With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.

Currently, for seemingly every type of cancer, dysregulated levels of non-coding RNAs (ncRNAs) are reported and non-coding transcripts are expected to be the next class of diagnostic and therapeutic tools in oncology. Recently, alterations to the ncRNAs transcriptome have emerged as a novel hallmark of cancer. Historically, ncRNAs were characterized mainly as regulators and little attention was paid to the mechanisms that regulate them. The role of modifications, which can control the function of ncRNAs post-transcriptionally, only recently began to emerge. Typically, these modifications can be divided into reversible (i.e., chemical modifications: m⁵C, hm⁵C, m⁶A, m¹A, and pseudouridine) and non-reversible (i.e., editing: ADAR dependent, APOBEC dependent and ADAR/APOBEC independent). The first research papers showed that levels of these modifications are altered in cancer and can be part of the tumorigenic process. Hence, the aim of this review paper is to describe the most common regulatory modifications (editing and chemical modifications) of the traditionally considered “non-functional” ncRNAs (i.e., microRNAs, long non-coding RNAs and circular RNAs) in the context of malignant disease. We consider that only by understanding this extra regulatory layer it is possible to translate the knowledge about ncRNAs and their modifications into clinical practice.

Gliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.

Introduction: Glioblastoma (GBM), the most aggressive primary brain tumor, poses a significant challenge in predicting patient survival due to its heterogeneity and resistance to treatment. Accurate survival prediction is essential for optimizing treatment strategies and improving clinical outcomes.Methods: This study utilized metadata from 135 GBM patients, including demographic, clinical, and molecular variables such as age, Karnofsky Performance Status (KPS), MGMT promoter methylation, and EGFR amplification. Six machine learning models—XGBoost, Random Forests, Support Vector Machines, Artificial Neural Networks, Extra Trees Regressor, and K- Nearest Neighbors—were employed to classify patients into predefined survival categories. Data preprocessing included label encoding for categorical variables and MinMax scaling for numerical features. Model performance was assessed using ROC-AUC and accuracy metrics, with hyperparameters optimized through grid search.Results: XGBoost demonstrated the highest predictive accuracy, achieving a mean ROC-AUC of 0.90 and an accuracy of 0.78. Ensemble models outperformed simpler classifiers, emphasizing the predictive value of metadata. The models identified key prognostic markers, including MGMT promoter methylation and KPS, as significant contributors to survival prediction.Conclusions: The application of machine learning to GBM metadata offers a robust approach to predicting patient survival. The study highlights the potential of ML models to enhance clinical decision-making and contribute to personalized treatment strategies, with a focus on accuracy, reliability, and interpretability.

Background/Objectives: Arteriovenous malformations (AVMs) are complex vascular anomalies that can present with significant complications, including intracranial hemorrhage. This report presents the case of a 36-year-old female with Prothrombin G20210A mutation-associated thrombophilia, highlighting its potential impact on AVM pathophysiology and management. Methods: The patient presented with a right paramedian intraparenchymal frontal hematoma, left hemiparesis, and seizures. Cerebral angiography identified a ruptured right parasagittal frontal AVM classified as Spetzler–Martin Grade II. A right interhemispheric frontal craniotomy was performed, enabling microsurgical resection of the AVM. Intraoperative findings included evacuation of a subcortical hematoma and excision of a 20 mm AVM nidus with arterial feeders from the A4 segment of the anterior cerebral artery and a single venous drainage into the superior sagittal sinus. Results: Postoperative recovery was favorable, with significant neurological improvement. The patient demonstrated resolution of hemiparesis and a marked reduction in seizure activity. The hypercoagulable state associated with Prothrombin G20210A mutation was identified as a contributing factor in the thrombosis of the AVM’s draining vein, potentially leading to increased venous pressure, rupture, and hemorrhage. Conclusions: This case underscores the importance of recognizing thrombophilia in patients with AVMs for optimal surgical planning and complication management. Despite the challenges posed by the hypercoagulable condition, microsurgical resection proved to be a viable and effective treatment option. Further research is warranted to elucidate the relationship between thrombophilic disorders and AVMs to enhance patient management strategies and outcomes.

Background/Objectives: Kissing aneurysms, a rare and intriguing cerebrovascular anomaly, challenge even the most advanced neurosurgical techniques. These lesions, characterized by two intimately apposed aneurysms with shared arterial walls, often masquerade as single, irregular aneurysms. This report documents a case of ruptured kissing aneurysms in the M1 segment of the right middle cerebral artery (MCA), complicated by ischemic stroke and pulmonary thromboembolism (PTE)—a convergence of severe complications rarely encountered. The case underscores the importance of precise diagnostics, innovative surgical strategies, and multidisciplinary care. Methods: A 55-year-old female presented with subarachnoid hemorrhage, confirmed by advanced imaging to arise from ruptured kissing aneurysms in the M1 segment of the right MCA. Surgical intervention via a right frontotemporal craniotomy and microsurgical clipping achieved definitive aneurysmal exclusion. Postoperatively, the patient experienced ischemic stroke and PTE, necessitating dynamic adjustments in anticoagulation therapy, intensive care, and rehabilitation protocols. Results: The dual aneurysms were successfully clipped, as confirmed by intraoperative and postoperative imaging. Despite developing significant complications, including left-sided motor deficits and PTE, a carefully orchestrated treatment strategy enabled the patient’s full recovery, with marked neurological and systemic improvement by her three-month follow-up. This favorable outcome highlights the resilience of a multidisciplinary approach in navigating such high-risk scenarios. Conclusions: This case showcases the formidable challenges of managing kissing aneurysms, particularly when compounded by stroke and PTE. It emphasizes the transformative role of cutting-edge imaging and surgical techniques in achieving successful outcomes. By illustrating how precision medicine and collaborative care can overcome rare and complex cases, this report contributes valuable insights to the evolving field of cerebrovascular surgery and postoperative management.