Impacts of the COVID-19 pandemic on pediatric and adult patients with haemophilia (preprint)

Background:The COVID-19-associated mortality rate of haemophilia patients is similar to that of the general population, but the risk of hospitalization and bleeding is higher. However, the specific impact of this infection on haemophilia patients has not been reported yet. In this study, we aimed to investigate the impact of the coronavirus disease 2019 (COVID-19) pandemic on the infection susceptibility, symptoms, drug use, and social intercourse of patients with haemophilia. Methods: A survey was distributed to a total of 265 patients with haemophilia [adult (n = 185) and pediatric patients (n = 80)] in the Fujian haemophilia therapeutic center (Fuzhou City, China) during the COVID-19 pandemic, and data were collected between January 2022 and January 2023. The impacts of SARS-CoV-2 infection on haemophilia symptoms, drug use, and social intercourse of these patients were investigated, and the association between the recovery time and disease conditions was explored in infected patients. Results: During the COVID-19 pandemic, compared with adult patients, pediatric patients had reduced social intercourse and outdoor activities because of the fear of contracting COVID-19 (85.0% vs. 66.5%; P = 0.002). Bleeding events were also significantly fewer in children than in adults (61.2% vs. 81.1%; P = 0.001). The SARS-CoV-2 infection rate was significantly higher in patients living in urban areas than in those living in rural areas (74.3% vs. 53.6%; P < 0.001). The duration of achieving symptomatic recovery from COVID-19 was not significantly associated with hemorrhage, type and classification of haemophilia, presence of inhibitors, complications, and vaccination status. Conclusion: Having COVID-19 infection did not significantly influence the symptoms and treatments in patients with haemophilia. Compared with adults, pediatric patients had significantly fewer bleeding events.

Post-COVID chronic neuropsychiatric symptoms persisting beyond one year from infection- a case-control study and network analysis (preprint)

Background Limited data about chronic post-COVID neuropsychiatric complaints exist in the literature. Aim: Our study aims to delineate the phenotypes of chronic neuropsychiatric symptoms among adult subjects recovering from their first COVID that occurred more than one year ago. We also aim to explore the clinical and socioeconomic risk factors of having a high loading of chronic neuropsychiatric symptoms. Methods We recruited a post-COVID group who suffered from their first pre-Omicron COVID more than a year ago, and a control group who had never had COVID. The subjects completed app-based questionnaires on demographic, socioeconomic and health status, a COVID symptoms checklist, mental and sleep health measures, and neurocognitive tests. Results The post-COVID group has a statistically significantly higher level of fatigue compared to the control group (p<0.001). Among the post-COVID group, the lack of any COVID vaccination before the first COVID and a higher level of material deprivation before the COVID pandemic predicts a higher load of chronic post-COVID neuropsychiatric symptoms. Partial correlation network analysis suggests that the chronic post-COVID neuropsychiatric symptoms can be clustered into two major (cognitive complaints -fatigue and anxiety-depression) and one minor (headache-dizziness) cluster. A higher level of material deprivation predicts a higher number of symptoms in both major clusters, but the lack of any COVID vaccination before the first COVID only predicts a higher number of symptoms in the cognitive complaints-fatigue cluster. Conclusions Our result suggests heterogeneity among chronic post-COVID neuropsychiatric symptoms, which are associated with the complex interplay of biological and socioeconomic factors.

CD4+ T cell senescence is associated with reduced reactogenicity in severe/critical COVID-19 (preprint)

Background: Aging is a critical risk factor for unfavorable clinical outcomes among COVID-19 patients and may affect vaccine efficacy. However, whether the senescence of T cells impact the progression to severe COVID-19 in the elderly individuals remains unclear. Methods: By using flow cytometry, we analyzed the frequency of senescent T cells (Tsens) in the peripheral blood from 100 elderly patients hospitalized for COVID-19 and compared the difference between mild/moderate and severe/critical illness. We also assessed correlations between the percentage of Tsens and the quantity and quality of spike-specific antibodies by ELISA, neutralizing antibody test kit and Elispot assay respectively, cytokine production profile of COVID-19 reactive T cells as well as plasma soluble factors by cytometric bead array (CBA). Results: We found a significant elevated level of CD4+ Tsens in severe/critical disease compared to mild/moderate illness and patients with a higher level of CD4+ Tsens (>19.78%) showed a decreased survival rate as compared to those with a lower level (<19.78%), especially in the breakthrough infection. The percentage of CD4+ Tsens was negatively correlated with spike-specific antibody titers, neutralization ability and COVID-19 reactive IL-2+ CD4+ T cells. Additionally, IL-2 producing T cells and plasma levels of IL-2 were positively correlated with antibody levels. Conclusion: Our data illustrated that the percentage of CD4+ Tsens in the peripheral blood could act as an efficient biomarker for the capacity of spike-specific antibody production and the prognosis of severe COVID-19, especially in the breakthrough infection. Therefore, restoration of the immune response of CD4+ Tsens is one of the key factors to prevent severe illness and improve vaccine efficacy in older adults.

Dynamics of Microbial Community and Potential Microbial Pollutants in Shopping Malls.

Shopping malls offer various niches for microbial populations, potentially serving as sources and reservoirs for the spread of microorganisms of public health concern. However, knowledge about the microbiome and the distribution of human pathogens in malls is largely unknown. Here, we examine the microbial community dynamics and genotypes of potential pathogens from floor and escalator surfaces in shopping malls and adjacent road dusts and greenbelt soils. The distribution pattern of microbial communities is driven primarily by habitats and seasons. A significant enrichment of human-associated microbiota in the indoor environment indicates that human interactions with surfaces might be another strong driver for mall microbiomes. Neutral community models suggest that the microbial community assembly is strongly driven by stochastic processes. Distinct performances of microbial taxonomic signatures for environmental classifications indicate the consistent differences of microbial communities of different seasons/habitats and the strong anthropogenic effect on homogenizing microbial communities of shopping malls. Indoor environments harbored higher concentrations of human pathogens than outdoor samples, also carrying a high proportion of antimicrobial resistance-associated multidrug efflux genes and virulence genes. These findings enhanced the understanding of the microbiome in the built environment and the interactions between humans and the built environment, providing a basis for tracking biothreats and communicable diseases and developing sophisticated early warning systems. IMPORTANCE Shopping malls are distinct microbial environments which can facilitate a constant transmission of microorganisms of public health concern between humans and the built environment or between human and human. Despite extensive investigation of the natural environmental microbiome, no comprehensive profile of microbial ecology has been reported in malls. Characterizing microbial distribution, potential pathogens, and antimicrobial resistance will enhance our understanding of how these microbial communities are formed, maintained, and transferred and help establish a baseline for biosurveillance of potential public health threats in malls.

Analysis of Spatiotemporal Transmission Characteristics of African Swine Fever (ASF) in Mainland China

In view of the rapid spread of African swine fever in Mainland China from 2018 to 2019, we used spatiotemporal statistical analysis methods to study the spatiotemporal transmission features of African swine fever. The results reveal that the hot spots of African swine fever were concentrated in some cities in Northeast and Southwest China. Seven spatiotemporal clusters of African swine fever were identified, and the most likely spatiotemporal cluster was located in the Buyi and Miao Autonomous Prefecture of QianNan in Guizhou Province, and the cluster date was from 19 June to 25 June 2019. The first secondary cluster covered five cities (Shenyang, Yingkou, Panjin, Anshan, and Liaoyang) in Liaoning Province from 1 August to 10 October 2018. In addition, from the global and local transmission direction and speed of African swine fever in Mainland China, the spatial transmission speed of ASF was found to be slow from August to October 2018, and fast from February to March 2019. Lastly, the global and local isolation and exposure of sites infected with ASF were calculated in Mainland China to reveal the infection risk of different susceptible sites and time periods. [ FROM AUTHOR]

Determinants and mechanisms driving energy-saving behaviours of long-stay hotel guests: Comparison of leisure, business and extended-stay residential cases

The global hospitality industry is fast-turning sustainable and environmentally friendly. Behaviour-driven energy conservation is an emerging green hotel operation strategy to support this change. The long-stay accommodation services have gained momentum in the hospitality sector since the COVID-19 pandemic. However, the characteristics of long-stay hotel guests are often overlooked in sustainable interventions. Based on an empirical survey in China, this study aims to explore the factors driving energy-saving behaviours of long-stay hotel guests and to compare their effects on guests for different visiting purposes (leisure, business, and extended-stay resident). The analysis indicates that attitude, personal norm and place attachment present a direct contribution to energy-saving behaviour. Besides, the results support that attitude and personal norm connect environmental values and energy-saving behaviour. Both altruistic and biospheric values have positive effects, while egoistic values seem to play a negative role. Biospheric values have stronger impact on attitude and personal norm of business guests. Place attachment has a stronger influence on extended-stay residents while its contribution to energy-saving behaviours of business guests is smaller than other guests. Besides, leisure guests are more sensitive to moral obligations. This research sheds novel lights on the psychological perspectives of the observed heterogeneity of energy-saving behaviours of hotel guests with different visiting purposes. The findings provide hotel operators with a novel theoretical reference for targeted energy-saving interventions to promote energy-saving actions of long-term hotel guests. The study, therefore, can contribute to sustainable tourism policymaking and behaviour-driven hotel energy management.

Using Social Media to Help Understand Long COVID Patient-Reported Health Outcomes: A Natural Language Processing Approach (preprint)

BackgroundThere remains significant uncertainty in the definition of the long COVID disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. ObjectiveWe aim to determine the validity and effectiveness of advanced NLP approaches built to derive insight into Long COVID-related patient-reported health outcomes from social media platforms. MethodologyWe use Transformer-based BERT models to extract and normalize long COVID Symptoms and Conditions (SyCo) from English posts on Twitter and Reddit. Furthermore, we estimate the occurrence and co-occurrence of SyCo terms at any point or across time and locations. Finally, we compare the extracted health outcomes with human annotations and highly utilized clinical outcomes grounded in the medical literature. ResultBased on our findings, the top three most commonly occurring groups of long COVID symptoms are systemic (such as "fatigue"), neuropsychiatric (such as "anxiety" and "brain fog"), and respiratory (such as "shortness of breath"). Regarding the co-occurring symptoms, the pair of fatigue & headaches is most common. In addition, we show that other conditions, such as infection, hair loss, and weight loss, as well as mentions of other diseases, such as flu, cancer, or Lyme disease, are among the top reported terms by social media users. ConclusionThe outcome of our social media-derived pipeline is comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms. Overall, this study provides unique insights into patient-reported health outcomes from long COVID and valuable information about the patients journey that can help healthcare providers anticipate future needs.

How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method.

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

Inhalable SARS-CoV-2 vaccines for single-dose dry-powder aerosol immunization and orchestrated mucosal/systemic immune responses (preprint)

The ongoing coronavirus disease pandemic has fostered major advances in vaccination technologies; however, there are urgent needs of mucosal immune responses and single-dose, non-invasive administration. Here, we develop a SARS-CoV-2 vaccine for single-dose, dry-powder aerosol inhalation that induces potent systemic and mucosal immune responses. Our vaccine encapsulates proteinaceous cholera toxin B subunit-assembled nanoparticles displaying the SARS-CoV-2 RBD antigen (R-CNP) within microcapsules of optimal aerodynamic size, and such unique nano-micro coupled structure supports efficient alveoli delivery, sustained R-CNP release, and antigen presenting cell uptake, which are favorable for invocation of immune responses. Moreover, our vaccine successfully induces robust serological IgG and secretory IgA production, collectively conferring effective protection from SARS-CoV-2 challenge (including pseudovirus and the authentic virus) in mice, hamsters, and non-human primates. Finally, we also demonstrate a “mosaic iteration” of our vaccine that co-displays ancestral and Omicron’s antigens, thus extending the breadth of antibody response against co-circulating strains and transmission of Omicron variant. These findings support our inhalable vaccine as a promising candidate to prevent SARS-CoV-2 infection, disease, and transmission.

Performance comparison of reusable versus disposable colonoscopes:a non-inferiority Trial (preprint)

Objective We herein compared the performance of reusable and disposable colonoscopes in patients scheduled to undergo colonoscopy with a view of preventing patient cross-infection, protecting the safety of clinical medical staff, reducing the risk of infection, and minimizing the decontamination process, particularly during the coronavirus disease 2019 pandemic. Methods We randomly divided patients meeting the enrollment criteria into reusable and disposable colonoscopy groups; the success rate of photographing customary anatomical sites with a non-inferiority margin of -8% was the primary endpoint. Secondary endpoints were the adenoma detection rate, operation time, endoscopic image quality score, endoscopic mucosal resection (EMR) success rate, and adverse events. Results We recruited patients who were treated using reusable or disposable (n = 45, each) colonoscopes. Both groups had 100% success rate for capturing images of customary anatomical sites, with no between-group differences. The lower limit of 95% CI was - 7.8654%, which was greater than the non-inferiority threshold of -8%. The disposable group had a significantly lower average image quality score (26.09 ±1.33 vs. 27.44±0.59, P < 0.001) than the reusable group. The groups did not significantly differ in maneuverability, safety, or device failure/defect rate. The en-bloc EMR success rate was 100% in both groups. EMR took significantly longer in the disposable group (466.18 s±180.56 s vs . 206.32 s±109.54 s, P < 0.001). The incidence of EMR-related bleeding and perforation did not significantly differ between the groups. Conclusions Disposable colonoscope endoscopy is safe and feasible for endoscopy examinations and EMR.

Psychological capital and organizational citizenship behavior among nurses during the COVID-19 epidemic: Mediation of organizational commitment (preprint)

Background: Nurses' organizational citizenship behavior, a spontaneous "altruistic work behavior", may be affected by psychological capital and organizational commitment, but its mechanism is not clear. The aim of this study was to investigate the characteristics and distribution of psychological capital, organizational commitment and organizational citizenship behavior among nurses during the COVID-19 epidemic, and explore the mediating role of organizational commitment in psychological capital and organizational citizenship behavior. Methods: Across-sectional survey was conducted among 746 nurses from 6 designated hospitals for treatment of COVID-19 in China. Descriptive statistics, Pearson correlation analysis, and structural equation model were used in this study. Results: Nurses' psychological capital, organizational commitment and organizational citizenship behavior scores were 103.12 ± 15.57, 46.53 ± 7.14 and 101.47 ± 12.14, respectively. Additionally, organizational commitment partially mediates between psychological capital and organizational citizenship behavior. Conclusions: Nurses' psychological capital, organizational commitment and organizational citizenship behavior were at the upper middle level during the COVID-19 epidemic, which was affected by social-demographic variables. Moreover, psychological capital can affect organizational citizenship behavior through the mediating effect of organizational commitment. The findings call for the nursing administrationshould develop and excavate nurses' psychological capital, enhance nurses' organizational commitment, so as to promote nurses' organizational citizenship behavior.

Safe-haven or speculation? Research on price and risk dynamics of Bitcoin

This paper investigates the price and risk dynamics of Bitcoin. Applying SVAR to study Bitcoin, gold and U.S. dollar in one system, we find that neither the gold nor U.S. dollar can explain Bitcoin pricing dynamics in the short-run. We further apply the DCC-MGARCH model to study the risk correlations. The results show that there exists volatility spillover effect and dynamic correlation between three markets, which is magnified with the advent of COVID-19. We can thus draw a conclusion that the boom of Bitcoin is just a hype and speculative bubble.

Green bonds and corporate performance: A potential way to achieve green recovery

In the post-COVID-19 era, the importance of green finance in green recovery is underlined. As a financial instrument serving green development, green bonds promise to raise funds to support climate- and environment-friendly projects. However, whether green bonds can improve corporate performance is of great concern to companies, affecting the long-term intrinsic driving force of green bond issuance. This study applies the dynamic difference-in-differences (DID) model to explore the impact of green bonds on corporate performance and its potential pathway by using the panel data of Chinese listed companies from 2010 to 2020. The main findings are as follows: (1) Green bonds can significantly increase corporate performance by an overall effect of 1.65%. (2) The positive effect of green bonds could be transferred and enhanced by possible mechanisms, including internal green patents and external social reputations. (3) The overall effect of green bonds varies among companies due to different ownership and eco-geographical locations. (4) Green bonds would motivate better corporate performance if the company is related to renewable energy or located in provinces with high renewable energy consumption. Therefore, it is necessary to promote green bonds, ultimately helping to achieve green recovery.

Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field (preprint)

Neural Radiance Field (NeRF) has widely received attention in Sparse-View (SV) CT reconstruction problems as a self-supervised deep learning framework. NeRF-based SVCT methods model the desired CT image as a continuous function that maps coordinates to intensities and then train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV measurement. Thanks to the continuous representation provided by NeRF, the function can be approximated well and thus the high-quality CT image is reconstructed. However, existing NeRF-based SVCT methods strictly suppose there is completely no relative motion during the CT acquisition because they require accurate projection poses to simulate the X-rays that scan the SV sinogram. Therefore, these methods suffer from severe performance drops for real SVCT imaging with motion. To this end, this work proposes a self-calibrating neural field that recovers the artifacts-free image from the rigid motion-corrupted SV measurement without using any external data. Specifically, we parametrize the coarse projection poses caused by rigid motion as trainable variables and then jointly optimize these variables and the MLP. We perform numerical experiments on a public COVID-19 CT dataset. The results indicate that our model significantly outperforms two latest NeRF-based methods for SVCT reconstruction with four different levels of rigid motion.

Safety and immunogenicity of a broad-spectrum mosaic vaccine as a booster dose against SARS-CoV-2 Omicron and other circulating variants (preprint)

BACKGROUNDThe rising breakthrough infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, especially Omicron and its sub-lineages, have raised an urgent need to develop broad-spectrum vaccines against coronavirus disease 2019 (COVID-19). We have developed a mosaic-type recombinant vaccine candidate, named NVSI-06-09, having immune potentials against a broad range of SARS-CoV-2 variants. METHODSAn ongoing randomized, double-blind, controlled phase 2 trial was conducted to evaluate the safety and immunogenicity of NVSI-06-09 as a booster dose in subjects aged 18 years and older from the United Arab Emirates (UAE), who had completed two or three doses of BBIBP-CorV vaccinations at least 6 months prior to the enrollment. The participants were randomly assigned with 1:1 to receive a booster dose of NVSI-06-09 or BBIBP-CorV. The primary outcomes were immunogenicity and safety against SARS-CoV-2 Omicron variant, and the exploratory outcome was cross-immunogenicity against other circulating strains. RESULTSA total of 516 participants received booster vaccination. Interim results showed a similar safety profile between NVSI-06-09 and BBIBP-CorV booster groups, with low incidence of adverse reactions of grade 1 or 2. For immunogenicity, by day 14 after the booster vaccination, the fold rises in neutralizing antibody geometric mean titers (GMTs) from baseline level elicited by NVSI-06-09 were remarkably higher than those by BBIBP-CorV against the prototype strain (19.67 vs 4.47-fold), Omicron BA.1.1 (42.35 vs 3.78-fold), BA.2 (25.09 vs 2.91-fold), BA.4 (22.42 vs 2.69-fold), and BA.5 variants (27.06 vs 4.73-fold). Similarly, the neutralizing GMTs boosted by NVSI-06-09 against Beta and Delta variants were also 6.60-fold and 7.17-fold higher than those boosted by BBIBP-CorV. CONCLUSIONSA booster dose of NVSI-06-09 was well-tolerated and elicited broad-spectrum neutralizing responses against SARS-CoV-2 prototype strain and immune-evasive variants, including Omicron and its sub-lineages. The immunogenicity of NVSI-06-09 as a booster vaccine was superior to that of BBIBP-CorV. (Funded by LIBP and BIBP of Sinopharm; ClinicalTrials.gov number, NCT05293548).

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

There is a centralization of the core content in the text information of the new crown epidemic notification. This paper proposes a joint learning text information extraction method: TBR-NER (topic-based recognition named entity recognition) based on topic recognition and named entity recognition to predict the labeled risk areas and epidemic trajectory information in text information. Transfer learning and data augmentation are used to solve the problem of data scarcity caused by the initial local outbreak of the epidemic, and mutual understanding is achieved by topic self-labeling without introducing additional labeled data. Taking the epidemic cases in Hebei and Jilin provinces as examples, the reliability and effectiveness of the method are verified by five types of topic recognition and 15 types of entity information extraction. The experimental results show that, compared with the four existing NER methods, this method can achieve optimality faster through the mutual learning of each task at the early stage of training. The optimal accuracy in the independent test set can be improved by more than 20%, and the minimum loss value is significantly reduced. This also proves that the joint learning algorithm (TBR-NER) mentioned in this paper performs better in such tasks. The TBR-NER model has specific sociality and applicability and can help in epidemic prediction, prevention, and control.

Transcriptome Analysis Reveals Hub Genes Regulating Autophagy in Patients With Severe COVID-19

Background: The COVID-19 pandemic has currently developed into a worldwide threat to humankind. Importantly, patients with severe COVID-19 are believed to have a higher mortality risk than those with mild conditions. However, despite the urgent need to develop novel therapeutic strategies, the biological features and pathogenic mechanisms of severe COVID-19 are poorly understood. Methods: Here, peripheral blood mononuclear cells (PBMCs) from four patients with severe COVID-19, four patients with mild COVID-19, and four healthy controls were examined by RNA sequencing (RNA-Seq). We conducted gene expression analysis and Venn diagrams to detect specific differentially expressed genes (DEGs) in patients with severe disease compared with those with mild conditions. Gene Ontology (GO) enrichment analysis was performed to identify the significant biological processes, and protein–protein interaction networks were constructed to extract hub genes. These hub genes were then subjected to regulatory signatures and protein–chemical interaction analysis for certain regulatory checkpoints and identification of potent chemical agents. Finally, to demonstrate the cell type-specific expression of these genes, we performed single-cell RNA-Seq analyses using an online platform. Results: A total of 144 DEGs were specifically expressed in severe COVID-19, and GO enrichment analysis revealed a significant association of these specific DEGs with autophagy. Hub genes such as MVB12A, CHMP6, STAM, and VPS37B were then found to be most significantly involved in the biological processes of autophagy at the transcriptome level. In addition, six transcription factors, including SRF, YY1, CREB1, PPARG, NFIC, and GATA2, as well as miRNAs, namely, hsa-mir-1-3p, and potent chemical agents such as copper sulfate and cobalt chloride, may cooperate in regulating the autophagy hub genes. Furthermore, classical monocytes may play a central role in severe COVID-19. Conclusion: We suggest that autophagy plays a crucial role in severe COVID-19. This study might facilitate a more profound knowledge of the biological characteristics and progression of COVID-19 and the development of novel therapeutic approaches to achieve a breakthrough in the current COVID-19 pandemic.