Search Results (368)

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

Background/Objectives: Glioblastoma (GBM) remains the most aggressive primary brain tumor, characterized by high malignancy, recurrence rate, and dismal prognosis, thereby demanding innovative therapeutic strategies. In this study, we report a novel in situ targeting inclusion complex hydrogel hybrid system (DOX/RGD-CD@Gel) that integrates doxorubicin (DOX) with RGD-conjugated cyclodextrin (RGD-CD) and a thermosensitive hydrogel for enhanced GBM therapy. Methods: The DOX/RGD-CD@Gel system was prepared by conjugating doxorubicin (DOX) with RGD-modified cyclodextrin (RGD-CD) and embedding it into a thermosensitive hydrogel. The drug delivery and antitumor efficacy of this system were evaluated in vitro and in vivo. Results: In vitro and in vivo evaluations demonstrated that DOX/RGD-CD@Gel significantly enhanced cytotoxicity compared to free DOX or DOX/CD formulations. The targeted delivery system effectively promoted apoptosis and inhibited cell proliferation and metastasis in GBM cells. Moreover, the hydrogel-based system exhibited prolonged drug retention in the brain, as evidenced by its temperature- and pH-responsive release characteristics. In a GBM mouse model, DOX/RGD-CD@Gel significantly suppressed tumor growth and improved survival rates. Conclusions: This study presents a paradigm of integrating a targeted inclusion complex with a thermosensitive hydrogel, offering a safe and efficacious strategy for localized GBM therapy with potential translational value. Full article

Flood susceptibility modeling (FSM) plays a key role in advancing proactive disaster risk reduction and spatial planning. This research developed FSM for the Buzău River catchment in Romania—a region historically vulnerable to recurrent flood events—using four state-of-the-art ensemble boosting algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. Initially, a comprehensive set of 13 flood conditioning factors was assessed, which was subsequently narrowed down to 9 essential factors through multi-tier feature selection strategies. Analysis of performance via receiver operating characteristic (ROC) andprecision–recall curves showed only marginal differences between the models; however, CatBoost excelled with an area under the ROC curve (AUC) of 0.972 and an average precision (AP) of 0.971, with XGBoost following closely behind. The SHAP (SHapley Additive exPlanations) analysis of the CatBoost model indicated that the Slope, Distance from Rivers, Topographic Wetness Index (TWI), and Land Use/Land Cover (LULC) are the key contributing factors. The novelty of this research is found in its comparative analysis of AdaBoost alongside three gradient boosting algorithms—CatBoost, LightGBM, and XGBoost—while utilizing explainable artificial intelligence (XAI) and a multi-tier feature selection strategy to create FSM that are precise and comprehensible. These strategies deliver robust tools for managing flood risks and reinforce the viability of data-driven modeling in the various catchments of Europe. 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

Background and Aim: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with poor prognosis despite maximal surgical resection, radiotherapy (RT), and temozolomide (TMZ) per the Stupp protocol. IDH-wild-type GBM, the predominant molecular subtype, frequently harbors EGFR amplification and is resistant to therapy, while MGMT promoter methylation predicts improved TMZ response. This study aimed to assess the prognostic impact of EGFR and MGMT status on survival and recurrence patterns in IDH-wild-type GBM. Materials and Methods: We retrospectively analyzed 218 patients with IDH-wild-type GBM treated at the Azienda Ospedaliero-Universitaria Senese (2016–2024). All patients underwent maximal safe surgical resection whenever feasible. The cohort includes patients who received gross total resection (GTR), subtotal resection (STR), or biopsy only, depending on tumor location and clinical condition, followed by intensity-modulated RT (59.4–60 Gy) with concurrent and adjuvant TMZ. EGFR amplification was assessed via FISH/NGS and immunohistochemistry; MGMT promoter methylation was determined using methylation-specific PCR. Progression-free survival (PFS), overall survival (OS), and recurrence patterns (in-field, marginal, out-field) were evaluated using Kaplan–Meier, Cox regression, and logistic regression analyses. Results: Among patients (64.7% male; mean age 61.8), 58.7% had EGFR amplification and 49.1% showed MGMT methylation. Median OS and PFS were 14 and 8 months, respectively. EGFR non-amplified/MGMT methylated tumors had the best outcomes (OS: 22.0 months, PFS: 10.5 months), while EGFR-amplified/MGMT unmethylated tumors fared worst (OS: 10.0 months, PFS: 5.0 months; p < 0.001). MGMT methylation was an independent positive prognostic factor (HR: 0.48, p < 0.001), while EGFR amplification predicted worse survival (HR: 1.57, p = 0.02) and higher marginal recurrence (OR: 2.42, p = 0.01). Conclusions: EGFR amplification and MGMT methylation significantly influence survival and recurrence dynamics in IDH-wild-type GBM. Incorporating these biomarkers into treatment planning may enable tailored therapeutic strategies, potentially improving outcomes in this challenging disease. Prospective studies are needed to validate biomolecularly guided management approaches. Full article

Glioblastoma multiforme (GBM) is a highly aggressive, treatment-resistant grade IV brain tumor with poor prognosis that grows rapidly and invades surrounding tissues, complicating surgery and frequently recurring. Although the crucial role of endogenous peptides has been highlighted for several tumors, the specific peptidomic profile of GBM remains unexplored to date. This study aimed to perform a preliminary characterization of the low molecular mass proteome fraction of Cavitron Ultrasonic Surgical Aspirator (CUSA) fluid collected from different tumor zones, i.e., the core and tumor periphery of newly diagnosed (ND) and recurrent (R) GBM. The samples, pooled by tumor type and collection zone, were centrifuged through molecular cut-off filter devices to collect the non-retained fraction of the proteome <10 kDa for direct full-length LC-MS analysis. A total of 40 and 24 peptides, fragments of 32 and 18 proteins, were marked as ND and R GBM COREs, respectively, while 132 peptides, fragments of 46 precursor proteins, were identified as common and included proteins which were cancer-related or involved in GBM pathophysiology. Besides providing a preliminary overview of the unexplored peptidome of GBM, this pilot study confirms peptidomics as a promising tool to discover potential GBM biomarkers in the perspective of clinical applications increasingly oriented towards a precision medicine approach. Data are available via ProteomeXchange with the identifier PXD060807. Full article

Background/Objectives: Cervical cancer is a leading cancer-related cause of death among women, with recurrence being a serious clinical issue. Recent evidence demonstrates that long non-coding RNAs (lncRNAs) affect cancer recurrence. This research investigates vitamin D’s regulatory actions in the recurrence of cervical cancer, centering on the involvement of lncRNA. Clinical data on 738 patients shows that greater serum vitamin D levels are linked to reduced recurrence rates and enhanced disease-free survival (DFS). Methods: A transcriptomic analysis of CaSki cervical cancer cells using data from the GEO dataset GSE267715 identified that vitamin D controls genes that prevent cervical cancer recurrence. Machine learning predictors CatBoost, LightGBM, Extra Trees, and Logistic Regression and feature selection methods such as ANOVA F-test, mutual information, Chi-squared test, and Recursive Feature Elimination (RFE) are used to identify predictors of recurrence, evaluating model performance using accuracy, precision, recall, ROC AUC, confusion matrices, and ROC curves. Result: CatBoost performs the best overall, producing an accuracy of 95.27%. CatBoost provided an ROC AUC of 0.9930, a precision of 0.9296, and a recall of 0.9706, and this implies a significant trade-off between the ability to detect metastatic cases correctly. Conclusions: These data identify the therapeutic potential of vitamin D as a regulatory compound and lncRNA as a potential therapeutic target in the recurrence of cervical cancer. Full article

Background: High-grade gliomas (HGGs), particularly glioblastoma (GBM), are associated with exceptionally high mortality and inevitable recurrence. In considering novel treatment options for these devastating diseases, immunotherapies represent promising candidates. Immunotherapies have demonstrated efficacy for several advanced tumors outside the central nervous system, highlighting a potential role for these agents in treating HGGs. However, multiple challenges to immunotherapy efficacy have tempered therapeutic benefit in practice, including local and systemic immunosuppression, intratumoral heterogeneity, and various mechanisms of intrinsic and acquired resistance. In the past 30 years, diverse immunotherapeutic subclasses have been assessed for benefit against HGGs. Methods: We performed a PubMed search for randomized clinical trials performed within the last 30 years evaluating the following immunotherapy agents for high-grade gliomas: immune checkpoint inhibitors, vaccines, oncologic viruses, cytokines, and CAR T-cells. The present review offers a critical analysis of key pre-clinical and clinical trials that have shaped the immunotherapy landscape for high-grade gliomas over the past two decades. Results/Conclusions: Across the different immunotherapeutic methods and modalities explored thus far, a recurring theme emerges: while therapeutic strategies with a compelling conceptual basis are continually under development and even demonstrate a benefit in preclinical and early-phase trials, larger and later-phase trials consistently fail to produce concordantly significant outcomes. To date, no large-scale clinical trial has demonstrated a benefit of sufficient consequence to change practice. Continued critical appraisal of the strengths and pitfalls of prior investigative work, optimization of treatment development and delivery, and innovative approaches to combination therapy design will collectively be integral to future therapeutic advancement. Full article

Background/Objectives: Glioblastoma (GBM) is the most common primary malignant central nervous system tumor, accounting for 50.9% of malignant CNS diagnoses and carrying a median survival of 15 months despite maximal standard therapy. High recurrence rates are driven by residual infiltrative tumor cells at the resection margin. Fluorescence-guided surgery (FGS) has emerged as a key innovation to improve intraoperative tumor visualization and maximize the extent of resection (EOR). This review examines the historical development, current clinical applications, and future directions of FGS in GBM surgery. Methods: A comprehensive literature review was conducted, covering the evolution of fluorophores (fluorescein, indocyanine green [ICG], and 5-aminolevulinic acid [5-ALA]), visualization technologies (wide- and narrow-field modalities), therapeutic adjuncts (photodynamic and sonodynamic therapies), and clinical adoption patterns and outcomes. Results: Early intraoperative fluorescence using fluorescein dates to 1947. ICG angiography has broad surgical utility, while 5-ALA received FDA approval in 2017, with phase III trials demonstrating gross total resection rates of 65% versus 36% with white-light surgery. Adjunct technologies—3D exoscopes, FGS-compatible loupes, and quantitative spectroscopy probes—enhance detection of residual tumor. Preliminary studies of intraoperative photodynamic and sonodynamic therapies show feasibility and potential survival benefits. Global adoption of 5-ALA FGS exceeds 75% among surveyed neurosurgeons. Conclusions: FGS significantly improves EOR in GBM surgery, translating into better patient outcomes. Ongoing clinical trials and technological refinements—novel fluorophores, quantitative imaging, and therapeutic applications—promise to further optimize tumor visualization and treatment. Full article

Glioblastoma (GBM) often exhibits distinct anatomical patterns of relapse after radiotherapy. Tumour cell migration along myelinated white matter tracts is a key driver of disease progression. The failure of conventional imaging to capture subclinical infiltration has driven interest in advanced imaging biomarkers capable of quantifying tumour–brain interactions. Diffusion tensor imaging (DTI), radiomics, and connectomics represent a triad of innovative, non-invasive approaches that map white matter architecture, predict recurrence risk, and inform biologically guided treatment strategies. This review examines the biological rationale and clinical applications of DTI-based metrics, radiomic signatures, and tractography-informed connectomics in GBM. We discuss the integration of these modalities into machine learning frameworks and radiotherapy/surgical planning, supported by landmark studies and multi-institutional data. The implications for personalised neuro-oncology are profound, marking a shift towards risk-adaptive, tract-aware treatment strategies that may improve local control and preserve neurocognitive function. Full article

This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. It explores key factors influencing the model performance, such as the type of time series task, dataset size, and the time interval of historical data. Additionally, this study investigates potential biases in model development and weighs the trade-offs between the computational costs and performance. A detailed analysis of the most used error metrics and hyperparameter tuning methods in the reviewed papers is included. Furthermore, this study evaluates the results from prominent forecasting competitions, such as M5 and M6, to enrich the analysis. The findings of this paper highlight that tree-based methods like LightGBM 4.6.0 and deep learning methods like recurrent neural networks deliver the best performance in time series forecasting, with tree-based methods offering a significant advantage in terms of their computational efficiency. This paper concludes with practical recommendations for approaching time series forecasting tasks, offering valuable insights and actionable strategies that can enhance the accuracy and reliability of predictions derived from time series data. Full article

Background: A glioblastoma (GBM) is a type of tumor originating from the glial brain cells, the astrocytes, and thus belongs to the astrocytoma group. Bevacizumab (BV) is a treatment for GBM. BV is the active ingredient in the drugs Avastin^®, Alymsys^®, Mvasi^® and ZiraBev^®. It is currently approved as second-line treatment for GBM recurrence in combination with radiotherapy, and as first-line treatment for other cancers, including advanced colorectal cancer, metastatic breast cancer and advanced non-small-cell lung cancer. The objective of this systematic review was to analyze the scientific evidence from the science-based literature on the therapeutic effect and adverse effects of the drug BV in patients with GBM or GBM multiforme. Methods: We systematically searched electronic databases for the literature search, including the MEDLINE (via PubMed), SCOPUS, Google Scholar, the Cumulative Index to Nursing and Allied Health Literature and Web of Science databases, covering records from their earliest data to December 2024. Randomized or controlled clinical trials that were published in English or Spanish were included. The following keywords were used in different combinations: “Bevacizumab therapy”, “Bevacizumab pharmaceutical”, “Glioblastoma”, “Glioma” and “multiform glioblastoma”. Results: The use of Bevacizumab has been extensively studied in the scientific literature, with beneficial effects in symptom control. However, the adverse effects of BV vary across different types of carcinomas, which is why it has already been established that these adverse effects must be taken into consideration. In our meta-analysis of adverse effects, we found 14 adverse effects and estimated their prevalence, with an average of 19% (CI: 4 to 44%). The most significant vascular adverse effect was thromboembolism, which led to a greater number of complications for patients with GBM. Finally, the most common adverse effects were nausea, vomiting, fatigue and hypertension. Conclusions: While the beneficial properties of this pharmacological therapy have been observed, its adverse effect profile requires constant evaluation, as it includes vascular, blood and symptomatic adverse effects, which must be analyzed on a case-by-case basis and with great attention, especially in the case of more serious complications such as thromboembolic events. Full article

GBM is the most common and aggressive primary brain tumor in adults, characterized by low survival rates, high recurrence, and resistance to conventional therapies. Traditional diagnostic and therapeutic methods remain limited due to the difficulty in permeating the blood–brain barrier (BBB), diffuse tumor cell infiltration, and tumor heterogeneity. In recent years, nano-based technologies have emerged as innovative approaches for the detection and treatment of GBM. A wide variety of nanocarriers, including dendrimers, liposomes, metallic nanoparticles, carbon nanotubes, carbon dots, extracellular vesicles, and many more demonstrate the ability to cross the BBB, precisely deliver therapeutic agents, and enhance the effects of radiotherapy and immunotherapy. Surface functionalization, peptide modification, and cell membrane coating improve the targeting capabilities of nanostructures toward GBM cells and enable the exploitation of their photothermal, magnetic, and optical properties. Furthermore, the development of miRNA nanosponge systems offers the simultaneous inhibition of multiple tumor growth mechanisms and the modulation of the immunosuppressive tumor microenvironment. This article presents current advancements in nanotechnology for GBM, with a particular focus on the characteristics and advantages of specific groups of nanoparticles, including their role in radiosensitization. Full article

IDH-wildtype glioblastoma (GBM) represents the most common malignant form of brain tumor and is still incurable despite comprehensive therapeutic efforts. Due to tumor location and patient condition, open surgical resection of recurrent GBM is not always feasible. In these cases, frame-based stereotactic biopsies represent a less invasive technique to obtain tissue samples for diagnostics. However, whether this material would also be sufficient to prepare tumor organoids (TOs) and perform drug screenings has not been addressed so far. In this study, we present our highly optimized workflow for generating standardized patient-derived GBM TOs from single-cell suspensions using limited biopsy-derived material. We highlight crucial steps within the procedure, such as reliable cell counting, viable cell recovery, enzymatic digestion, and the requirement of an extracellular matrix as a scaffold. Furthermore, we showcase the potential of personalized drug testing as a promising application of GBM TOs. In conclusion, we successfully developed a robust workflow that effectively utilizes the limited material derived from stereotactic biopsies to reproducibly form standardized TOs. Moreover, we demonstrate that biopsy-derived TOs represent a valuable tool for testing drug vulnerabilities in a personalized setting, which might be especially useful in the case of non-resectable GBM. Full article