Publications by authors named "Zhijiao Yue"
Article Synopsis
- The COVID-19 pandemic has impacted the spread of non-SARS-CoV-2 respiratory viruses, leading to a study that used wastewater surveillance to monitor SARS-CoV-2 and influenza A virus in three major Chinese port cities.
- Researchers employed a novel machine learning algorithm that combined Gaussian and random forest models to predict the trends of these infections, revealing two waves of SARS-CoV-2 in mid-2023 and two waves of influenza A.
- Their predictions from October 2023 to April 2024 closely matched observed data, indicating that combining wastewater surveillance with machine learning can significantly enhance public health responses to respiratory viral diseases.
Norovirus (NoV) is the primary cause of acute gastroenteritis (AGE) on a global scale. Numerous studies have demonstrated the immense potential of wastewater surveillance in monitoring the prevalence and spread of NoV within communities. This study employed a one-step reverse transcription-quantitative PCR to quantify NoV GI/GII in wastewater samples (n = 2574), which were collected once or twice a week from 38 wastewater treatment plants from March 2023 to February 2024 in Shenzhen.
View Article and Find Full Text PDFIntroduction: The emergence of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineage, BA.2.86, has sparked global public health concerns for its potential heightened transmissibility and immune evasion.
View Article and Find Full Text PDFObjective: To understand the prevalence, genetic characteristics and drug resistance features of Salmonella Kentucky ST314 in Shenzhen.
Methods: Whole genome sequencing of 14 strains of Salmonella Kentucky ST314 collected from 2010-2021 by the Foodborne Disease Surveillance Network of Shenzhen Center for Disease Control and Prevention for phylogenetic evolutionary analysis, drug resistance gene and plasmid detection; drug susceptibility experiments were performed by micro-broth dilution method.
Results: A total of 57 strains of Salmonella Kentucky were collected from the foodborne disease surveillance network, 14 of which were ST314.
The prolonged course of the COVID-19 pandemic necessitates sustained surveillance of emerging variants. This study aimed to develop a multiplex real-time polymerase chain reaction (rt-PCR) suitable for the real-time tracking of Omicron subvariants in clinical and wastewater samples. Plasmids containing variant-specific mutations were used to develop a MeltArray assay.
View Article and Find Full Text PDF