Design and evaluation of a multiplex vesicular rash PCR for the detection of varicella zoster virus and herpes simplex virus

Schmid, B.; Affolter, Marianne; Buttafuoco, Antonino; Bosshard, Philipp P.

doi:10.1111/jdv.17198

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Herpes zoster virus primers: 5’-GATTACAGGCGAGCCCATTAGA-3’ and 5’-CGTATCGGGACTTCAACCAGAA-3’. ( Schmid et al., 2021 ) Herpes simplex virus primers: 5’-GGGGTGATCGGCGAGTAYTG-3’ and 5’-ATCTGCTGGCCGTCGTARATG-3’. ( Tan et al., 2013 ) Vaccinia virus primers: 5’-AAACACACACTGAGAAACAGCATAAA-3’ and 5’-ACTATCGGCGAATGATCTGATTA-3’.…”

Section: Methodsmentioning

confidence: 99%

Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network

Ren

Liu

et al. 2022

Front. Cell. Infect. Microbiol.

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Both healthy and diseased human ocular surfaces possess their own microbiota. If allowed, opportunistic pathogens within the ocular microbiota may cause microbial keratitis (MK). However, the nonpathogenic component of the ocular microbiota has been proven to undermine the performance of culture, the gold standard of the etiological diagnosis for MK. As the conjunctival bacterial microbiota generates unique alterations with various oculopathies, this study aimed to evaluate the feasibility of distinguishing MK using machine learning based on the characteristics of the conjunctival bacterial microbiome associated with various types of MK. This study also aimed to reveal which bacterial genera constitute the core of the interaction network of the conjunctival bacterial microbiome. Conjunctival swabs collected from the diseased eyes of MK patients and the randomly chosen normal eyes of healthy volunteers were subjected for high-throughput 16S rDNA sequencing. The relative content of each bacterial genus and the composition of bacterial gene functions in every sample were used to establish identification models with the random forest algorithm. Tenfold cross validation was adopted. Accuracy was 96.25% using the bacterial microbiota structure and 93.75% using the bacterial gene functional composition. Therefore, machine learning with the conjunctival bacterial microbiome characteristics might be used for differentiation of MKs as a noninvasive supplementary approach. In addition, this study found that Actinobacteria, Lactobacillus, Clostridium, Helicobacter, and Sphingomonas constitute the core of the interaction network of the conjunctival bacterial microbiome.

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Section: Methodsmentioning

confidence: 99%