Search Results (371)

Among brain regions, the cerebellum (CBL) has traditionally been associated with motor control. However, increasing evidence from connectomics and functional imaging has expanded this view, revealing its involvement in a wide range of cognitive and integrative processes. Despite this emerging relevance, the CBL has received comparatively less attention in aging research, which has focused mainly on other central nervous system (CNS) regions such as the neocortex and hippocampus. This review synthesizes the current evidence on glial cell aging across the CNS, emphasizing how cerebellar circuits follow distinct trajectories in terms of cellular remodeling, transcriptional reprogramming, and structural vulnerability. Recent findings highlight that cerebellar astrocytes and microglia exhibit specific signatures related to aging compared to their cortical counterpart, including moderate reactivity, selective immune response, and spatial reorganization. Cerebellar white matter (WM) undergoes structural alteration, suggesting that oligodendroglial cells may undergo region-specific alterations, particularly within WM tracts, although these aspects remain underexplored. Despite the presence of glial remodeling, the CBL maintains a notable degree of structural and functional integrity during aging. This resilience may be the result of the CBL’s ability to maintain synaptic adaptability and homeostatic balance, supported by its highly organized and compartmentalized architecture. A better understanding of the dynamics of cerebellar glial cells in aging may provide new insight into the mechanisms of brain maintenance and identify potential biomarkers for healthy brain aging. Full article

S100 proteins, a family of Ca²⁺-binding proteins, play numerous roles in cellular processes such as proliferation, differentiation, and apoptosis. Recent evidence has highlighted their critical involvement in neuroinflammation, a pathological hallmark of various neurodegenerative disorders including Alzheimer’s disease, multiple sclerosis, and Parkinson’s disease. Among these proteins, S100B and S100A8/A9 are particularly implicated in modulating inflammatory responses in the CNS. Acting as DAMPs, they interact with pattern recognition receptors like RAGE and TLRs, triggering pro-inflammatory signaling cascades and glial activation. While low concentrations of S100 proteins may support neuroprotective functions, increased levels are often associated with exacerbated inflammation and neuronal damage. This review explores the dualistic nature of S100 proteins in neuroinflammatory processes, their molecular interactions, and their potential as biomarkers and therapeutic targets in neurodegenerative disease management. Full article

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. Full article

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Neurodegenerative diseases (NDDs) such as Alzheimer’s, Parkinson’s, ALS, and Huntington’s pose a growing global challenge due to their complex pathobiology and aging demographics. Once considered as cellular debris, small extracellular vesicles (sEVs) are now recognized as active mediators of intercellular signaling in NDD progression. These nanovesicles (~30–150 nm), capable of crossing the blood–brain barrier, carry pathological proteins, RNAs, and lipids, facilitating the spread of toxic species like Aβ, tau, TDP-43, and α-synuclein. sEVs are increasingly recognized as valuable diagnostic tools, outperforming traditional CSF biomarkers in early detection and disease monitoring. On the therapeutic front, engineered sEVs offer a promising platform for CNS-targeted delivery of siRNAs, CRISPR tools, and neuroprotective agents, demonstrating efficacy in preclinical models. However, translational hurdles persist, including standardization, scalability, and regulatory alignment. Promising solutions are emerging, such as CRISPR-based barcoding, which enables high-resolution tracking of vesicle biodistribution; AI-guided analytics to enhance quality control; and coordinated regulatory efforts by the FDA, EMA, and ISEV aimed at unifying identity and purity criteria under forthcoming Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines. This review critically examines the mechanistic roles, diagnostic potential, and therapeutic applications of sEVs in NDDs, and outlines key strategies for clinical translation. Full article

The effective suppression of inflammation using disease-modifying therapies is essential in the treatment of multiple sclerosis (MS). Anti-CD20 monoclonal antibodies are commonly used long-term as maintenance therapies, largely due to the lack of reliable biomarkers to guide dosing and evaluate treatment response. However, prolonged use increases the risk of infections and other immune-mediated side effects. The unique ability of brain-derived blood extracellular vesicles (EVs) to cross the blood–brain barrier and reflect the central nervous system (CNS) immune status has sparked interest in their potential as biomarkers. This study aimed to assess whether blood-derived L1CAM⁺ EVs could serve as biomarkers of treatment response to rituximab (RTX) in patients with relapsing-remitting MS (RRMS). Serum samples (n = 25) from the baseline (month 0) and after 6 months were analyzed from the RTX arm of the ongoing randomized clinical trial OVERLORD-MS (comparing anti-CD20 therapies in RRMS patients) and were compared with serum samples from healthy controls (n = 15). Baseline cerebrospinal fluid (CSF) samples from the same study cohort were also included. EVs from both serum and CSF samples were characterized, considering morphology, size, and concentration, using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The immunophenotyping of EV surface receptors was performed using flow cytometry with the MACSPlex exosome kit, while label-free quantitative proteomics of EV protein cargo was conducted using a proximity extension assay (PEA). TEM confirmed the presence of EVs with the expected round morphology with a diameter of 50–150 nm. NTA showed significantly higher concentrations of L1CAM⁺ EVs (p < 0.0001) in serum total EVs and EBNA1⁺ EVs (p < 0.01) in serum L1CAM⁺ EVs at baseline (untreated) compared to in healthy controls. After six months of RTX therapy, there was a significant reduction in L1CAM⁺ EV concentration (p < 0.0001) and the downregulation of TNFRSF13B (p = 0.0004; FC = −0.49) in serum total EVs. Additionally, non-significant changes were observed in CD79B and CCL2 levels in serum L1CAM⁺ EVs at baseline compared to in controls and after six months of RTX therapy. In conclusion, L1CAM⁺ EVs in serum showed distinct immunological profiles before and after rituximab treatment, underscoring their potential as dynamic biomarkers for individualized anti-CD20 therapy in MS. Full article

Background: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer’s Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods: An ensemble classification approach was designed by integrating Extra Trees, Random Forest, and Light Gradient Boosting Machine (LightGBM) algorithms. Feature selection emphasized clinically relevant biomarkers, including Amyloid-β 42, phosphorylated tau, diastolic blood pressure, age, and gender. The dataset was split into training and held-out test sets. A probability thresholding strategy was employed to flag uncertain predictions for potential deferral, enhancing model reliability in borderline cases. Results: The final ensemble model achieved an accuracy of 83.2%, a recall of 80.2%, and a precision of 86.3% on the independent test set. The probability thresholding mechanism flagged 23.3% of cases as uncertain, allowing the system to abstain from low-confidence predictions. This strategy improved clinical interpretability and minimized the risk of misclassification in ambiguous cases. Conclusions: The proposed AI-driven ensemble model demonstrates strong performance in classifying MCI versus CN individuals using multimodal ADNI data. Incorporating a deferral mechanism through uncertainty estimation further enhances the model’s clinical utility. These findings support the integration of machine learning tools into early screening workflows for cognitive impairment. Full article

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases. Full article

Breast cancer is a leading cause of central nervous system (CNS) metastases in women, often associated with poor prognosis and limited therapeutic options. However, molecular differences between primary tumors and CNS metastases remain underexplored. We aimed to characterize transcriptomic differences between primary breast tumors and matched CNS metastases and identify immune-related biomarkers associated with metastatic progression and patient outcomes. Transcriptomic profiling was based on 11 matched FFPE sample pairs (primary tumor and CNS metastasis). Paired formalin-fixed paraffin-embedded (FFPE) samples from primary tumors (T1) and CNS metastases (T2) were analyzed using the NanoString nCounter^® platform and the PanCancer IO 360™ Gene Expression Panel. Differential gene expression, Z-score transformation, and heatmap visualization were performed in R. In silico survival analyses for overall survival (OS) and recurrence-free survival (RFS) were conducted using publicly available TCGA and GEO datasets. Forty-five genes were significantly differentially expressed between the T1 and T2 samples. Immune-related genes such as CXCL9, IL7R, CD79A, and CTSW showed consistent downregulation in CNS metastases. High expression of CXCL9 and CD79A was associated with improved OS and RFS, whereas high IL7R and CTSW expression correlated with worse outcomes. These findings indicate immune suppression as a hallmark of CNS colonization. Comparative transcriptomic analysis further underscored the distinct molecular landscapes between primary and metastatic tumors. This study highlights transcriptional signatures associated with breast cancer CNS metastases, emphasizing the role of immune modulation in metastatic progression. The identified genes have potential as prognostic biomarkers and therapeutic targets, supporting the need for site-specific molecular profiling in metastatic breast cancer management. Full article

Medulloblastoma (MB) is the most common malignant brain tumor in children and typically arises in the cerebellum, likely due to disruptions in neuronal precursor development. The primary inhibitory neurotransmitter in the central nervous system (CNS), γ-aminobutyric acid (GABA), exerts its effects through GABA_A, GABA_B, and GABA_C receptors. GABA receptor activity regulates the development and function of cerebellar neurons, including glutamatergic cerebellar granule cells (CGCs). Beyond the nervous system, GABA is also a common metabolite in non-neuronal cell types. An increasing body of evidence indicates that GABA can influence cell proliferation, differentiation, and migration in several types of adult solid tumors, including brain cancers. GABA and GABA_A receptor agonists can impair the viability and survival of MB cells, primarily acting on GABA_A receptors containing the α5 subunit. A marked expression of the gene encoding the α5 subunit is found across all MB tumor molecular subgroups, particularly Group 3 MB, which has a poor prognosis. Importantly, high levels of the γ-aminobutyric acid type A receptor subunit α5 (GABRA5) gene are associated with shorter patient overall survival in Group 3 and Group 4 MB. In contrast, high γ-aminobutyric acid type A receptor subunit β1 (GABRB1) gene expression is related to longer survival in all MB subgroups. The GABAergic system may, therefore, regulate MB cell function and tumor progression and influence patient prognosis, and is worthy of further investigation as a biomarker and therapeutic target in MB. Full article

In clinical applications of surface-enhanced Raman spectroscopy (SERS) immunosensors, accurately determining analyte biomarker concentrations is essential. This study presents a non-invasive approach for quantifying various breast cancer biomarkers—including human epidermal growth factor receptor II (HER-II) (2+, 3+ (I), 3+ (II), 3+ (III), and positive IV) and CA 15-3—using a directional, plasmonically active, label-free SERS sensor. Each stage of sensor functionalization, conjugation, and biomarker interaction was verified by UV–Vis spectroscopy. Atomic force microscopy (AFM) characterized the morphology of gold nanourchin (GNU)-immobilized printed circuit board (PCB) substrates. An enhancement factor of ≈ 0.5 × 10⁵ was achieved using Rhodamine 6G as the probe molecule. Calibration curves were initially established using standard HER-II solutions at concentrations ranging from 1 to 100 ng/mL and CA 15-3 at concentrations from 10 to 100 U/mL. The SERS signal intensities in the 620–720 nm region were plotted against concentration, yielding linear sensitivity with R² values of 0.942 and 0.800 for HER-II and CA15-3, respectively. The same procedure was applied to breast cancer serum (BCS) samples, allowing unknown biomarker concentrations to be determined based on the corresponding calibration curves. SERS data were processed using the filtfilt filter from scipy.signal for smoothing and then baseline-corrected with the Improved Asymmetric Least Squares (IASLS) algorithm from the pybaselines.Whittaker library. Principal Component Analysis (PCA) effectively distinguished the sample groups and revealed spectral differences before and after biomarker interactions. Key Raman peaks were attributed to functional groups including N–H (primary and secondary amines), C–H antisymmetric stretching, C–N (amines), C=O antisymmetric stretching, NH₃⁺ (amines), carbohydrates, glycine, alanine, amides III, C=N stretches, and NH₂ in primary amides. Full article

Traumatic brain injury (TBI) is a major neurological condition affecting millions of individuals each year. Mild TBI (mTBI) manifests differently, with some individuals experiencing persistent, debilitating symptoms while others recover more rapidly. Despite its classification as “mild,” mTBI leads to both short- and long-term neurological effects, many of which occur due to functional changes in the brain. TBI-induced environmental changes within the brain play a critical role in shaping these functional outcomes. The importance of astrocytes in maintaining central nervous system (CNS) homeostasis has been increasingly recognized for their pivotal role in the brain’s response to TBI. Previous studies showed significant TBI-associated metabolic dysregulations. Therefore, we sought to analyze how astrocytes might adapt to persistent metabolic stressors in the post-injury microenvironment and identify injury-induced shifts occurring in vivo that may contribute to chronic metabolic dysfunction. We used an astrocyte-specific genome-scale metabolic model that allowed for the input of biologically relevant uptake rates corresponding to healthy astrocytes to analyze how the activity of metabolic pathways differed in hypoxic and acidic conditions. Additionally, these fluxes were integrated with mass spectrometry-based proteomics from male Sprague-Dawley rats subjected to mTBI to identify chronic adaptive neural responses post-injury. Comparison of modeled metabolic fluxes and experimental proteomic data demonstrated remarkable alignment, with both predicting significant changes in key metabolic processes including glycolysis, oxidative phosphorylation, the TCA cycle, and the Pentose Phosphate Pathway. These overlapping signatures may represent core survival strategies, offering insight into metabolic priorities and potentially serving as biomarkers of injury adaptation or recovery capacity. Full article

Hemolytic uremic syndrome (HUS), a thrombotic microangiopathy primarily affecting the kidneys, can also involve the central nervous system (CNS), often leading to significant morbidity and mortality. Neurologic manifestations are among the most severe extra-renal complications, particularly in children and during outbreaks of Shiga toxin-producing Escherichia coli (STEC)-associated HUS (typical (tHUS)). This review explores the clinical spectrum, pathophysiology, diagnostic workup, and age-specific outcomes of neurologic involvement in both typical (tHUS) and atypical (aHUS). Neurologic complications occur in up to 11% of pediatric and over 40% of adult STEC-HUS cases in outbreak settings. Presentations include seizures, encephalopathy, focal deficits, movement disorders, and posterior reversible encephalopathy syndrome (PRES). Magnetic resonance imaging (MRI) commonly reveals basal ganglia or parieto-occipital lesions, though subtle or delayed findings may occur. Laboratory workup typically confirms microangiopathic hemolytic anemia (MAHA), thrombocytopenia, and kidney damage, with additional markers of inflammation or metabolic dysregulation. Eculizumab is the first-line treatment for aHUS with CNS involvement, while its utility in STEC-HUS remains uncertain. Although many children recover fully, those with early CNS involvement are at greater risk of developing epilepsy, cognitive delays, or fine motor deficits. Adults may experience lingering neurocognitive symptoms despite apparent clinical recovery. Differences in presentation and imaging findings between age groups emphasize the need for tailored diagnostic and therapeutic strategies. Comprehensive neurorehabilitation and long-term follow-up are crucial for identifying residual deficits. Continued research into predictive biomarkers, neuroprotective interventions, and standardized treatment protocols is needed for improving outcomes in HUS patients with neurological complications. Full article

Background/Objectives: In adults with B-cell acute lymphoblastic leukemia (B-ALL), central nervous system (CNS) involvement represents a significant clinical challenge due to its association with adverse outcomes. Infiltration of blast cells into the CNS is primarily detected via cerebrospinal fluid (CSF) microscopy, the current gold standard diagnostic method, although it has limitations in terms of sensitivity. Quantitative polymerase chain reaction (qPCR) offers higher sensitivity and can support the diagnosis of CNS infiltration. This study assessed the mRNA expression levels of CD79A and IL7R in CSF to evaluate their potential for detecting CNS involvement in adults with B-ALL. Methods: CSF samples were collected from adults with B-ALL. The classification criteria for CNS Leukemia (CNS status) were used to evaluate CNS involvement. RNA was extracted from the CSF, and quantitative reverse transcription PCR (RT-qPCR) was used to measure the CD79A and IL7R mRNA expression levels. Results: A total of 19 treatment-naïve adult patients with B-ALL were enrolled over a 19-month period. Four (21%) patients had CNS3 status. Four (21%) patients had CNS3 status. The results also showed that the expression levels of CD79A and IL7R mRNA were significantly higher (median fold change = 0.62 and 2.12, p < 0.05, respectively) in the group with CNS3. Furthermore, using the Haldane-Anscombe correction and Fisher’s exact test, we demonstrated an association between IL7R and CNS3 expression (odds ratio = ∞, due to zero CNS+ in the IL7R group, p < 0.05). Conclusions: CD79A and IL7R mRNA levels in CSF could be potential biomarkers for detecting CNS involvement in adult patients with B-ALL. Full article