Rapid Multiplex Real-Time PCR Method for the Detection and Quantification of Selected Cariogenic and Periodontal Bacteria

Lochman, Jan; Zapletalová, Martina; Poskerová, Hana; Hollá, Lýdie Izakovičová; Linhartová, Petra Bořilová

doi:10.3390/diagnostics10010008

Cited by 23 publications

(16 citation statements)

References 33 publications

(35 reference statements)

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“…To rapidly detect specific oral pathogens, methods based on the detection of specific bacterial DNA sequences have become invaluable in basic dental science and translational research [ 38 ]. In particular, multiplex real-time PCR is a sensitive method for detecting and quantifying a small number of bacteria in clinical samples.…”

Section: Effective and Rapid Detection Of Oral Microbiota Diversitymentioning

confidence: 99%

Progress in Oral Microbiome Related to Oral and Systemic Diseases: An Update

et al. 2021

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The human oral microbiome refers to an ecological community of symbiotic and pathogenic microorganisms found in the oral cavity. The oral cavity is an environment that provides various biological niches, such as the teeth, tongue, and oral mucosa. The oral cavity is the gateway between the external environment and the human body, maintaining oral homeostasis, protecting the mouth, and preventing disease. On the flip side, the oral microbiome also plays an important role in the triggering, development, and progression of oral and systemic diseases. In recent years, disease diagnosis through the analysis of the human oral microbiome has been realized with the recent development of innovative detection technology and is overwhelmingly promising compared to the previous era. It has been found that patients with oral and systemic diseases have variations in their oral microbiome compared to normal subjects. This narrative review provides insight into the pathophysiological role that the oral microbiome plays in influencing oral and systemic diseases and furthers the knowledge related to the oral microbiome produced over the past 30 years. A wide range of updates were provided with the latest knowledge of the oral microbiome to help researchers and clinicians in both academic and clinical aspects. The microbial community information can be utilized in non-invasive diagnosis and can help to develop a new paradigm in precision medicine, which will benefit human health in the era of post-metagenomics.

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Section: Effective and Rapid Detection Of Oral Microbiota Diversitymentioning

confidence: 99%

Progress in Oral Microbiome Related to Oral and Systemic Diseases: An Update

et al. 2021

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“…The R 2 -values, ranging from 0.984 to 1.000, indicate a linear correspondence between the logarithmic genome copy number and their Cq-values. The efficiency (E), as one of the most important indicators of the performance of a qPCR assay, was between 86.4 and 104.7% in simplex, and between 90.6 and 103.4% in multiplex assays, which proves a good performance of the primer-probe sets (Baudy et al, 2019;Lochman et al, 2019). In all four assays, PCR probes appeared to be very specific and no loss of activity was observed upon multiplexing.…”

Section: Performance Of Simplex and Multiplexed Qpcr Assaysmentioning

confidence: 79%

“…High costs for labeling of each end of the probes with different dyes is in comparison to the use of intercalating dyes is compensated by higher selectivity and a reduced process time in multiplexed assays. Multiplex TaqMan qPCR recently appeared to be a very useful tool for the identification and quantification of different microbial communities (Farkas et al, 2017;Baudy et al, 2019;Enora et al, 2019;Lochman et al, 2019). Accordingly, this study aims to establish a multiplex qPCR approach for the simultaneous detection and quantification of eleven frequently reported bacteria and yeast species of milk kefirs.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Major Bacteria and Yeast Species in Kefir Consortia by Multiplex TaqMan qPCR

et al. 2020

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Kefir grains are complex microbial systems of several groups of microorganisms. The identification and quantification of the microbial composition of milk kefirs was described in several studies, which provided an insight into the microbial consortia in this complex ecosystem. Nevertheless, the current methods for identification and quantification are not appropriate for deeper studies on kefir consortia, e.g., population dynamics and microbial interactions in kefir grains. This requires another sensitive and reliable quantitative method. Therefore, this study aims to develop multiplexed qPCR assays to specifically detect and quantify, as an example, several microorganisms of the milk kefir microbial community. Primer-probe sets, which target species-specific genes in six bacteria and five yeasts, were designed, and their sensitivity and specificity to the target species was analyzed in simplex as well as four multiplex qPCR assays. The self-designed multiplex assays were applied for the detection of target bacteria and yeast species in milk kefirs, in both, grain and beverage fractions. Detection of all target microorganisms in simplex and multiplex qPCR was achieved by good linearity, efficiency, repeatability and reproducibility in all assays. When the designed assays were applied on six kefirs, all target microorganisms were detected in different samples, but not all in one kefir sample. The two ubiquitous lactobacilli Lactobacillus kefiranofaciens and Lb. kefiri were present in all six kefirs studied, but were associated with different other yeasts and bacteria. Especially on the yeast community a significant diversity was observed. In general, multiplex TaqMan qPCR as developed here was proven to have high potential for specific identification of target microorganisms in kefir samples and for the first time, eleven target bacteria and yeasts of kefir microbiota were rapidly detected and quantified. This study, thus, provides a fast and reliable protocol for future studies on kefir and other similar microbial ecosystems.

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“…Modified Hopfield neural network [26] or advanced fuzzy decision making [28] would be the solution to our problem, which warrants further research. Moreover, similar to the research in [29,30], the proposed algorithms need to be applied to different datasets. In future works, we will make the implementations on a wider range of different, complex dental image datasets to verify the suitability of this algorithm.…”

Section: Discussionmentioning

confidence: 99%

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

et al. 2020

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In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals. By using object detection models and algorithms, this work is much easier and needs less experienced medical practitioners to clear their doubts while diagnosing a medical case. In this paper, we propose a dental defect recognition model by the integration of Adaptive Convolution Neural Network and Bag of Visual Word (BoVW). In this model, BoVW is used to save the features extracted from images. After that, a designed Convolutional Neural Network (CNN) model is used to make quality prediction. To evaluate the proposed model, we collected a dataset of radiography images of 447 patients in Hanoi Medical Hospital, Vietnam, with third molar complications. The results of the model suggest accuracy of 84% ± 4%. This accuracy is comparable to that of experienced dentists and radiologists.

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Rapid Multiplex Real-Time PCR Method for the Detection and Quantification of Selected Cariogenic and Periodontal Bacteria

Cited by 23 publications

References 33 publications

Progress in Oral Microbiome Related to Oral and Systemic Diseases: An Update

Progress in Oral Microbiome Related to Oral and Systemic Diseases: An Update

Quantification of Major Bacteria and Yeast Species in Kefir Consortia by Multiplex TaqMan qPCR

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

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