Immunogenicity profiling and distinct immune response in liver transplant recipients vaccinated with SARS-CoV-2 inactivated vaccines

SARS-CoV-2 vaccination has been recommended for liver transplant (LT) recipients. However, our understanding of inactivated vaccine stimulation of the immune system in regulating humoral and cellular immunity among LT recipients is inadequate. Forty-six LT recipients who received two-dose inactivated vaccines according to the national vaccination schedule were enrolled. The clinical characteristics, antibody responses, single-cell peripheral immune profiling, and plasma cytokine/chemokine/growth factor levels were recorded. Sixteen (34.78%) LT recipients with positive neutralizing antibody (nAb) were present in the Type 1 group. Fourteen and 16 LT recipients with undetected nAb were present in the Type 2 and Type 3 groups, respectively. Time from transplant and lymphocyte count were different among the three groups. The levels of anti-RBD and anti-S1S2 decreased with decreasing neutralizing inhibition rates. Compared to the Type 2 and Type 3 groups, the Type 1 group had an enhanced innate immune response. The proportions of B, DNT, and CD3+CD19+ cells were increased in the Type 1 group, whereas monocytes and CD4+ T cells were decreased. High CD19, high CD8+CD45RA+ cells, and low effector memory CD4+/naïve CD4+ cells of the T-cell populations were present in the Type 1 group. The Type 1 group had higher concentrations of plasma CXCL10, MIP-1 beta, and TNF-alpha. No severe adverse events were reported in all LT recipients. We identified the immune responses induced by inactivated vaccines among LT recipients and provided insights into the identification of immunotypes associated with the responders.

Exploring the Effects of Carpooling on Travelers’ Behavior during the COVID-19 Pandemic: A Case Study of Metropolitan City

Transportation accounts for more than a quarter of the greenhouse gas emissions that are causing climate change. Carpooling is a subset of the sharing economy, in which individuals share their vehicle with commuters to save travel expenses. In recent decades, carpooling has been promoted as a feasible alternative to car ownership with the potential to alleviate traffic congestion, parking demand, and environmental problems. Unstable economic conditions, cultural norms, and lack of infrastructure make cultural exchange activities and mobility habits different in developing nations to those in developed countries. The rapid evolution of sharing mobility has reshaped travelers’ behavior and created a dire need to determine the travel patterns of commuters living in megacities in developing countries. To obtain data, a web-based stated choice (SC) experiment was used in this study. It used mode-related variables, socioeconomic demographic variables, and a coronavirus disease 2019 (COVID-19) precautionary measure variable. Logit models, namely the mixed logit regression model (ML) and the multinomial logit regression model (MNL), were applied to analyze the available data. According to modeling and survey data, economic variables associated with modes of transport, such as trip time and trip cost, were determined to be significant. Additionally, the results revealed that commuters were more conscious of COVID-19 preventive measures, which was determined to be highly significant. The findings showed that the majority of residents in the COVID-19 pandemic continue to rely on automobiles and motorcycles. It is noteworthy that individuals with more than two members in their family and a travel distance of less than seven miles were more likely to prefer a carpooling service. This study’s findings will provide a basis for researchers to aid existing operators in the field of transportation, as well as offer guidelines for governments in developing countries to enhance the utility of transportation networks.

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data (preprint)

A bstract Single-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

Accessing the syndemic of COVID-19 and malaria intervention in Africa (preprint)

Background: The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. Methods: : We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results: : In this paper, we conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R_0 and the duration of infection D_I) of COVID-19 in each country are estimated as follows: Ethiopia (R_0=1.57, D_I=5.32), Nigeria (R_0=2.18, D_I=6.58), Tanzania (R_0=2.47, D_I=6.01), and Zambia (R_0=2.12, D_I=6.96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions: : By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential. Keywords : COVID-19 pandemic; Non-pharmaceutical interventions; Particle Markov chain Monte Carlo; Insecticide-treated nets; Vectorial capacity; Malaria transmission potential