Lanjun Shao

China Tobacco

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Lili Cai

University of Illinois Urbana-Champaign

Yidong Zou

Hunan Agricultural University

Pingnan Chen

Pingliang People's Hospital

Can Wang

Southwest Jiaotong University

Yanqiu Jing

Henan Agricultural University

Qiyun Zhou

Central China Normal University

Ruiwen Hu

Nanjing Tech University

Hongfei Yin

China Tobacco

Junhui Li

Institute of Vegetables and Flowers

Meijie Tian

Hunan Agricultural University

Cooperative Institutions

China Tobacco

Hunan Agricultural University

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Ministry of Agriculture and Rural Affairs

Henan Agricultural University

Tobacco Research Institute

Zhejiang University

First Affiliated Hospital of Zhengzhou University

Nanchang University

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Nicotine detection in tobacco samples via a molecularly imprinted polymers sensing platform

International Journal of Electrochemical Science (2024)

Yaoguang Li Lanjun Shao Qianjin Liu Lei Li Yinghao Zhang

A highly sensitive and selective molecularly imprinted polymer (MIP) sensing platform was developed for the detection of nicotine in tobacco samples. The MIP sensor was fabricated by integrating a PtNPs/GO nanocomposite with an electropolymerized nicotine-imprinted film on a glassy carbon electrode. Under optimal conditions (pH 7.4, 50 mM pyrrole, and 30 min elution time), the MIP sensor exhibited a wide linear range (0.01-0.5 μM and 0.5-100 μM), low detection limit (0.015 μM), good reproducibility (RSD < 4.2%), and long-term stability (93.2% retention after 30 days). The MIP sensor demonstrated excellent selectivity towards nicotine, with selectivity coefficients ranging from 7.2 to 9.5 against interfering substances. The practical applicability of the MIP sensor was validated by analyzing nicotine content in cigarettes and chewing tobacco samples, with recoveries ranging from 97.5% to 102.8% and relative errors within ±5% compared to the reference GC-MS method. The proposed MIP-based electrochemical sensing platform offers a rapid, simple, and cost-effective approach for sensitive and selective detection of nicotine in complex tobacco matrices, showing great potential for applications in the tobacco industry and public health sector.

Molecularly imprinted polymer

Linear range

10.1016/j.ijoes.2024.100592

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Dissemination and characterization of Stenotrophomonas maltophilia isolates from Dairy Cows in Northeast China

Polish Journal of Microbiology (2023)

Lanjun Shao Xiaojing Liu Yi Liu Jie Shen Ruishan Liu

This work investigated the genetic relationship among Stenotrophomonas maltophilia strains in fecal samples from dairy cows in northeast China and identified the dominant β-lactamase genotype. One hundred and six samples were collected from two randomly selected cow farms in northeast China, and the isolates were identified with MALDI-TOF/MS. Whole-genome sequencing was conducted using Illumina HiSeq 4000-PE150 platform (Illumina, Inc., USA). The antimicrobial resistance genes were detected using CGE services. The phylogenetic analysis of S. maltophilia strains was performed by Roary and MEGA X. In total, 24 S. maltophilia isolates were isolated. The results of resistome analysis showed all S. maltophilia strains carrying blaL1 gene, which was the only β-lactamase genotype. In addition, the aminoglycoside resistance genes aac(6')-Iz and aph(3')-IIc were found. The phylogenetic tree indicated the clonal diversity of S. maltophilia in these two regions and the clonal relatedness of the strains from these regions. This study first investigated the dissemination and characterization of S. maltophilia isolates from dairy cows in northeast China and provided evidence of the potential transmission between two provinces. Furthermore, it indicated blaL1 was the most prevalent genotype of β-lactamase in these regions.

Stenotrophomonas maltophilia

Resistome

10.33073/pjm-2023-032

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Well-developed root systems and a nitrogen-rich rhizosphere recruit key bacterial taxa to resist disease invasion of field crop

Agriculture Ecosystems & Environment (2024)

Junhui Li Yidong Zou Kun Yang Yi Zhu Qiyun Zhou

10.1016/j.agee.2024.109279

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