Liver diseases are important causes of morbidity and mortality worldwide. The aim of this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from subjects with six different liver pathologies and from control livers. ANOVA was employed to obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database) was used to predict target genes. A miRNA–gene differential regulatory (MGDR) network was constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We identified important DEMs common and specific to the different patient groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog (PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.
Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in the pediatric population, for 45% of soft tissue sarcomas in children. Commonly located in the head and neck, urogenital system, extremities and torso, RMS of the biliary system is rare. We present a specific case of RMS in the gallbladder of a 4-year-old girl who presented with epigastric abdominal pain and vomiting. Her work-up revealed transaminitis and tumefactive sludge in the gallbladder neck. After she had persistent symptoms and repeated work-up, the patient underwent a laparoscopic cholecystectomy and was found to have cholecystitis intraoperatively with cholelithiasis. However, pathology revealed botryoid embryonal RMS with positive microscopic margins. This case demonstrates the difficulty in diagnosing gallbladder RMS and the importance of considering malignancy in pediatric biliary disease.
Abstract Background Currently, teledermatology assumes a progressively greater role in the modern healthcare system, especially in consultation, diagnosis, or examining lesions and skin cancers. One of the major challenges facing teledermatology systems is determining the optimal image compression method to efficiently reduce the space needed for electronic storage and data transmission. Objective To the objective and subjective assessment of HEIC compression method on dermatological color images and benchmarking the performance of High‐Efficiency Image Coding (HEIC) with different algorithms to a feasibility study of the method for teledermatology. Methods Twenty‐five clinical and five skin histopathology images were taken in department of dermatology, Imam Reza Hospital, Mashhad, Iran. For each image, a set of 24 compressed images with different compression rates, which is composed of eight JPEG, eight JPEG2000, and eight HEIC images, has been prepared. Compressed and original images were shown simultaneously to three dermatologists and one dermatopathologist with different experiences. Each dermatologist scored quality and suitability of compressed images for diagnostic, as well as educational/scientific purposes. An objective evaluation was performed by calculating the mean “distance” of pixel colors and peak signal‐to‐noise ratio (PSNR). Results All compression rates for HEIC were objectively better than JPEG and JPEG2000, particularly at PSNR. Moreover, mean “color distance” per pixel for compressed images using HEIC was lower than others. The subjective image quality assessment also confirms the results of objective evaluation. In both educational and clinical diagnostic applications, HEIC compressed images have the highest score. Conclusion In consideration of objective and subjective evaluation, the HEIC algorithm represents an optimal performance in dermatology images compression compared with JPEG and JPEG2000.
Abstract Background and Aims Hepatitis C virus (HCV) is an important infectious disease that imposes a significant burden on healthcare systems. Determining the prevalence of HCV genotypes in a area is essential for the successful implementation of HCV elimination programs and allocation of financial resources to direct‐acting antiviral direct‐acting antivirals (DAA) treatments against prevalent HCV genotypes. Accordingly, we conducted a registry‐based cross‐sectional cohort study to investigate the prevalence of HCV genotypes and factors associated with cirrhosis, fatty liver, and viral load in Kermanshah Province, Western Iran. Methods Patients presenting to the Hepatitis Clinic of the Research Center for Infectious Diseases affiliated with Kermanshah University of Medical Sciences between 1999 and 2023 were enrolled in this study. Serum samples were collected to assess HCV genotypes and viral load. Additionally, demographic data and the status of cirrhosis and fatty liver were extracted from the registry system records throughout the study period. Results Records of 828 patients with an average age of 40.38 ± 11.72 years (range: 11–80 years) were included in the study that 721 individuals were male, and 107 were female. The prevalence of fatty liver and cirrhosis was 30.3% and 12.9%, respectively. Four genotypes (1, 2, 3, and 4) and four subtypes (1a, 1b, 3a, and 3b) were identified, with subtype 3a (55.7%) being the most prevalent, followed by subtype 1a (34.3%). None of the variables including age, gender, viral load level, and genotypes 1 and 3 were associated with fatty liver or cirrhosis. However, age, gender, and genotype were correlated with the viral load ( p ≤ 0.05). Conclusion The most common HCV subtypes in Kermanshah were 3a and 1a. Genotypes 2 and 4 were identified in one case each. Further studies on identifying HCV subtypes in different regions of the country are recommended to manage HCV infection and predict the prognosis.
Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (−6.32 kcal/mol) and diosmin (−6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV.
One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.
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