Comparison between two PCR‐based bacterial identification methods through artificial neural network data analysis

Wen, Jie; Zhang, Xiaohui; Gao, Peng; Qiuhong, Jiang

doi:10.1002/jcla.20224

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…For example, many microfluidic DEP devices are solely designed for cell sorting which could be followed by an identification method not carried out within the study . On the other hand, many identification methods, such as Raman spectroscopy , PCR , or FTIR , already assume a sample free of debris, making them ill‐equipped to handle environmental or clinical samples which are bound to have debris. Only few combined isolation and identification designs exist, of which many require labels such as Raman reporters or antibodies.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous isolation and label‐free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy

et al. 2019

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The traditional bacterial identification method of growing colonies on agar plates can take several days to weeks to complete depending on the growth rate of the bacteria. Successfully decreasing this analysis time requires cell isolation followed by identification. One way to decrease analysis time is by combining dielectrophoresis (DEP), a common technique used for cell sorting and isolation, and Raman spectroscopy for cell identification. DEP‐Raman devices have been used for bacterial analysis, however, these devices have a number of drawbacks including sample heating, cell‐to‐electrode proximity that limits throughput and separation efficiency, electrode fouling, or inability to address sample debris. Presented here is a contactless DEP‐Raman device to simultaneously isolate and identify particles from a mixed sample while avoiding common drawbacks associated with other DEP designs. Using the device, a mixed sample of bacteria and 3 μm polystyrene spheres were isolated from each other and a Raman spectrum of the trapped bacteria was acquired, indicating the potential for cDEP‐Raman devices to decrease the analysis time of bacteria.

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Section: Resultsmentioning

confidence: 99%

Simultaneous isolation and label‐free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy

et al. 2019

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Application of probabilistic neural network in bacterial identification by biochemical profiles

Jia

Liang

Cao

et al. 2013

Journal of Microbiological Methods

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Amplification refractory mutation system polymerase chain reaction versus optimized polymerase chain reaction restriction-fragment length polymorphism for apolipoprotein E genotyping of majorly depressed patients

You

Chen²,

Zhou³

et al. 2015

Molecular Medicine Reports

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Major depressive disorder (MDD) is a prevalent, debilitating mood disorder that has been associated with several genetic polymorphisms. One such polymorphism, namely that of apolipoprotein E (APOE), has three allelic forms (ε2, ε3 and ε4) that encode for six unique isoforms of the APOE protein. A growing number of techniques have been developed for APOE genotyping; however, not all polymerase chain reaction (PCR)‑based genotyping techniques are equally accurate or cost‑effective. In order to find a more accurate and cost‑effective APOE genotyping method for MDD screening in large populations, the present study comparatively evaluated two genotyping methods, amplification refractory mutation system PCR (ARMS‑PCR) and optimized PCR restriction‑fragment length polymorphism (PCR‑RFLP), in blood samples taken from a population of 708 MDD patients. Although either of the two methods were able to detect all six unique APOE genotypes, comparisons of the two methods with Sanger sequencing demonstrated that ARMS‑PCR (94%) was significantly more accurate than optimized PCR‑RFLP (82%). ARMS‑PCR should prove useful in quickly verifying ambiguous results obtained by other APOE genotyping methods and can be cost-effectively performed in the setting of a small laboratory or a population-based screening program.

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Comparison between two PCR‐based bacterial identification methods through artificial neural network data analysis

Cited by 3 publications

References 19 publications

Simultaneous isolation and label‐free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy

Simultaneous isolation and label‐free identification of bacteria using contactless dielectrophoresis and Raman spectroscopy

Application of probabilistic neural network in bacterial identification by biochemical profiles

Amplification refractory mutation system polymerase chain reaction versus optimized polymerase chain reaction restriction-fragment length polymorphism for apolipoprotein E genotyping of majorly depressed patients

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