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Start Over Author "Tang, Feng" Publisher elsevier b.v.
319 results on '"Tang, Feng"'

29. Genetic relatedness, antibiotic resistance, and virulence of Klebsiella pneumoniae isolated from health care and food sources in Wuhan, China.

Author

Tang, Feng, Chen, Zhi, Zhu, Hanjue, Xi, Lei, Li, Changzhen, and Luo, Wanjun

Abstract

To investigate genetic relatedness and antibiotic resistance of Klebsiella pneumoniae from retail meat samples, clinical source samples, and hospital environmental samples in Wuhan, China. Hypermucoviscosity and biofilm formation of K. pneumoniae were assessed by string test and crystal violet staining. MICs of 18 antimicrobials were determined by broth microdilution. PCR detected 14 antibiotic resistance genes. Genetic relatedness and clonal dissemination were analyzed by PFGE. Among 5,730 samples, 46 were tested positive for K pneumoniae , with higher rates observed in meat (23.4%) than in clinical samples (0.6%) and hospital environmental samples (8.0%). Meat-derived isolates showed high resistance to tetracycline (36.4%, 4/11), sulfonamide (27.3%, 3/11), and gentamicin (27.3%, 3/11), whereas clinical isolates exhibited significant resistance to ampicillin-sulbactam (32.3%, 10/31). Multidrug resistance was observed in 17.4% (8/46) of the isolates, particularly in hospital environmental samples (3/4). Biofilm production was observed in 88.1% (37/42) of K pneumoniae. Pulsed-field gel electrophoresis analysis revealed patient-to-patient K pneumoniae transmission, transmission between patients and hospital environment, as well as cross-contamination between markets. The findings underscore the importance of comprehensive surveillance, infection control, and judicious antibiotic use in mitigating the impact of K pneumoniae on public health, especially in the food chain and health care settings. • Detection of Klebsiella pneumoniae in pediatric patients, clinical settings, and foodstuff. • Virulence phenotypes and biofilm formation of K pneumoniae from different sources. • Antibiotic resistance and resistance genes of K pneumoniae from different sources. • Homology analysis indicates its transmission between different sources. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Global pattern and determinant for interaction of seasonal influenza viruses.

Author

Chen, Yilin, Tang, Feng, Cao, Zicheng, Zeng, Jinfeng, Qiu, Zekai, Zhang, Chi, Long, Haoyu, Cheng, Peiwen, Sun, Qianru, Han, Wenjie, Tang, Kang, Tang, Jing, Zhao, Yang, Tian, Dechao, and Du, Xiangjun

Abstract

The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza. [ABSTRACT FROM AUTHOR]

Published
2024
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