The Ilam University of Medical Sciences is a university in Ilam, Iran. It was established in 1986. IUMS is the official medical university of the Ilam province which is associated with the Iranian Ministry of Health and Medical Education. IUMS provides healthcare and treatment services to the community. IUMS with seven vice-chancellors, five faculties, 11 hospitals, and 63 healthcare centers tries to accomplish its research, education, and healthcare missions. This university welcomes qualified students and researchers to educate and investigate a wide range of medical fields, including medicine, pharmacology, allied medical sciences, dentistry, health sciences, nursing, and midwifery. The education schedule is semester-based.
A. baumannii is a critical nosocomial pathogen with increasing antibiotic resistance. Colistin heteroresistance (CHR), which cannot be detected by standard methods, threatens the effectiveness of this definitive treatment. This study aimed to characterize carbapenem resistance, detect CHR, and investigate its molecular mechanisms. One hundred forty seven A. baumannii isolates were characterized. Carbapenemase genes were detected by PCR, and antibiotic susceptibility was determined by disc diffusion and broth microdilution. Heteroresistance (HR) was detected using population analysis profiling (PAP). The expression of efflux pump (adeB, adeG) and porin (ompA, carO) genes in HR isolates quantified by Real-time PCR analysis. The predominant carbapenemase genes were blaOXA-51 (97
Burkholderia cenocepacia biofilm and persister cells cause chronic, hard-to-treat infections. This study investigated the role of the MqsR/MqsA toxin-antitoxin (TA) system in these processes. B. cenocepacia persisters were induced with ciprofloxacin (10x MIC). Gene expression of TA systems was analyzed via qRT-PCR in biofilms and persisters. Following key target identification, in silico modeling, docking, and peptide design were performed. Ciprofloxacin induced a persister state. The MqsR toxin gene was significantly upregulated during biofilm formation (6.8-fold) and in persister cells (p < 0.05). Structural modeling defined the MqsR/MqsA interaction interface, enabling the computational prediction of a candidate inhibitory peptide (KLLKILDRHPDLLAEV) that was predicted to dock effectively with the toxin in silico. The significant upregulation of MqsR in B. cenocepacia biofilms and persisters suggests a correlation between this toxin and the persistence phenotype. In silico modeling predicted a potential inhibitory peptide that could disrupt the MqsR/MqsA interaction; however, these computational findings require experimental validation.
Colorectal cancer (CRC) is a major global health challenge with a high mortality rate, necessitating the identification of novel therapeutic approaches. The purified Antigen B (AgB) from hydatid cyst fluid is an immunoactive component with known abilities to modulate host immune responses. Given the increasing role of inflammatory pathways in regulating tumor progression or inhibition, this study aimed to investigate the effect of AgB on cell viability and the expression of inflammasome-related genes (including NLRP3, IL-1β, IL-8, and Caspase-1) in human colon cancer cell lines, SW480 and SW948. In this experimental study, AgB was collected and purified from ovine hydatid cysts. The SW480 and SW948 cell lines were treated with different concentrations of AgB (10–100 µg/mL) for 24 h. Cell viability was evaluated using the MTT assay. Subsequently, the expression of NLRP3, IL-1β, IL-8, and Caspase-1 genes, as key components of the inflammasome axis, was examined using the quantitative real-time PCR (qRT-PCR) technique. The MTT assay results showed that AgB had a dose-dependent cytotoxic effect on both cell lines, leading to a significant decrease in cell viability. The IC50 value for AgB was determined to be 150 µg/mL in both cell lines. The qRT-PCR analysis indicated a significant increase in the expression of NLRP3, IL-1β, and Caspase-1 genes in both SW480 and SW948 cell lines treated with AgB compared to the control group (P < 0.05). However, the increase in IL-8 gene expression was not statistically significant (P > 0.05). These findings suggest that AgB modulates the expression of key genes in the inflammasome pathway in colon cancer cells. The data from this study demonstrate that AgB can affect inflammasome-dependent pathways in colorectal cancer cell lines. These results provide a new perspective for utilizing AgB as an immunomodulatory compound in preclinical cancer studies and highlight its potential in the development of novel therapeutic strategies against colon cancer.
Abstract Bladder cancer represents the ninth most commonly diagnosed malignancy worldwide, with substantial morbidity and mortality. Conventional diagnostic modalities—including cystoscopy, urine cytology, cross-sectional imaging, and histopathological examination—are limited by operator-dependent variability, modest sensitivity for early-stage disease, and significant interobserver discordance. Artificial intelligence (AI), and particularly deep learning (DL)-based approaches, has emerged as a transformative paradigm to enhance diagnostic accuracy and standardize assessment across the complete diagnostic continuum. This narrative review critically synthesizes contemporary evidence on AI applications in bladder cancer diagnosis, encompassing cystoscopic tumor detection, urine cytology analysis, radiomics-based staging, computational pathology, multimodal fusion architectures, and intraoperative guidance. Across validation cohorts, AI-enhanced cystoscopy achieves sensitivity of 91–99% and specificity of 87–99%. AI-augmented urine cytology demonstrates substantial sensitivity improvements, with the VisioCyt system achieving 84.9% overall sensitivity compared with 43% for conventional cytology. Radiomics and deep learning approaches for imaging analysis achieve area under the curve values ranging from 0.834 to 0.997 for staging and muscle-invasion prediction. Computational pathology systems achieve diagnostic accuracy meeting or exceeding that of experienced pathologists while providing standardized, reproducible assessments. Notwithstanding these advances, challenges including data standardization, model interpretability, prospective clinical validation, regulatory harmonization, and health-economic evaluation must be addressed to enable widespread clinical implementation. This review identifies critical research priorities and discusses pathways for responsible translation of AI innovations into routine urologic practice.
OBJECTIVE: The computed tomography-severity score (CT-SS) quantifies the severity of pulmonary involvement and is significantly associated with disease severity, intensive care unit (ICU) admissions, and mortality in coronavirus disease-2019 (COVID-19) patients. There is very limited information on the prognostic value of CT-SS when used in machine learning (ML) models to predict ICU admission in COVID-19 patients. In this study, the prognostic significance of CT-SS in ML model-based prediction of ICU admission among COVID-19 patients was evaluated. MATERIAL AND METHODS: In this retrospective study, a hospital-based database from 6,854 COVID-19 patients was reviewed. To evaluate the prognostic significance of CT-SS in predicting ICU admission in patients, seven ML methods were trained separately using the most important features, with and without CT-SS data, and their performances were compared. RESULTS: After applying exclusion criteria, 815 COVID-19 patients remained. Just over half of the patients (54.85%) were male, and the mean age was 57.22±16.76 years. The CT-SS index was the strongest predictor among the parameters examined, and integrating this index into the training dataset enhanced ML model performance. The k-nearest neighbors model with 93.3% accuracy, 97.3% sensitivity, 89.4% specificity, and an area under the curve of approximately 98.8% showed the best performance for predicting ICU admission in COVID-19 patients. CONCLUSION: The results showed that CT-SS is a key predictor for ML models of ICU admission in COVID-19 patients. The ML models developed using a dataset including CT-SS are efficient risk stratification tools for identifying critical COVID-19 patients, thereby facilitating optimal allocation of limited hospital resources.
