Dataset Information

Evaluation of a smartphone-based rapid fluorescent diagnostic system for H9N2 virus in specific-pathogen-free chickens.

ABSTRACT: Repeated interspecies transmission of H9N2 virus from poultry to humans and human infections transmitted via aerosols highlight the need for a highly sensitive, rapid diagnostic system for the detection of this virus. However, no such test exhibiting high performance has been developed. In this study, the performance of a smartphone-based rapid fluorescent diagnostic system (SRFDS) was optimized for the diagnosis of an H9N2-virus-infected animal. To suppress the nonspecific reactivity of the bioconjugate in oropharyngeal (OP) and cloacal (CL) samples derived from chickens, different blocking reagents were tested, and a mixture of casein and sucrose was found to be optimal. To assess the performance of SRFDS, OP and CL samples were obtained from specific-pathogen-free chickens and used for comparison of this method with real-time reverse transcription PCR (rRT-PCR) at time points of three, five, and seven days postinfection (dpi). The limit of detection of SRFDS was found to be 7.5 PFU/mL, which was 138-fold higher than that of a conventional colloidal-gold-based avian influenza rapid diagnostic test. In the animal study, the presence of viral antigen was monitored with SRFDS, and the relative sensitivity (relative to rRT-PCR results) was 94.44 % (17/18) and 95.23 % (20/21) in OP and CL specimens, respectively. The specificity of SRFDS was 100 %. These results imply that the diagnostic performance of SRFDS might be comparable to that of rRT-PCR for diagnosis of H9N2 in chickens and that this test can be used as a highly sensitive rapid diagnostic method in field studies on broiler poultry and wild birds.

SUBMITTER: Yeo SJ

PROVIDER: S-EPMC4947103 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Evaluation of a smartphone-based rapid fluorescent diagnostic system for H9N2 virus in specific-pathogen-free chickens.

Yeo Seon-Ju SJ Cuc Bui Thi BT Sung Haan Woo HW Park Hyun H

Archives of virology 20160610 8

Repeated interspecies transmission of H9N2 virus from poultry to humans and human infections transmitted via aerosols highlight the need for a highly sensitive, rapid diagnostic system for the detection of this virus. However, no such test exhibiting high performance has been developed. In this study, the performance of a smartphone-based rapid fluorescent diagnostic system (SRFDS) was optimized for the diagnosis of an H9N2-virus-infected animal. To suppress the nonspecific reactivity of the bio ...[more]

PMID: 27287435

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Project description:BackgroundMicroscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.MethodsA total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.ResultsMalaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.ConclusionMalaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.

Dataset Information

Evaluation of a smartphone-based rapid fluorescent diagnostic system for H9N2 virus in specific-pathogen-free chickens.

Publications

Evaluation of a smartphone-based rapid fluorescent diagnostic system for H9N2 virus in specific-pathogen-free chickens.

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