Publications by authors named "N Ashraf"
Article Synopsis
- - The study explores how variations in the PRKCG gene's non-coding regions, specifically the 3' and 5' UTRs, affect gene expression and post-transcriptional regulation, focusing on variants that could influence transcription factor binding and RNA interactions.
- - Out of 419 UTR variants analyzed, 325 were deemed functionally significant, with specific variants linked to RNA binding proteins and regulatory mechanisms like histone modifications, as well as interactions with certain miRNAs in cancer.
- - Findings reveal that these non-coding variants may alter mRNA structure and affect splicing efficiency, emphasizing their potential role in developing targeted therapies for cancer and other diseases.
Background The opioid crisis has severely impacted health outcomes in the United States, particularly in rural areas, where barriers to medication-based treatment for opioid use disorder (OUD) persist. Although medication-assisted treatment (MAT) for OUD is effective, access remains limited, especially in these communities. Aim This study identifies and examines barriers to accessing office-based OUD treatment in rural areas of the United States.
View Article and Find Full Text PDFBackground: Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations.
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- Deep Learning (DL) models are being effectively used to analyze MRI scans for Alzheimer's Disease (AD), leveraging Cloud Computing to manage computational demands.
- The article provides a systematic tutorial on medical imaging datasets, presenting a case study that compares three DL models: Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach for AD MRI classification.
- Results indicate that CNN achieved the highest accuracy at 99.285%, while VGG-16 and the ensemble model scored lower, emphasizing the effectiveness of the proposed cloud-based framework for secure and efficient medical image processing.
Background: New strains of SARS-CoV-2 are continually emerging worldwide. Recently, WHO warned of a severe new wave in Europe. Current vaccines cannot fully prevent reinfection in vaccinated individuals.
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