The Tashkent Medical Academy or TMA (Uzbek: Toshkent tibbiyot akademiyasi; TMA) is a public undergraduate and graduate medical school and research university based in Tashkent, Uzbekistan. The TMA is one of the oldest and largest Universities in Uzbekistan, the center of medical education and research in medicine and Life Sciences in the Republic.It was founded in 1920 as Faculty of Medicine at the Turkestan State University and renamed to the Tashkent Medical Institute (ru.wikipedia.org: Ташкентский медицинский институт) in 1931, and became two separate Medical Universities: the First Tashkent State Medical Institute and the Second Tashkent State Medical Institute in 1990. The Tashkent Medical Academy was formed in 2005 by the Decree of the President of the Republic of Uzbekistan Islam Karimov by merging the First and Second Tashkent State Medical Institutes.The main campuses are located in Tashkent, the capital of Uzbekistan. The TMA includes six faculties, 52 departments, Multidisciplinary Clinic of TMA, as well as the Interuniversity Research Scientific Laboratory. There are three branches of TMA which function as independent institutions in the cities of Urgench, Termez and Fergana.
CAR-T cell therapy has been transformative in treating certain blood malignancies and is also being adopted for treating other malignancies, including solid tumors. Despite its undeniable successes, CAR-T cell therapy is frequently associated with severe and potentially life-threatening side effects and toxicities, including cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity (ICANS), graft-versus-host disease (GvHD) in allogeneic settings, secondary CAR-T-derived malignancies, and long-term immunosuppression-induced risk of infections. Recent advances in integrating gene-editing technology and nanomedicine into CAR-T cell therapy have opened new avenues to enhance the safety profile of CAR-T cell therapy and broaden its clinical applications. Gene-editing tools enable targeted modulation of the CAR-T cells' genome, thereby improving their safety profile by preventing related side effects. In parallel, nanomedicine can be used at various stages, including manufacturing and post-treatment, to prevent their occurrence or manage them. This review highlights the current preclinical and clinical landscape, explores the emerging combinatorial strategies, and discusses future directions to achieve a safe and more controllable CAR-T cell therapy.
Mild cognitive impairment (MCI) represents a transitional phase between normal aging and Alzheimer's disease (AD) and is characterized by subtle cognitive deficits as well as structural changes in the brain. Volumetric analysis of gray matter (GM) and white matter (WM) using MRI provides crucial biomarkers for early diagnosis and monitoring of disease progression. This study investigated volumetric differences between healthy controls (HC) and MCI patients using advanced neuroimaging and AI-assisted analysis techniques. High-resolution T1-weighted MRI scans were used to quantify the volume changes of GM and WM in both groups. Automated segmentation and volumetric analysis were performed using the deep neural network Vb-Net, which is optimized for precise quantification of brain structures. Boxplots were generated to visualize the regional volume distribution between HC and MCI. In addition, a k-nearest neighbors (KNN) classifier was used to distinguish between HC and MCI based on volumetric features. Classification performance was evaluated using ROC curves. MCI patients showed a significant reduction in GM volume in key regions of cognitive processing, particularly in the hippocampus, medial temporal lobe, and precuneus ( p < 0.05). WM volume also showed significant decreases, particularly in frontal and temporal regions, suggesting early neurodegenerative changes. Boxplot analyses showed a clear separation of regional volume distributions between HC and MCI. The KNN classifier achieved high discrimination between HC and MCI, with a promising area under the ROC curve (AUC). Compared to other available AI tools, Vb-Net has been adopted due to its outstanding ability to precisely segment and volumetric quantify brain structures. While traditional deep learning models such as U-Net or VoxelMorph are designed for general image segmentation tasks, Vb-Net is specifically developed for neuroanatomical analysis. Its deep residual and 3D feature extraction mechanisms enable higher accuracy in distinguishing small volume changes, which are crucial for early diagnosis of MCI.
Patient and healthcare provider knowledge were previously found to be significantly associated with viral hepatitis testing in Uzbekistan. However, no survey has assessed awareness and knowledge of viral hepatitis among the general population. In 2022, we conducted a cross-sectional population-based survey among persons aged≥18 years in seven of Uzbekistan’s 14 regions representing 60
The Aspergillus species are the potent and promising bio-objects for the fabrication of nanoparticles. Among them, Aspergillus nidulansand Aspergillus terreusare the ideal biocatalysts for the synthesis of AgNPs. The AgNPs were prepared in laboratory of Allied Health Sciences Department, Sarhad University, Peshawar. Whereas, the nanoparticles were characterized in Centralized Resource Laboratory (CRL), University of Peshawar. The tube test was used to analyze the antifungal activity of nanoparticles against pathogenic fungi (Dermatophyte, Malasseziaand Trichuristrichura). A noticeable visual change in color from colorless to a dark brown confirmed the formation of AgNPs. The SEM characterization of A. terreus-derived AgNPsshowed the presence of amorphous and distinctly asymmetrical particles. On the other hand, the SEM characterization of A. nidulansderived AgNPs showed anisotropic shapes i-e., spherical and triangular morphologies. The XRD pattern ofA. terreus-derived AgNPs exhibited a diffused pattern with no intense and clear peaks across the entire 2θ range from 5° to 80°, which showed that the AgNPs are amorphous. The synthesized AgNPs showed inhibitory activity against different fungal speciesincluding Malassezia(M-1), Malassezia (M-2), Dermatophyte (D-1), Dermatophyte (D2) and Trichuristrichiura(T-1) and Trichuristrichiura(T-2). The AgNPs synthesized using A. terreus specie showed highest inhibition rate (76.66 %) against Trichuristrichiura(T-1). Whereas, less inhibitory effect was observed against Malassezia(M-1). The A. nidulans synthesized AgNPs also showed inhibitory activity against the tested fungal species. In this case, highest antifungal activity was observed against Malassezia (M-2), followed by Dermatophyte (D-2).
