Publications by authors named "A Omari"
Background: Supratentorial function-eloquent brain tumour surgeries challenge the balance between maximal tumour resection and preservation of neurological function. This study aims to evaluate the efficacy of preoperative and intraoperative mapping techniques on resection outcomes and post-operative deficits.
Methods: This systematic review and meta-analysis examined literature up to March 2023, sourced from PubMed, Embase, and Medline.
Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach.
Adv Appl Bioinform Chem
January 2025
Purpose: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.
Patients And Methods: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases).
Hepatitis E virus (HEV) is one of the most common causes of viral hepatitis. We examined HEV seroprevalence and associations of sociodemographic and lifestyle characteristics with HEV immunoglobulin G (IgG) seropositivity in the Arab population. A cross-sectional single-centre study was conducted among adults in the Nazareth area during 2022.
View Article and Find Full Text PDFBackground: We aim to investigate the potential association between the administration of Lumbosacral epidural steroid injections (LESIs) and the subsequent onset or progression of Lumbosacral epidural lipomatosis (LEL) in patients devoid of established risk factors for LEL.
Methods: We carried out a single-center retrospective study on patients who underwent LESI. We collected demographics including age and gender as well as clinical and radiological characteristics including the type of corticosteroids utilized, the number of LESIs administered, the use of epidural guidance during LESI procedures, and LEL grading.
Article Synopsis
- Antimicrobial peptides (AMPs) are essential for combating infections, prompting the need for new, more effective AMPs and leveraging machine learning to enhance their development.
- Machine learning and deep learning techniques were applied to predict the effectiveness of 1,360 peptide sequences against the Gram-negative bacterium Escherichia coli, yielding accuracy rates of 74% and 92.9% for two different methodologies.
- This research not only shows promising results for E. coli but also sets the groundwork for future applications in other antimicrobial, antiviral, and anticancer peptide discoveries, potentially saving time and costs in drug development.