A 10-valent composite mRNA vaccine against both influenza and COVID-19 (preprint)

The COVID-19 pandemic caused by SARS-CoV-2 viruses has had a persistent and significant impact on global public health for four years. Recently, there has been a resurgence of seasonal influenza transmission worldwide. The co-circulation of SARS-CoV-2 and seasonal influenza viruses results in a dual burden on communities. Additionally, the pandemic potential of zoonotic influenza viruses, such as avian Influenza A/H5N1 and A/H7N9, remains a concern. Therefore, a combined vaccine against all these respiratory diseases is in urgent need. mRNA vaccines, with their superior efficacy, speed in development, flexibility, and cost-effectiveness, offer a promising solution for such infectious diseases and potential future pandemics. In this study, we present FLUCOV-10, a novel 10-valent mRNA vaccine created from our proven platform. This vaccine encodes hemagglutinin (HA) proteins from four seasonal influenza viruses and two avian influenza viruses with pandemic potential, as well as spike proteins from four SARS-CoV-2 variants. A two-dose immunization with the FLUCOV-10 elicited robust immune responses in mice, producing IgG antibodies, neutralizing antibodies, and antigen-specific cellular immune responses against all the vaccine-matched viruses of influenza and SARS-CoV-2. Remarkably, the FLUCOV-10 immunization provided complete protection in mouse models against both homologous and heterologous strains of influenza and SARS-CoV-2. These results highlight the potential of FLUCOV-10 as an effective vaccine candidate for the prevention of influenza and COVID-19.

Virological traits of the SARS-CoV-2 BA.2.87.1 lineage (preprint)

The highly mutated SARS-CoV-2 BA.2.87.1 lineage was recently detected in South Africa, but its transmissibility is unknown. Here, we report that BA.2.87.1 efficiently enters human cells but is more sensitive to antibody-mediated neutralization than the currently dominating JN.1 variant. Acquisition of adaptive mutations might thus be needed for high transmissibility.

HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the Peripheral Blood Mononuclear Cells of COVID-19 patients (preprint)

Background Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking. Long noncoding RNAs (LncRNAs) are ncRNAs longer than 200 nucleotides, regulating the transcription and translation of protein‐coding genes and they can be found in the peripheral blood, thus holding a promising biomarker potential. Specifically, peripheral blood mononuclear cells (PBMCs) have emerged as a source of indirect biomarkers mirroring the conditions of tissues: they include monocytes, B and T lymphocytes, and natural killer T cells (NKT), being highly informative for immune-related events. Methods We profiled by RNA-Sequencing a panel of 2,906 lncRNAs to investigate their modulation in PBMCs of a pilot group of COVID-19 patients, followed by qPCR validation in 111 hospitalized COVID-19 patients. Results The levels of four lncRNAs were found to be decreased in association with COVID-19 mortality and disease severity: HLA Complex Group 18-242 and -244 (HCG18-242 and HCG18-244), Lymphoid Enhancer Binding Factor 1-antisense 1 (LEF1-AS1) and lncCEACAM21 (i.e. ENST00000601116.5, a lncRNA in the CEACAM21 locus). Interestingly, these deregulations were confirmed in an independent patient group of hospitalized patients and by the re-analysis of publicly available single-cell transcriptome datasets. The identified lncRNAs were expressed in all of the PBMC cell types and inversely correlated with the neutrophil/lymphocyte ratio (NLR), an inflammatory marker. In vitro, the expression of LEF1-AS1 and lncCEACAM21 was decreased upon THP-1 monocytes exposure to a relevant stimulus, hypoxia. Conclusion The identified COVID-19-lncRNAs are proposed as potential innovative biomarkers of COVID-19 severity and mortality.

Robust Evidence C-Means Clustering Combining Spatial Information for Image Segmentation (preprint)

Although evidence c-means clustering (ECM) based on evidence theory overcomes the limitations of fuzzy theory to some extent and improves the capability of fuzzy c-means clustering (FCM) to express and process the uncertainty of information, the ECM does not consider the spatial information of pixels, which makes it to be unable to effectively deal with noise pixels. Applying ECM directly to image segmentation cannot obtain satisfactory results. This paper proposes a robust evidence c-means clustering combining spatial information for image segmentation algorithm. Firstly, an adaptive noise distance is constructed by using the local information of pixels to improve the ability to detect noise points. Secondly, the pixel’s original, local and non-local information are introduced into the objective function through adaptive weights to enhance the robustness to noise. Then, the entropy of pixel membership degree is used to design an adaptive parameter to solve the problem of distance parameter selection in credal c-means clustering (CCM). Finally, the Dempster’s rule of combination was improved by introducing spatial neighborhood information, which is used to assign the pixels belonging to the meta-cluster and the noise cluster into the singleton cluster. Experiments on synthetic images, real images and remote sensing SAR images demonstrate that the proposed algorithm not only suppress noise effectively, but also retain the details of the image. Both the segmentation visual effect and evaluation indexes indicate its effectiveness in image segmentation.

Predicting unseen antibodies’ neutralizability via adaptive graph neural networks

The effects of novel antibodies are hard to predict owing to the complex interactions between antibodies and antigens. Zhang and colleagues use a graph-based method to learn a dynamic representation that allows for predictions of neutralization activity and demonstrate the method by recommending probable antibodies for human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, influenza and dengue. Most natural and synthetic antibodies are 'unseen'. That is, the demonstration of their neutralization effects with any antigen requires laborious and costly wet-lab experiments. The existing methods that learn antibody representations from known antibody-antigen interactions are unsuitable for unseen antibodies owing to the absence of interaction instances. The DeepAAI method proposed herein learns unseen antibody representations by constructing two adaptive relation graphs among antibodies and antigens and applying Laplacian smoothing between unseen and seen antibodies' representations. Rather than using static protein descriptors, DeepAAI learns representations and relation graphs 'dynamically', optimized towards the downstream tasks of neutralization prediction and 50% inhibition concentration estimation. The performance of DeepAAI is demonstrated on human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, influenza and dengue. Moreover, the relation graphs have rich interpretability. The antibody relation graph implies similarity in antibody neutralization reactions, and the antigen relation graph indicates the relation among a virus's different variants. We accordingly recommend probable broad-spectrum antibodies against new variants of these viruses.

Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study (preprint)

The increasing number of people living with Long COVID requires the development of more personalized care, as for now limited treatment options and rehabilitation programs adapted to the variety of Long COVID presentations are available. Our objective was to design an easy-to-use Long COVID classification to help stratifying people with Long COVID. Individual characteristics and a detailed set of 62 self-reported persisting symptoms together with quality of life indexes 12 months after initial COVID-19 infection were collected in a cohort of SARS-CoV-2 infected people in Luxembourg. A hierarchical ascendant classification (HAC) was used to identify clusters of people. We identified 3 patterns of Long COVID symptoms with a gradient in disease severity. Cluster-Mild encompassed almost 50% of the study population and was composed of participants with less severe initial infection, fewer comorbidities, and fewer persisting symptoms (mean=2.9). Cluster-Moderate was characterized by a mean of 11 persisting symptoms and a poor sleep and respiratory quality of life. Cluster-Severe was characterized by a higher proportion of women and smokers as in the other clusters, with a higher number of Long COVID symptoms, in particular of vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified in 3 sub-categories in terms of severity. If replicated in other populations, this simple classification will help clinicians to personalize the care of people with Long COVID.

The Integration Mechanism of Music Intangible Cultural Heritage and Tourism Industry in the Internet of Things Environment

This work aims to optimize and innovate the protection, inheritance, and dissemination of Intangible Cultural Heritage (ICH). Based on the Internet of Things (IoT), this work designs the music ICH development path under the cultural concept by integrating music ICH with the tourism industry in the smart city environment. Specifically, it innovatively proposes the integration mechanism of immersive scene design of music ICH with smart city’s tourism industry in the Internet of Things (IoT) context. Consequently, the music ICH brand equity evaluation model is built. This model can evaluate the importance of cultural and asset value of music ICH. The comprehensive evaluation index system of music ICH mainly includes four primary indexes (with eight secondary indexes): the live performance, historical value, music audience, and the tourism industry integration. In addition, at the beginning of the model iteration, the pattern recognition accuracy of the traditional model is only 67.5%. Then, the recognition accuracy of the traditional model can reach 72.5% at the 500th iteration. In contrast, the average recognition accuracy of the improved model can reach 80%, and the highest can reach 82.5%. This work can provide technical support and guarantee for formulating and optimizing music ICH protection strategies.

SARS-CoV-2-Specific Adaptive Immunity in COVID-19 Survivors With Asthma

Background Asthma patients potentially have impaired adaptive immunity to virus infection. The levels of SARS-CoV-2-specific adaptive immunity between COVID-19 survivors with and without asthma are presently unclear. Methods COVID-19 survivors (patients with asthma n=11, with allergies n=8, and COVID-19 only n=17) and non-COVID-19 individuals (asthmatic patients n=10 and healthy controls n=9) were included. The COVID-19 patients were followed up at about 8 months and 16 months after discharge. The clinical characteristics, lymphocyte subsets, memory T cells, and humoral immunity including SARS-CoV-2 specific antibodies, SARS-CoV-2 pseudotyped virus neutralization assay, and memory B cells were analyzed in these subjects. Results The strength of virus-specific T cell response in COVID-19 survivors was positively correlated with the percentage of blood eosinophils and Treg cells (r=0.4007, p=0.0188;and r=0.4435, p=0.0086 respectively) at 8-month follow-up. There were no statistical differences in the levels of SARS-CoV-2-specific T cell response between the COVID-19 survivors with, and without, asthma. Compared to those without asthma, the COVID-19 with asthma survivors had higher levels of SARS-CoV-2-specific neutralizing antibodies (NAbs) at the 8-month follow-up (p<0.05). Moreover, the level of NAbs in COVID-19 survivors was positively correlated with the percentage of Treg and cTfh2 cells (r=0.5037, p=0.002;and r=0.4846, p=0.0141), and negatively correlated with the percentage of Th1 and Th17 cells (r=-0.5701, p=0.0003;and r=-0.3656, p=0.0308), the ratio of Th1/Th2, Th17/Treg, and cTfh1/cTfh2 cell (r=-0.5356, r=-0.5947, r=-0.4485;all p<0.05). The decay rate of NAbs in the COVID-19 survivors with asthma was not significantly different from that of those without asthma at 16-month follow-up. Conclusion The level of SARS-CoV-2-specific NAbs in COVID-19 survivors with asthma was higher than that of those without asthma at 8-month follow-up. The SARS-CoV-2-specific T cell immunity was associated with blood eosinophils and Treg percentages. The SARS-CoV-2-specific humoral immunity was closely associated with cTfh2/cTfh1 imbalance and Treg/Th17 ratio. According to the findings, asthmatic patients in COVID-19 convalescent period may benefit from an enhanced specific humoral immunity, which associates with skewed Th2/Th1 and Treg/Th17 immune.

Development of a vocal biomarker for fatigue monitoring in people with COVID-19 (preprint)

Objective To develop a vocal biomarker for fatigue monitoring in people with COVID-19. Design Prospective cohort study. Setting Predi-COVID data between May 2020 and May 2021. Participants A total of 1772 voice recordings was used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone s operating system (Android/iOS). The recordings were collected from 296 participants tracked for two weeks following SARS-CoV-2 infection. primary and secondary outcome measures Four machine learning algorithms (Logistic regression, k-nearest neighbors, support vector machine, and soft voting classifier) were used to train and derive the fatigue vocal biomarker. A t-test was used to evaluate the distribution of the vocal biomarker between the two classes (Fatigue and No fatigue). Results The final study population included 56% of women and had a mean (SD) age of 40 (13) years. Women were more likely to report fatigue (P

A Voice-Based Biomarker for Monitoring Symptom Resolution in Adults with COVID-19: Findings from the Prospective Predi-COVID Cohort Study (preprint)

Background: People with COVID-19 may experience impairing, and sometimes persisting, symptoms that would require enhanced surveillance. Our objective was to train an artificial intelligence-based model to predict the presence of COVID-19 symptoms and derive a digital vocal biomarker for easily and quantitatively monitoring symptom resolution. Methods: We used data from 272 participants in the prospective Predi-COVID cohort study recruited between May 2020 and May 2021, with both voice recordings and simultaneous COVID-19 related symptom assessment during the first two weeks of follow-up. A total of 6473 voice features were derived from recordings of participants reading a standardized pre-specified text. Models were trained separately for Android devices (3gp audio format) and iOS devices (m4a audio format). A binary outcome (symptomatic versus asymptomatic) was considered, based on a list of 14 frequent symptoms: dry cough, fatigue, sore throat, loss of taste and smell, diarrhea, fever, respiratory problems, increase in respiratory problems, difficulty eating or drinking, skin rash, conjunctivitis or eye pain, muscle pain/unusual aches, chest pain, overall pain level. Performances of the predictive models were evaluated according to the AUC, balanced accuracy, precision, recall, F1 score, and Matthews correlation coefficient. Findings: A total of 1775 audio recordings were analyzed (6.5 recordings per participant on average), including 1049 corresponding to symptomatic cases and 726 to asymptomatic ones. The best performances were obtained from Support Vector Machine models for both audio formats. We observed an elevated predictive capacity for both Android (AUC=0.91, balanced acc=0.85) and iOS (AUC=0.85, balanced accuracy=0.77) as well as good calibrations (Brier Scores = 0.11 and 0.16 respectively for Android and iOS). The vocal biomarker derived from the predictive models accurately discriminated asymptomatic and symptomatic individuals with COVID-19 (t-test P-values<0.001 for both Android and iOS devices). Interpretation: In this prospective cohort study, we have demonstrated that using a simple, reproducible task of reading a standardized pre-specified text of 25 seconds enabled us to derive a vocal biomarker for monitoring the resolution of COVID-19 related symptoms with elevated accuracy and calibration. An external validation, especially in other languages, would now be required before integrating such a vocal biomarker in telemonitoring solutions or in clinical practice.Trial Registration: This study was registered in ClinicalTrials.gov (NCT04380987)Funding: The Predi-COVID study is supported by the Luxembourg National Research Fund (FNR)(Predi-COVID, grant number 14716273), the André Losch Foundation, and the LuxembourgInstitute of Health.Declaration of Interest: Ethical Approval: This study was approved by the National Research Ethics Committee ofLuxembourg (study number 202003/07) in April 2020.

An Ultrapotent Neutralizing Bispecific Antibody with Broad Spectrum Against SARS-CoV-2 Variants (preprint)

Some variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are threatening our global efforts of herd immunity, novel and more efficacious agents are urgently needed. We have developed a bispecific antibody, 2022, which bonds with high affinity to two non-overlapping epitopes on the receptor-binding domain (RBD) simultaneously, blocks the binding of RBD to human angiotensin-converting enzyme 2 (ACE2), and potently neutralizes SARS-CoV-2 and all of the variants tested, including variants carrying mutations known to resist neutralizing antibodies approved under Emergency Use Authorization (EUA) and reduce the efficacy of existing vaccines. In a mouse model of SARS-CoV-2, 2022 showed strong prophylactic and therapeutic effects. A single administration of 2022 completely protected all mice from bodyweight loss, as compared with up to 20% loss of bodyweight in placebo treated mice, reduced the lung viral titers to undetectable in all mice treated with 2022 either prophylactically or therapeutically, as compared with around 1X105 pfu/g lung tissue in placebo treated mice. In summary, bispecific antibody 2022 showed potent binding and neutralizing activity across a variety of SARS-CoV-2 variants and could be an attractive weapon to combat the ongoing waves of the COVID-19 pandemic.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning (preprint)

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

An ultrapotent neutralizing bispecific antibody with broad spectrum against SARS-CoV-2 variants (preprint)

In spite of the successful development of effective countermeasures against Covid-19, variants have and will continue to emerge that could compromise the efficacy of currently approved neutralizing antibodies and vaccines. Consequently, novel and more efficacious agents are urgently needed. We have developed a bispecific antibody, 2022, consisting of two antibodies, 2F8 and VHH18. 2F8 was isolated from our proprietary fully synthetic human IDEAL (Intelligently Designed and Engineered Antibody Library)-VH/VL library and VHH18 is a single domain antibody isolated from IDEAL-nanobody library. 2022 was constructed by attaching VHH18 to the C-terminal of Fc of 2F8. 2022 binds two non-overlapping epitopes simultaneously on the RBD of the SARS-CoV-2 spike protein and blocks the binding of RBD to human angiotensin-converting enzyme 2 (ACE2). 2022 potently neutralizes SARS-CoV-2 and all of the variants tested in both pseudovirus and live virus assays, including variants carrying mutations known to resist neutralizing antibodies approved under EUA and that reduce the protection efficiency of current effective vaccines. The half-maximum inhibitory concentration (IC50) of 2022 is 270 pM, 30 pM, 20 pM, and 1 pM, for wild-type, alpha, beta, and delta pseudovirus, respectively. In the live virus assay, 2022 has an IC50 of 26.4 pM, 13.3 pM, and 88.6 pM, for wild-type, beta, and delta live virus, respectively. In a mouse model of SARS-CoV-2, 2022 showed strong prophylactic and therapeutic effects. A single administration of 2022 intranasal (i.n.) or intraperitoneal (i.p.) 24 hours before virus challenge completely protected all mice from bodyweight loss, as compared with up to 20% loss of bodyweight in placebo treated mice. In addition, the lung viral titers were undetectable (FRNT assay) in all mice treated with 2022 either prophylactically or therapeutically, as compared with around 1x105 pfu/g lung tissue in placebo treated mice. In summary, bispecific antibody 2022 showed potent binding and neutralizing activity across a variety of SARS-CoV-2 variants and could be an attractive weapon to combat the ongoing waves of the COVID-19 pandemic propagated mainly by variants, especially, the much more contagious delta variant.

Plasma cell-free DNA promise disease monitoring and tissue injury assessment of COVID-19 (preprint)

COVID-19 is a huge threat to global health. Due to the lack of definitive etiological therapeutics currently, effective disease monitoring is of high clinical value for better healthcare and management of the large number of COVID-19 patients. In this study, we recruited 37 COVID-19 patients, collected 176 blood samples upon diagnosis and during treatment, and analyzed cell-free DNA (cfDNA) in these samples. We report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA characteristics reflect patient-specific physiological conditions during treatment. Further analysis on tissue origin tracing of cfDNA reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, we demonstrate the translational merit of cfDNA as valuable analyte for effective disease monitoring, as well as tissue injury assessment in COVID-19 patients.

NSUN2-mediated m5C methylation of IRF3 mRNA negatively regulates type I interferon responses (preprint)

5-Methylcytosine (m 5 C) is a widespread post-transcriptional RNA modification and is reported to be involved in manifold cellular responses and biological processes through regulating RNA metabolism. However, its regulatory role in antiviral innate immunity has not yet been elucidated. Here, we report that NSUN2, a typical m 5 C methyltransferase, can negatively regulate type I interferon responses during viral infection. NSUN2 specifically mediates m 5 C methylation of IRF3 mRNA and accelerates its degradation, resulting in low levels of IRF3 and downstream IFN-β production. Knockout or knockdown of NSUN2 could enhance type I interferon responses and downstream ISG expression after viral infection in vitro . And in vivo , the antiviral innate responses is more dramatically enhanced in Nsun2 +/− mice than in Nsun2 +/+ mice. Four highly m 5 C methylated cytosines in IRF3 mRNA were identified, and their mutation could enhance the cellular IRF3 mRNA levels. Moreover, infection with Sendai virus (SeV), vesicular stomatitis virus (VSV), herpes simplex virus 1 (HSV-1), Zika virus (ZIKV), or especially SARS-CoV-2 resulted in a reduction in endogenous levels of NSUN2. Together, our findings reveal that NSUN2 serves as a negative regulator of interferon response by accelerating the fast turnover of IRF3 mRNA, while endogenous NSUN2 levels decrease after viral infection to boost antiviral responses for the effective elimination of viruses. Our results suggest a paradigm of innate antiviral immune responses ingeniously involving NSUN2-mediated m 5 C modification.

SARS-CoV-2 mutations acquired in mink reduce antibody-mediated neutralization (preprint)

Transmission of SARS-CoV-2 from humans to farmed mink was observed in Europe and the US. In the infected animals viral variants arose that harbored mutations in the spike (S) protein, the target of neutralizing antibodies, and these variants were transmitted back to humans. This raised concerns that mink might become a constant source of human infection with SARS-CoV-2 variants associated with an increased threat to human health and resulted in mass culling of mink. Here, we report that mutations frequently found in the S proteins of SARS-CoV-2 from mink were mostly compatible with efficient entry into human cells and its inhibition by soluble ACE2. In contrast, mutation Y453F reduced neutralization by an antibody with emergency use authorization for COVID-19 therapy and by sera/plasma from COVID-19 patients. These results suggest that antibody responses induced upon infection or certain antibodies used for treatment might offer insufficient protection against SARS-CoV-2 variants from mink.

Urinary SARS-CoV-2 RNA is An Indicator For The Progression and Prognosis of COVID-19 Disease (preprint)

Background: We aimed to analyse clinical characteristics and find potential factors predicting poor prognosis in patients with coronavirus disease 2019 (COVID-19). Methods: We analyzed the demographic and clinical data of COVID-19 patients and detected SARS-CoV-2 RNA in urine sediments collected from 53 COVID-19 patients enrolled in Renmin Hospital of Wuhan University from January 31, 2020 to February 18, 2020 with qRT-PCR analysis, and then classified those patients based on clinical conditions (severe or non-severe syndrome) and urinary SARS-CoV-2 RNA (URNA- or URNA+). Results: We found that COVID-19 patients with severe syndrome (severe patients) showed significantly higher positive rate (11 of 23, 47.8%) of urinary SARS-CoV-2 RNA than non-severe patients (4 of 30, 13.3%, p = 0.006). URNA+ patients or severe URNA+ subgroup exhibited higher prevalence of inflammation and immune discord, cardiovascular diseases, liver damage and renal disfunction, and higher risk of death than URNA- patients. To understand the potential mechanisms underlying the viral urine shedding, we performed renal histopathological analysis on postmortems of patients with COVID-19 and found that severe renal vascular endothelium lesion characterized by increase of the expression of thrombomodulin and von Willebrand factor, markers to assess the endothelium dysfunction. We proposed a theoretical and mathematic model to depict the potential factors determining the urine shedding of SARS-CoV-2. Conclusions: This study indicated that urinary SARS-CoV-2 RNA detected in urine specimens can be used to predict the progression and prognosis of COVID-19 severity. 

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data (preprint)

A bstract Single-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

Liver Injury Is Related to the Severity and Prognosis of Novel Coronavirus Disease 2019 (preprint)

Background: Previous studies revealed that liver injury is common in the novel coronavirus disease 2019 (COVID-19) patients. However, risk factors of liver injury and whether liver injury is related to the severity or prognosis of COVID-19 is unclarified. Methods: COVID-19 patients were retrospectively recruited from a tertiary hospital in Wuhan, China. General information, past medical history, symptoms, signs, laboratory examination results and clinical outcomes were collected. Incidence and potential risk factors of liver injury were analyzed. Laboratory examination results, incidence of other symptoms and the incidence of the main outcomes, disease progression and death, were compared between patients with or without liver injury. Findings: About 57·7% patients showed liver injury. The incidence of liver injury was higher in male patients. More patients with liver injury were diagnosed as critically ill at admission. Patients with liver injury had higher incidence of dyspnea, lower SpO2 and higher maximum body temperature. They also had many worse laboratory examination results regarding inflammation, cytokines, renal function, coagulation function and myocardial injury. And, the incidence of disease progression and mortality was much higher in patients with liver injury. In further multivariable logistic analysis, direct bilirubin, hs-CRP and NT-proBNP were independent predictors for disease progression, and chronic pulmonary disease, hemoptysis, cholesterol and IL-2R were independent predictors for death. Interpretation: Liver injury is common in COVID-19 patients, and male patients are at higher risk to have liver injury. Patients with liver injury can be severer and have a higher incidence of disease progression than those without. Funding: The present study was not supported by any funding.Declaration of Interests: All the authors declare no conflicts of interests.Ethics Approval Statement: The study protocol is approved by the Institutional Ethics Committee of Peking University First Hospital and all the methods were carried out in accordance with the approved guidelines of the committee and with the Helsinki Declaration. Informed consent is waived.