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The segmentation of liver margins in computed tomography (CT) images presents significant challenges due to the complex anatomical variability of the liver, with critical implications for medical diagnostics and treatment planning. In this study, we leverage a substantial dataset of over 4,200 abdominal CT images, meticulously annotated by expert radiologists from Taleghani Hospital in Kermanshah, Iran. Now made available to the research community, this dataset serves as a rich resource for enhancing and validating various neural network models. We employed two advanced deep neural network models, U-Net and Detectron2, for liver segmentation tasks. In terms of the Mask Intersection over Union (Mask IoU) metric, U-Net achieved an Mask IoU of 0.903, demonstrating high efficacy in simpler cases. In contrast, Detectron2 outperformed U-Net with an Mask IoU of 0.974, particularly excelling in accurately delineating liver boundaries in complex cases where the liver appears segmented into two distinct regions within the images. This highlights Detectron2’s advanced potential in handling anatomical variations that pose challenges for other models. Our findings not only provide a robust comparative analysis of these models but also establish a framework for further enhancements in medical imaging segmentation tasks. The initiative aims not just to refine liver margin detection but also to facilitate the development of automated systems for diagnosing liver diseases, with potential future applications extending these methodologies to other abdominal organs, potentially transforming the landscape of computational diagnostics in healthcare.

Cancer is caused by abnormal proliferation of cells and aberrant recognition of the immune system. According to recent studies, natural products are most likely to be effective at preventing cancer without causing any noticeable complications. Among the bioactive flavonoids found in fruits and vegetables, quercetin is known for its anti-inflammatory, antioxidant, and anticancer properties. This review aims to highlight the potential therapeutic effects of quercetin on some different types of cancers including blood, lung and prostate cancers.

Human papillomavirus (HPV) is a viral infection that, if does not go away, can cause health problems like genital warts and cancer. The national immunization schedules for individuals before sexual debut, significantly decreased HPV-associated mortality and it will be affordable. However, immunization programs remain vulnerable to macroeconomic factors such as inflation, fiscal policy, employment levels, and national income. This review aims to investigate the association between national income in lower-middle-income countries to explore recent advances and potential issues, as well as how to deal with challenges.

Myasthenia gravis (MG) is a type of muscle paralysis created by immune responses against acetylcholine receptor proteins in neuromuscular synapses. This disease is characterized by muscle weakness, especially ocular weakness symptoms that could be ptosis (fall of the upper eyelid) or diplopia (double vision of a single object). Some patients also identified with speech and swallowing problems. The main goals of MG therapeutic approaches are to achieve remission, reduce symptoms, and improve life quality. Recently, other studies have revealed the potential role of various microRNAs (miRNAs) in the development of MG through different mechanisms and have proposed these molecules as effective biomarkers for the treatment of MG. This review was aimed at providing an overview of the critical regulatory roles of various miRNAs in the pathogenesis of this autoimmune disease focusing on human MG studies and the interaction between different miRNAs with important cytokines and immune cells during the development of this autoimmune disease.