Endoscopic characteristics and management of Subepithelial Lesions in Video-Gastascopie

Current chemotherapy uses compounds of organometallic nature that act with different mechanisms of action. Many pharmacological studies are directed toward the creation of compounds with more specific and selective activity toward tumor targets, including telomerase. The design and synthesis of such compounds with specific antitelomerase activity must consider the mechanism of action of the enzyme and its structure. The discovery of a close correlation between telomerase activation, cell immortalization and oncogenesis has suggested that telomerase inhibitors could be potent therapeutic agents, capable of selectively killing cancer cells. Inhibition of telomerase is expected to lead toward shortening of telomeres to a critical length, such that replicative senescence and cell death due to irreparable chromosomal damage can result. It has been observed that cancer cells generally have shorter telomeres than the normal replicative cell population, probably because the malignant cells have undergone more divisions. Therefore, the inhibition telomeres of cancer cells after a few cycles of cell division, without the normal cells suffering harmful consequences during therapy. Telomerase is certainly an interesting target on which to continue to study molecules that inhibit its function to obtain a specificity of therapeutic intervention and a reduction of the nonspecific cytotoxicity of chemotherapy.

Telomerase and Anticancer Treatment

Attinà

Ruggiero

2022

Hybrid Model: Deep Learning method for Early Detection of Alzheimer’s disease from MRI images

Vashishtha,

Acharya,

Swain

2023

Alzheimer's disease is a neurodegenerative brain disease that kills neurons. The global prevalence of the disease is gradually growing. In all leading countries, it is one of the senior citizens' leading causes of death. So, much research shows that early detection of illness is the most critical factor in improving patient care and treatment outcomes. Currently, AD is diagnosed by the manual study of magnetic resonance imaging, biomarker tests, and cognitive tests. Machine learning algorithms are used for automatic diagnosis. However, they have certain limits in terms of accuracy. Another issue is that models trained on class-unbalanced datasets often have poor results. Therefore, the main objective of the proposed work is to include a pre-processing method before the hybrid model to improve classification accuracy. This research presents a hybrid model based on a deep learning approach to detect Alzheimer’s disease. Which, we are using the SMOTE method to equally distribute the classes to prevent the issue of class imbalance. The hybrid model uses Inception V3 and Resnet50 to detect characteristics of Alzheimer's disease from magnetic resonance imaging. Finally, a dense layer of convolution neural network is used for classification. The hybrid approach achieves 99% accuracy in classifying MRI datasets, which is better than the old work. These results are better than existing approaches based on accuracy, specificity, sensitivity, and other characteristics.

A Comparative Diagnostic Study for Using the Contrast Agent in Active and Non-Active Multiple Sclerosis by Region of Interest Parameter

Sulaiman,

abbas,

Al-Samydai

et al. 2023

Background: Multiple sclerosis (MS) is a neuropathological disease diagnosed by a magnetic resonance imaging device. Early patients affected with MS are named active. Objectives: This study assessed the difference between active and non-active MS using the region of interest value. Methods: Twenty patients with MS included in this study were examined with T1 weighted image (T1W) with and without contrast agent injection to increase the brightness of darker regions after only 10 minutes. Also, T2 weighted images (T2W) and Fluid-attenuated inversion recovery (FLAIR) were scanned. The area of interest option was calculated for all cases. Results: The result shows that the region of interest (ROI) value was significantly higher for T1 weighted image (T1W) with contrast than without. Furthermore, the range of FLAIR values was higher than the (T2W). Conclusion: The ROI is an effective parameter for diagnosing active MS early by values instead of the radiographic picture.