Dataset Information

Molecular Correlates and Recent Advancements in the Diagnosis and Screening of FMR1-Related Disorders.

ABSTRACT: Fragile X syndrome (FXS) is the most common monogenic cause of intellectual disability and autism. Molecular diagnostic testing of FXS and related disorders (fragile X-associated primary ovarian insufficiency (FXPOI) and fragile X-associated tremor/ataxia syndrome (FXTAS)) relies on a combination of polymerase chain reaction (PCR) and Southern blot (SB) for the fragile X mental retardation 1 (FMR1) CGG-repeat expansion and methylation analyses. Recent advancements in PCR-based technologies have enabled the characterization of the complete spectrum of CGG-repeat mutation, with or without methylation assessment, and, as a result, have reduced our reliance on the labor- and time-intensive SB, which is the gold standard FXS diagnostic test. The newer and more robust triplet-primed PCR or TP-PCR assays allow the mapping of AGG interruptions and enable the predictive analysis of the risks of unstable CGG expansion during mother-to-child transmission. In this review, we have summarized the correlation between several molecular elements, including CGG-repeat size, methylation, mosaicism and skewed X-chromosome inactivation, and the extent of clinical involvement in patients with FMR1-related disorders, and reviewed key developments in PCR-based methodologies for the molecular diagnosis of FXS, FXTAS and FXPOI, and large-scale (CGG)n expansion screening in newborns, women of reproductive age and high-risk populations.

SUBMITTER: Rajan-Babu IS

PROVIDER: S-EPMC5083926 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Molecular Correlates and Recent Advancements in the Diagnosis and Screening of FMR1-Related Disorders.

Rajan-Babu Indhu-Shree IS Chong Samuel S SS

Genes 20161014 10

Fragile X syndrome (FXS) is the most common monogenic cause of intellectual disability and autism. Molecular diagnostic testing of FXS and related disorders (fragile X-associated primary ovarian insufficiency (FXPOI) and fragile X-associated tremor/ataxia syndrome (FXTAS)) relies on a combination of polymerase chain reaction (PCR) and Southern blot (SB) for the fragile X mental retardation 1 (<i>FMR1</i>) CGG-repeat expansion and methylation analyses. Recent advancements in PCR-based technologie ...[more]

PMID: 27754417

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Project description:Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

Dataset Information

Molecular Correlates and Recent Advancements in the Diagnosis and Screening of FMR1-Related Disorders.

Publications

Molecular Correlates and Recent Advancements in the Diagnosis and Screening of FMR1-Related Disorders.

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