Explore the vast range of titles available.

Background Liquid biopsy assays using cerebrospinal fluid (CSF) can revolutionize care for children with central nervous system (CNS) tumors by enabling precise monitoring of therapeutic responses and detecting recurrence or measurable residual disease (MRD). These assays can detect cell-free, circulating tumor DNA (ctDNA) via somatic alterations, though accurately measuring low-abundance ctDNA in CSF is challenging. Methods Our research focused on the optimization of next-generation sequencing library preparation from cell-free DNA (cfDNA), evaluating four commercial kits to address the low nucleic acid yield in CSF-derived cfDNA. The selected kit minimized false positives and detected somatic variants at 5% variant allele frequency using 0.1 ng input of synthetic cfDNA, suitable for low-volume CSF samples. Results We then applied our optimized workflow to six children with CNS tumors using a personalized hybrid-capture sequencing strategy (“MRD4U”), in which individualized panels were designed based on each patient’s tumor sequencing. Using MRD4U, we identified ctDNA in two samples, even though neither patient had radiographic or clinical evidence of disease at the time of liquid biopsy. Notably, one ctDNA-positive patient developed radiographic recurrence four months later, demonstrating the assay’s potential to detect molecular relapse ahead of conventional clinical measures. Conclusions These findings demonstrate applicability of our personalized MRD4U assay in early detection of disease recurrence. Unlike non-targeted or tumor-agnostic CSF liquid biopsy approaches, MRD4U leverages patient-specific genomic information to enable sensitive, tumor-informed monitoring that can be deployed across a wide range of pediatric CNS tumors. Our approach is broadly applicable to any tumor type with existing genomic data, enabling ctDNA detection across diverse diagnoses. Ultimately, this strategy may inform clinical decision-making and enable earlier therapeutic intervention.

Resveratrol, a naturally occurring polyphenolic compound found in various plants, has been extensively studied for its broad spectrum of beneficial biological effects. These encompass its potent antioxidant properties, anti-inflammatory activities, anti-aging capabilities, cardioprotective functions, and neuroprotective potential. The diverse biological actions of resveratrol extend beyond these well-established properties. It also exerts a significant impact on metabolic processes and bioavailability, and critically, it demonstrates the ability to effectively traverse the blood–brain barrier. This capacity to penetrate the central nervous system renders resveratrol a promising therapeutic agent for the management of central nervous system malignancies, as it has been shown to inhibit tumor cell proliferation, induce apoptosis, and modulate key signaling cascades, such as PI3K/Akt, JAK/STAT, and NF-kB. The multifaceted nature of resveratrol’s biological effects, including its influence on diverse physiological processes, underscores its potential as a valuable therapeutic option for the treatment of central nervous system tumors.

Background Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification. Results This paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls. Conclusions The proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.

Type 2 diabetes mellitus (T2DM) is a pivotal chronic disease with an increasing prevalence. Recent studies have found associations between T2DM and the development of central nervous system (CNS) tumours, a special class of solid tumours with an unclear pathogenesis. In this study, we aimed to explore the relationship between T2DM and certain glycaemic factors with common CNS tumours by using genetic data to conduct Mendelian randomization (MR) and co-localisation analysis. We found a causal relationship between T2DM and glioblastoma, fasting glucose and spinal cord tumours, glycated haemoglobin and spinal cord tumours, and insulin-like growth factor-1 and spinal cord tumours, pituitary tumours, and craniopharyngiomas. These results clarify the relationship between T2DM, glucose-related factors, and common CNS tumours, and they provide valuable insight into further clinical and basic research on CNS tumours, as well as new ideas for their diagnosis and treatment.

Sirtuins (SIRTs), a family of NAD+-dependent enzymes, play crucial roles in epigenetic regulation, metabolism, DNA repair, and stress response, making them relevant to glioma biology. This review systematically summarizes the molecular mechanisms and context-specific functions of SIRT1–SIRT7 in central nervous system tumors, with particular focus on gliomas. SIRT1, SIRT3, SIRT5, and SIRT7 are often overexpressed and promote glioma cell proliferation, stemness, therapy resistance, and metabolic adaptation. Conversely, SIRT2, SIRT4, and SIRT6 generally exhibit tumor-suppressive functions by inducing apoptosis, inhibiting invasion, and counteracting oncogenic signaling. Preclinical studies have identified several sirtuin modulators—both inhibitors and activators—that alter tumor growth, sensitize cells to temozolomide, and regulate pathways such as JAK2/STAT3, NF-κB, and mitochondrial metabolism. Emerging evidence positions sirtuins as promising targets for glioma therapy. Future studies should evaluate sirtuin modulators in clinical trials and explore their potential for patient stratification and combined treatment strategies.

Intraoperative cytology is an important diagnostic modality improving on the accuracy of the frozen sections. It has shown to play an important role especially in the intraoperative diagnosis of central nervous system tumors. To study the diagnostic accuracy of squash preparation and frozen section (FS) in the intraoperative diagnosis of central nervous system (CNS) tumors. This prospective study of 114 patients with CNS tumors was conducted over a period of 18 months (September 2004 to February 2006). The cytological preparations were stained by the quick Papanicolaou method. The squash interpretation and FS diagnosis were later compared with the paraffin section diagnosis. Of the 114 patients, cytological diagnosis was offered in 96 cases. Eighteen nonneoplastic or noncontributory cases were excluded. Using hematoxylin and eosin-stained histopathology sections as the gold standard, the diagnostic accuracy of cytology was 88.5% (85/96) and the accuracy on FS diagnosis was 90.6% (87/96). Among these cases, gliomas formed the largest category of tumors (55.2%). The cytological accuracy in this group was 84.9% (45/53) and the comparative FS figure was 86.8% (46/53). In cases where the smear and the FS diagnosis did not match, the latter opinion was offered. Squash preparation is a reliable, rapid and easy method and can be used as a complement to FS in the intraoperative diagnosis of CNS tumors.

Controversies in Neurosurgery II reflects real-world situations where multiple treatment options are often considered for difficult neurosurgical cases. Each chapter begins with an opening case in which experts describe the pros and cons of different treatment methodologies and operative techniques, helping neurosurgeons select the best treatment plan for individual patients in their practice. It is informed with the expertise of more than 100 of the world's preeminent neurosurgeons. Key features: * Experts from all over the world offer their opinions on more than 20 controversial \"hot\" topics in neurosurgery, including a discussion about whether transcranial or endoscopic approaches are the better option for resecting colloid cysts and a lively debate about the optimal treatment of giant ophthalmic artery aneurysms * All treatment options are presented concisely in one volume, so that neurosurgeons don't have to spend time consulting other sources * The moderators' conclusion at the end of each chapter explains the scientific evidence about the case and synthesizes the views presented Controversies in Neurosurgery II will be treasured by neurosurgeons and neurosurgery residents as well as by neurologists and neuroradiologists and will be a key volume in their medical libraries for years to come.

After 8 years, an update of central nervous system (CNS) tumors was published in 2016 after 2007. First time ever, molecular markers along with histology have been used in classification of any tumor. Major changes are seen in glioma and medulloblastoma groups. Few entities have been added such as diffuse midline glioma, H3 K27M-mutant, RELA fusion-positive ependymoma, embryonal tumor with multilayered rosettes, C19MC-altered, and hybrid nerve sheath tumors. Few variants and patterns that no longer have diagnostic and/or biological relevance and have been deleted such as glioblastoma cerebri, protoplasmic and fibrillary astrocytoma, and cellular ependymoma. Other changes include deletion of term “primitive neuroectodermal tumor,” addition of criterion of brain invasion in atypical meningioma, separation of melanotic schwannoma from other schwannoma, and combination of solitary fibrous tumors and hemangiopericytoma as one entity. There is also expansion of entities in nerve sheath tumors and hematopoietic/lymphoid tumors of the CNS. In this review article, we tried to review CNS tumors 2016 classification update in a simplified manner; comparing the differences between 2016 and 2007 CNS tumors classifications with brief description of important molecular markers and finally utility as well as challenges of this classification.

Multidrug resistance (MDR) commonly leads to cancer treatment failure because cancer cells often expel chemotherapeutic drugs using ATP-binding cassette (ABC) transporters, which reduce drug levels within the cells. This study investigated the clinical characteristics and single nucleotide variant (SNV) in ABCB1, ABCC1, ABCC2, ABCC4, and ABCG2, and their association with mortality in pediatric patients with central nervous system tumors (CNST). Using TaqMan probes, a real-time polymerase chain reaction genotyped 15 SNPs in 111 samples. Patients were followed up until death or the last follow-up day using the Cox proportional hazards model. An association was found between the rs1045642 (ABCB1) in the recessive model (HR = 2.433, 95% CI 1.098–5.392, p = 0.029), and the ICE scheme in the codominant model (HR = 9.810, 95% CI 2.74–35.06, p ≤ 0.001), dominant model (HR = 6.807, 95% CI 2.87–16.103, p ≤ 0.001), and recessive model (HR = 6.903, 95% CI 2.915–16.544, p = 0.038) significantly increased mortality in this cohort of patients. An association was also observed between the variant rs3114020 (ABCG2) and mortality in the codominant model (HR = 5.35, 95% CI 1.83–15.39, p = 0.002) and the dominant model (HR = 4.421, 95% CI 1.747–11.185, p = 0.002). A significant association between the ICE treatment schedule and increased mortality risk in the codominant model (HR = 6.351, 95% CI 1.831–22.02, p = 0.004, HR = 9.571, 95% CI 2.856–32.07, p ≤ 0.001), dominant model (HR = 6.592, 95% CI 2.669–16.280, p ≤ 0.001), and recessive model (HR = 5.798, 95% CI 2.411–13.940, p ≤ 0.001). The genetic variants rs3114020 in the ABCG2 gene and rs1045642 in the ABCB1 gene and the ICE chemotherapy schedule were associated with an increased mortality risk in this cohort of pediatric patients with CNST.

Central nervous system (CNS) malignancies are associated with poor prognosis, as well as exceptional morbidity and mortality, likely as a result of low rates of early diagnosis and limited knowledge of the tumor growth and resistance mechanisms, dissemination, and evolution in the CNS. Monitoring patients with CNS malignancies for treatment response and tumor recurrence can be challenging because of the difficulty and risks of brain biopsies and the low specificity and sensitivity of the less invasive methodologies that are currently available. Therefore, there is an urgent need to detect and validate reliable and minimally invasive biomarkers for CNS tumors that can be used separately or in combination with current clinical practices. The circulating tumor DNA (ctDNA) of cerebrospinal fluid (CSF) samples can outline the genetic landscape of entire CNS tumors effectively and is a promising, suitable biomarker, though its role in managing CNS malignancies has not been studied extensively. This review summarizes recent studies that explore the diagnostic, prognostic, and predictive roles of CSF-ctDNA as a liquid biopsy with primary and metastatic CNS malignancies.