Search Results (216)

Diffuse gliomas are the most common primary brain tumors in adults in the Western world. According to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the assessment of isocitrate dehydrogenase (IDH1/2)-mutation status is essential for accurate patient stratification. In this study, we performed a comprehensive evaluation of IDH-mutation status in the intraoperative setting using the Idylla platform. The reference cohort comprised 30 formalin-fixed paraffin-embedded (FFPE) tissue samples with known IDH status, while the exploration cohort included 35 intraoperative snap-frozen and native-tissue specimens. The results were compared with those of a standard next-generation sequencing (NGS) analysis. Our findings demonstrate that the Idylla IDH-mutation assay provides 100% concordance compared with NGS analysis for both FFPE and intraoperative tissue samples. The Idylla system delivers results within approximately 90 min, significantly outperforming NGS, which requires between 7 and 27 days. This rapid turnaround facilitates timely interdisciplinary case discussions and enables timely therapy planning, within the framework of neuro-oncological molecular tumor boards. The ultra-fast intraoperative IDH-mutation analysis using the Idylla platform, in combination with intraoperative histopathological assessment, enables rapid patient stratification and treatment planning in diffuse gliomas. Full article

Background: Alexander disease (AxD) is a rare severe leukodystrophy that has no cure to date. A pathogenic gain-of-function variant in the GFAP gene affects the astrocytes and subsequently the function of the white matter in the CNS. Methods: We retrospectively analyzed the most frequent symptoms of nine AxD cases, classified them according to published classifications, and described the need of care and support. Results: The description of the courses of disease of nine cases with AxD reflects the broad spectrum of different phenotypes of AxD, with often occurring apnoea. Data about care and support for AxD patients indicate a high and heterogeneous need of support. Treatment with steroids reduced symptoms in two patients. Some patients showed lasting improvement during their course of disease. Conclusions: The course of AxD is very heterogeneous. Thus, we extracted relevant key features to describe the severity of the disease, namely feeding problems, epilepsy, age-appropriate motor function, failure to thrive, age-appropriate language and apnoea. We recommend early evaluation for clinical care and support. For some AxD patients, treatment with steroids may alleviate symptoms. Further development of efficient treatments is necessary. Full article

Objective: The T classification of oral squamous cell carcinoma (OSCC) is linear according to the tumor size, excluding T4a by its criteria of invasion into adjacent structures, such as cortical bone. This may lead to the upstaging of otherwise small tumors. The objective was to analyze patients with OSCC and negative nodal staging to assess the impact of T-staging with tumor size on the incidence of occult cervical lymph node metastasis (CLNM) and regional neck failure. Methods: This retrospective cohort study included patients with OSCC and clinically negative necks (cN0), treated surgically between 2010 and 2024. All T4a OSCC classified due to bone invasion were additionally reclassified into T1–T3 based on size and depth of invasion according to the current staging manual. The primary endpoint of this study was the association between OSCC stratified by T-stage and tumor size as well as the presence of occult CLNM. Results: A total of 642 patients were included, with an overall occult CLNM rate of 20.2%. Bone invasion in T1-sized tumors was significantly associated with occult CLNM (OR 6.38, 95% CI: 1.48–27.42), whereas no such association was observed in T2 or T3 tumors (OR 0.80, 95% CI: 0.37–1.73; and OR 0.77, 95% CI: 0.37–1.62, respectively). Additionally, in T1–T2 tumors, bone invasion did not correlate with worse survival outcomes. Conclusions: Bone invasion was not significantly associated with occult CLNM in T2-3 sized OSCC, suggesting that the prognostic relevance is size-dependent. These findings question the uniform upstaging to T4a and support a more differentiated approach, potentially enabling neck management de-escalation in selected early-stage cases. Full article

The search for markers applicable for efficient differential diagnosis and personalized therapy is a priority task of experimental neuro-oncology. Modern molecular methods allow us to analyze human biopsy material; however, further actions with this extracted tumor tissue are limited. Relevant and sophisticated CNS tumor models are required for precise therapy development. Although it is possible to use human biomaterial to create 2D and 3D cultures and implant them into xenograft animals, the data generated from such models is limited. Due to changes in the classification of the CNS tumors in 2021, a representative model should have not only morphological similarity to human tumors but also key genetic aberrations for studying the mechanisms of carcinogenesis and personalized therapy (such as PDGFRa, Olig1/2, Sox2, and Mki67) for different glioma models such as astrocytoma, oligodendroglioma, and glioblastoma. On the basis of a unique scientific facility “The Collection of experimental tumors of the nervous system and neural tumor cell lines” (Avtsyn Research Institute of Human Morphology of “Petrovsky National Research Center of Surgery”), there is a biobank of chemically induced transplantable tumors of laboratory animals. Their properties, mechanisms, and progression closely correlate with malignant CNS neoplasms in humans. These are potentially useful for identifying novel signaling pathways associated with oncogenesis in the nervous system and personalizing therapeutic approaches. In our work, we characterized a tissue-transplantable brain tumor strain of rat glioma101.8 using MRI, IHC, scRNA-seq, and qPCR-RT methods. According to this study, the cellular composition of the tissue-transplantable rat glioma 101.8 strain was determined, as well as the major genetic signature characteristics of each cell population of this tissue-transplantable strain and its microenvironment. Full article

Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a new method based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT). In particular, CWT led to the maximum classification accuracy, which was ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use. Full article

Background: Burkitt lymphoma is a rare, aggressive B-cell neoplasm with frequent central nervous system (CNS) involvement, treated with intensive multidrug regimens associated with rituximab. The aim of this study was to assess the efficacy, safety, and feasibility of the LMB protocol in adults with BL in a real-world setting. Methods: We included 55 patients with BL diagnosis according to the 2008 WHO classification, treated with LMB protocol associated with rituximab. Low-risk patients (no bone marrow or CNS involvement) were treated in the group B arm, while high-risk patients were placed in group C, which was further stratified by age and CNS infiltration. Results: Thirty-four patients (62%) were treated in group B and 21 patients (38%) were treated in group C, with a median age of 34 years (16–77). Extranodal infiltration was present in 71% patients, including 11 (20%) with CNS involvement. After a median follow up time of 7 years, the complete remission rate was 85%, and progression-free and overall survival at 3 years were 79% and 84%, respectively. Patients with CNS infiltration had an inferior survival rate (55% at 3 years). Grade 3–4 toxicities were frequent, mainly hematologic, infectious, and mucosal. Treatment required substantial supportive care, including 1604 transfusions and 4696 days of hospitalization. Patients over 60 years had poorer outcomes and higher toxicity. Conclusions: The LMB protocol demonstrated high survival rates in adult BL, although at the cost of significant toxicity and considerable health care resource utilization. Outcomes remained suboptimal in patients with CNS involvement despite treatment intensification. Full article

Ependymomas are a heterogeneous group of central nervous system tumors originating from ependymal cells, exhibiting significant variability in clinical behavior, prognosis, and treatment response based on anatomical location and molecular profile. Historically, diagnosis and grading relied on histopathological features, often failing to predict outcomes accurately across tumor subtypes. With the integration of molecular and epigenetic profiling, the classification and management of ependymomas have undergone a significant transformation, culminating in the updated 2021 World Health Organization Classification of Tumors of the Central Nervous System. This molecularly driven system emphasizes the relevance of DNA methylation patterns and fusion oncogenes, offering a more biologically accurate stratification of disease. These insights enhanced diagnostic accuracy and informed prognostic assessments, paving the way for new targeted therapies. Although conventional treatment primarily consists of surgical resection and radiotherapy, emerging preclinical and early-phase clinical studies suggest a potential for molecularly guided interventions targeting specific oncogenic pathways. Despite these advances, effective targeted therapies remain limited, highlighting the need for further research and molecular stratification in clinical trial design. Additionally, the practical implementation of molecular diagnostics in standard-of-care settings is challenged by cost, accessibility, and institutional variability, which may impede equitable integration. This review summarizes the evolution of ependymoma classification, current molecular subtypes, gaps in clinical application and their implications for personalized therapy and future clinical research. Full article

Background: Periprosthetic joint infection (PJI) is one of the most serious complications following total joint arthroplasty. The debridement, antibiotics, irrigation, and implant retention (DAIR) procedure is commonly employed to treat acute, early-stage infections, but its success is highly variable, influenced by factors such as pathogen virulence and antibiotic susceptibility profiles. This study aimed to evaluate the impact of pathogens responsible for these infections on the outcome of DAIR. Methods: This retrospective, single-center study analyzed the microbiological profiles of 116 patients (66 hips and 50 knees) treated for acute periprosthetic joint infections (PJIs) with DAIR between 2018 and 2022. Acute PJI was defined as a duration of symptom less than three weeks, according to the criteria established by the Tsukayama and Izakovicova classification. Preoperative joint aspirations, intraoperatively collected tissue samples, and sonication of the exchanged mobile parts were analyzed for each case. We differentiated between monomicrobial PJI, polymicrobial PJI (defined as the identification of more than one microorganism from preoperative joint fluid aspiration or intraoperative samples), and difficult-to-treat (DTT) pathogens. Results: In this cohort, the following pathogen profiles were identified: culture-negative cases accounted for 11.1% of infections, while 64.2% were attributed to Gram-positive bacteria, 19.8% to Gram-negative bacteria, and 4.9% to fungal pathogens. Among the identified microorganisms, coagulase-negative staphylococci (CNS) were the most frequently detected, exhibiting a notable oxacillin resistance rate of 52.9% and rifampicin resistance rate of 28.7%. Additionally, no significant difference in revision-free implant survival was found between patients with DTT pathogens and/or polymicrobial PJI and those without such infections. Conclusions: This study highlights that pathogens in prosthetic joint infections (PJIs) do not solely determine outcomes, as patient-specific factors (comorbidities, implant type) may also play a key role. Regional variations in pathogens and antibiotic resistance patterns should guide empirical therapy. For instance, this study found a high reliance on vancomycin due to high oxacillin resistance in CNS, the most frequent causative pathogen. Full article

Histone deacetylases (HDACs) are key epigenetic regulators that influence chromatin remodeling, gene expression, and cellular plasticity in the central nervous system (CNS). This review provides a comprehensive overview of the classification and functional diversity of HDACs, with particular emphasis on their roles in neural progenitor cells, mature neurons, and glial populations. In neural stem and progenitor cells, HDACs modulate neurogenesis, fate specification, and lineage commitment. In differentiated neurons, HDACs govern synaptic plasticity, memory formation, and survival. In glial cells, including astrocytes and microglia, HDACs orchestrate inflammatory responses, redox balance, and metabolic adaptations. We further examine the dysregulation of HDAC expression and activity in major neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. Evidence from human post-mortem brain studies reveals region- and isoform-specific alterations in HDAC expression, which are closely associated with cognitive decline, mitochondrial dysfunction, and neuroinflammation. Preclinical studies support the use of HDAC inhibitors (HDACi) as neuroprotective agents, capable of restoring acetylation homeostasis, reducing neuroinflammation, and improving neuronal function. Given the relevance of HDACi, we summarize current clinical studies assessing the safety of these compounds in the context of tumor biology, as well as their potential future applications in neurodegenerative diseases. Together, this review underscores the dual significance of HDACs as biomarkers and therapeutic targets in the context of CNS disorders. Full article

Cardiovascular disease (CVD) is a term used for disorders affecting the heart. Globally, it is the most common cause of death. The main purpose of this study was the rapid detection of CVD, which is essential for effective cure and inhibition. Early detection may lower the risk of myocardial infarction (MI) and reduce the death rate in CVD patients. Laser-induced breakdown spectroscopy (LIBS) is a non-invasive and less sample preparation technique for early detection of CVD. LIBS technique investigated the variation in intensities of different biochemical elements such as Calcium (Ca), Nitrogen (N), Sodium (Na), Carbon (C) and CN-band in the spectra of healthy and CVD patients. Machine learning algorithms applied to LIBS spectral data for the determination of validation accuracy and classification between CVD and healthy individuals. Several models achieved a perfect 100% highest accuracy, which showed the exceptional precision in the given configuration. The Narrow Neural Network achieved 100% accuracy on both the validation and test datasets in a short duration of 10.008 s. This preliminary research of LIBS combined with machine learning may provide a complementary method over existing analytical techniques for early detection of CVD. Full article

Background/Objectives: Recurrent meningiomas remain difficult to manage due to the absence of effective systemic therapies and comparatively high treatment failure rates, particularly in high-grade tumors. Stereotactic radiosurgery (SRS) offers a minimally-invasive and precise option, particularly for tumors in surgically complex locations. However, the risks associated with re-irradiation, and recent changes in the WHO classification of CNS tumors highlight the need for more personalized and strategic treatment approaches. This systematic review evaluates the safety, efficacy, and clinical considerations for use of SRS for recurrent meningiomas. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted using the PubMed, Scopus, and Web of Science databases for studies reporting outcomes of SRS in recurrent, pathologically confirmed intracranial meningiomas. Studies were excluded if they were commentaries, reviews, case reports with fewer than three cases, or had inaccessible full text. The quality and risk of bias of the included studies were assessed using the modified Newcastle-Ottawa Scale. Data on patient and tumor characteristics, SRS treatment parameters, clinical outcomes, adverse effects, and statistical analysis results were extracted. Results: Sixteen studies were included. For WHO Grade I tumors, 3- to 5-year progression-free survival (PFS) ranged from 85% to 100%. Grade II meningiomas demonstrated more variable outcomes, with 3-year PFS ranging from 23% to 100%. Grade III tumors had consistently poorer outcomes, with reported 1-year and 2-year PFS rates as low as 0% and 46%, respectively. SRS performed after surgery alone was associated with superior outcomes, with local control rates of 79% to 100% and 5-year PFS ranging from 40.4% to 91%. In contrast, tumors previously treated with radiotherapy, with or without surgery, showed substantially poorer outcomes, with 3- to 5-year PFS ranging from 26% to 41% and local control rates as low as 31%. Among patients with prior radiotherapy, outcomes were particularly poor in Grade II and III recurrent tumors. Toxicity rates ranged from 3.7% to 37%, and were generally higher for patients with prior radiation. Predictors of worse PFS included prior radiation, older age, and Grade III histology. Conclusions: SRS may represent a reasonable salvage option for carefully selected patients with recurrent meningioma, particularly following surgery alone. Outcomes were notably worse in high-grade recurrent meningiomas following prior radiotherapy, emphasizing the prognostic significance of both histological grade and treatment history. Notably, the lack of molecular and genetic data in most existing studies represents a key limitation in the current literature. Future prospective studies incorporating molecular profiling may improve risk stratification and support more personalized treatment strategies. Full article

Medulloblastoma (MB), the most common malignant pediatric brain tumor, has undergone reclassification from a histologically defined disease to a genetically stratified spectrum of distinct subgroups: WNT, SHH, Group 3, and Group 4. Advances in molecular profiling, as captured in the 2021 WHO CNS5 classification, have shown meaningful heterogeneity in terms of tumor biology, prognosis, and therapeutic response. However, translating these insights into precise, less toxic treatments remains an ongoing challenge. This review synthesizes current knowledge on MB subgroup biology, treatment strategies, and emerging therapies such as subgroup-specific inhibitors, immunotherapies, and novel chemotherapeutic regimens. This review also explores risk-adapted approaches while addressing global disparities in access to diagnostics and care. As the field moves toward individualized medicine, closing the gap between molecular understanding and equitable implementation will be crucial to improving outcomes and quality of life for children with medulloblastoma worldwide. Full article

This review outlines the technological principles of neural–computer interface (NCI) construction, classifying them according to: (1) the degree of intervention (invasive, semi-invasive, and non-invasive); (2) the direction of signal communication, including BCI (brain–computer interface) for converting neural activity into commands for external devices, CBI (computer–brain interface) for translating artificial signals into stimuli for the CNS, and BBI (brain–brain interface) for direct brain-to-brain interaction systems that account for agency; and (3) the mode of user interaction with technology (active, reactive, passive). For each NCI type, we detail the fundamental data processing principles, covering signal registration, digitization, preprocessing, classification, encoding, command execution, and stimulation, alongside engineering implementations ranging from EEG/MEG to intracortical implants and from transcranial magnetic stimulation (TMS) to intracortical microstimulation (ICMS). We also review mathematical modeling methods for NCIs, focusing on optimizing the extraction of informative features from neural signals—decoding for BCI and encoding for CBI—followed by a discussion of quasi-real-time operation and the use of DSP and neuromorphic chips. Quantitative metrics and rehabilitation measures for evaluating NCI system effectiveness are considered. Finally, we highlight promising future research directions, such as the development of electrochemical interfaces, biomimetic hierarchical systems, and energy-efficient technologies capable of expanding brain functionality. Full article

Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice. Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding. Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization. Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery. Full article

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article