemolysans and Leptotrichia hofstadii were notably depleted in the pharynges of COVID-19 patients, while Prevotella histicola, Streptococcus sanguinis, and Veillonella dispar were relatively increased. The abundance of G. haemolysans and L. hofstadii were significantly positively associated with serum CME, which might be an anti-SARS-CoV-2 bacterial metabolite. This study provides important information to explore the linkage between nasopharyngeal microbiota and disease susceptibility. The findings were based on a very limited number of patients enrolled in this study; a larger size of cohort will be appreciated for further investigation.

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification .

Upper Respiratory Tract Viral Ribonucleic Acid Load at Hospital Admission Is Associated With Coronavirus Disease 2019 Disease Severity.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has aroused global public health concerns. Multiple clinical features relating to host profile but not for virus have been identified as the risk factors for illness severity and/or the outcomes in COVID-19. METHODS: The clinical features obtained from a cohort of 195 laboratory-confirmed, nasopharynx-sampled patients with COVID-19 in Guangdong, China from January 13 to February 29, 2020 were enrolled to this study. The differences in clinical features among 4 groups (mild, moderate, severe, and critical) and between 2 groups (severe vs nonsevere) were compared using one-way analysis of variance and Student's t test, respectively. Principal component analysis and correlation analysis were performed to identify the major factors that account for illness severity. RESULTS: In addition to the previously described clinical illness severity-related factors, including older age, underlying diseases, higher level of C-reactive protein, D-dimer and aspartate aminotransferase, longer fever days and higher maximum body temperature, larger number of white blood cells and neutrophils but relative less lymphocytes, and higher ratio of neutrophil to lymphocytes, we found that the initial viral load is an independent factor that accounts for illness severity in COVID-19 patients. CONCLUSIONS: The initial viral load of severe acute respiratory syndrome coronavirus 2 is a novel virological predictor for illness severity of COVID-19.