Pre-existing humoral immunity and CD4+ T cell response correlate with cross-reactivity against SARS-CoV-2 Omicron subvariants after heterologous prime-boost vaccination.

BACKGROUND: Information regarding the heterologous prime-boost COVID vaccination has been fully elucidated. The study aimed to evaluate both humoral, cellular immunity and cross-reactivity against variants after heterologous vaccination. METHODS: We recruited healthcare workers previously primed with Oxford/AstraZeneca ChAdOx1-S vaccines and boosted with Moderna mRNA-1273 vaccine boost to evaluate the immunological response. Assay used: anti-spike RBD antibody, surrogate virus neutralizing antibody and interferon-γ release assay. RESULTS: All participants exhibited higher humoral and cellular immune response after the booster regardless of prior antibody level, but those with higher antibody level demonstrated stronger booster response, especially against omicron BA.1 and BA.2 variants. The pre-booster IFN-γ release by CD4+ T cells correlates with post-booster neutralizing antibody against BA.1 and BA.2 variant after adjustment with age and gender. CONCLUSIONS: A heterologous mRNA boost is highly immunogenic. The pre-existing neutralizing antibody level and CD4+ T cells response correlates with post-booster neutralization reactivity against the Omicron variant.

T-fiber, a highland barley fiber-rich powder, alleviates hyperglycemia and improves kidney pathology in Db/db mice

The role of dietary fiber in highland barley in lowering blood lipids has been continuously studied in recent years. However, its effects on diabetes and diabetic nephropathy are rarely studied. Considering that highland barley bran is rich in dietary fiber, the effective use of dietary fiber in highland barley bran can not only alleviate the symptoms of diabetes but also improve the local economy. This article aimed to study the effects of highland barley fiber-rich powder (T-fiber) with a high-quality natural dietary fiber ratio (insoluble fiber/soluble fiber = 3 : 1) on the symptoms of hyperglycemia in a diabetic mouse model. Compared with the model group's blood glucose level (30.80 mmol/L), glucose tolerance (28.57 mmol/L), and glycosylated serum protein (2.43 mmol/L), T-fiber presented significant reductions in blood glucose (23.69 mmol/L), better glucose tolerance (21.32 mmol/L), and glycosylated serum protein (1.78 mmol/L) in the diabetic mouse model. Meanwhile, T-fiber effectively alleviated hepatocellular lesions. In addition, T-fiber not only improved kidney function by reducing the 24-hour urine output (8.25 ml), urine protein levels (11.51 mg), and serum creatinine (13.80 mol/L) but also alleviated renal pathology, including glomerular hypertrophy, mesangial expansion, and fibrosis. The above results proved the ability of T-fiber to reduce blood glucose and alleviate liver and renal function in diabetic mice. Altogether, T-fiber is a capable formula for utilizing highland barley bran dietary fiber, which alleviates diabetes symptoms and endows highland barley with promising value.

Materials-based vaccines for infectious diseases.

Infectious diseases that result from pathogen infection are among the leading causes of human death, with pathogens such as human immunodeficiency virus, malaria, influenza, and ongoing SARS-COV-2 viruses constantly threatening the global population. While the mechanisms behind various infectious diseases are not entirely clear and thus retard the development of effective therapeutics, vaccines have served as a universal approach to containing infectious diseases. However, conventional vaccines that solely consist of antigens or simply mix antigens and adjuvants have failed to control various highly infective or deadly pathogens. Biomaterials-based vaccines have provided a promising solution due to their ability to synergize the function of antigens and adjuvants, troubleshoot delivery issues, home and manipulate immune cells in situ. In this review, we will summarize different types of materials-based vaccines for generating cellular and humoral responses against pathogens and discuss the design criteria for amplifying the efficacy of materials-based vaccines against infectious diseases. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease.

A TLR7-nanoparticle adjuvant promotes a broad immune response against heterologous strains of influenza and SARS-CoV-2.

The ideal vaccine against viruses such as influenza and SARS-CoV-2 must provide a robust, durable and broad immune protection against multiple viral variants. However, antibody responses to current vaccines often lack robust cross-reactivity. Here we describe a polymeric Toll-like receptor 7 agonist nanoparticle (TLR7-NP) adjuvant, which enhances lymph node targeting, and leads to persistent activation of immune cells and broad immune responses. When mixed with alum-adsorbed antigens, this TLR7-NP adjuvant elicits cross-reactive antibodies for both dominant and subdominant epitopes and antigen-specific CD8+ T-cell responses in mice. This TLR7-NP-adjuvanted influenza subunit vaccine successfully protects mice against viral challenge of a different strain. This strategy also enhances the antibody response to a SARS-CoV-2 subunit vaccine against multiple viral variants that have emerged. Moreover, this TLR7-NP augments antigen-specific responses in human tonsil organoids. Overall, we describe a nanoparticle adjuvant to improve immune responses to viral antigens, with promising implications for developing broadly protective vaccines.

Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work-home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.

Discrimination of influenza A virus subtypes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Influenza is a contagious respiratory illness caused by influenza viruses which possess the enormous threat to older people and young children. Rapid and precise discrimination of virus subtypes are quite crucial for the early therapy, prophylaxis and the prevention of epidemic outbreaks. Herein, a universal strategy, with influenza A virus (IAV) as a model, is proposed for the discrimination of virus subtypes based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Reference library based on nine IAVs subtypes (i.e., H1N1, H3N2, H4N8, H5N8, H6N6, H7N7, H9N2, H10N8, and H11N8) was set up for matching various IAVs subtypes. The simulative test spectra from IAVs showed that the corresponding IAVs subtypes could be distinguished in 90 min, accurately. Furthermore, the principal component analysis results also show that nine virus subtypes can be reliably distinguished. More importantly, this strategy provides an alternative method for identifying and distinguishing other viruses with high variability characteristics, such as SARS-CoV-2, which could be helpful for implementing public health strategies to counter pandemics.

Parallel use of pluripotent human stem cell lung and heart models provide new insights for treatment of SARS-CoV-2 (preprint)

SARS-CoV-2 primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe COVID-19. To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR-Cas9 mediated knock-out of ACE2, we demonstrated that angiotensin converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but further processing in lung cells required TMPRSS2 while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems.

JAK-STAT signaling as an ARDS therapeutic target: status and future trends (preprint)

Acute respiratory distress syndrome (ARDS) is an acute respiratory disease which is characterized by non-cardiogenic pulmonary oedema. It has a high mortality rate and lacks effective pharmacotherapy. As the outbreak of COVID worldwide, the mortality of ARDS has increased correspondingly, which makes it urgent to find effective targets and strategies for the treatment of ARDS. Recent clinical trials of Janus kinase (JAK) inhibitors in treating COVID induced ARDS have shown a positive outcome, which makes the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway a potential therapeutic target for treating ARDS. Here, we review the complex cause of ARDS, the molecular pathway of JAK/STAT involved in ARDS pathology, and the progress that has been made in strategies of targeting JAK/STAT to treat ARDS Specially, JAK/STAT signaling directly participates in the progression of ARDS or collude with other pathways to aggravate ARDS. We summarize JAK and STAT inhibitors with ARDS treatment benefits, including inhibitors in clinical trials and pre-clinical studies and natural products, and discuss the side effects of the current JAK inhibitors to reveal the future trends in designing of JAK inhibitors, which will help to develop effective treatment strategies for ARDS in the future.

Built environment's impact on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program (preprint)

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has led to millions of deaths. Effectively cutting the transmission of COVID-19 is essential to reduce the impact. Previous studies have observed the potential relationship between the built environment and COVID-19 transmission, however, to date, stringent studies investigating these relationships at the individual level are still insufficient. Here, we aim to examine the relationship between household types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the All of Us Research Program COVID-19 Participant Experience (COPE) Survey data. Design: Based on 62,664 participants' responses to COPE from May to July 2020, we matched the cases of self-reported COVID-19 status, anxiety, or stress, with controls of the same race, sex, age group, and survey version. We conducted multiple logistic regressions between one of the outcomes and household type under the adjustment of other related covariates, such as ethnicity, age, social distancing behavior, and house occupancy. Results: Household type with a shared component was significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2x10-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1x10-6), and stress (OR=1.29, 95% CI 1.2 to 1.4, p=4.3x10-10) as compared to free-standing houses after adjusted for the abovementioned confounding factors. Further, frequent nonessential shopping or outings, another indicator of built environment, was also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Conclusion: Our study demonstrated that the built environment of houses with a shared component tends to increase the risk of COVID-19 transmission, which consequently led to more anxiety and stress for their dwellers. It also suggested the necessity to improve the quality of the built environment through planning, design, and management toward a more resilient society in coping with future pandemics.

Intensity-Based Computation Tomography (CT) Evaluation of Temporal Changes in Lung Abnormalities During the Recovery Stage in COVID-19 In-Hospital Patients (preprint)

Background: The coronavirus disease-19 (COVID-19) and its variants have increased rapidly worldwide since December 2019, with respiratory disease being a prominent complication. As such, optimizing evaluation methods and identifying factors predictive of disease progress remain critical. The purpose of the study was to assess late phase (≥3 weeks) pulmonary changes using intensity-based computed tomography (CT) scoring in COVID-19 patients and determine the clinical characteristics predicting lung abnormalities and recovery. Methods: : We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. Two scoring methods were assessed: (1) area-based scoring (ABS) and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by the calculating the averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results: Using the ABS system, temporal changes in lung abnormalities during recovery were highly variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). Strong relationships were observed with D-dimer and C-reactive protein (CRP) levels on admission, with hazard ratios (HR)(95%CI) of 5.32 (1.25-22.6)(P=0.026) and 1.05 (1.10-1.09)(P=0.017), respectively. Conclusion: Our results suggest COVID-19-mediated pulmonary abnormalities persist well-beyond 3-weeks of symptom onset, with intensity-weighted rather than area-based scoring being more sensitive. Moreover, D-dimer and CRP levels were predictive of the recovery from the disease.

IL-6 and D-Dimer at Admission Predicts Cardiac Injury and Early Mortality during SARS-CoV-2 Infection (preprint)

BACKGROUND We recently described mortality of cardiac injury in COVID-19 patients. Admission activation of immune, thrombotic biomarkers and their ability to predict cardiacinjury and mortality patterns in COVID-19 is unknown. METHODS This retrospective cohort study included 170 COVID-19 patients with cardiac injury at admission to Tongji Hospital in Wuhan from January 29–March 8, 2020. Temporal evolution of inflammatory cytokines, coagulation markers, clinical, treatment and mortality were analyzed. RESULTS Of 170 patients, 60 (35.3%) died early (<21d) and 61 (35.9%) died after prolonged stay. Admission lab work that correlated with early death were elevate levels of interleukin 6 (IL-6) (p<0.0001), Tumor Necrosis Factor-a (TNF-a) (p=0.0025), and C-reactive protein (CRP) (p<0.0001). We observed the trajectory of biomarker changes after admission, and determined that early mortality had a rapidly increasing D-dimer, gradually decreasing platelet and lymphocyte counts. Multivariate and simple linear regression models showed that death risk was determined by immune and thrombotic pathway activation. Increasing cTnI levels were associated with those of increasing IL-6 (p=0.03) and D-dimer (p=0.0021). Exploratory analyses suggested that patients that received heparin has lower early mortality compared to those who did not (p =0.07), despite similar risk profile. CONCLUSIONS In COVID-19 patients with cardiac injury, admission IL-6 and D-dimer predicted subsequent elevation of cTnI and early death, highlighting the need for early inflammatory cytokine-based risk stratification in patients with cardiac injury. Condensed Abstract COVID-19 with cardiac injury is associated with worse survival. Admission activation of immune, thrombotic biomarkers and their ability to predict cardiac injury and mortality patterns in COVID-19 is unknown. This study proved that cardiac injury in these patients is closely related to the activation of immunological and thrombotic pathways and can be predicted by admission biomarkers of these pathways. This study supports the strategy of biomarker-guided, point-of-care therapy that warrants further studies in a randomized manner to develop anti-immune and anti-thrombotic treatment regimens in severe COVID-19 patients with cardiac injury.

Prognostic analysis of neutralizing antibody levels in patients with COVID-19 (preprint)

Background: In the high incidence period of COVID-19, it is very important to quickly classify and evaluate the prognosis of patients through limited clinical antibody data. Methods Chemiluminescence immunoassay was used to detect serum IgM and IgG concentrations in 1951 patients diagnosed with COVID-19, and R language was used to analyze the influence of factors such as antibody, age, gender and concomitant diseases on the prognosis of SARS-CoV-2 patients. Results The results showed that the incidence of COVID-19 was consistent with the characteristics of the elderly, and patients with hypertension, diabetes, stroke, hypoalbuminemia and anemia were at increased risk of critical illness ( p  

A Spread Model of COVID-19 With Some Strict Anti-epidemic Measures (preprint)

In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibriums and the basic regenerative number of the model are widely analyzed. As a validation, the model is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated and the model is validated by the actual data related to COVID-19 in Hubei. Simulation results show that themodel can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures.

Clinical Characteristics, Risk Factors and Cardiac Manifestations of Cancer Patients with COVID-19 (preprint)

Background: Coronavirus disease 2019 (COVID-19) has been associated with cardiovascular features, which may be deteriorated in cancer patients.Methods: We retrospectively assessed 1,244 COVID-19 patients from February 1st to August 31st (140 cancer and 1104 non-cancer patients). Clinical data and laboratory findings were obtained and compared between cancer and non-cancer groups. Risk factors for in-hospital mortality were identified by multivariable COX regression models.Findings: The median age of cancer group was older than that in non-cancer patients [61 (57-67) vs. 56 (40-68), p < 0.001]. For cancer group, 56% were in severe and critical status, while the proportion was 10% for non-cancer group. Cancer patients had increased levels of leukocyte, neutrophil count and BUN (all p < 0.01), while lymphocyte count was significantly lower (p < 0.001). The most common solid tumor types were gastrointestinal cancer (26%), lung cancer (21%), breast and reproductive cancer (both 19%). There is a rising for cardiac biomarkers, including Pro-BNP, cTnI, MYO, CK-MB, and D-Dimer in COVID-19 cancer population, especially in deceased cancer subjects. The 30-day in hospital mortality in cancer group was dramatically raised than that in non-cancer group (12.9% vs. 4.0%, p<0.01). In multivariable COX regression models, fever, disease severity status, underlying diseases were risk factors for mortality. The Pro-BNP and D-Dimer levels were significantly increased in cancer subjects with cardiovascular disease.Interpretation: COVID-19 cancer patients relate to deteriorating conditions, increased risk of admission to intensive care units and in-hospital mortality. They display severely impaired myocardium, damaged heart function and imbalanced homeostasis of coagulation, which warrants more aggressive treatment. Funding: The current work was supported by the National Natural Science Foundation Project of China (Grant No. 81670304) and the Fundamental Research Funds for the Central Universities of China (NO.2042019kf0058).Declaration of Interests: All authors report no relationship or conflict of interest to disclose.Ethics Approval Statement: This study was approved by the National Health Commission of China and the institutional review board at Renmin Hospital of Wuhan University (Wuhan, China).

Computation Tomography (CT)-Based Evaluation of Temporal Changes in Lung Abnormalities During the Recovery Stage in COVID-19 In-Hospital Patients (preprint)

Objectives: To assess the late phase CT changes of COVID-19 patients, and figure out factors predicting lung abnormality in late phase.Methods: We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19 admitted between February 7, 2020 and March 27, 2020. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. CT images were analyzed by 2 independent radiologists using different scoring: (1) area-based scoring (ABS); and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results: Temporal changes in lung abnormalities during recovery (weeks 3 through 8) of CT findings using the ABS system were variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during the late phase of recovery in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). In assessing the correlation between averaged AUCs and clinical characteristics, strong relationships were observed with D-dimer and C-reactive protein (CRP) levels on admission, with hazard ratios (HR)(95%CI) of 5.32 (1.25-22.6)(P=0.026) and 1.05 (1.10-1.09)(P=0.017), respectively. Conclusion: Our results suggest an intensity-weighted rather than area-based scoring system is more sensitive to detect subtle temporal CT changes in COVID-19, with D-dimer and CRP levels on admission being predictive of the time course of late phase recovery from the disease.

Prediction of COVID-19 Prognosis By Heterogeneity Analysis Based on Chest CT Scans (preprint)

BackgroundGround-glass opacity (GGO) and consolidation opacity (CLO) are the common CT lung opacities, and their heterogeneity may have potential for prognosis in COVID-19 patients. This study aimed to estimate clinical outcome in individual COVID-19 patient by using histogram heterogeneity analysis based on CT opacities. Methods71 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China, from January 24th to February 28th at the early stage of pandemic. Two characteristic lung abnormity opacities, GGO and CLO were drawn on CT images to identify the heterogeneity by using quantitative histogram analysis. The parameters (mean, mode, kurtosis, skewness) were derived from histograms to evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. Results A total of 57 cases were eligible for the study cohort after exclusion 14 cases. The most highly frequency of lung abnormalities was GGO mixed with CLO in both survival population (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as following: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, AUC=0.746. Nomogram based on histogram parameters has the ability to predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively.ConclusionsThe histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients. 

The Impact of CKD on the Prognosis of COVID-19: A Protocol for Systematic Review and Meta-analysis (preprint)

Background: COVID-19 is a serious respiratory disease currently causing a global pandemic. However, few studies have evaluated the prognosis of chronic kidney disease (CKD) patients infected with COVID-19. Given the comorbidities evident in CKD patients, we speculate that they are more likely to be susceptible to COVID-19 infection relative to healthy individuals. However, a systematic study is necessary to confirm the relationship between these two conditions.Methods: The Wanfang, China Science Journal Citation Report (VIP database), EMBASE, CNKI, Web of Science, PubMed, and Cochrane Library databases will be reviewed to identify relevant studies. The PRISMA-P and Cochrane Handbook guidelines were used to prepare a standardized table to extract data from all relevant studies in a uniform manner. Risk and quality assessment analyses will be conducted for all included studies, with Revman 5.3 and Stata 13.0 being used for all data analyses. The primary study outcome is the assessment of whether CKD is a risk factor associated with COVID-19 infection, and to establish whether CKD increases the risk of severe illness in those infected with COVID-19. Secondary outcomes include mortality rates in CKD patients with COVID-19.Results: This study approach will synthesize extant studies into a single systematic review and meta-analysis in order to establish whether or not CKD is a risk factor associated with the development of critical COVID-19 illness, and whether CKD patients are at a higher risk of being infected by COVID-19.Conclusion: These results will provide a basis for the clinical treatment of COVID-19 in those with CKD.Systematic Review registration: CRD42020216330（PRORPERO registration number）

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans (preprint)

This study aimed to estimate clinical outcome in individual COVID-19 patient by using histogram heterogeneity analysis based on CT opacities. 57 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China. Two characteristic lung abnormity opacities, ground-glass opacity (GGO) and consolidation opacity (CLO) were drawn on CT images to identify the heterogeneity by using quantitative histogram analysis. The parameters (mean, mode, kurtosis, skewness) derived from histograms evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. The most highly frequency of lung abnormalities was GGO mixed with CLO in both survival population (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as following: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. The histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

Re-positive discharged COVID-19 patients are at low transmission risk for SARS-CoV-2 infection– a finding from recovered COVID-19 patients in Wuhan, China (preprint)

Discharged COVID-19 patients have been found to be retested positive for SARS-CoV-2 (re-positive), which has widely raised concern among the public. We investigated the prevalence and transmission risk of re-positive cases in discharged COVID-19 patients and their SARS-CoV-2-specific antibody levels in Wuhan, China. Of 1065 discharged COVID-19 patients investigated, 518 (48.64%) patients were males; the mean age was 53.29 ± 14.91 years, with a median duration of 40 (IQR: 31–47) days since discharge. 63 patients were tested re-positive for SARS-CoV-2, with the re-positive prevalence to be 5.92% (95%CI: 4.50%-7.33%). The re-positive prevalence was higher in females (7.86%, 95%CI: 5.61%-10.12%) than that in males (3.86%, 95%CI: 2.20%-5.52%, P = 0.006). Re-positive prevalence was similar in patients tested positive and negative for IgG (6.01% vs 5.56%, P = 0.821) or IgM (6.38% vs 5.07%, P = 0.394). Illness severity and duration from illness onset to retest were not associated with the risk of positive results for SARS-CoV-2 after discharge. All 196 environmental samples collected from 49 re-positive patients were tested negative for SAR-CoV-2. Only one close contact to the re-positive patient had been tested positive for SARS-CoV-2; however, he might be a previous COVID-19 case but had not been detected before. Viral culture of 6 nasopharyngeal specimens presented no cytopathic effect of Vero E6 cells. Virus sequencing of 11 nasopharyngeal specimens indicated genomic fragments of SARS-CoV-2. 898 (84.72%) patients and 705 (66.51%) patients were tested positive for SARS-CoV-2-specific IgG and IgM, respectively. Self-report symptoms at the survey were similar, regardless of the level of antibody. All the re-positive patients and their matched non-re-positive patients were tested negative for SARS-CoV-2 four months later. These findings indicate that Testing re-positive of SARS-CoV-2 is common in discharged COVID-19 patients, but no evidence showed the transmission risk of these re-positive cases. Further isolation of recovered COVID-19 patients is unnecessary. However, only 85% recovered COVID-19 patients had SARS-CoV-2-specific antibody, which suggested discharged COVID-19 patients still had potential re-infection risk.

Two Color Single Molecule Sequencing on GenoCare 1600 Platform to Facilitate Clinical Applications (preprint)

With the rapid development of precision medicine industry, DNA sequencing becomes increasingly important as a research and diagnosis tool. For clinical applications, medical professionals require a platform which is fast, easy to use, and presents clear information relevant to definitive diagnosis. We have developed a single molecule desktop sequencing platform, GenoCare 1600. Fast library preparation (without amplification) and simple instrument operation make it friendlier for clinical use. Here we presented sequencing data of E. coli sample from GenoCare 1600 with consensus accuracy reaches 99.99%. We also demonstrated sequencing of microbial mixtures and COVID-19 samples from throat swabs. Our data show accurate quantitation of microbial, sensitive identification of SARS-CoV-2 virus and detection of variants confirmed by Sanger sequencing.