Recent studies have highlighted the role of plasma biomarkers in differentiating Alzheimer's disease (AD) from other neurodegenerative disorders. For instance, Palmqvist et al. demonstrated that plasma phospho-tau217 exhibited a high discriminative accuracy for AD, achieving an area under the curve (AUC) of 0.89 in neuropathologically defined cases and 0.96 for clinical AD dementia, outperforming other biomarkers such as plasma p-tau181 and neurofilament light chain (NfL) (ref: Palmqvist doi.org/10.1001/jama.2020.12134/). Lantero Rodriguez et al. further supported the predictive value of plasma p-tau181, showing significant increases in levels up to 8 years before post-mortem diagnosis, indicating its potential for early detection of AD pathology (ref: Lantero Rodriguez doi.org/10.1007/s00401-020-02195-x/). Additionally, Chatterjee et al. explored the association between plasma metabolites and neurodegeneration, providing insights into biochemical changes that precede clinical symptoms, which could inform future therapeutic targets (ref: Chatterjee doi.org/10.1111/jnc.15128/). These findings collectively underscore the importance of plasma biomarkers in understanding and diagnosing neurodegenerative diseases, although the methodologies and specificities of these biomarkers vary, necessitating further validation in diverse cohorts. Moreover, the identification of molecular signatures in rare CNS tumors has expanded our understanding of tumor biology. Łastowska et al. introduced a diagnostic method using NanoString technology to classify four distinct CNS tumor types based on gene methylation profiles, emphasizing the potential of molecular profiling in clinical diagnostics (ref: Łastowska doi.org/10.1186/s40478-020-00984-9/). This approach aligns with the growing trend of integrating molecular data into neuropathological assessments, which may enhance diagnostic accuracy and treatment stratification.

The integration of artificial intelligence (AI) into neuropathology has shown promise in enhancing diagnostic accuracy, particularly in glioma classification. Jin et al. demonstrated that deep learning algorithms applied to hematoxylin and eosin stained slide images could effectively classify glioma subtypes, suggesting that AI could complement traditional histopathological methods (ref: Jin doi.org/10.1093/neuonc/). This study highlights the potential for AI to streamline diagnostic workflows and improve consistency in tumor classification. Furthermore, Torre et al. investigated gliomas harboring NTRK fusions, revealing that these alterations, present in less than 2% of gliomas, can significantly influence treatment decisions due to the efficacy of TRK inhibitors (ref: Torre doi.org/10.1186/s40478-020-00980-z/). The use of next-generation sequencing and methylation profiling in their study underscores the importance of molecular characterization in guiding targeted therapies. In addition to AI and molecular profiling, Choi et al. explored the application of radiomics as an imaging biomarker for glioblastoma (GBM), demonstrating that imaging subtypes derived from radiomic features correlate with genomic characteristics (ref: Choi doi.org/10.3390/cancers12071707/). This study suggests that radiomics could serve as a non-invasive tool for assessing tumor biology, potentially aiding in personalized treatment approaches. Kessler et al. further emphasized the clinical relevance of molecular profiling in glioblastoma, reporting on the integration of comprehensive molecular analyses into routine clinical practice, which may enhance patient outcomes through tailored therapies (ref: Kessler doi.org/10.1093/noajnl/). Collectively, these studies illustrate a shift towards more sophisticated, data-driven approaches in neuropathology that leverage AI, molecular profiling, and imaging techniques to improve diagnostic accuracy and therapeutic strategies.

The tumor microenvironment plays a crucial role in the progression and treatment response of various CNS tumors, particularly glioblastomas (GBM). Iba et al. investigated the relationship between neuroinflammation and T cell infiltration in Lewy body disease and α-synuclein transgenic models, suggesting that aberrant immune activation may contribute to neurodegeneration in these disorders (ref: Iba doi.org/10.1186/s12974-020-01888-0/). This study highlights the potential for targeting immune pathways as a therapeutic strategy in neurodegenerative diseases. In the context of GBM, Eoli et al. reported that dendritic cell immunotherapy, when combined with temozolomide, led to an expansion of effector and memory T cells, which was associated with improved survival in recurrent GBM patients (ref: Eoli doi.org/10.1093/noajnl/). This finding underscores the importance of the immune response in shaping treatment outcomes and suggests that enhancing T cell activity could be a viable strategy for improving GBM therapy. Moreover, Lohmann et al. explored the effects of interferon-β on glioblastoma stem cells, revealing that exposure to this cytokine induced a fragile phenotype characterized by reduced migratory and MAPK pathway activity (ref: Lohmann doi.org/10.1093/noajnl/). This suggests that manipulating the tumor microenvironment through cytokine exposure could alter the behavior of glioma-initiating cells, potentially impacting tumor aggressiveness and treatment resistance. Additionally, Foltyn et al. assessed the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant gliomas, confirming its specificity for lower-grade gliomas while noting limitations in sensitivity (ref: Foltyn doi.org/10.1093/noajnl/). This study highlights the ongoing need for reliable imaging biomarkers that can inform clinical decision-making in glioma management. Together, these findings illustrate the complex interplay between the tumor microenvironment and immune response, emphasizing the need for integrated therapeutic strategies that consider both aspects.

Molecular profiling has emerged as a pivotal component in the development of targeted therapies for various tumors, including meningiomas and non-small cell lung cancer (NSCLC). Deng et al. investigated the therapeutic potential of the CREB-binding protein inhibitor ICG-001 in sporadic meningiomas with Neurofibromin 2 gene mutations, demonstrating that low Merlin expression correlated with tumor proliferation and poor clinical outcomes (ref: Deng doi.org/10.1093/noajnl/). Their findings suggest that ICG-001 may selectively inhibit tumor growth in patients with low Merlin expression, highlighting the importance of molecular characterization in guiding treatment decisions. This aligns with the broader trend of utilizing molecular alterations to inform therapeutic strategies in oncology. In the context of NSCLC, Haentschel et al. conducted a prospective study to evaluate the impact of biopsy techniques on the detection of molecular genetic alterations, emphasizing the need for standardized approaches to optimize mutation detection rates (ref: Haentschel doi.org/10.3390/diagnostics10070459/). Their findings underscore the critical role of accurate molecular characterization in tailoring individualized treatment plans for patients with advanced NSCLC. Furthermore, Willenbacher et al. reported on the clinical outcomes of patients with high-grade B cell lymphomas characterized by double or triple copy number gains, revealing that these patients had similarly poor prognoses compared to those with rearrangements of MYC and BCL2/BCL6 (ref: Willenbacher doi.org/10.1007/s00277-020-04124-0/). This study highlights the need for comprehensive molecular profiling to better understand the prognostic implications of various genetic alterations in lymphomas. Collectively, these studies illustrate the transformative potential of molecular profiling in oncology, paving the way for more effective and personalized therapeutic approaches.

The application of artificial intelligence (AI) and machine learning in pathology is revolutionizing the diagnosis and characterization of CNS tumors. Gojo et al. utilized single-cell RNA sequencing to dissect the cellular hierarchies within pediatric ependymomas, revealing insights into their developmental trajectories and intratumoral heterogeneity (ref: Gojo doi.org/10.1016/j.ccell.2020.06.004/). This study underscores the potential of single-cell technologies to enhance our understanding of tumor biology and inform therapeutic strategies. Additionally, Hallal et al. focused on extracellular vesicles (EVs) as a source of biomarkers for glioblastoma, demonstrating that small non-coding RNAs within EVs could distinguish GBM from lower-grade astrocytomas, highlighting the promise of liquid biopsies in CNS tumor diagnostics (ref: Hallal doi.org/10.3390/ijms21144954/). Moreover, the relationship between binge eating behaviors and brain structure was explored by Abdo et al., who examined subcortical volumes and cortical thickness in individuals with binge eating disorder (BED) (ref: Abdo doi.org/10.1016/j.jad.2019.10.032/). While not directly related to tumor pathology, this study illustrates the broader applicability of AI and machine learning techniques in analyzing complex datasets to uncover associations between behavior and brain morphology. The integration of AI into pathology not only enhances diagnostic accuracy but also facilitates the identification of novel biomarkers and therapeutic targets, paving the way for more personalized approaches to patient care.

Genomic and transcriptomic analyses are providing critical insights into the molecular underpinnings of CNS tumors, with implications for diagnosis and treatment. Sorce et al. constructed a genome-wide atlas of mRNA abundance and splicing alterations during prion disease progression, revealing that specific molecular events are tightly linked to disease pathogenesis (ref: Sorce doi.org/10.1371/journal.ppat.1008653/). This study emphasizes the potential for transcriptomic profiling to identify early biomarkers of disease and inform therapeutic strategies. Similarly, Ferrandi et al. conducted a transcriptome analysis of skeletal muscle in a mouse model of cerebral ischemic stroke, uncovering altered gene expressions associated with proteolytic pathways and neuromuscular junctions (ref: Ferrandi doi.org/10.3390/genes11070726/). Their findings highlight the importance of understanding the systemic effects of CNS pathologies on peripheral tissues, which could inform rehabilitation strategies. Additionally, Nessler et al. investigated a mitochondrial PCK2 missense variant in Shetland Sheepdogs with paroxysmal exercise-induced dyskinesia, providing insights into the genetic basis of this condition (ref: Nessler doi.org/10.3390/genes11070774/). This study underscores the relevance of genomic studies in elucidating the molecular mechanisms underlying neurological disorders. Collectively, these investigations illustrate the transformative impact of genomic and transcriptomic research in advancing our understanding of CNS tumors and related conditions, ultimately guiding the development of targeted therapies.

The clinical implications of biomarkers in neuropathology are becoming increasingly evident, particularly in understanding disease mechanisms and guiding therapeutic interventions. Lampl et al. identified a novel mechanism by which virus-infected hepatocytes can selectively undergo apoptosis through reduced mitochondrial resilience, highlighting the role of TNF receptor signaling in this process (ref: Lampl doi.org/10.1016/j.jhep.2020.06.026/). This finding may have broader implications for understanding cell death mechanisms in various neurological disorders. In a different context, Mekbib et al. implemented a virtual reality-based limb mirroring therapy for stroke rehabilitation, demonstrating proactive motor functional recovery in patients (ref: Mekbib doi.org/10.1007/s13311-020-00882-x/). This innovative approach underscores the potential for integrating technology into clinical practice to enhance rehabilitation outcomes. Furthermore, Fendt et al. profiled mitochondrial DNA heteroplasmy in oral squamous cell carcinoma, revealing insights into the role of mitochondrial genetics in tumorigenesis (ref: Fendt doi.org/10.3390/cancers12071933/). Their findings contribute to the growing body of evidence linking mitochondrial dysfunction to cancer progression. Collectively, these studies illustrate the critical role of biomarkers in elucidating disease mechanisms, informing treatment strategies, and ultimately improving patient outcomes in neuropathology.