Household Transmission of SARS-CoV-2 along the Evolution of Pandemic (preprint)

Over the past three years, we have gained some understanding of the transmission mechanisms of COVID-19. One of the key findings that experts have agreed on is that household transmission is an important pathway for the pandemic. However, most studies on the transmission patterns of COVID-19 focus on the community transmission only, while the equally important study on household transmission has lagged behind. We developed a stochastic dynamic model motivated by the cluster growth algorithm in Erdös–Rényi Random Graph to differentiate the COVID-19 transmission within households from that in the community by noting only a small fraction of the total susceptible population, replenished dynamically by the community transmission events, are indeed vulnerable to household transmission. Thus the model allowed us to the role and characteristics of household transmission within the full framework of virus transmission, beyond the intrinsic characteristics of household transmission. It was then applied to a comprehensive individual-level pandemic dataset collected in Yichang, China. Our findings showed that household transmission accounted for 25.1% and 38.5% of total infections before and during the lockdown, respectively, and that 80.9% of infections were unavoidable. Our model suggests that household-level contact tracing could have reduced the number of infections by over 50% and advanced the clearance date of active infection by 72 days. This model can be used to fit COVID-19 data outside Yichang or other infectious diseases, though modifications might be needed.

Integrated network pharmacology analysis, molecular docking, LC-MS analysis and bioassays revealed the potential active ingredients and underlying mechanism of Scutellariae radix for COVID-19

Scutellariae radix (“Huang-Qin” in Chinese) is a well-known traditional herbal medicine and popular dietary supplement in the world, extensively used in prescriptions of TCMs as adjuvant treatments for coronavirus pneumonia 2019 (COVID-19) patients in China. According to the differences in its appearance, Scutellariae radix can be classified into two kinds: ZiQin (1∼3 year-old Scutellariae baicalensis with hard roots) and KuQin (more than 3 year-old S. baicalensis with withered pithy roots). In accordance with the clinical theory of TCM, KuQin is superior to ZiQin in cooling down the heat in the lung. However, the potential active ingredients and underlying mechanisms of Scutellariae radix for the treatment of COVID-19 remain largely unexplored. It is still not clear whether there is a difference in the curative effect of ZiQin and KuQin for the treatment of COVID-19. In this research, network pharmacology, LC-MS based plant metabolomics, and in vitro bioassays were integrated to explore both the potential active components and mechanism of Scutellariae radix for the treatment of COVID-19. As the results, network pharmacology combined with molecular docking analysis indicated that Scutellariae radix primarily regulates the MAPK and NF-κB signaling pathways via active components such as baicalein and scutellarin, and blocks SARS-CoV-2 spike binding to human ACE2 receptors. In vitro bioassays showed that baicalein and scutellarein exhibited more potent anti-inflammatory and anti-infectious effects than baicalin, the component with the highest content in Scutellariae radix. Moreover, baicalein inhibited SARS-CoV-2’s entry into Vero E6 cells with an IC50 value of 142.50 μM in a plaque formation assay. Taken together, baicalein was considered to be the most crucial active component of Scutellariae radix for the treatment of COVID-19 by integrative analysis. In addition, our bioassay study revealed that KuQin outperforms ZiQin in the treatment of COVID-19. Meanwhile, plant metabolomics revealed that baicalein was the compound with the most significant increase in KuQin compared to ZiQin, implying the primary reason for the superiority of KuQin over ZiQin in the treatment of COVID-19.

The rapid change of mental health in college students after on-campus quarantine in Shanghai 2022 Covid lockdown (preprint)

Importance: Social isolation during outbreaks of epidemics always challenge psychological resilience profoundly. However, it still remains unclear how students’ mental health would be affected by strict o n-campus quarantine, which means students stayed at dormitories or on-campus for quarantine. We analyzed the data from one of the earliest universities releasing strict on-campus quarantine to prevent the Omicron spread-out in Shanghai, to evaluate the time course of the psychological resilience in the influence of an unexpected change in the living space in university students. Objectives: ： The campus was locked down on 9 th March. From 9 th to 23 rd , all students on campus were asked to stay in their dormitries, and teachers sent food supplies three times a day. From 24 th March to early April, students were gradually allowed to enter the campus for necessary activities. We quantify dynamic changes in depression severity during the campus locked down. Method: All participants were undergraduate or graduate students. The survey was released on 5th, April. All survey was dropped a public link on www.wjx.cn and administered from April 5th to 7th, 2022. Following the schedule of quarantine area, we assessed depressive symptoms in SJTU students from Period 1 (two weeks before 9 th , March), Period 2 (9 th to 23 rd , March), and Period 3 (24 th March to 5 th April). 274 students completed the online survey, and two incomplete data were excluded for further analysis. All these participants aged range from 18 to 25 years old and were quarantined in dormitories since 9 th March 2022. Findings: A remarkable increase in depression was observed in university students after stringent quarantine. The portion of students with depression started at 9.1% before quarantine and rapidly increased to 36.1% right after quarantine. No more increase in depressive symptoms was observed with time though quarantine was ongoing, which can be predicted by the food supplement satisfaction and the duration of physical fitness per day. Interestingly, the impact of a sudden change in living space on the psychological resilence was more evident in students who have a romatic relationship than singles.

Rapid increase in depression within the first month of the Shanghai Covid lockdown in 2022 (preprint)

In the global efforts to combat Covid-19, researchers have increasingly recognized the profound impacts of society lockdown on population mental health. However, the fine temporal evolution of negative psychological consequences induced by lockdowns remains poorly understood. Here we report a rapid and systematic increase in depression due to the Shanghai Covid lockdown in March 2022. Measured by Beck Depression Inventory-2, 10% of the participants experienced at least mild depression before the official citywide lockdown started, and two and four weeks later this number increased to 21% and 36 %, respectively. Regression analyses show that lockdown duration and physical restriction jointly contribute to worsening depression. Furthermore, the time of sleep and social communication during the lockdown are associated with the severity of depression symptoms. These results highlight the fast development of depression during lockdowns and call for special attention to early psychological interventions once a lockdown is initiated.

NcPath: A novel tool for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways (preprint)

Noncoding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178,308 human experimentally-validated miRNA-target interactions (MTIs), 36,537 experimentally-verified lncRNA target interactions (LTIs), and 4,879 experimentally-validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 553,523 reliable lncRNA-PCG interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-Histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and protein-coding genes. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using an hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which noncoding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal noncoding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/.

Time Series Based Data Explorer and Stream Analysis for Anomaly Prediction

All over the world, time series-based anomaly prediction plays a vital role in all walks of life such as medical monitoring in hospitals and climate and environment risks. In the present study, a survey on the methods and techniques for time series data mining and proposes is carried, in order to solve a brand-new problem, time series progressive anomaly prediction. In terms of contents, the first part sketches out the methods that have captured most of the interest of researchers, which include an overview of abnormal prediction problems, a summary of main characteristics of anomaly prediction, and an introduction of anomaly prediction methodology in literature. The second part focuses on the future research trends on the phase/staged abnormal prediction of time series, where a novel time series compression method and a corresponding similarity measure will be designed, which can be explored subsequently. Finally, the related challenges to take this trend are mentioned. It is hoped that this paper can provide a profound understanding of anomaly prediction for the time series-based data mining research field.

Surveillance on soil-transmitted nematode infections in population in Guangxi Zhuang Autonomous Region during 2016-2020

Objective: To understand and analyze the epidemic status and development trends of soil-transmitted nematodiasis in Guangxi Zhuang Autonomous Region (Guangxi), to provide scientific basis for formulating the disease control strategy, and evaluating the control effect.

Low-dose IL-2 reduces IL-21+ T cells and induces a long-lived anti-inflammatory gene expression signature inversely modulated in COVID-19 patients (preprint)

Despite early clinical successes, the mechanisms of action of low-dose interleukin-2 (LD-IL-2) immunotherapy remain only partly understood. Here, we examined the effects of interval administration of low-dose recombinant IL-2 (iLD-IL-2) using high-resolution, single-cell multiomics and flow cytometry. We confirmed that iLD-IL-2 selectively expands thymic-derived FOXP3 + HELIOS + Tregs and CD56 br NK cells, and showed that treatment reduced the frequency of IL-21-producing CD4 + T cells and of two subsets of innate-like CD8 + T cells, mucosal-associated invariant T cells and V γ9 V δ2 T cells. The cellular changes induced by LD-IL-2 were associated with an anti-inflammatory gene expression signature, which remains detectable in all T and NK cell subsets analysed one month after treatment. The anti-inflammatory nature of this gene expression signature was supported by the observation that the same genes were also modulated in COVID-19 patients, but in the opposite direction. These findings warrant continued investigations of the potential clinical benefits of iLD-IL-2 in immunotherapy and further understanding of the development of long-term sequelae in convalescent COVID-19 patients.

Self-organization's responses to the COVID-19 pandemic in China

Self-organization was a key informal channel used to fight COVID-19 in Wuhan when the local government and other formal organizations failed to respond in the very early stages of the pandemic. The present study aims to investigate the effectiveness of self-organization during an emergency from both a demand-driven and a social mission identity perspective. Using 14 cases, this study examines the lifecycles of self-organization in alignment with the consistent changes in social demands. Volunteers' social mission identity and a flexible organizational structure have been identified as two major attributes of the outstanding performance of self-organization. Moreover, self-organization demonstrates a striking feature of noncontiguous service provision with the assistance of social media. This study extends the literature on the changing roles of governments and civil society to the emergency context.

Statistical Inference Using GLEaM Model with Spatial Heterogeneity and Correlation between Regions (preprint)

A better understanding of the various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the celebrated GLEaM model (Balcan et al., 2010[1]), this paper proposes a pioneering stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.

L-2 Regularized maximum likelihood for $β$-model in large and sparse networks (preprint)

The $\beta$-model is a powerful tool for modeling network generation driven by degree heterogeneity. Its simple yet expressive nature particularly well-suits large and sparse networks, where many network models become infeasible due to computational challenge and observation scarcity. However, existing estimation algorithms for $\beta$-model do not scale up; and theoretical understandings remain limited to dense networks. This paper brings several significant improvements to the method and theory of $\beta$-model to address urgent needs of practical applications. Our contributions include: 1. method: we propose a new $\ell_2$ penalized MLE scheme; we design a novel fast algorithm that can comfortably handle sparse networks of millions of nodes, much faster and more memory-parsimonious than all existing algorithms; 2. theory: we present new error bounds on $\beta$-models under much weaker assumptions than best known results in literature; we also establish new lower-bounds and new asymptotic normality results; under proper parameter sparsity assumptions, we show the first local rate-optimality result in $\ell_2$ norm; distinct from existing literature, our results cover both small and large regularization scenarios and reveal their distinct asymptotic dependency structures; 3. application: we apply our method to large COVID-19 network data sets and discover meaningful results.

Graphdiyne-based flexible respiration sensors for monitoring human health

Monitoring respiration is a pivotal way to sense the health of the human body and deal with respiratory diseases. Here, we utilize the porous graphdiyne (GDY) material to construct printed respiration sensors suitable for life and health monitoring. These sensors exhibit ultrasensitive responses and rapid recovery performance under real-time respiratory conditions, realizing non-contact detection with a long distance of 40 cm. Even the instantaneous change of weak fingertip moisture can also be easily monitored by such respiratory sensors. Synchronous density functional theory calculations reveal the critical role of GDY’s unique acetylene structure on the easy adsorption/desorption of water molecules, which results in enhanced conductivity during the entire sensing process. These results provide a new carbon-based material as ink to print ultrasensitive respiration sensors, which can be used to develop large-scale industrialized integrated smart breathing masks or skin monitors, significantly expanding the prospects of carbon materials in multifunctional electronic skin.

Association of Depression with Sperm Quality: a Multicentre Cross-Sectional Study in China During the COVID-19 Pandemic (preprint)

Background: Depression is a potential factor affecting semen quality. However, the relationship between depression and semen quality remains to be fully elucidated. We investigated the association between depression and semen quality in men during the COVID-19 pandemic. Methods: : Cross‐sectional, participants completed a questionnaire assessing lifestyle factors the Patient Health Questionnaire-9 (PHQ-9) was used to evaluate the psychological depression status of subjects. Semen quality assessment implements the requirements of the fifth edition of the World Health Organization (WHO )manual ,multivariable logistic regression analysis and linear regression analysis were conducted to assess the associations between depression and semen quality. Results: : Of the 896 participants, 173 were depressed (19.30%). A Linear regression model was fitted to assess the strength and significant level of the association between depressive symptoms and semen quality. After adjusting for potential confounders, depression was significantly associated with 9.27 (95% confidence interval [CI] 3.39, 15.15), 0.57(95% CI 0.27, 0.87), 62.20 (95% CI 32.93, 91.48), and 12.96 (95% CI 10.55, 15.37) reduction in sperm concentration, sperm volume, total sperm count, and progressive motility, respectively. Which are consistent with the logistic regression analysis results. The semen concentration and total sperm count were linearly correlated with depression scores, whereas the semen volume and progressive motility of sperm were nonlinearly correlated with depression scores. Conclusions: : Depression were associated with lower levels of semen concentration, semen volume, total sperm count, and progressive motility, which could affect male reproductive health.

A recombinant receptor-binding domain in trimeric form generates completely protective immunity against SARS-CoV-2 infection in nonhuman primates (preprint)

Safe and effective vaccination is critical to combatting the COVID-19 pandemic. Here, we developed a trimeric SARS-CoV-2 receptor-binding domain (RBD) subunit vaccine candidate that simulates the natural structure of the spike (S) trimer glycoprotein. Immunization with RBD-trimer induced robust humoral and cellular immune responses and a high level of neutralizing antibodies that were maintained for at least 4 months. Moreover, the antibodies that were produced in response to the vaccine effectively neutralized the SARS-CoV-2 501Y.V2 variant. Of note, when the titers of the antibodies dropped to a sufficiently low level, only one boost quickly activated the anamnestic immune response, resulting in complete protection against the SARS-CoV-2 challenge in rhesus macaques without typical histopathological changes or viral replication in the lungs and other respiratory tissues. Our results indicated that immunization with SARS-CoV-2 RBD-trimer could raise long-term and broad immunity protection in nonhuman primates, thereby offering an optimal vaccination strategy against COVID-19.

Deploying Resilience Enablers to Mitigate Risks in Sustainable Fashion Supply Chains

The complex structure of supply chains makes them vulnerable to risk, so enhancing their resilience is an important goal. In particular, fashion supply chain research has identified two important issues that need to be addressed: sustainability and risk. However, investigation of these issues is relatively sparse and has primarily been independent with little combinatory research. Therefore, it is crucial to develop a risk mitigation method that can maximize the resilience of sustainable supply chains for fashion companies. The objective of this study is to develop an integrated quality function deployment approach and to mitigate supply chain risk by deploying resilience capabilities and resilience-enhancing features, thus ultimately providing the fashion industry with a useful approach for the development of resilient, sustainable supply chains. Using a fashion company as an example, the practicability of the proposed approach is verified. To strengthen resilience and thus mitigate key risks, it is found that the most urgent tasks are to reallocate the company’s resources, to carry out the real-time monitoring of risk on the spot, to share the risk responsibility, and to establish an incentive system. When these features are strengthened, agility and adaptability can be improved, and finally, the risks of supplier delays, natural disasters, political instability, and problematic supplier materials with the greatest impact can be alleviated. This study provides a new strategy for the fashion industry for the implementation of resilient, sustainable supply chains to mitigate risks.

Incidence and Associated Factors for Neuraxial Anaesthesia-Related Hypotension in COVID-19 Parturients Undergoing Caesarean Delivery: A Multicenter Case-Control Study (preprint)

Background: COVID-19 continues to spread globally and results in additional challenges for perioperative management in parturients. The purpose of this study was to determine the incidence and identify associated factors for neuraxial anaesthesia-related hypotension in COVDI-19 parturients during caesarean delivery.Methods: We performed a multicenter case-control study at 3 medical institutions in Hubei province, China form 1th January to 30th May 2020. All ASA Physical Status II full termed pregnant women who received caesarean delivery under neuraxial anaesthesia were eligible for inclusion. The univariate analysis and binary logistic regression analysis were used to identified the independent predictors of neuraxial anaesthesia-related hypotension.Results: Present study included 102 COVID-19 parturients. The incidence of neuraxial anaesthesia-related hypotension was 58%. Maternal abnormal lymphocyte count (OR = 3.41, p = 0.03), full stomach (OR = 3.22, p = 0.04), baseline heart rate (OR = 1.04, p = 0.03), experience of anaesthetist (OR = 0.86, p = 0.02) and surgeon (OR = 0.76, p = 0.03), and combined spinal-epidural anaesthesia technique (OR = 3.27, p = 0.02) were associated with neuraxial anaesthesia-related hypotension. The area under the receiver operating characteristic curve achieved 0.83 which was significantly higher than 0.5 (p < 0.001). And the sensitivity, specificity and percentage correct were 75%, 79% and 75%, respectively. The Hosmer-Lemeshow test showed a good calibration of the model (H = 2.01, DF = 8, p = 0.98).Conclusions: Maternal abnormal lymphocyte count, full stomach, baseline heart rate, experience of anaesthetist and surgeon, and combined spinal-epidural anaesthesia technique were identified as the independent predictors of neuraxial anaesthesia-related hypotension.

CD127 imprints functional heterogeneity to diversify monocyte responses in human inflammatory diseases (preprint)

Studies on human monocytes historically focused on characterization of bulk responses, whereas functional heterogeneity is largely unknown. Here, we identified an inducible population of CD127-expressing human monocytes under inflammatory conditions and named the subset M127. M127 is nearly absent in healthy individuals yet abundantly present in patients with infectious and inflammatory conditions such as COVID-19 and rheumatoid arthritis. Multiple genomic and functional approaches revealed unique gene signatures of M127 and unified anti-inflammatory properties imposed by the CD127-STAT5 axis. M127 expansion correlated with mild COVID-19 disease outcomes. Thereby, we phenotypically and molecularly characterized a human monocyte subset marked by CD127 that retained anti-inflammatory properties within the pro-inflammatory environments, uncovering remarkable functional diversity among monocytes and signifying M127 as a potential therapeutic target for human inflammatory disorders.