Daxi Wang

Dr. Daxi Wang


Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 April 2024 in Science
Verified
Journal Article
Closed access
LZLiming Zhang
Liming Zhang
DWDaxi Wang
Daxi Wang
PSPeibo Shi
Peibo Shi
JLJuzhen Li
Juzhen Li
JNJichen Niu
Jichen Niu
Gong ChengGong Cheng
Gong Cheng
The commensal microbiota of the mosquito gut plays a complex role in determining the vector competence for arboviruses. In this study, we identified a bacterium from the gut of fieldAedes albopictusmosquitoes namedRosenbergiellasp. YN46 (Rosenbergiella_YN46) that rendered mosquitoes refractory to infection with dengue and Zika viruses. Inoculation of 1.6 × 103colony forming units (CFUs) ofRosenbergiella_YN46 intoA. albopictusmosquitoes effectively prevents viral infection. Mechanistically, this bacterium secretes glucose dehydrogenase (RyGDH), which acidifies the gut lumen of fed mosquitoes, causing irreversible conformational changes in the flavivirus envelope protein that prevent viral entry into cells. In semifield conditions,Rosenbergiella_YN46 exhibits effective transstadial transmission in field mosquitoes, which blocks transmission of dengue virus by newly emerged adult mosquitoes. The prevalence ofRosenbergiella_YN46 is greater in mosquitoes from low-dengue areas (52.9 to ~91.7%) than in those from dengue-endemic regions (0 to ~6.7%).Rosenbergiella_YN46 may offer an effective and safe lead for flavivirus biocontrol.

22 March 2024 in Nature Ecology & Evolution
Verified
Journal Article
Closed access
PDF file
YPYuan-Fei Pan
Yuan-Fei Pan
HZHailong Zhao
Hailong Zhao
QGQin-Yu Gou
Qin-Yu Gou
PSPei-Bo Shi
Pei-Bo Shi
JTJun-Hua Tian
Jun-Hua Tian
MSMang Shi
Mang Shi

21 May 2025 in Microorganisms
Verified
Journal Article
Open access
PDF file
溦朱溦荷 朱
溦荷 朱
DWDaxi Wang
Daxi Wang
PLPengsong Li
Pengsong Li
HDHaohao Deng
Haohao Deng
ZDZiqing Deng
Ziqing Deng
Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial–temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management.



5 March 2022 in Parasites & Vectors
Verified
Journal Article
Open access
PDF file
YZYanping Zhao
Yanping Zhao
DGDunzhu Gesang
Dunzhu Gesang
LWLi Wan
Li Wan
JLJiandong Li
Jiandong Li
GQGezhen Qiangba
Gezhen Qiangba
WCWenxue Chen
Wenxue Chen
Background: Molecular markers are essential to identify Echinococcus species and genotypes in areas with multiple Echinococcus species to understand their epidemiology and pathology. Tibet Autonomous Region (TAR) is one of the areas worst hit by echinococcosis. However, molecular epidemiology is still missing among echinococcosis patients in TAR. This research explored the Echinococcus species and genotypes infecting humans in TAR and the population diversity and the possible origin of G1 in TAR. Methods: Cyst samples were collected in one echinococcosis-designated hospital in TAR. Echinococcus species and genotypes were identified through a maximum-likelihood approach with near-complete/complete mtDNA using IQ-TREE. Phylogenetic networks were built with PopART, and the phylogeographical diffusion pattern was identified using a Bayesian discrete phylogeographic method. Results: Using phylogenetic trees made with near-complete/complete mtDNA obtained from 92 cysts from TAR patients, the Echinococcus species and genotypes infecting humans in TAR were identified as Echinococcus granulosus (s.s.) G1 (81, 88.04%), accounting for the majority, followed by G6 of the E. canadensis cluster (6, 6.52%), E. granulosus (s.s.) G3 (3, 3.26%), and E. multilocularis (2, 2.17%). An expansion trend and a possible recent bottleneck event were confirmed among the G1 samples in TAR. Adding the other near-complete mtDNA of G1 samples globally from the literature, we identified the possible phylogeographic origin of the G1 samples in TAR as Turkey. Conclusions: Using near-complete/complete mtDNA sequences of Echinococcus spp. obtained from echinococcosis patients, a variety of Echinococcus species and genotypes infecting humans throughout TAR were identified. As far as we know, this is the first comprehensive molecular investigation of Echinococcus species and genotypes infecting humans throughout TAR. We identified, for the first time to our knowledge, the possible origin of the G1 in TAR. We also enriched the long mtDNA database of Echinococcus spp. and added two complete E. multilocularis mtDNA sequences from human patients. These findings will improve our knowledge of echinococcosis, help to refine the targeted echinococcosis control measures, and serve as a valuable baseline for monitoring the Echinococcus species and genotypes mutations and trends of the Echinococcus spp. population in TAR. Graphical