Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Combining radiomics and connectomics in MRI studies of the human brain: A systematic literature review

Advances in MRI techniques continue to open new avenues to investigate the structure and function of the human brain. Radiomics, involving the extraction of quantitative image features, and connectomics, involving the estimation of structural and functional neural connections, from large amounts and different types of MRI data sets, represent two key research areas for advancing neuroimaging while exploiting progress in computational and theoretical modelling applied to MRI. This systematic literature review aimed at exploring the combination of radiomics and connectomics in human brain MRI studies, highlighting how the combination of these approaches can provide novel or additional insights into the human brain under normal and pathological conditions. The review was conducted according to the Preferred Reported Item for Systematic Reviews and Meta-Analyses (PRISMA) statement, seeking documents from Scopus and PubMed archives. Eleven studies (out of the initial 675 records) have met the established criteria and reported combined approaches from radiomics and connectomics. Three subgroups of approaches were identified, based on the MRI modalities used to obtain radiomic and connectomic features. The first group of 3 studies combined radiomics and connectomics applied to structural MRI (sMRI) data sets; the second group of 5 studies combined radiomics applied to sMRI data and connectomics applied to diffusion (dMRI) and/or functional MRI (fMRI) data sets; the third group of 3 studies combined radiomics and connectomics applied to fMRI. This review highlighted the recent growing interest in combining MRI-based radiomics and connectomics to explore the human brain for neurological, psychiatric, and oncological conditions. Current methodologies and challenges were discussed, pointing out future research directions to improve or standardize these approaches and the gaps to be filled to advance the field.

Context aware machine learning techniques for brain tumor classification and detection - A review

Background

Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis.

Objectives

This review aims to predict gene alterations and establish structured correlations among various tumor types, extending the prediction of genetic mutations and structures using the latest machine learning techniques. Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis.

Methods

The review encompasses the most recent developments in MRI, and histology image processing methods across multiple tumor classes, including Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. It identifies challenges in tumor classification, segmentation, datasets, and modalities, employing various neural network architectures. A competitive analysis assesses the performance of CNN. Furthermore it also implies K-MEANS clustering to predict Genetic structure, Genes Clusters prediction and Molecular Alteration of various types and grades of tumors e.g. Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma.

Results

CNN and KNN structures, with their ability to extract highlights in image-based information, prove effective in tumor classification and segmentation, surmounting challenges in image analysis. Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning.

Conclusion

Machine learning, especially through CNN and SVM algorithms, demonstrates significant potential in the accurate diagnosis and classification of brain tumors based on imaging and histo-pathological data. Further advancements in this area hold promise for improving the accuracy and efficiency of intra-operative tumor diagnosis and treatment.

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, and 18F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy

BACKGROUND

Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.

MATERIALS AND METHODS

We retrospectively retrieved 154 cases of recurrent HGG from multiple centers. Tumor segmentation was performed by expert radiologists and a convolutional neural network (CNN). From the segmented tumors, 2553 radiomic features were extracted for each case. A robust feature subset was selected using intraclass correlation coefficient analysis between manual and automated segmentations. The data was divided into a 9:1 ratio and validated through ten-fold cross-validation and tested on a rotating test set. Features selection was done by the Kruskal-Wallis test. The Radiomics-based OS predictions, generated using Support Vector Machine (SVM), were compared between the two segmentation approaches and against OS prediction by the CNN model adapted for classification. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The clinical model AUC for OS prediction was 0.640 ± 0.013 (mean ± 95% confidence interval) in the training set and 0.610 ± 0.131 in the test set. The radiomics prediction of OS based on manual segmentation outperformed automatic segmentation (AUC of 0.662 ± 0.122 vs. 0.471 ± 0.086, respectively) in the test set. Robust features improved the performance of manual segmentation to AUC of 0.700 ± 0.102, of automated segmentation to 0.554 ± 0.085. The CNN prognosis model demonstrated promising results, with an average AUC of 0.755 ± 0.071 for training sets and 0.700 ± 0.101 for the test set.

CONCLUSION

Manual segmentation-derived radiomic features outperformed automated segmentation-derived features for predicting OS in recurrent high-grade glioma patients undergoing immunotherapy. The end-to-end CNN prognosis model performed similarly to radiomics modeling using manual-segmentation-derived features without the need for segmentation. The potential time-saving must be weighed against the lower interpretability of end-to-end black box modeling.

Recent developments in translational imaging of in vivo gene therapy outcomes

Gene therapy achieves therapeutic benefits by delivering genetic materials, packaged within a delivery vehicle, to target cells with defective genes. This approach has shown promise in treating various conditions, including cancer, metabolic disorders, and tissue-degenerative diseases. Over the past 5 years, molecular imaging has increasingly supported gene therapy development in both preclinical and clinical studies. High-quality images from positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and computed tomography (CT) enable quantitative and reliable monitoring of gene therapy. Most reported studies have applied imaging biomarkers to non-invasively evaluate the outcomes of gene therapy. This review aims to inform researchers in molecular imaging and gene therapy about the integration of these two disciplines. We highlight recent developments in using imaging biomarkers to monitor the outcome of in vivo gene therapy, where the therapeutic delivery vehicle is administered systemically. In addition, we discuss prospects for further incorporating imaging biomarkers to support the development and application of gene therapy.

Review on Photoacoustic Monitoring after Drug Delivery: From Label-Free Biomarkers to Pharmacokinetics Agents

Photoacoustic imaging (PAI) is an emerging noninvasive and label-free method for capturing the vasculature, hemodynamics, and physiological responses following drug delivery. PAI combines the advantages of optical and acoustic imaging to provide high-resolution images with multiparametric information. In recent decades, PAI's abilities have been used to determine reactivity after the administration of various drugs. This study investigates photoacoustic imaging as a label-free method of monitoring drug delivery responses by observing changes in the vascular system and oxygen saturation levels across various biological tissues. In addition, we discuss photoacoustic studies that monitor the biodistribution and pharmacokinetics of exogenous contrast agents, offering contrast-enhanced imaging of diseased regions. Finally, we demonstrate the crucial role of photoacoustic imaging in understanding drug delivery mechanisms and treatment processes.

Enhancing the Understanding of Breast Vascularity Through Insights From Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Comprehensive Review

Breast vascularity plays a crucial role in both physiological and pathological processes, particularly in the development and progression of breast cancer. Understanding vascular changes within breast tissue is essential for accurate diagnosis, treatment planning, and monitoring therapeutic response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has emerged as a valuable tool for evaluating breast vascularity due to its ability to provide detailed functional and morphological insights. DCE-MRI utilizes contrast agents to highlight blood flow and vessel permeability, making it especially useful in differentiating between benign and malignant lesions. This review explores the significance of DCE-MRI in breast vascularity assessment, highlighting its principles, clinical applications, and role in detecting malignancy through vascular changes. We also examine its utility in monitoring treatment response and quantitative analysis of perfusion metrics such as Ktrans and extracellular-extravascular volume (Ve). While DCE-MRI offers remarkable diagnostic accuracy, challenges remain regarding its cost, accessibility, and potential overlap of enhancement patterns between benign and malignant conditions. The review further discusses emerging technologies and future directions for DCE-MRI, including advanced imaging techniques and machine learning-based quantification. Overall, DCE-MRI stands out as a powerful tool in the comprehensive evaluation of breast vascularity, with significant potential to improve patient outcomes in breast cancer management.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

The role of macrophage plasticity in neurodegenerative diseases

Tissue-resident macrophages and recruited macrophages play pivotal roles in innate immunity and the maintenance of brain homeostasis. Investigating the involvement of these macrophage populations in eliciting pathological changes associated with neurodegenerative diseases has been a focal point of research. Dysregulated states of macrophages can compromise clearance mechanisms for pathological proteins such as amyloid-β (Aβ) in Alzheimer's disease (AD) and TDP-43 in Amyotrophic lateral sclerosis (ALS). Additionally, recent evidence suggests that abnormalities in the peripheral clearance of pathological proteins are implicated in the pathogenesis and progression of neurodegenerative diseases. Furthermore, numerous genome-wide association studies have linked genetic risk factors, which alter the functionality of various immune cells, to the accumulation of pathological proteins. This review aims to unravel the intricacies of macrophage biology in both homeostatic conditions and neurodegenerative disorders. To this end, we initially provide an overview of the modifications in receptor and gene expression observed in diverse macrophage subsets throughout development. Subsequently, we outlined the roles of resident macrophages and recruited macrophages in neurodegenerative diseases and the progress of targeted therapy. Finally, we describe the latest advances in macrophage imaging methods and measurement of inflammation, which may provide information and related treatment strategies that hold promise for informing the design of future investigations and therapeutic interventions.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Nano-Micron Combined Hydrogel Microspheres: Novel Answer for Minimal Invasive Biomedical Applications

Hydrogels, key in biomedical research for their hydrophilicity and versatility, have evolved with hydrogel microspheres (HMs) of micron-scale dimensions, enhancing their role in minimally invasive therapeutic delivery, tissue repair, and regeneration. The recent emergence of nanomaterials has ushered in a revolutionary transformation in the biomedical field, which demonstrates tremendous potential in targeted therapies, biological imaging, and disease diagnostics. Consequently, the integration of advanced nanotechnology promises to trigger a new revolution in the realm of hydrogels. HMs loaded with nanomaterials combine the advantages of both hydrogels and nanomaterials, which enables multifaceted functionalities such as efficient drug delivery, sustained release, targeted therapy, biological lubrication, biochemical detection, medical imaging, biosensing monitoring, and micro-robotics. Here, this review comprehensively expounds upon commonly used nanomaterials and their classifications. Then, it provides comprehensive insights into the raw materials and preparation methods of HMs. Besides, the common strategies employed to achieve nano-micron combinations are summarized, and the latest applications of these advanced nano-micron combined HMs in the biomedical field are elucidated. Finally, valuable insights into the future design and development of nano-micron combined HMs are provided.

Establishment of glioma prognosis nomogram based on the function of meox1 in promoting the progression of cancer

Background

Gliomas stand out as highly predominant malignant nervous tumors and are linked to adverse treatment outcomes and short survival periods. Current treatment options are limited, emphasizing the need to identify effective therapeutic targets. The heterogeneity of tumors necessitates a personalized treatment approach with an effective grouping system. Meox1 has been implicated in promoting tumor progression in diverse cancers; nonetheless, its role in gliomas remains unelucidated.

Material/methods

Utilized immunohistochemistry to assess the expression of Meox1 protein in glioma tissues. Proliferation and invasion assays were conducted on wild-type and meox1-overexpressed glioma cells using the CCK8 and Transwell assays, respectively. The expression levels of meox1 and its related genes in gliomas were obtained from Chinese Glioma Genome Atlas (CGGA), along with the corresponding patient survival periods. LASSO regression modeling was employed to construct a scoring system for patients with gliomas, categorizing them into high-/low-risk groups. Additionally, a nomogram for predicting the survival period of patients with glioma was developed using multivariate logistic analysis.

Results

We attempted, for the first time, to demonstrate heightened expression of Meox1 in glioma tumor tissues, correlating with significantly increased invasion and proliferation abilities of glioma cells following meox1 overexpression. The scoring system effectively stratified patients with glioma into high-/low-risk groups, revealing differences in the survival period and immunotherapy efficacy between the two groups. The integration of this scoring system with other clinical indicators yielded a nomogram capable of effectively predicting the survival period of individuals with gliomas.

Conclusions

Our study established a stratified investigation system based on the levels of meox1 and its related genes, providing a novel, cost-effective model for facilitating the prognosis prediction of individuals with glioma.

Radiopharmaceuticals for Skeletal Muscle PET Imaging

The skeletal muscles account for approximately 40% of the body weight and are crucial in movement, nutrient absorption, and energy metabolism. Muscle loss and decline in function cause a decrease in the quality of life of patients and the elderly, leading to complications that require early diagnosis. Positron emission tomography/computed tomography (PET/CT) offers non-invasive, high-resolution visualization of tissues. It has emerged as a promising alternative to invasive diagnostic methods and is attracting attention as a tool for assessing muscle function and imaging muscle diseases. Effective imaging of muscle function and pathology relies on appropriate radiopharmaceuticals that target key aspects of muscle metabolism, such as glucose uptake, adenosine triphosphate (ATP) production, and the oxidation of fat and carbohydrates. In this review, we describe how [18F]fluoro-2-deoxy-D-glucose ([18F]FDG), [18F]fluorocholine ([18F]FCH), [11C]acetate, and [15O]water ([15O]H2O) are suitable radiopharmaceuticals for diagnostic imaging of skeletal muscles.

Gold and Silver Nanoparticles as Biosensors: Characterization of Surface and Changes in the Adsorption of Leucine Dipeptide under the Influence of Substituent Changes

Early detection of diseases can increase the chances of successful treatment and survival. Therefore, it is necessary to develop a method for detecting or sensing biomolecules that cause trouble in living organisms. Disease sensors should possess specific properties, such as selectivity, reproducibility, stability, sensitivity, and morphology, for their routine application in medical diagnosis and treatment. This work focuses on biosensors in the form of surface-functionalized gold (AuNPs) and silver nanoparticles (AgNPs) prepared using a less-time-consuming, inexpensive, and efficient synthesis route. This allows for the production of highly pure and stable (non-aggregating without stabilizers) nanoparticles with a well-defined spherical shape, a desired diameter, and a monodisperse distribution in an aqueous environment, as confirmed by transmission electron microscopy with energy-dispersive X-ray spectroscopy (TEM-EDS), X-ray diffraction (XRD), photoelectron spectroscopy (XPS), ultraviolet-visible (UV-VIS) spectroscopy, and dynamic light scattering (DLS). Thus, these nanoparticles can be used routinely as biomarker sensors and drug-delivery platforms for precision medicine treatment. The NPs' surface was coated with phosphonate dipeptides of L-leucine (Leu; l-Leu-C(R1)(R2)PO3H2), and their adsorption was monitored using SERS. Reproducible spectra were analyzed to determine the orientation of the dipeptides (coating layers) on the nanoparticles' surface. The appropriate R2 side chain of the dipeptide can be selected to control the arrangement of these dipeptides. This allows for the proper formation of a layer covering the nanoparticles while also simultaneously interacting with the surrounding biological environment, such as cells, tissues, and biological fluids.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

Navigating Nutritional Strategies: A Comprehensive Review of Early and Delayed Enteral Feeding in Acute Pancreatitis

This review critically examines enteral feeding strategies in managing acute pancreatitis, focusing on the contrasting early and delayed initiation approaches. Acute pancreatitis, marked by pancreatic inflammation, poses complex challenges, and nutritional interventions are pivotal in patient outcomes. Early enteral feeding, initiated within 24-48 hours, is associated with positive outcomes such as shortened hospital stays and reduced complications. However, controversies persist, with studies questioning its universal benefits. Conversely, delayed enteral feeding, employing a cautious approach, gains prominence in high-risk and severe cases. The identification of high-risk patients becomes paramount in decision-making. Practical recommendations for clinicians advocate an individualized approach, considering the severity of pancreatitis and regular monitoring. As the landscape of acute pancreatitis management evolves, staying abreast of emerging guidelines is essential. This review aims to provide a comprehensive understanding of critical findings, offering practical insights to guide clinicians in navigating the complexities of enteral feeding decisions in acute pancreatitis.

Machine learning-based CT texture analysis in the differentiation of testicular masses

Purpose

To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively.

Methods

We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers.

Results

985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively.

Conclusion

Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.

Effect of Niosomal Encapsulation of Quercetin and Silymarin and their Combination on Dimethylnitrosoamine-induced and Phenobarbital promoted Hepatocellular Carcinoma in Rat Model

BACKGROUND

Hepatocellular carcinoma is a particularly dangerous and severe kind of liver cancer. Many anticancer drugs fail to complete the treatment of hepatocellular carcinoma without any side effects. There should be appropriate and without side effective treatments for hepatocellular carcinoma.

OBJECTIVE

The objective of the current study was to evaluate how quercetin and silymarin in a niosomal formulation affected hepatocyte carcinoma caused by diethylnitrosamine.

METHODS

Five groups were created from the thirty male rats. Normal control (untreated group), tumor group (administered dimethylnitrosoamine 200 mg/kg), treatment group I (administered 50 mg/kg of niosomal encapsulated quercetin), treatment group II (administered 50 mg/kg of niosomal encapsulated silymarin), and treatment group III (administered 50 mg/kg of niosomal encapsulated quercetin + silymarin). Then, biochemical estimation, serum analysis, and histopathological examination were carried out.

RESULTS

Treatment group III, treated with niosomal encapsulation of a combination of quercetin + silymarin 50 mg/kg, demonstrated the significant restoration of alpha-fetoprotein and carcinoembryonic antigen and also antioxidants like superoxide dismutase and nitric oxide. The histopathological examination showed improved liver architecture in this group compared to other treatment groups.

CONCLUSION

Our findings revealed that a potent anticancer effect was observed in treatment group III as niosomal formulation increased the bioavailability of the drug within the body. In order to completely understand the underlying processes and evaluate the therapeutic effectiveness of these chemicals in the therapy of hepatocellular carcinoma, further investigation and clinical trials are required.

Exploring VEGF-Linked Pathways: Investigating Multiple miRNAs for Their Therapeutic Potential in Angiogenesis Targets and as Biomarkers in Recurrent Glioblastoma Multiforme

Alternative pathways frequently operate as the origins of resistance to drugs that block the vascular endothelial growth factor (VEGF) pathway. To find possible therapeutic targets and indicators, this study explored the VEGF pathway and how miRNAs control it in recurrent glioblastoma multiforme (rGBM). Differentially expressed miRNAs (DEmiRNAs) were identified by using GBM GSE profiles (GSE32466). To find pathways containing DEmiRNAs, VEGF pathway genes, and their related genes, DIANA-miRPath v3.0 and the ToppGene database were utilized. miRNAs linked to VEGF signaling pathway genes, interactional genes, and DEmiRNAs were discovered by extracting common pathways. The ability of these miRNAs to distinguish rGBM patients from those with primary GBM was assessed using ROC analysis. The study revealed that in rGBM, 30 miRNAs were significantly up-regulated and 49 miRNAs were considerably down-regulated. Among them, the VEGF pathway was connected to 22 up-regulated miRNAs and 29 down-regulated miRNAs. The MAPK pathway shared the most genes with the VEGF pathway, accounting for 1,014 of the interacting genes, which were discovered to have interactions with VEGF signaling pathway genes. Furthermore, 14 miRNAs were identified as having a great deal of potential as molecular biomarkers and therapeutic targets for rGBM. The results indicate that the VEGF pathway in rGBM is regulated by a number of interrelated pathways. The discovered miRNAs hold promise as rGBM biomarkers and therapeutic targets, offering possibilities for novel therapy strategies and aiding rGBM diagnosis and prognosis.