Safety and immunogenicity of a heterologous booster with an RBD virus-like particle vaccine following two- or three-dose inactivated COVID-19 vaccine (preprint)

Background: Reduced protection against COVID-19 due to the waning vaccine-induced immunity over time and emergence of immune-evading SARS-CoV-2 variants of concern (VOCs) indicate the need for vaccine boosters. LYB001 is an innovative recombinant SARS-CoV-2 vaccine which displays a repetitive array of the Spike glycoprotein's receptor binding domain (RBD) on a virus-like particle (VLP) vector to boost the immune system, produced using a Covalink plug-and-display protein binding technology. Methods: The safety and immunogenicity of LYB001 as a heterologous booster at an interval of 6-12 months was assessed in 119 participants receiving a booster with (1) 30g LYB001 (I-I-30L) or CoronaVac (I-I-C), (2) escalated dose of 60g LYB001 (I-I-60L) or CoronaVac in a ratio of 2:1 after two-dose primary series of inactivated COVID-19 vaccine in part 1 of this study, or (3) 30g LYB001 (I-I-I-30L) after three-dose primary series of inactivated COVID-19 vaccine in part 2 of this study. Results: A well-tolerated reactogenicity profile was observed for LYB001 as a heterologous booster, with adverse reactions predominantly being mild in severity and transient. The peak neutralizing antibody response was observed at 28 days after booster, with GMT (95%CI) against prototype SARS-CoV-2 being 1237.8 (747.2, 2050.6), 554.3 (374.6, 820.2), 181.9 (107.6, 307.6) and 1200.2 (831.5, 1732.3) in the I-I-30L, I-I-60L, I-I-C, and I-I-I-30L groups, respectively. LYB001 also elicited a cross-neutralizing antibody response against the BA.4/5 strain, dominant during the study period, with GMT being 201.1 (102.7, 393.7), 63.0 (35.1, 113.1), 29.2 (16.9, 50.3) and 115.3 (63.9, 208.1) at 28 days after booster in the I-I-30L, I-I-60L, I-I-C, and I-I-I-30L groups, respectively. Additionally, RBD-specific IFN-{gamma}, IL-2, IL-4 secreting T cells, as measured by ELISpot assay, dramatically increased (more than 10 times versus baseline) at 14 days after a single LYB001 booster. Conclusions: Our data confirm the favorable safety and immunogenicity profile of the LYB001 vaccine when used as a heterologous booster, and support the continued clinical development of this promising candidate that utilize VLP platform to provide protection against COVID-19.

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification (preprint)

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL.

Characterization and Evaluation of the Pathogenicity of a Natural Gene-Deleted Transmissible Gastroenteritis Virus in China

Porcine transmissible gastroenteritis virus is the major pathogen that causes fatal diarrhea in newborn piglets. In this study, a TGEV strain was isolated from the small intestine of diarrhea piglets in Sichuan Province, China, and designated SC2021. The complete genomic sequence of TGEV SC2021 was 28561 bp, revealing a new natural deletion TGEV strain. Based on phylogenetic analyses, TGEV SC2021 belonged to the Miller cluster and was closely related to CN strains. The newborn piglets orally challenged with TGEV SC2021 showed typical watery diarrhea. In addition, macro and micropathological changes in the lungs and intestines were observed. In conclusion, we isolated a new natural deletion virus strain and confirmed that the virus strain has high pathogenicity in newborn piglets. Moreover, macroscopic and microscopic lesions were observed in the lungs and intestines of all TGEV SC2021-infected piglets. In summary, we isolated a new natural deletion TGEV strain and demonstrated that the natural deletion strain showed high pathogenicity in newborn piglets. These data enrich the diversity of TGEV strains and help us to understand the genetic evolution and molecular pathogenesis of TGEV.

Efficacy and safety of granulocyte–macrophage colony-stimulating factor (GM-CSF) antibodies in COVID-19 patients: a meta-analysis

Objective This study aims to determine the efficacy and safety of granulocyte–macrophage colony-stimulating factor (GM-CSF) antibodies in COVID-19 patients. Methods We searched Cochrane Library, PubMed, Embase, and ClinicalTrials.gov databases until July 27, 2022. Both randomized control trials (RCTs) and cohort studies were included and analyzed separately. The outcomes included mortality, incidence of invasive mechanical ventilation (IMV), ventilation improvement rate (need oxygen therapy to without oxygen therapy), secondary infection, and adverse events (AEs). The odds ratio (OR) with a 95% confidence interval (CI) was calculated by a random-effects meta-analysis model. Results Five RCTs and 2 cohort studies with 1726 COVID-19 patients were recruited (n = 866 in the GM-CSF antibody group and n = 891 in the control group). GM-CSF antibodies treatment reduced the incidence of IMV, which was supported by two cohort studies (OR 0.16;95% CI 0.03, 0.74) and three RCTs (OR 0.62;95% CI 0.41, 0.94). GM-CSF antibodies resulted in slight but not significant reductions in mortality (based on two cohort studies and five RCTs) and ventilation improvement (based on one cohort study and two RCTs). The sensitive analysis further showed the results of mortality and ventilation improvement rate became statistically significant when one included study was removed. Besides, GM-CSF antibodies did not increase the risks of the second infection (based on one cohort study and five RCTs) and AEs (based on five RCTs). Conclusion GM-CSF antibody treatments may be an efficacious and well-tolerant way for the treatment of COVID-19. Further clinical evidence is still warranted. Supplementary Information The online version contains supplementary material available at 10.1007/s10787-022-01105-9.

Pathogen diversity in meta-population networks

The pathogen diversity means that multiple strains coexist, and widely exist in the biology systems. The new mutation of SARS-CoV-2 leading to worldwide pathogen diversity is a typical example. What are the main factors of inducing the pathogen diversity? Previous studies indicated the pathogen mutation is the most important reason for inducing the pathogen diversity. The traffic network and gene network are crucial in shaping the dynamics of pathogen contagion, while their roles for the pathogen diversity still lacking a theoretical study. To this end, we propose a reaction–diffusion process of pathogens with mutations on meta-population networks, which includes population movement and strain mutation. We extend the Microscopic Markov Chain Approach (MMCA) to describe the model. Traffic networks make pathogen diversity more likely to occur in cities with lower infection densities. The likelihood of pathogen diversity is low in cities with short effective distances in the traffic network. Star-type gene network is more likely to lead to pathogen diversity than lattice-type and chain-type gene networks. When pathogen localization is present, infection is localized to strains that are at the endpoints of the gene network. Both the increased probability of movement and mutation promote pathogen diversity. The results also show that the population tends to move to cities with short effective distances, resulting in the infection density is high.

Urban-Rural Disparities for COVID-19: Evidence from 10 Countries and Areas in the Western Pacific

Background Limited evidence on the effectiveness of various types of social distancing measures, from voluntary physical distancing to a community-wide quarantine, exists for the Western Pacific Region (WPR) which has large urban and rural populations. Methods We estimated the time-varying reproduction number (Rt) in a Bayesian framework using district-level mobility data provided by Facebook (i) to assess how various social distancing policies have contributed to the reduction in transmissibility of SARS-COV-2 and (ii) to examine within-country variations in behavioural responses, quantified by reductions in mobility, for urban and rural areas. Results Social distancing measures were largely effective in reducing transmissibility, with Rt estimates decreased to around the threshold of 1. Within-country analysis showed substantial variation in public compliance across regions. Reductions in mobility were significantly lower in rural and remote areas than in urban areas and metropolitan cities (p < 0.001) which had the same scale of social distancing orders in place. Conclusions Our findings provide empirical evidence that public compliance and consequent intervention effectiveness differ between urban and rural areas in the WPR. Further work is required to ascertain the factors affecting these differing behavioural responses, which can assist in policy-making efforts and increase public compliance in rural areas where populations are older and have poorer access to healthcare.

An FcRn-targeted mucosal vaccine against SARS-CoV-2 infection and transmission (preprint)

SARS-CoV-2 and its variants cause COVID-19, which is primarily transmitted through droplets and airborne aerosols. To prevent viral infection and reduce viral spread, vaccine strategies must elicit protective immunity in the airways. FcRn transfers IgG across epithelial barriers; we explore FcRn-mediated respiratory delivery of SARS-CoV-2 spike (S). A monomeric IgG Fc was fused to a stabilized S protein; the resulting S-Fc bound to S-specific antibodies (Ab) and FcRn. A significant increase in Ab responses was observed following the intranasal immunization of mice with S-Fc formulated in CpG as compared to the immunization with S alone or PBS. Furthermore, we intranasally immunize adult or aged mice and hamsters with S-Fc. A significant reduction of virus replication in nasal turbinate, lung, and brain was observed following nasal challenges with SARS-CoV-2, including Delta and Omicron variants. Intranasal immunization also significantly reduced viral transmission between immunized and naive hamsters. Protection was mediated by nasal IgA, serum-neutralizing Abs, tissue-resident memory T cells, and bone marrow S-specific plasma cells. Hence FcRn delivers an S-Fc antigen effectively into the airway and induces protection against SARS-CoV-2 infection and transmission. Based on these findings, FcRn-targeted non-invasive respiratory immunizations are superior strategies for preventing highly contagious respiratory viruses from spreading.

Immune response of primary and booster immunity of SARS-CoV-2 vaccination among patients with chronic liver disease: a real-world study (preprint)

Aim: The present study discussed the humoral immune response and antibody dynamics after primary and booster immunity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines among patients with chronic liver disease (CLD) in the real world. Thus, it provided data to develop SARS-CoV-2 vaccination strategy. Methods: Patients with confirmed CLD and completed primary or booster immunity of SARS-CoV-2 vaccines were enrolled. Serological specimens were collected after primary or booster immunity of SARS-CoV-2 vaccines to detect novel coronavirus neutralizing antibody (nCoV NTAb) and novel coronavirus spike receptor-binding domain antibody (nCoV S-RBD). Thus, we could evaluate the humoral immune response and antibody dynamics after primary and booster immunity of SARS-CoV-2 vaccines among patients with CLD. Simultaneously, baseline demographics, liver disease-related situations, comorbidity-related situations, SARS-CoV-2 vaccination information, and laboratory examination-related indicators of patients were collected. Results: A total of 315 patients received SARS-CoV-2 vaccines, including 223 patients who completed the primary immunity of SARS-CoV-2 vaccines, 114 patients who completed booster immunity of SARS-CoV-2 vaccines, and 22 patients who underwent the antibody detection of SARS-CoV-2 vaccines after both primary and booster immunities. The positive rate of nCoV NTAb was 59.64% in Primary and 87.72% in Booster (P<0.001). The median level of nCoV NTAb was 11.53 AU/mL in Primary and 31.98 AU/mL in Booster (P<0.001). The positive rate of nCoV S-RBD was 69.06% in Primary and 91.23% in Booster (P<0.001). The median level of nCoV S-RBD was 21.60AU/mL in Primary and 112.65 AU/mL in Booster (P<0.001). After booster immunity of SARS-CoV-2 vaccines in 22 patients, the positive rate of nCoV NTAb increased from 59.09% to 86.36%, and that of nCoV S-RBD increased from 68.18% to 90.91%. The median level of nCoV NTAb increased from 11.24 AU /mL to 59.14 AU /mL after booster immunity. The median level of nCoV S-RBD increased from 27.28 AU/mL to 219.10 AU/mL. Compared to the antibody level of primary immunity, the median level of nCoV NTAb and nCoV S-RBD in 22 patients was increased by 5.26 and 8.03 times, respectively. Among 22 patients, 9 were negative for nCoV NTAb after primary immunity, while 6 were transformed positive after booster immunity, and the positive conversion rate of nCoV NTAb was 66.7%. On the other hand, 7 patients were negative for nCoV S-RBD after primary immunity, while 5 were transformed positive after booster immunity, and the positive conversion rate of nCoV S-RBD was 71.4%. Conclusion: Patients with CLD show improved humoral immune response after completing primary and booster immunity of SARS-CoV-2 vaccines, while booster immunity further improves the positive rate and antibody level of patients with CLD. Finally, the positive conversion rate among patients with primary immunity failure also can be improved after booster immunity. Keywords: immune response; primary and booster immunity; SARS-CoV-2 vaccination; chronic liver disease

The Geography of Information Acquisition

Using detailed data on company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms both in and outside of their portfolios, but the ease of travel between fund and firm locations can substantially alleviate geographic distance constraints. Company visits by mutual funds are strongly associated with both fund trading activities and fund trading performance. Our results show that geographic constraints and costly information acquisition amplify information asymmetry in financial markets.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

When are the effects of economic policy uncertainty on oil–stock correlations larger? Evidence from a regime-switching analysis

Economic policy uncertainty (EPU) is an important driver of the correlation in the oil–stock nexus. However, whether the effect of EPU on oil–stock correlations across different market conditions is heterogeneous remains unclear. To fill this gap, we combine a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to explore the market-state-dependent effects of EPU on oil–stock correlations under different regimes. Empirical results indicate that the impacts of EPU on oil–stock correlations are regime-dependent both at the aggregate and industry levels, with stronger effects in high-correlation regimes, and these effects are more significant in times of economic turmoil. Moreover, the impact of EPU on oil–stock correlations is larger during the COVID-19 pandemic than it was during the Global Financial Crisis. These findings highlight the need to consider the nonlinear impact of EPU under different market conditions.

Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study (preprint)

Background In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai. Methods We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmacological interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number (Rt). Findings A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest coronavirus disease 2019 (COVID-19) outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai. The spatial distribution of the infections was highly heterogeneous, with 40% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 544 to 325 meters/day) prior to the citywide lockdown. During Phase 2, Rt remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing Rt below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak. Interpretation Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for successfully containing Omicron outbreaks.

Specific Pupylation as IDEntity Reporter (SPIDER) for the identification of Protein-Biomolecule interactions (preprint)

Protein-biomolecule interactions play pivotal roles in almost all biological processes, the identification of the interacting protein is essential. By combining a substrate-based proximity labelling activity from the pupylation pathway of Mycobacterium tuberculosis , and the streptavidin (SA)-biotin system, we developed S pecific P upylation as IDE ntity R eporter (SPIDER) for identifying protein-biomolecular interactions. As a proof of principle, SPIDER was successfully applied for global identification of interacting proteins, including substrates for enzyme (CobB), the readers of m 6 A, the protein interactome of mRNA, and the target proteins of drug (lenalidomide). In addition, by SPIDER, we identified SARS-CoV-2 Omicron variant specific receptors on cell membrane and performed in-depth analysis for one candidate, Protein-g. These potential receptors could explain the differences between the Omicron variant and the Prototype strain, and further serve as target for combating the Omicron variant. Overall, we provide a robust technology which is applicable for a wide-range of protein-biomolecular interaction studies.

Clinical characteristics of hospitalized patients in Binzhou, China during periods of the SARS-CoV-2 Omicron variant BA.2 predominance: a single-centered, retrospective, observational study (preprint)

Background: Since the first identification of the novel SARS-CoV-2 variant of concern Omicron in South Africa, it has rapidly spread around the world. This study aimed to evaluate the clinical and laboratory characteristics of patients infected with the SARS-CoV-2 Omicron variant BA.2. Methods: In this retrospective study, we extracted data for 422 patients in Binzhou COVID-19 treatment centerl from March 11 to April 28, 2022. Cases were analyzed on the basis of demographic, clinical, and laboratory data as well as radiological features. Results: Of 422 hospitalized patients with SARS-CoV-2 Omicron Variant BA.2, there were 311 (73.7%) asymptomatic, 102 (24.1%) mild cases and 9 (2.1%) moderate cases. The median age was 38 years (IQR, 14 to 58) for all the participants, and the cohort included 207 men and 215 women. Compared with asymptomatic patients, moderate patients were older and had more chronic comorbidities (P<0.001). For all patients, Only 23 (5.5%) of 422 patients had never received any COVID-19 vaccine dose. Nonvaccination rate was significant difference between asymptomatic group and moderte group (4.5% vs 33.3%, p=0.001), respectively. The most common symptoms at onset of illness were fever, fatigue. Moderate patients had more ground-glass opacity, and patchy shadowing. Lymphopenia was present in 6.6% of all patients, which was more common in moderate patients than asymptomatic patients (44.4% vs 4.8%, P<0.001). Conclusion: Unvaccinated and older patients (>65 years) with comorbidities are at increased risk of moderate infection. Lymphopenia, increased D-dimer, ground-glass opacity, and patchy shadowing are common in moderate patients. 

COVID-19 Hospitalizations Forecasts Using Internet Search Data (preprint)

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics. Inspired by the strong association between public search behavior and hospitalization admission, we extended previously-proposed influenza tracking model, ARGO (AutoRegression with GOogle search data), to predict future 2-week national and state-level COVID-19 new hospital admissions. Leveraging the COVID-19 related time series information and Google search data, our method is able to robustly capture new COVID-19 variants’ surges, and self-correct at both national and state level. Based on our retrospective out-of-sample evaluation over 12-month comparison period, our method achieves on average 15% error reduction over the best alternative models collected from COVID-19 forecast hub. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist health-care officials and decision making for the current and future infectious disease outbreak.

COVID-19 Hospitalizations Forecasts Using Internet Search Data (preprint)

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics. Inspired by the strong association between public search behavior and hospitalization admission, we extended previously-proposed influenza tracking model, ARGO (AutoRegression with GOogle search data), to predict future 2-week national and state-level COVID-19 new hospital admissions. Leveraging the COVID-19 related time series information and Google search data, our method is able to robustly capture new COVID-19 variants' surges, and self-correct at both national and state level. Based on our retrospective out-of-sample evaluation over 12-month comparison period, our method achieves on average 15\% error reduction over the best alternative models collected from COVID-19 forecast hub. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist health-care officials and decision making for the current and future infectious disease outbreak.