Search Results (503)

Background/Objectives: Alzheimer’s disease (AD) is the most prevalent form of dementia and is characterized by a decline in cognition that may be due, in part, to deficient cholinergic signalling. Cholinesterase inhibitors (ChEIs) are the first-line pharmacotherapies for treating the diminished cholinergic function in AD patients. Plant phytochemicals may provide useful ChEIs and mitigate other elements of AD pathology, including oxidative stress. Methods: Herein, the phytochemicals present in Moringa oleifera aqueous and methanolic extracts were identified by LC-MS/MS and the potential of several phytochemicals (4-O-caffeoylquinic acid (4-CQA), quercetin 3-β-D-glucoside (Q3-β-D), chlorogenic acid (CGA), and rutin) to act as ChEIs and antioxidants was assessed. Results: The phytochemicals inhibited human acetylcholinesterase (AChE) in the following order of potency: 4-CQA > Q3-β-D > CGA > rutin; for AChE from Electrophorus electricus, the order of potency was Q3-β-D > 4-CQA > CGA > rutin. For human butyrylcholinesterase (hBuChE), the order of potency was rutin > 4-CQA > Q3-β-D > CGA and for equine serum BuChE, it was 4-CQA > Q3-β-D > rutin > CGA. Molecular docking validated the binding of the phytochemicals to cholinesterases, with binding affinities comparable to or higher than those of ChEI drugs. All the phytochemicals displayed potent radical-scavenging and antioxidant activities across six assays. 4-CQA was the most effective antioxidant in three of the assays. Conclusions: M. oleifera contains phytochemicals with weak ChEI activity and potent antioxidant capacity, with potential use as nutraceuticals to treat the cholinergic signalling deficit and oxidative stress that typifies AD pathology. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

Glioblastoma (GBM) is a highly aggressive brain tumour characterised by a poor prognosis and resistance to anti-angiogenic treatments. Vasculogenic mimicry (VM), in which tumour cells form vessel-like structures independent of endothelial cells, has emerged as a key mechanism hindering the efficacy of anti-angiogenic therapies. Recent research highlights the central role of RNA-binding proteins (RBPs) in regulating VM through diverse post-transcriptional mechanisms, including mRNA decay induction and translational repression. Several oncogenic RBPs, such as HuR and HNRNPs, promote VM and tumour aggressiveness, while others, including RBMS3, act as suppressors of VM. Despite the prominent oncogenic roles of multiple RBPs, RBP-targeting compounds aimed at suppressing VM in GBM have remained at an early stage due to a number of limitations. This review summarises the role of VM in the treatment resistance of GBM, RBP regulation of VM, and the current landscape and future direction of RBP-targeted therapies aimed at overcoming VM-mediated treatment resistance in GBM. Full article

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

Microtubules play a key role in cell division and cell migration. Thus, microtubule-targeting agents (MTAs) are pivotal in cancer therapy due to their ability to disrupt cell division microtubule dynamics. Traditionally divided into stabilizers and destabilizers, MTAs are increasingly being repurposed for central nervous system (CNS) applications, including brain malignancies such as gliomas and neurodegenerative diseases like Alzheimer’s and Parkinson’s. Microtubule-stabilizing agents, such as taxanes and epothilones, promote microtubule assembly and have shown efficacy in both tumour suppression and neuronal repair, though their CNS use is hindered by blood–brain barrier (BBB) permeability and neurotoxicity. Destabilizing agents, including colchicine-site and vinca domain binders, offer potent anticancer effects but pose greater risks for neuronal toxicity. This review highlights the mapping of nine distinct tubulin binding pockets—including classical (taxane, vinca, colchicine) and emerging (tumabulin, pironetin) sites—that offer new pharmacological entry points. We summarize the recent advances in structural biology and drug design, enabling MTAs to move beyond anti-mitotic roles, unlocking applications in both cancer and neurodegeneration for next-generation MTAs with enhanced specificity and BBB penetration. We further discuss the therapeutic potential of combination strategies, including MTAs with radiation, histone deacetylase (HDAC) inhibitors, or antibody–drug conjugates, that show synergistic effects in glioblastoma models. Furthermore, innovative delivery systems like nanoparticles and liposomes are enhancing CNS drug delivery. Overall, MTAs continue to evolve as multifunctional tools with expanding applications across oncology and neurology, with future therapies focusing on optimizing efficacy, reducing toxicity, and overcoming therapeutic resistance in brain-related diseases. Full article

Background/Objectives: Paclitaxel (PTX) is widely used in the treatment of a variety of solid tumours due to its broad-spectrum anti-tumour activity, but its use in brain gliomas is limited by insufficient blood–brain tumour barrier (BBTB) penetration and systemic toxicity. The aim of this study was to develop a Solutol HS-15-based micellar nanoparticle (PSM) to enhance the brain glioma targeting of PTX and reduce toxicity. Methods: PSMs were prepared by solvent injection and characterised for particle size, encapsulation rate, haemolysis rate and in vitro release properties. A C6 in situ glioma mouse model was used to assess the brain targeting and anti-tumour effects of the PSM by in vivo imaging, tissue homogenate fluorescence analysis and bioluminescence monitoring. Meanwhile, its safety was evaluated by weight monitoring, serum biochemical indexes and histopathological analysis. Results: The particle size of PSMs was 13.45 ± 0.70 nm, with an encapsulation rate of 96.39%, and it demonstrated excellent cellular uptake. In tumour-bearing mice, PSMs significantly enhanced brain tumour targeting with a brain drug concentration 5.94 times higher than that of free PTX. Compared with Taxol, PSMs significantly inhibited tumour growth (terminal luminescence intensity <1 × 10⁶ p/s/cm²/Sr) and did not cause significant liver or kidney toxicity or body weight loss. Conclusions: PSMs achieve an efficient accumulation of brain gliomas through passive targeting and EPR effects while significantly reducing the systemic toxicity of PTX. Its simple preparation process and excellent therapeutic efficacy support its use as a potential clinically translational candidate for glioma treatment. Full article

Background: Pseudoprogression is known to occur after immune-checkpoint inhibitor (ICI) therapy in brain metastasis and can complicate clinical decision-making. Still, its incidence, timing, and clinical presentation remain unclear. A retrospective cohort study in melanoma and non-small cell lung cancer brain metastasis patients was conducted to address this. Materials and Methods: Brain metastasis patients showing progression on MRI according to response assessment in neuro-oncology brain metastases criteria after starting ICI therapy were included, irrespective of prior irradiation. Lesions were classified as tumour progression (TP) or pseudoprogression based on three-month radiological follow-up or histopathology. TP was assigned if progression was again shown at three months. Pseudoprogression was assigned if lesions showed stability, partial, or complete response at three months. ‘Non-classified’ lesions were those with new or changed treatment during follow-up. Results: A cohort of 98 patients with 233 lesions was included over a 13-year period; 170 lesions were considered non-classified, and 41 and 22 lesions were classified as TP and pseudoprogression respectively. This resulted in a lesion- and patient-specific incidence for pseudoprogression of 9.4% and 17.3% respectively. Due to the large number of lesions that could not be classified, as is the case in clinical practice, the reported incidence in this study is likely an underestimation and can be seen as a ‘minimum’ incidence rate. Ten pseudoprogression (45.5%) and 13 (31.7%) TP lesions were previously irradiated. Pseudoprogression occurred at a median of 2.7 months after starting ICI therapy. The only clinical feature distinguishing patients with TP from pseudoprogression was that TP patients were more likely to need dexamethasone for neurological symptoms. Conclusions: Pseudoprogression has a lesion-specific incidence rate of at least 9.4% and occurs at a median of 2.7 months after starting ICI therapy. Severe neurological symptoms requiring dexamethasone may be a clinical feature typical for TP. Full article

Brain tumours challenge the structural and functional integrity of the brain, yet remarkable neuroplastic adaptations often preserve critical functions. This review synthesises the current knowledge of the molecular events underlying neuroplasticity in the context of tumoural growth, spanning from early genetic and protein alterations to macroscopic functional reorganisation. We discuss the roles of stress-regulated molecules, synaptic proteins, trophic factors, and morphological changes in driving adaptive responses. Furthermore, we bridge the gap between microscopic molecular events and large-scale network adaptations, emphasising clinical implications for glioma surgery and patient outcomes. Despite advances, knowledge gaps persist regarding the dynamics, predictors, and therapeutic modulation of plasticity, underscoring the need for future longitudinal and translational research. Understanding and leveraging these molecular mechanisms holds promise for improving functional recovery and quality of life in patients with brain tumours. Full article

Glioblastoma is the most common and aggressive malignant primary brain tumour. Patients with glioblastoma have a median survival of only around 14.6 months after diagnosis, despite the availability of various conventional multimodal treatments including chemotherapy, radiation therapy, and surgery. Therefore, photodynamic therapy (PDT) has emerged as an advanced, selective and more controlled therapeutic approach, which has minimal systemic toxicity and fewer side effects. PDT is a less invasive therapy that targets all cells or tissues that possess the photosensitizer (PS) itself, without affecting the surrounding healthy tissues. Polymeric NPs (PNPs) as carriers can improve the targeting ability and stability of PSs and co-deliver various anticancer agents to achieve combined cancer therapy. Because of their versatile tuneable features, these PNPs have the capacity to open tight junctions of the blood–brain barrier (BBB), easily transport drugs across the BBB, protect against enzymatic degradation, prolong the systemic circulation, and sustainably release the drug. Conjugated polymer NPs, poly(lactic-co-glycolic acid)-based NPs, lipid–polymer hybrid NPs, and polyethylene-glycolated PNPs have demonstrated great potential in PDT owing to their unique biocompatibility and optical properties. Although the combination of PDT and PNPs has great potential and can provide several benefits over conventional cancer therapies, there are several limitations that are hindering its translation into clinical use. This review aims to summarize the recent advances in the combined use of PNPs and PDT in the case of glioblastoma treatment. By evaluating various types of PDT and PNPs, this review emphasizes how these innovative approaches can play an important role in overcoming glioblastoma-associated critical challenges, including BBB and tumour heterogeneity. Furthermore, this review also discusses the challenges and future directions for PNPs and PDT, which provides insight into the potential solutions to various problems that are hindering their clinical translation in glioblastoma treatment. Full article

Tissue hypoxia is commonly observed in head cancers and contributes to both molecular and functional changes in tumour cells. It is known to stimulate erythropoiesis, angiogenesis, and metabolic alterations within tumour cells. Glioblastoma, a type of brain tumour, is characterized by rapid proliferation and aggressive growth. Recent studies have indicated that natural products may hold potential as components of cancer therapy. Among these, Polish propolis and its active compound, quercetin, have demonstrated promising anti-cancer properties. The aim of this study was to evaluate the concentrations of selected cytokines—specifically IL-6, IL-9, vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF-BB), interferon gamma-induced protein 10 (IP-10), and monocyte chemoattractant protein-1 (MCP-1)—produced by astrocytes of the CCF-STTG1 cell line. The cytotoxic effects of ethanolic extract of propolis (EEP) and quercetin were assessed using the MTT assay. Astrocytes were stimulated with lipopolysaccharide (LPS, 200 ng/mL) and/or IFN-α (100 U/mL), followed by treatment with EEP or quercetin (25–50 µg/mL) under hypoxic conditions for two hours. Cytokine concentrations were measured using the xMAP Luminex Multiplex Immunoassay and the Multiplex Bead-Based Cytokine Kit. Our study demonstrated that Polish propolis and its component quercetin modulate the tumour microenvironment in vitro, primarily by altering the levels of specific cytokines. The HCA analysis revealed that IL-6 and MCP-1 formed a distinct cluster at the highest linkage distance (approximately 100% of Dmax), suggesting that their expression patterns are significantly different from those of the other cytokines and that they are more similar to each other than to the rest. PCA analysis showed that EEP-PL (50 μg/mL) with IFN-α and EEP-PL (50 μg/mL) with LPS exert similar activities on cytokine secretion by astrocytes. Similar effects were demonstrated for EEP-PL 50 μg/mL + LPS + IFN-α, EEP-PL 25 μg/mL + IFN-α and EEP-PL 25 μg/mL + LPS + IFN-α. Our findings suggest that Polish propolis and quercetin may serve as promising natural agents to support the treatment of stage IV malignant astrocytoma. Nonetheless, further research is needed to confirm these results. Full article

Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively. Full article

Background: Protein arginine methyltransferase 3 (PRMT3), a type I family PRMT, regulates the activity of downstream substrates by catalyzing the asymmetric dimethylation of arginine residues. While PRMT3 activity has been reported to be deregulated in many cancers, including glioblastoma (GBM), the underlying signalling mechanisms that contribute to disease progression are largely unknown. Methods: We tested the efficacy of a PRMT3 chemical probe, SGC707, in a cohort of GBM patient-derived primary and recurrent brain tumour stem cell (BTSC) lines. RNA-sequencing, CRISPR-cas9 knockout, and inducible overexpression methods were used to investigate the molecular mechanisms regulated by the aberrant activity of PRMT3 in different BTSC lines. Results: We show that expression of the tumour suppressor protein 4.1B, a negative regulator of PRMT3, predicts the response of GBM BTSCs to the PRMT3 chemical probe, SGC707. Furthermore, PRMT3 modulates the stability and subcellular localization of the downstream effector, UHRF1, a member of the DNA methylation complex. These findings suggest that UHRF1 and DNMT1 may suppress the expression of 4.1B through the increased promoter methylation of EPB4.1L3. Intriguingly, the inducible overexpression of EPB4.1L3 in the BT248^EPB4.1L3low BTSC line mimicked the effects of the pharmacologic and genetic inhibition of PRMT3. In contrast, knockout of EPB4.1L3 in BT143^EPB4.1L3high cells reduced the interactions between PRMT3 and 4.1B proteins, resulting in increased sensitivity of knockout cells to SGC707 treatment. Conclusions: These findings show that 4.1B, PRMT3, and UHRF1/DNMT1 function together to promote BTSC growth. Thus, targeting PRMT3 or UHRF1/DNMT1, especially in tumours with low endogenous 4.1B protein, may have high therapeutic relevance. Full article

Glioblastoma is the most prevalent and aggressive form of brain malignancy. Actual treatments face several challenges due to its high aggressiveness and poor prognosis. The chemotherapeutic agent temozolomide (TMZ) has limited therapeutic efficacy, and mutations in the tumour protein p53 gene (TP53) have been associated with treatment resistance. Thus, this study aimed to explore an innovative therapeutic strategy to enhance treatment efficacy of GBM. Previously, our team had developed a WRAP5 cell-penetrating peptide (CPP) functionalized with a transferrin receptor ligand (Tf) for the targeted delivery of TMZ and a p53-encoding plasmid to glioma cells. Our research had elucidated the circadian oscillations of the clock genes in the U87 glioma cells by employing two different computational models and observed that T16 and T8 time points revealed the highest circadian activity for Bmal1 and Per2 genes, respectively. Similar analysis was conducted for the transferrin receptor, which revealed that T7 and T8 were the key time points for its expression. A confocal microscopy study indicated the highest intracellular uptake of complexes and p53 mRNA expression at T8, the time point with the highest Per2 and transferrin receptor expression. Following mRNA analysis, the evaluation of p53 levels confirmed transcriptional changes at the protein level, and that T16 appears to be a favourable time point for enhancing therapeutic efficacy in U87 glioblastoma cells. These findings suggested that synchronizing the complexes’ administration with the biological clock of GBM cells may significantly improve glioblastoma therapeutics. Full article

Disulfiram (DSF), a well-known anti-alcoholism drug, exhibits potent anticancer activity via its metabolite, diethyldithiocarbamate (DDC), which forms a cytotoxic copper complex that selectively targets cancer stem cells. However, its clinical utility is limited by poor solubility and rapid plasma metabolism. This study explores saccharide-linked DDCs as novel prodrugs designed to enhance stability, solubility, and tumour-selective activation. These compounds feature thioglycosidic bonds that shield the DDC moiety from premature degradation while retaining its metal-chelating function to form the active copper(II)bis(N,N-diethyldithiocarbamate) (Cu(DDC)₂) complex. The synthesised derivatives were characterised and evaluated for serum stability and in vitro cytotoxicity across several cancer cell lines, including colorectal, breast, lung, and brain cancers. Copper-complexed saccharide-DDC prodrugs demonstrated remarkable cytotoxicity, with improved biostability and solubility profiles. These findings highlight the potential of saccharide-linked DDCs as stable, copper-activated prodrugs for cancer therapy. Further in vivo studies are warranted to validate their pharmacokinetics and clinical relevance. Full article

Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on p-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection. Full article