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Clinical Microbiology and Infection :... Jan 2013Molecular diagnostic techniques for viral testing have undergone rapid development in recent years. They are becoming more widely used than the classical virological... (Review)
Review
Molecular diagnostic techniques for viral testing have undergone rapid development in recent years. They are becoming more widely used than the classical virological assays in the majority of clinical virology laboratories, and now represent a new method for the diagnosis of human viral infections. Recently, new techniques based on multiplex RT-PCR amplification followed by microarray analysis have been developed and evaluated. On the basis of amplification of viral genome-specific fragments by multiplex RT-PCR and their subsequent detection via hybridization with microorganism-specific binding probes on solid surfaces, they allow simultaneous detection and identification of multiple viruses in a single clinical sample. The management of viral central nervous system and respiratory tract infections currently represents the two main applications of the microarrays in routine virological practice. Microarrays have shown reliable results in comparison with those of referenced (RT)-PCR assays, and appear to be of major interest for the detection of a broad range of respiratory and neurotropic viruses, assessment of the pathogenicity of newly discovered or neglected viruses, and identification of multiple viral infections in clinical samples. Despite several limitations observed during the different studies performed, this new technology might improve the clinical management of patients by enlarging the range of the viruses detected, in particular in cases of severe infections leading to patient hospitalization in the intensive-care unit. They might also help in the prevention of nosocomial transmission in hospital departments by contributing to the development of new epidemiological surveillance systems for viral infections.
Topics: Central Nervous System Viral Diseases; Humans; Microarray Analysis; Molecular Diagnostic Techniques; Respiratory Tract Infections; Virology
PubMed: 23034051
DOI: 10.1111/1469-0691.12024 -
Journal of Cancer Research and... 2012Cancer can be considered a "developmental disorder" because it involves a disruption in the normal development of cells, in terms of both differentiation and... (Review)
Review
Cancer can be considered a "developmental disorder" because it involves a disruption in the normal development of cells, in terms of both differentiation and proliferation (Dean M,1998). Cancer cells generally contain the full complement of biomolecules that are necessary for survival, proliferation, differentiation, cell death, and expression of cell type-specific function. Human Cancer diagnosis and classification by Microarray analysis has yet to be widely accepted despite the exponential increase in microarray studies reported in the literatures. Additionally, recent microarrays were inspired by the nucleotide-based technology, which have created to better define the molecular basis of malignancy which have shown that microarray have clinical utility in cancer diagnosis, risk stratification, and patient management.
Topics: Gene Expression Profiling; Humans; Microarray Analysis; Neoplasms; Prognosis
PubMed: 22531505
DOI: 10.4103/0973-1482.95166 -
Cell Transplantation 2021To screen the differential expression cytokines (DECs) in hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome, establish its differential cytokines...
Identification of Differential Expression Cytokines in Hemolysis, Elevated Liver Enzymes, and Low Platelet Syndrome by Proteome Microarray Analysis and Further Verification.
To screen the differential expression cytokines (DECs) in hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome, establish its differential cytokines spectra, and provide the clues for its diagnosis and pathogenic mechanism researches. Sera from four HELLP syndrome patients and four healthy controls were detected by proteome microarray. Then the analysis of Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network were performed and possible hub proteins were selected out, further verified by Enzyme Linked Immunosorbent Assay (ELISA) in sera from 21 HELLP syndrome patients and 21 healthy controls. Thirty DECs were defined according to -value and fold change between HELLP group and control group. GO enrichment analysis showed that DECs were mainly involved in the regulation of inflammatory response and have relationship to growth factor binding, transmembrane receptor protein kinase, and cytokine receptor activity. Seven possible hub proteins were defined by PPI analysis, including IGFBP-3/Follistatin-like 1/FLRG/Fetuin A and MMP-13/Thrombospondin-5/Aggrecan. ELISA showed higher serum levels of Fetuin A/IGFBP-3/FLGR/MMP-13/Thrombospondin-5 in HELLP group than those in controls, while the levels of Follistatin-like 1 and Aggrecan were lower in HELLP patients (all < 0.05 or <0.01).The serological DECs spectra of HELLP syndrome was established and seven possible hub proteins that may be more closely related to the disease have been verified, providing new clues for its pathogenesis, diagnosis, and clinical treatment.
Topics: Adult; Cytokines; Female; HELLP Syndrome; Humans; Liver; Microarray Analysis; Pregnancy; Proteome
PubMed: 33757334
DOI: 10.1177/0963689720975398 -
Ultrasound in Obstetrics & Gynecology :... May 2019To assess the added value of chromosomal microarray analysis (CMA) over conventional karyotyping to assess the genetic causes in stillbirth. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To assess the added value of chromosomal microarray analysis (CMA) over conventional karyotyping to assess the genetic causes in stillbirth.
METHODS
To identify relevant studies, published in English or Spanish and without publication time restrictions, we performed a systematic search of PubMed, SCOPUS and ISI Web of Science databases, The Cochrane Library and the PROSPERO register of systematic reviews, for case series of fetal loss ≥ 20 weeks of gestation, with normal or suspected normal karyotype, undergoing CMA and with at least five subjects analyzed. To investigate quality, two reviewers evaluated independently the risk of bias using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. For the meta-analysis, the incremental yield of CMA over karyotyping was assessed by single-proportion analysis using a random-effects model (weighting by inverse variance). We assessed heterogeneity between studies and performed a sensitivity analysis and a subgroup analysis of structurally abnormal (malformed or growth-restricted) and normal fetuses.
RESULTS
Included in the meta-analysis were seven studies involving 903 stillborn fetuses which had normal karyotype. The test success rate achieved by conventional cytogenetic analysis was 75%, while that for CMA was 90%. The incremental yield of CMA over conventional karyotyping based on the random-effects model was 4% (95% CI, 3-5%) for pathogenic copy-number variants (pCNVs) and 8% (95% CI, 4-17%) for variants of unknown significance. Subgroup analysis showed a 6% (95% CI, 4-10%) incremental yield of CMA for pCNVs in structurally abnormal fetuses and 3% (95% CI, 1-5%) incremental yield for those in structurally normal fetuses. The pCNV found most commonly was del22q11.21.
CONCLUSIONS
CMA, incorporated into the stillbirth work-up, improves both the test success rate and the detection of genetic anomalies compared with conventional karyotyping. To achieve a genetic diagnosis in stillbirth is particularly relevant for the purpose of counseling regarding future pregnancies. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
Topics: Chromosome Aberrations; Female; Fetal Diseases; Humans; Karyotyping; Microarray Analysis; Pregnancy; Stillbirth
PubMed: 30549343
DOI: 10.1002/uog.20198 -
Journal of Clinical Laboratory Analysis Dec 2020To compare karyotype and chromosomal microarray (CMA) analysis of aneuploid chromosome mosaicism in amniocentesis samples.
OBJECTIVE
To compare karyotype and chromosomal microarray (CMA) analysis of aneuploid chromosome mosaicism in amniocentesis samples.
MATERIALS AND METHODS
A total of 2091 amniocentesis samples from pregnant women were collected from March 1, 2019, to January 31, 2020. Karyotype analysis was performed using G-banding and CMA analysis used the Affymetrix CytoScan 750K SNP microarray.
RESULT
Thirteen cases with aneuploid chromosome mosaicism were detected and compared between the karyotype and CMA methods. Seven of these cases were trisomic mosaicism, and the levels of mosaicism calculated from CMA were higher than those detected from karyotype analysis; noting three cases of trisomy mosaicism were not detected by karyotype analysis. Four cases exhibited monomeric mosaicism, and the levels of mosaicism detected in three of these cases were higher in karyotype compared with CMA analysis; one case had equivalent levels of monomeric mosaicism from both karyotype and CMA analysis. Two other cases from karyotype analysis were a mix of monosomic and trisomic mosaicism, whereas the CMA result was restricted to monosomic mosaicism for these cases.
CONCLUSION
Both karyotype and CMA analysis can be used to detect aneuploid chromosome mosaicism. However, the two methods produced different results. CMA and karyotype analysis have their own advantages in detecting aneuploid mosaicism, and the combination of these methods provides a more rigorous diagnosis.
Topics: Aneuploidy; Chromosome Disorders; Cytogenetic Analysis; Female; Humans; Karyotyping; Microarray Analysis; Mosaicism; Pregnancy; Prenatal Diagnosis
PubMed: 32864771
DOI: 10.1002/jcla.23514 -
International Review of Neurobiology 2014Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental... (Review)
Review
Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.
Topics: Animals; Central Nervous System; Chromosome Mapping; Gene Expression; History, 20th Century; Humans; Microarray Analysis; Quantitative Trait Loci; Transcriptome
PubMed: 25172476
DOI: 10.1016/B978-0-12-801105-8.00008-4 -
Molecular Oncology Apr 2011A typical array experiment yields at least tens of thousands of measurements on often not more than a hundred patients, a situation often denoted as the curse of... (Review)
Review
A typical array experiment yields at least tens of thousands of measurements on often not more than a hundred patients, a situation often denoted as the curse of dimensionality. With a focus on prognostic multi-biomarker scores derived from microarrays, we highlight the multidimensionality of the problem and the issues in the multidimensionality of the data. We go over several statistical challenges raised by this curse occurring in each step of microarray analysis on patient data, from the hypothesis and the experimental design to the analysis methods, interpretation of results and clinical utility. Different analytical tools and solutions to answer these challenges are provided and discussed.
Topics: Animals; Biomarkers; Genomics; Humans; Microarray Analysis; Models, Statistical
PubMed: 21349780
DOI: 10.1016/j.molonc.2011.01.002 -
Human Heredity 2014The detection of consanguinity by the presence of multiple regions of homozygosity (ROH) is not an uncommon occurrence in clinical laboratories performing SNP microarray... (Review)
Review
The detection of consanguinity by the presence of multiple regions of homozygosity (ROH) is not an uncommon occurrence in clinical laboratories performing SNP microarray analysis. Reporting practices amongst laboratories are highly variable, due in part to differences in testing platforms, threshold parameters, language utilized, and laboratory policies. While guidance documents have provided a framework for detection and reporting practices, and will doubtless serve to harmonize the field, there are still many facets of the testing that remain at the discretion of the performing laboratory. Clinician and patient education remain a high priority. In the clinical laboratory, these homozygous segments are often examined to identify genes associated with a phenotype that matches that of the proband and autosomal recessive inheritance. While the detection of these ROH is possible with whole genome sequencing, it currently requires special algorithms be utilized, an uncommon practice in most clinical laboratories currently performing this type of testing.
Topics: Consanguinity; Genes, Recessive; Genetic Counseling; Genetic Techniques; Homozygote; Humans; Microarray Analysis; Polymorphism, Single Nucleotide
PubMed: 25060286
DOI: 10.1159/000362448 -
BMC Genomics Oct 2011The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology,...
BACKGROUND
The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software.
RESULTS
Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.
CONCLUSIONS
Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.
Topics: Algorithms; Databases, Genetic; Gene Expression Regulation; MicroRNAs; Microarray Analysis; Software; User-Computer Interface
PubMed: 21999641
DOI: 10.1186/1471-2164-12-507 -
BMC Pregnancy and Childbirth Nov 2020To explore the application value of chromosomal microarray analysis (CMA) in prenatal diagnosis. (Comparative Study)
Comparative Study
BACKGROUND
To explore the application value of chromosomal microarray analysis (CMA) in prenatal diagnosis.
METHODS
The results of chromosome karyotype analysis and CMA of 477 cases undergoing amniocentesis were analyzed. The results of the no ultrasound abnormality group and the ultrasound abnormality group were compared separately. Within the ultrasound abnormality group, the results of the ultrasound structural malformation group, the ultrasound soft index abnormality group, and other ultrasound abnormality (including abnormal amniotic fluid volume and fetal growth restriction) groups were compared.
RESULTS
Abnormal chromosome and CMA results were found in a total of 71 cases (15.88%, 71/447), which can be broken down into a total of 23 karyotype abnormalities (5.15%, 23/447), consisting of 18 cases of aneuploidy (4.03%, 18/447), 2 cases of unbalanced chromosome rearrangements (0.44%, 2/447), and 3 cases of chimerism (0.67%, 3/447); 17 cases with detection of pathogenic copy number variations (pCNVs) (3.80%, 17/447); and 31 cases of detection of clinical variants of unknown significance (VOUS) (6.93%, 31/447). CMA detected 3.8% more genetic abnormalities than karyotype analysis (in addition to the abnormalities detected simultaneously by karyotype analysis). Between the no ultrasound abnormality group and the ultrasound abnormality group, there was an extremely significant difference in the detection rate of an abnormal chromosomal karyotype (P < 0.01) and of VOUS (P < 0.01), but there was no significant difference in the detection rate of pCNV (P > 0.05). Comparing the ultrasound structural malformation group, the ultrasound soft index abnormality group, and the other ultrasound abnormality group, there were no significant differences in the detection rate of abnormal chromosomal karyotypes (P > 0.05), pCNV (P > 0.05) or VOUS (P > 0.05).
CONCLUSIONS
The detection rate of chromosomal karyotype abnormalities in prenatal diagnosis in cases with no ultrasound abnormalities was higher. For cases with fetal ultrasound structural abnormalities, when compared with traditional karyotype analysis, CMA can improve the detection rate of fetal genetic abnormalities. However, the no ultrasound abnormality group also had a high VOUS abnormality detection rate, so it is necessary to strictly define the CMA indications.
Topics: Adult; Amniocentesis; Chromosome Disorders; DNA Copy Number Variations; Female; Fetus; Genetic Testing; Humans; Karyotyping; Microarray Analysis; Pregnancy; Prenatal Diagnosis; Ultrasonography, Prenatal; Young Adult
PubMed: 33198662
DOI: 10.1186/s12884-020-03368-y