Modified transport medium for improving influenza virus detection.


Journal

Frontiers in cellular and infection microbiology
ISSN: 2235-2988
Titre abrégé: Front Cell Infect Microbiol
Pays: Switzerland
ID NLM: 101585359

Informations de publication

Date de publication:
2024
Historique:
received: 12 03 2024
accepted: 18 06 2024
medline: 19 7 2024
pubmed: 19 7 2024
entrez: 19 7 2024
Statut: epublish

Résumé

Accurate detection of influenza virus in clinical samples requires correct execution of all aspects of the detection test. If the viral load in a sample is below the detection limit, a false negative result may be obtained. To overcome this issue, we developed a modified transport medium (MTM) for clinical sample transportation to increase viral detection sensitivity. We first validated the MTM using laboratory-stocked influenza A viruses (IAVs: H1N1, H3N2, H7N3, H9N2) and influenza B viruses (IBVs: Yamagata, Victoria). We also tested clinical samples. A total of 110 patients were enrolled and a pair of samples were collected to determine the sensitivity of real-time polymerase chain reaction (RT-PCR) following MTM treatment. After 24 h culturing in MTM, the viral loads were increased, represented by a 10-fold increase in detection sensitivity for H1N1, H9N2, and IBVs, a 100-fold increase for H3N2, and a 1,000-fold increase for H7N3. We further tested the effects of MTM on 19 IAV and 11 IBV stored clinical samples. The RT-PCR results showed that the positive detection rate of IAV samples increased from 63.16% (12/19) without MTM culturing to 78.95% (15/19) after 48 h culturing, and finally 89.47% (17/19) after 72 h culturing. MTM treatment of IBV clinical samples also increased the positive detection rate from 36.36% (4/11, 0 h) to 63.64% (7/11, 48 h) to 72.73% (8/11, 72 h). For clinical samples detected by RT-PCR, MTM outperformed other transport mediums in terms of viral detection rate (11.81% increase, P=0.007). Our results demonstrated that the use of MTM for clinical applications can increase detection sensitivity, thus facilitating the accurate diagnosis of influenza infection.

Sections du résumé

Background UNASSIGNED
Accurate detection of influenza virus in clinical samples requires correct execution of all aspects of the detection test. If the viral load in a sample is below the detection limit, a false negative result may be obtained. To overcome this issue, we developed a modified transport medium (MTM) for clinical sample transportation to increase viral detection sensitivity.
Method UNASSIGNED
We first validated the MTM using laboratory-stocked influenza A viruses (IAVs: H1N1, H3N2, H7N3, H9N2) and influenza B viruses (IBVs: Yamagata, Victoria). We also tested clinical samples. A total of 110 patients were enrolled and a pair of samples were collected to determine the sensitivity of real-time polymerase chain reaction (RT-PCR) following MTM treatment.
Result UNASSIGNED
After 24 h culturing in MTM, the viral loads were increased, represented by a 10-fold increase in detection sensitivity for H1N1, H9N2, and IBVs, a 100-fold increase for H3N2, and a 1,000-fold increase for H7N3. We further tested the effects of MTM on 19 IAV and 11 IBV stored clinical samples. The RT-PCR results showed that the positive detection rate of IAV samples increased from 63.16% (12/19) without MTM culturing to 78.95% (15/19) after 48 h culturing, and finally 89.47% (17/19) after 72 h culturing. MTM treatment of IBV clinical samples also increased the positive detection rate from 36.36% (4/11, 0 h) to 63.64% (7/11, 48 h) to 72.73% (8/11, 72 h). For clinical samples detected by RT-PCR, MTM outperformed other transport mediums in terms of viral detection rate (11.81% increase, P=0.007).
Conclusion UNASSIGNED
Our results demonstrated that the use of MTM for clinical applications can increase detection sensitivity, thus facilitating the accurate diagnosis of influenza infection.

Identifiants

pubmed: 39027137
doi: 10.3389/fcimb.2024.1399782
pmc: PMC11254831
doi:

Substances chimiques

Culture Media 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1399782

Informations de copyright

Copyright © 2024 Zeng, Li, Guo, Liu, Liang, Lai, Fang, Li, Xue, Zhan, Lin, Guan and Yang.

Déclaration de conflit d'intérêts

YL was employed by Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd. HG, LL and QX were employed by BaSO Diagnostics Inc. Zhuhai. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Auteurs

Zhiqi Zeng (Z)

Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.

Qianying Li (Q)

Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.

Hua Guo (H)

BaSO Diagnostics Inc., Zhuhai, Guangzhou, China.

Yong Liu (Y)

Kingmed Virology Diagnostic and Translational Center, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, China.

Lixi Liang (L)

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Yuanfang Lai (Y)

Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.

Yi Fang (Y)

Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.

Lei Li (L)

BaSO Diagnostics Inc., Zhuhai, Guangzhou, China.

Qiuting Xue (Q)

BaSO Diagnostics Inc., Zhuhai, Guangzhou, China.

Yangqing Zhan (Y)

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Zhengshi Lin (Z)

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Wenda Guan (W)

Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Zifeng Yang (Z)

Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.
State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Guangzhou Laboratory, Guangzhou, Guangdong, China.

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