Antibody response and safety of inactivated SARS-CoV-2 vaccines in chronic hepatitis B patients with and without cirrhosis.

Background: The immune response and safety of inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines among patients with chronic hepatitis B (CHB), especially those with cirrhosis, are not clear. Therefore, this study was conducted to evaluate the efficacy and safety of inactivated SARS-CoV-2 vaccines among CHB patients with and without cirrhosis. Patients and methods: A total of 643 CHB patients who received two doses of inactivated SARS-CoV-2 vaccines (BBIBP-CorV and CoronaVac) were enrolled. Serum samples were collected and tested for SARS-CoV-2 S-receptor-binding domain (S-RBD) immunoglobulin G (IgG) at enrollment. Data on adverse events (AEs) within 7 days after the second dose were obtained using a questionnaire. Results: A total of 416 non-cirrhotic and 227 cirrhotic patients were included in the analysis. Cirrhotic patients had lower antibody titers than non-cirrhotic patients after adjusting for age, sex, and time interval (2.45 vs. 2.60 ng/ml, p = 0.034). Furthermore, the study revealed that cirrhotic patients demonstrated a slower rate of seropositivity increase, with the highest rate being recorded at week 4 and reaching 94.7%. On the other hand, among non-cirrhotic patients, the seropositivity rate peak was observed at week 2 and reached 96.0%. In addition, cirrhotic patients displayed a more rapid decline in the seropositivity rate, dropping to 54.5% after ≥16 weeks, while non-cirrhotic patients exhibited a decrease to 67.2% after the same time period. The overall incidence of AEs was low (18.4%), and all AEs were mild and self-limiting. In addition, 16.0% of participants had mild liver function abnormalities, and half of them returned to normality within the next 6 months without additional therapy. The participants who experienced liver function abnormalities showed a higher seropositivity rate and antibody titer than those who did not (91.6% vs. 79.5%, p = 0.005; 2.73 vs. 2.41 ng/ml, p < 0.001). Conclusion: Cirrhotic CHB patients had lower antibody titers to inactivated SARS-CoV-2 vaccines than non-cirrhotic patients. The vaccines were generally well tolerated in both non-cirrhotic and cirrhotic CHB patient groups. Patients with abnormal liver function may have a better antibody response than those without.

SARS-CoV-2 infection of kidney tissues from severe COVID-19 patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests diverse clinical pathologies involving multiple organs. While the respiratory tract is the primary SARS-CoV-2 target, acute kidney injury is common in COVID-19 patients, displaying as acute tubular necrosis (ATN) resulting from focal epithelial necrosis and eosinophilia, glomerulosclerosis, and autolysis of renal tubular cells. However, whether any renal cells are infected by SARS-CoV-2 and the mechanism involved in the COVID-19 kidney pathology remain unclear. METHODS: Kidney tissues obtained at autopsy from four severe COVID-19 patients and one healthy subject were examined by hematoxylin and eosin staining. Indirect immunofluorescent antibody assay was performed to detect SARS-CoV-2 spike protein S1 and nonstructural protein 8 (NSP8) together with markers of different kidney cell types and immune cells to identify the infected cells. RESULTS: Renal parenchyma showed tissue injury comprised of ATN and glomerulosclerosis. Positive staining of S1 protein was observed in renal parenchymal and tubular epithelial cells. Evidence of viral infection was also observed in innate monocytes/macrophages and NK cells. Positive staining of NSP8, which is essential for viral RNA synthesis and replication, was confirmed in renal parenchymal cells, indicating the presence of active viral replication in the kidney. CONCLUSIONS: In fatal COVID-19 kidneys, there are SARS-CoV-2 infection, minimally infiltrated innate immune cells, and evidence of viral replication, which could contribute to tissue damage in the form of ATN and glomerulosclerosis.

Infectivity of pseudotyped SARS-CoV-2 variants of concern in different human cell types and inhibitory effects of recombinant spike protein and entry-related cellular factors.

Since the report of the first COVID-19 case in 2019, SARS-CoV-2 variants of concern (VOCs) have continued to emerge, manifesting diverse infectivity, evasion of host immunity and pathology. While ACE2 is the predominant receptor of SARS-CoV-2, TMPRSS2, Kim-1, NRP-1, CD147, furin, CD209L, and CD26 have also been implicated as viral entry-related cofactors. To understand the variations in infectivity and pathogenesis of VOCs, we conducted infection analysis in human cells from different organ systems using pseudoviruses of VOCs including Alpha, Beta, Gamma, and Delta. Recombinant spike S1, RBD, ACE2, Kim-1, and NRP-1 proteins were tested for their ability to block infection to dissect their roles in SARS-CoV-2 entry into cells. Compared with wild type SARS-CoV-2 (WT), numerous VOCs had significant increases of infectivity across a wide spectrum of cell types. Recombinant ACE2 protein more effectively inhibited the infection of VOCs including Delta and Omicron (BA.1 and BA.2) than that of WT. Interestingly, recombinant S1, RBD, Kim-1, and NRP-1 proteins inhibited the infection of all pseudoviruses in a manner dependent on the levels of ACE2 expression in different cell types. These results provide insights into the diverse infectivity of SARS-CoV-2 VOCs, which might be helpful for managing the emergence of new VOCs.

Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients.

Neutralizing antibodies to SARS-CoV-2 variants of concern including Delta and Omicron in subjects receiving mRNA-1273, BNT162b2, and Ad26.COV2.S vaccines.

SARS-CoV-2 vaccines have contributed to the control of COVID-19 in some parts of the world. However, the constant emergence of variants of concern (VOCs) challenges the effectiveness of SARS-CoV-2 vaccines over time. In particular, Omicron contains a high number of mutations in the spike (S) protein gene, on which most vaccines were developed. In this study, we quantitated neutralizing antibodies in vaccine recipients at various times postvaccination using S protein-based pseudoviruses derived from wild type (WT) SARS-CoV-2 and five VOCs including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). We found that two-dose mRNA-1273 and BNT162b2 vaccines elicited robust neutralizing antibodies against WT, Alpha, Beta, Gamma, and Delta, but wanned after 6 months with a faster decline observed for BNT162b2. Both mRNA-1273 and BNT162b2 elicited weak neutralizing antibodies against Omicron. One dose of Ad26.COV2.S vaccine induced weaker neutralizing antibodies against WT and most VOCs than mRNA-1273 and BNT162b2 did but moderate neutralizing antibodies against Delta and Omicron, which lasted for 6 months. These results support current recommendations of the Centers for Disease Control and Prevention for a booster 5 months after full immunization with an mRNA-based vaccine and the use of an mRNA-based vaccine 2 months after Ad26.COV2.S vaccination.

AI-Enabled Opportunities and Transformation Challenges for SMEs in the Post-pandemic Era: A Review and Research Agenda.

The negative impact of COVID-19 pandemic has seen SME's struggling around the world. With many quickly adopting digital technologies, such as AI, in their manufacturing or services operations to achieve sustainable development. This study aims to develop a framework that informs AI-enabled sustainable development for SMEs by integrating the relevant research in the field. In this framework, we identify the opportunities that the deployment of AI technology can do to alleviate the plights of SMEs in the post-pandemic era, including the impacts on work, organizations, and performance. We further explore the challenges that SMEs face in AI transformation and recommend strategies to take on those challenges. Finally we propose an agenda for future research based on technological challenges and environmental threats.

Improving timeliness in the neglected tropical diseases preventive chemotherapy donation supply chain through information sharing: A retrospective empirical analysis.

BACKGROUND: Billions of doses of medicines are donated for mass drug administrations in support of the World Health Organization's "Roadmap to Implementation," which aims to control, eliminate, and eradicate Neglected Tropical Diseases (NTDs). The supply chain to deliver these medicines is complex, with fragmented data systems and limited visibility on performance. This study empirically evaluates the impact of an online supply chain performance measurement system, "NTDeliver," providing understanding of the value of information sharing towards the success of global health programs. METHODS: Retrospective secondary data were extracted from NTDeliver, which included 1,484 shipments for four critical medicines ordered by over 100 countries between February 28, 2006 and December 31, 2018. We applied statistical regression models to analyze the impact on key performance metrics, comparing data before and after the system was implemented. FINDINGS: The results suggest information sharing has a positive association with improvement for two key performance indicators: purchase order timeliness (ß = 0.941, p = 0.003) and-most importantly-delivery timeliness (ß = 0.828, p = 0.027). There is a positive association with improvement for three variables when the data are publicly shared: shipment timeliness (ß = 2.57, p = 0.001), arrival timeliness (ß = 2.88, p = 0.003), and delivery timeliness (ß = 2.82, p = 0.011). CONCLUSIONS: Our findings suggest that information sharing between the NTD program partners via the NTDeliver system has a positive association with supply chain performance improvements, especially when data are shared publicly. Given the large volume of medicine and the significant number of people requiring these medicines, information sharing has the potential to provide improvements to global health programs affecting the health of tens to hundreds of millions of people.

Unsupervised PM2.5 anomalies in China induced by the COVID-19 epidemic.

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we examine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities.