Bibliometrics and visualization analysis of land use regression models in ambient air pollution research / 中华流行病学杂志

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives. Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Series study on the relationship between air quality index and tuberculosis incidence in Beijing / 中华流行病学杂志

Objective: To analyze the effect of air quality index (AQI) on the incidence of tuberculosis (TB) in Beijing, and to provide evidence for setting up a better program regarding prevention and control of tuberculosis. Methods: Generalized additive model (GAM) was used to analyze the association between AQI and the incidence of tuberculosis in Beijing, from January 1, 2014 to November 9, 2016. Confounding factors as meteorological conditions and time trends were under control. Results: In Beijing, a total of 14 533 TB cases with definite dates of onset were collected during the study period, with 36 children excluded from the study. Finally, 14 497 cases were included in the study, including 9 513 men and 4 984 women, with 11 290 adults (15-59 years old) and 3 207 elderly (≥60 years old). Data from the optimal single-day lag effect of GAM showed that,with every 10 increase of AQI, the percent of increase on the onsets of overall, male, female and adult; tuberculosis cases were 0.85% (95%CI: 0.26%-1.44%), 0.83% (95%CI: 0.24%-1.42%), 0.93% (95%CI: 0.24%-1.62%) and 0.88% (95%CI: 0.29%-1.46%), respectively. The optimal lag time of the single-day effects were 15 days (lag15), but 16 days (lag16) for male. The optimal cumulative lag effect showed that with every 10 AQI increase, the percent of increase on the onsets of overall, male, female and adult tuberculosis cases were 1.92% (95%CI: 0.23%-3.16%), 1.94% (95%CI:0.15%-3.72%), 2.04% (95%CI: 0.10%-3.97%) and 2.00% (95%CI: 0.30%-3.69%), respectively, with the optimal lag time of cumulative delayed effects as 17 days (lag0_17), 18 days (lag0_18), 16 days (lag0_16) and 17 days (lag0_17), respectively. However, there were no statistical significances noticed in the elderly cases. Conclusion: There was a positive correlation between AQI and the number of TB cases in Beijing, and the effects of AQI on the number of TB cases in different genders and age groups were different.

High Throughput Designing and Mutational Mapping of RBD-ACE2 Interface Guide Non-Conventional Therapeutic Strategies for COVID-19

Considering the current status of the SARS-CoV-2 pandemic, sequence variations and possibly structural changes in the rapidly evolving SARS-CoV-2 is highly expected in the coming months. The SARS-CoV-2 spike (S) protein is responsible for mediating viral attachment and fusion with cell membranes. Mutations in the receptor-binding domain (RBD) of the S-protein occur at the most variable part of the SARS-CoV-2 genome, and specific sites of S-protein have undergone positive selection impacting the viral pathogenicity. In the present work, we used high-throughput computation to design 100,000 mutants in RBD interfacial residues and identify novel affinity-enhancing and affinity-weakening mutations. Our data suggest that SARS-CoV-2 can establish a higher rate of infectivity and pathogenesis when it acquires combinatorial mutations at the interfacial residues in RBD. Mapping of the mutational landscape of the interaction site suggests that a few of these residues are the hot-spot residues with a very high tendency to undergo positive selection. Knowledge of the affinity-enhancing mutations may guide the identification of potential cold-spots for this mutation as targets for developing a possible therapeutic strategy instead of hot-spots, and vice versa. Understanding of the molecular interactions between the virus and host protein presents a detailed systems view of viral infection mechanisms. The applications of the present research can be explored in multiple antiviral strategies, including monoclonal antibody therapy, vaccine design, and importantly in understanding the clinical pathogenesis of the virus itself. Our work presents research directions for the exploitation of non-conventional solutions for COVID-19.

A novel structure-based approach for identification of vertebrate susceptibility to SARS-CoV-2: implications for future surveillance programmes

Understanding the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a highly debatable and unsolved challenge for the scientific communities across the world. A key to dissect the susceptibility profiles of animal species to SARS-CoV-2 is to understand how virus enters into the cells. The interaction of SARS-CoV-2 ligands (RBD on spike protein) with its host cell receptor, angiotensin-converting enzyme 2 (ACE2), is a critical determinant of host range and cross-species transmission. In this study, we developed and implemented a rigorous computational approach for predicting binding affinity between 299 ACE2 orthologs from diverse vertebrate species and the SARS-CoV-2 spike protein. The findings show that the spike protein of SARS-CoV-2 can bind to many vertebrate species carrying evolutionary divergent ACE2, implying a broad host range at the virus entry level, which may contribute to cross-species transmission and further viral evolution. Additionally, the present study facilitated the identification of genetic determinants that may differentiate susceptible from the resistant host species based on the conservation of ACE2-spike protein interacting residues in vertebrate host species known to facilitate SARS-CoV-2 infection; however, these genetic determinants warrant in vivo experimental confirmation. The molecular interactions associated with varied binding affinity of distinct ACE2 isoforms in a specific bat species were identified using protein structure analysis, implying the existence of diversified susceptibility of bat species to SARS-CoV-2. The findings from current study highlight the importance of intensive surveillance programs aimed at identifying susceptible hosts, particularly those with the potential to transmit zoonotic pathogens, in order to prevent future outbreaks.

A novel consensus-based computational pipeline for rapid screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including omicron variant

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to evolve carrying flexible amino acid substitutions in the spike proteins receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with three-fold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero-dimeric and -trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concern (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variants RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow_3D11, and C118) -- that may likely neutralize the recently emerged omicron variant -- facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.