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Molecular Genetics and Diagnostic Testing
Published in Merlin G. Butler, F. John Meaney, Genetics of Developmental Disabilities, 2019
DNA microarrays are the most rapidly developing innovations of molecular technology. Thousands of different DNA molecules can be attached to small regions of glass microscope slides. The patient DNA is usually labeled with a fluorescent dye and allowed to hybridize to complementary DNA on the array. Automated detection of the fluorescent hybridization signals allows thousands of sequences to be analyzed simultaneously. Among many applications, microarrays can be used to detect heterogeneous mutations and to assess levels of transcription of genes. Although this technology is currently used for research purposes, it may soon have widespread clinical use.
Encephalitozoon
Published in Dongyou Liu, Handbook of Foodborne Diseases, 2018
Alexandra Valencakova, Lenka Luptakova, Monika Halanova, Olga Danisova
The report about the implementation of methods of DNA chips (DNA “microarray”) for the parallel detection of several species of microsporidia (E. cuniculi, E. hellem, and E. intestinalis) in clinical samples75 is very interesting. The great advantage of a DNA “microarray” compared to the PCR method is the ability to diagnose a high number of unknown samples. Unlike PCR, DNA is not obtained through laborious processing of spores by preextraction steps, but by using FTA filters, which not only eliminates these labor-intensive procedures, but also helps avoid the significant loss of DNA and effectively removes inhibitors from fecal samples. Compared to the commercial DNA extraction kits, this method results in lower financial costs, requires less technical training, requires less equipment, and can process a larger number of samples simultaneously.76 The disadvantage of DNA “microarray” is not only expensive laboratory instrumentation, but also the synthesis of large amounts of primers. But once the preparation of the DNA microarray is completed, the actual execution of tests is considerably cheaper. The method of the DNA microarray, as described by Wang et al. (2005),75 represents a combination of the PCR method, which is followed by hybridization of the amplicons using more specific probes immobilized on a microchip. The fluorescence intensity correlates with the abundance of DNA in a sample.
Two-Dimensional Microfluidic Bioarray for Nucleic Acid Analysis
Published in Iniewski Krzysztof, Integrated Microsystems, 2017
Nucleic acid hybridization techniques feature the use of a probe nucleic acid molecule to detect a target nucleic acid molecule. Here, probe molecules are usually short single-stranded nucleic acids (DNA or RNA) or oligonucleotides with known sequences; whereas target molecules are prepared from polymerase chain reaction (PCR) amplification of genomic extracts. Probe-target hybridization leads to the formation of a double-stranded molecule, called duplex. The concept of DNA microarray was evolved from Southern blotting technology based on solid-phase hybridization in the early 1990s [1]. This method relies on the immobilization of the probe molecules onto the solid surface to recognize their complementary DNA target sequence by hybridization. Up to millions of features have been integrated into a standard glass slide or silicon chip by microprinting or in situ synthesis of oligonucleotides [2,3]. The relative abundance of nucleic acid sequences in the target can be measured from chip-hybridization results optically, electrochemically, or radioactively, with proper detection labels [4]. DNA microarrays have dramatically accelerated many types of investigations including gene expression profiling, comparative genomic hybridization, protein–DNA interaction study (chromatin immunoprecipitation), single-nucleotide polymorphism (SNP) detection, and nucleic acid diagnostic applications. The advances in DNA microarray technology during the last couple of years have been summarized in many books and reviews [4–8].
Novel strategies for rapid identification and susceptibility testing of MRSA
Published in Expert Review of Anti-infective Therapy, 2020
Masako Mizusawa, Karen C Carroll
The commercially available molecular-based assays for detection of MRSA in blood cultures are categorized into four groups: 1) multiplex or monoplex nucleic acid amplification-based assays, 2) an in situ hybridization-based assay, 3) a DNA microarray-based assay, and 4) a combination of multiplex nucleic acid amplification and in situ hybridization. Most of the multiplex assays allow blood culture specimens to be tested within 8 h after the blood culture bottles are identified as positive by a continuous monitoring blood culture system. A number of studies on the utility of rapid molecular-based diagnostic tests for blood cultures demonstrated the positive impact on clinical outcomes such as time to effective/appropriate antibiotic therapy, length of hospital stay, and associated costs, but mostly when combined with antimicrobial stewardship interventions [116]. The analytic performance of the FDA-cleared and CE-marked assays for MRSA detection in blood cultures is summarized in Table 2.
Impaired expression of innate immunity-related genes in IgG4-related disease: A possible mechanism in the pathogenesis of IgG4-RD
Published in Modern Rheumatology, 2020
Takuji Nakamura, Tomomi Satoh-Nakamura, Akio Nakajima, Takafumi Kawanami, Tomoyuki Sakai, Yoshimasa Fujita, Haruka Iwao, Miyuki Miki, Yasufumi Masaki, Toshiro Okazaki, Yasuhito Ishigaki, Mitsuhiro Kawano, Kazunori Yamada, Shoko Matsui, Takako Saeki, Terumi Kamisawa, Motohisa Yamamoto, Hideaki Hamano, Tomoki Origuchi, Shintaro Hirata, Yoshiya Tanaka, Hiroto Tsuboi, Takayuki Sumida, Kazuichi Okazaki, Masao Tanaka, Tsutomu Chiba, Tsuneyo Mimori, Hisanori Umehara
Methods used to prepare DNA microarrays have been described in detail [23]. To eliminate any possible gender-related differences in gene expression, DNA microarrays were prepared from samples obtained from male patients and controls. Total RNA was reverse transcribed to cDNA using Ambion WT Expression kits (Applied Biosystems, Foster City, CA), labeled with GeneChip WT Terminal Labeling and Controls kits (Affymetrix, Santa Clara, CA) and hybridized to GeneChip Human Gene 1.0 ST Arrays (Affymetrix). Digitalized image data were processed using GeneChip Operating Software (Affymetrix). Following background correction and normalization to the 50th percentile, the microarray results were analyzed using GeneSpring version 11.0 software (Agilent Technologies, Santa Clara, CA). The microarray expression data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible with GEO Series accession number GSE66465 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66465).
Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma
Published in Expert Review of Hematology, 2019
R. Andrew Harkins, Andres Chang, Sharvil P. Patel, Michelle J. Lee, Jordan S. Goldstein, Selin Merdan, Christopher R. Flowers, Jean L. Koff
Utilization of DNA microarray and, more recently, high-throughput sequencing technologies have spawned large amounts of gene expression data. Integration of gene expression data with clinical, histological, imaging, demographic, and epidemiological information could provide insights for improving cancer diagnosis and prognosis. However, the enormity and complexity of data obtained from cancer-related gene expression studies present great challenges in making accurate predictions of clinical outcomes. Machine learning methods are designed to organize, process, and discover actionable knowledge in high-dimensional settings. As such, several different types of machine learning methods have been adapted to achieve three fundamental predictive tasks in cancer research: 1) prediction of cancer susceptibility (risk assessment); 2) prediction of cancer recurrence; and 3) prediction of cancer survival outcomes [89,90]. An important challenge in translating high-dimensional data into accurate predictions for clinical decision-making is to identify informative features (e.g., clinical risk factors and genes) that contribute most to the prediction. Firstly, a more compact model will be more useful and interpretable in predicting outcomes for future patients. Secondly, selecting informative features is critical to avoiding overfitting and improving the accuracy and speed of prediction systems. Lastly, informative features allow investigators to understand the underlying cancer mechanisms that generated the data.