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Filename: drivers/Session_files_driver.php
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Filename: Session/Session.php
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File: /var/www/html/index.php
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Function: require_once
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Message: Undefined array key "choices"
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Filename: controllers/Detail.php
Line Number: 249
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
Line Number: 249
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File: /var/www/html/application/controllers/Detail.php
Line: 249
Function: _error_handler
File: /var/www/html/index.php
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Function: require_once
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Function: _error_handler
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Filename: models/Detail_model.php
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File: /var/www/html/application/models/Detail_model.php
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Function: strpos
File: /var/www/html/application/controllers/Detail.php
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Function: insertAPISummary
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Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
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Function: str_replace
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Function: formatAIDetailSummary
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Message: Trying to access array offset on value of type null
Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
Line: 256
Function: _error_handler
File: /var/www/html/index.php
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Filename: controllers/Detail.php
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File: /var/www/html/application/controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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File: /var/www/html/index.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Function: require_once
The broad use of next-generation sequencing and microarray platforms in research and clinical laboratories has led to an increasing appreciation of the role of germline mutations in genes involved in hematopoiesis and lineage differentiation that contribute to myeloid neoplasms. Despite implementation of the American College of Medical Genetics and Genomics and Association for Molecular Pathology 2015 guidelines for sequence variant interpretation, the number of variants deposited in ClinVar, a genomic repository of genotype and phenotype data, and classified as having uncertain significance or being discordantly classified among clinical laboratories remains elevated and contributes to indeterminate or inconsistent patient care. In 2018, the American Society of Hematology and the Clinical Genome Resource co-sponsored the Myeloid Malignancy Variant Curation Expert Panel to develop rules for classifying gene variants associated with germline predisposition to myeloid neoplasia. Herein, we demonstrate application of our rules developed for the gene to variants in six examples to show how we would classify them within the proposed framework.
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http://dx.doi.org/10.3324/haematol.2018.214221 | DOI Listing |
Front Genet
December 2024
Genomenon, Ann Arbor, MI, United States.
Accurate variant classification is critical for genetic diagnosis. Variants without clear classification, known as "variants of uncertain significance" (VUS), pose a significant diagnostic challenge. This study examines AlphaMissense performance in variant classification, specifically for VUS.
View Article and Find Full Text PDFPathol Res Pract
December 2024
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. Electronic address:
Background: Adenocarcinoma of the esophagus and stomach demands a deeper molecular understanding to advance treatment strategies and improve patient outcomes. Here, we profiled the genome and transcriptome landscape of these cancers, explored molecular characteristics that are undetectable by other sequencing platforms, and analyzed their potential clinical ramifications.
Methods: Our study employed state-of-the-art integrative analyses of whole genome and transcriptome sequencing on 51 matched tumor and germline samples from 46 patients.
Lancet Neurol
January 2025
Department of Clinical Sciences, Neurosciences, Umeå University, Umeå, Sweden.
Pathogenic variants in the superoxide dismutase 1 (SOD1) gene were the first identified genetic cause of amyotrophic lateral sclerosis (ALS), in 1993. This discovery enabled the development of transgenic rodent models for studying the biology of SOD1 ALS. The understanding that SOD1 ALS is driven by a toxic gain-of-function mutation has led to therapeutic strategies that aim to lower concentrations of SOD1 protein, an endeavour that has been complicated by the phenotypic heterogeneity of SOD1 ALS.
View Article and Find Full Text PDFComput Biol Med
December 2024
Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel; The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Electronic address:
Background: The identification and drug targeting of cancer causing (driver) genetic alterations has seen immense improvement in recent years, with many new targeted therapies developed. However, identifying, prioritizing, and treating genetic alterations is insufficient for most cancer patients. Current clinical practices rely mainly on DNA level mutational analyses, which in many cases fail to identify treatable driver events.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!