Search Results (161)

Objective: To evaluate microstructural and morphological alterations of the extraocular optic nerve in patients with idiopathic Parkinson’s disease (IPD) using MRI-based texture analysis and to compare these findings with those of healthy controls. Methods: This retrospective study included 74 participants (37 IPD patients and 37 age- and sex-matched controls). IPD diagnosis was established according to the UK Parkinson’s Disease Society Brain Bank criteria. Coronal T2-weighted images obtained from a 1.5-T MRI system were retrospectively analyzed. A round region of interest was placed on the intraorbital extraocular segment of the optic nerve for histogram-based and radiomic texture analyses. Optic nerve sheath diameter (ONSD) was also measured. As no significant differences were observed between right and left eyes, the mean value of both eyes was used for statistical analyses. Statistical analyses included independent samples t-test, Mann–Whitney U test, and chi-square test. Results: No significant differences were observed between groups regarding age or sex (p > 0.05). ONSD was significantly greater in the IPD group than in healthy controls (5.2 ± 0.8 mm vs. 3.2 ± 0.5 mm, p < 0.05). Histogram analysis demonstrated significantly lower entropy values in patients with IPD (p < 0.05). In GLRLM analysis, short-run emphasis and high gray-level run emphasis were significantly lower, whereas long-run emphasis and low gray-level run emphasis were significantly higher in the IPD group (p < 0.05). GLSZM analysis revealed increased small zone emphasis and decreased large zone emphasis parameters in patients with IPD compared with controls (p < 0.05). Conclusions: MRI-based texture analysis reveals significant structural and microstructural alterations in the extraocular optic nerve in IPD, supporting its potential role as a non-invasive imaging biomarker for subclinical visual pathway involvement. Full article

Quantum dots (QDs) are semiconductor nanocrystals with unique photophysical properties, rendering them promising for applications in biomedical imaging, neuroscience, and various industrial sectors. However, the rapid expansion of their production and application inevitably leads to the release of QDs into the environment throughout their life cycle, classifying them as an emerging class of contaminants of concern. Their potential neurotoxicity not only represents a major bottleneck obstructing their clinical translation but also poses environmental and health risks that warrant serious attention. This review summarizes recent advances in the neurotoxicity of QDs, with a focus on their adverse effects on the central and peripheral nervous systems. It indicates that the mechanisms of QD neurotoxicity involve a complex network comprising oxidative stress, metabolic reprogramming, neuroinflammation, and multiple cell death pathways. Notably, the peripheral nervous system is highlighted as an early-warning target, and the significant risks associated with long-term, low-dose environmental exposure are emphasized. Full article

Improving the decoding accuracy and information transfer rate (ITR) of steady-state visual evoked potential brain–computer interface (SSVEP-BCI) systems, while enhancing cross-subject generalization and reducing calibration cost, is essential for practical deployment. This study proposes an end-to-end framework that integrates adaptive filtering with semi-supervised domain adaptation. The framework incorporates a Gabor adaptive filter bank (G-AFB) to optimize time–frequency representations and extract features matched to individual neural responses. It also introduces a three-stage semi-supervised domain-adversarial neural network (TriS-DANN), which combines unsupervised pre-alignment and supervised fine-tuning to align cross-subject feature distributions and enable lightweight calibration. On the 1.0 s public benchmark dataset, G-AFB-tCNN achieved 89.13% accuracy, a 4.63 percentage-point improvement over its conventional filter-bank counterpart. On the 0.4 s in-house dataset, G-AFB-tCNN achieved 91.85% accuracy, a 3.22 percentage-point improvement over the conventional fixed filter bank. In transfer learning, TriS-DANN reached 86.60% accuracy using 0.4 s segments extracted from the stimulation period and only 23.07% of the available target-domain training/calibration trials, demonstrating higher efficiency and stability than conventional fine-tuning. These results support the proposed framework as a feasible route toward reliable, low-calibration SSVEP-BCI systems. Full article

Fatigue monitoring and detection are crucial for improving efficiency and safety due to their influence on reducing cognitive and physical performance that may result in safety-related incidents. This paper proposes a filter bank-based approach that decomposes electroencephalography (EEG) signals into delta, theta, alpha, beta, and gamma sub-bands for feature extraction to enhance fatigue detection using a wearable EEG-based brain–computer interface (BCI). The study utilized a publicly available EEG dataset from 40 participants collected with a dry-EEG headband while performing two cognitive tasks: a Cognitive Vigilance Task (CVT) and a Multi-Modal Integration Task (MMIT). The data was previously investigated for stress detection on the MMIT. In this study, we investigate fatigue detection on the CVT. Subjects who were not fatigued post-CVT were iteratively removed. Two models were trained with five models to classify the fatigued state from the non-fatigued state, one using features extracted from a broadband filter approach and the other from the proposed filter bank approach. Leave-one-subject-out cross-validation yielded accuracies of 75.8% $\pm$10.4% (95% confidence interval) from the broadband filter approach, and 86.4% $\pm$8.3% (95% confidence interval) from the proposed filter bank approach, yielding an overall increase of 10.6%. These results demonstrate the potential of filter bank-based feature extraction for fatigue detection in wearable EEG-based BCI systems. Full article

Background: Major depressive disorder (MDD) is increasingly recognized as a highly heterogeneous disorder. Although the person-based similarity index (PBSI) provides a useful framework for characterizing individualized brain structural similarity, existing studies in MDD remain limited by either small samples or a lack of integration across different morphological features. Methods: We used structural MRI data from 1442 patients with MDD and 1277 healthy controls to calculate PBSI scores of cortical morphology measures based on cortical thickness (CT), cortical volume (CV), cortical surface area (SA), and sulcal depth (SD). Group comparisons of whole-brain PBSI and regional contributions to PBSI scores were then performed, and a subgroup analysis in 243 first-episode, drug-naive (FEDN) patients with MDD was further conducted. Results: Patients with MDD showed significant alterations in PBSI. Specifically, PBSI scores were significantly reduced for CT, CV, and SD, whereas no significant group difference was observed for SA in the main analysis. Analyses of regional contributions to PBSI further revealed significant between-group differences across multiple cortical regions. These alterations were mainly distributed in the default mode, ventral attention, and visual networks for CT; in the default mode, ventral attention, sensorimotor, and visual networks for CV; and in the default mode, dorsal attention, frontoparietal, and sensorimotor networks for SD. Similar patterns were also observed in the FEDN MDD subgroup. Conclusions: These findings provide neurobiological evidence for the marked structural heterogeneity of MDD and highlight the potential of PBSI as an individualized neuroimaging marker for more precise diagnosis and personalized intervention. Full article

Background: The absence of disease-modifying treatments for Parkinson’s disease (PD)—a neurodegenerative condition with escalating global incidence—represents a critical unmet medical need. Traditionally utilized for both dietary consumption and medicinal preparations, the fruit derived from Rosa roxburghii Tratt demonstrates a remarkably rich profile of biologically active compounds, with flavonoids, triterpenoids, and organic acids representing the predominant classes. Experimental evidence indicates that these compounds elicit robust antioxidative, anti-inflammatory, and neuroprotective effects, making them promising candidates for neurodegenerative disease modulation. This study aimed to systematically evaluate the neuroprotective effects of Rosa roxburghii Tratt juice concentrate powder (RRJCP) across the preventive, interventional, and therapeutic stages of PD and to elucidate its underlying molecular mechanisms. Methods: Rosa roxburghii Tratt juice was subjected to rotary evaporation concentration and vacuum freeze-drying to obtain the juice concentrate powder. C57BL/6 mice were randomly assigned to three main groups (prevention, intervention, and treatment), each containing subgroups including a normal control, an MPTP model group, low-, medium-, and high-dose RRCJP groups (50, 100, and 200 mg/kg), and a positive control Madopar group, totaling 18 subgroups. A chronic MPTP-induced PD mouse model was established. Motor function was assessed via the open field test, pole test, and wire hang test. Substantia nigra neuronal morphology was examined by hematoxylin and eosin staining. The area of tyrosine hydroxylase (TH)-positive regions was measured by immunohistochemistry. The levels of oxidative stress indicators in serum were measured using biochemical kits. Network pharmacology was employed to predict core targets, and the expression of PI3K/AKT pathway and apoptosis-related proteins was determined by Western blotting. Results: Compared with the MPTP model group, RRCJP (200 mg/kg) significantly increased the total distance traveled in the open field, shortened the pole climbing time, and improved the wire hang score. It attenuated the morphological disorganization and nuclear pyknosis of substantia nigra neurons, increased the TH-positive area and TH protein expression, reduced serum MDA content, and elevated the activities of SOD and GSH-Px. Network pharmacology analysis indicated that the PI3K/AKT signaling pathway was among the core targets. Western blotting results further showed that the juice concentrate powder upregulated the expression of p-PI3K, p-AKT, and Bcl-2, while downregulating Bax and Cleaved Caspase-3 levels, which was consistent with the network pharmacology prediction. Conclusions: RRCJP exerts neuroprotective effects across the preventive, interventional, and therapeutic stages in PD model mice, the mechanisms of which may be associated with activation of the PI3K/AKT signaling pathway, attenuation of oxidative stress, and inhibition of neuronal apoptosis. Full article

Improving the efficiency of distribution systems (DSs) through reactive power compensation using shunt capacitor banks is a widely applied practice, as it enhances the voltage profile and reduces operating costs. Owing to the nonlinear nature of DSs, heuristic algorithms—along with other optimization tools—are frequently employed to support techno-economic decision-making in DS design. In this study, we employ the neural population dynamics optimization algorithm (NPDOA), a recently developed heuristic approach inspired by brain neuroscience. The simulation and optimization model adopted in this research is based on quasi-static time-series analysis, which enables the planning problem and DS constraints to be examined from a probabilistic perspective. A comparative analysis with the genetic algorithm (GA) and the whale optimization algorithm (WOA) indicates that NPDOA provides a similar solution with comparable computational time. Specifically, the results show that NPDOA produces a solution only 0.02% higher than GA, with improvement probabilities of 27.42% and 10.94%, respectively. In comparison with WOA, NPDOA yields a solution that is 0.05% lower, with a corresponding probability of improvement of 10.76%. Furthermore, the installation of shunt capacitor banks optimized using NPDOA reduces the net present cost by 33%. Full article

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts. Full article

Background: Polygenic risk scores for Alzheimer’s disease (AD), organized by gene networks shared between the blood and brain, may provide insights into underlying disease mechanisms common to both tissues. Methods: We derived a blood–brain network-based polygenic risk score (nbPRS) from AD-associated genetic variants for three blood-brain networks, selected by the preservation of blood and brain gene co-expression networks, and AD association. Participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 1109), Framingham Heart Study (FHS, n = 8310), the Religious Orders Study Memory Aging Project (ROSMAP, n = 1215), and Mount Sinai Brain Bank (MSBB, n = 323) were stratified into low- and high-nbPRS subgroups, then profiled using longitudinal and cross-sectional data. We compared the conversion from normal cognition to AD between nbPRS subgroups. Genes differentially expressed among low- and high-nbPRS individuals were profiled with classical neuropathological markers and we investigated potential biologically relevant pathways for the genes significantly expressed in high-risk individuals. Results: Individuals with high nbPRS in three AD-associated networks (M2, M6, M14) demonstrated significant impairment in executive function and memory performance, whereas high-risk individuals in networks M2 and M14 had significantly reduced hippocampal volume. We observed high-risk individuals in M2 and M14 developed AD at twice the rate of low-risk individuals in these networks. HLA genes were differentially expressed with transcriptome-wide significance among low- and high-nbPRS individuals in M14 and associated with neuroinflammatory and tau pathology. Conclusions: Polygenic risk scores derived from blood and brain networks can differentiate individuals with a high risk of AD conversion. Full article

Background/Objectives: Traumatic brain injury (TBI) affects more than 50 million people annually worldwide. Challenges in managing moderate-to-severe TBI include high rates of hospital-acquired infections and substantial variability in discharge disposition, and these combined challenges contribute significantly to the cost and trajectory of health recovery. Although current strategies such as antibiotic-impregnated external ventricular drains (EVDs) offer some benefit in controlling infections, they remain limited by high cost and inconsistent implementation. A clearer understanding of clinical and demographic factors associated with infection risk and discharge disposition are essential for improving care pathways. This study aims to identify and quantify key determinants of infection and discharge outcomes in patients with TBI. Methods: The National Trauma Database (NTDB) was queried using structured query language (SQL) based on predefined inclusion criteria (adult patients with ICD-coded TBI), input variables (basic demographics, injury location and severity, and vital signs), and specified outcome variables (emergency department discharge disposition, infection, and sepsis) to identify and filter the eligible patient cohort. A set of machine learning models were trained for each outcome (e.g., Emergency Department (ED) discharge, types of infections, and sepsis). Results: Data from 310,494 patients were extracted. The prediction model we developed, the Predictive TBI-Disposition Model (PTDM), was able to predict the outcome of a patient’s discharge with 96% accuracy. The accuracy of the models for infection and sepsis was 93% and 94%, respectively. Conclusions: Demographic and clinical factors significantly influence the discharge disposition and infection risk among TBI patients. Machine learning models demonstrated strong predictive performance, suggesting their utility in early risk stratification and targeted clinical decision-making. Full article

Background/Objectives: Traumatic brain injury (TBI) remains a major global health concern, contributing substantially to mortality and long-term disability. Although sex hormones have been proposed to influence TBI outcomes, sex has not been incorporated into widely used prognostic models. Given the rapidly aging population in Japan, this study aimed to investigate the impact of sex on post-TBI outcomes. Methods: We analyzed data from the Japan Neurotrauma Data Bank, comprising four prospective multicenter cohorts (P1998, P2004, P2009, P2015). Patients with Glasgow Coma Scale (GCS) scores ≥9 at admission were included. Multivariate logistic regression identified predictors of unfavorable outcomes (death, vegetative state, or severe disability) on the Glasgow Outcome Scale. Subgroup analyses stratified by sex and age were performed. Results: Of 717 eligible patients, 195 (27.2%) were females. Females were significantly older than males (median age: 68 vs. 58.5 years). Traffic accidents were more common among females, whereas non-traffic injuries predominated in males. Independent predictors of unfavorable outcomes included age ≥51 years, male sex, GCS 9–12, Injury Severity Score ≥ 16, hypoxia, targeted temperature management, and subarachnoid hemorrhage. Stratified analysis showed that females aged ≥75 years had significantly better outcomes. Conclusions: Female sex was independently associated with more favorable functional outcomes among patients with TBI presenting with admission GCS ≥ 9, particularly among those aged ≥75 years. Although prior studies have reported potential biological influences, the underlying mechanisms remain uncertain. Further investigation of sex differences and associated risk factors may help inform the development of more individualized management strategies for patients with TBI. Full article

Background: Brain–Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel online compensation method to compensate for systematic effects in the Refresh Rate (RR) of XR displays, enhancing SSVEP classification without requiring additional training or invasive measurements. Methods: A non-invasive monitoring module was incorporated into the developed BCI pipeline to measure frame rate variations in the XR display, allowing deviations between nominal RR and measured values to be automatically detected and compensated for. Classification performance was evaluated using Filter Bank Canonical Correlation Analysis (FBCCA). Statistical significance was assessed using Student’s t-test. Materials: Two datasets were used: a dataset based on Moverio BT-350, including 9 subjects, and a dataset based on HoloLens 2, including 30 subjects, all collected by the authors. Results: The proposed compensation method led to significant improvements in SSVEP classification accuracy, proportional to the magnitude of fps deviations. In some cases, classification accuracy increased by up to 300% relative to its original value. Statistical analyses confirmed the reliability of the results across subjects and datasets. Conclusions: These findings show that the proposed method effectively enhances SSVEP-based BCIs in XR environments and provides a robust foundation for practical applications requiring high reliability. Full article

Background: Two natural steroids derived from cholesterol pathways are testosterone and progesterone, androgen and antiandrogen receptor binding. Steroid androgen antagonists can be prescribed to treat an array of diseases and disorders such as gender dysphoria. In men, androgen antagonists are frequently used to treat prostate cancer and hyperplasia. Sex hormones regulate the expression of the viral receptors in COVID-19 progression, and these hormones may act as a metabolic signal-mediating response to changes in glucose and Reactive Oxygen Species (ROS). The objective of the present study is to use artificial intelligence (AI) applications in healthcare to predict the targets and to assess biological assays of novel steroid derivatives prepared in house from the commercially available 16-dehydropregnenolone acetate (DPA^®) aimed at achieving the metabolic stability of glucose and steroid brain homeostasis. This suggests the introduction of aromatic or aliphatic structures in the steroid B-ring and D-ring. This is important since the roles of 5α-reductase and ROS in brain control of glucose and novel steroids homeostasis remain unclear. Methods: A tool prediction was used as a tuned algorithm, with the novel steroid derivatives data in web interface to carry out their pharmacological evaluation. The new steroidal derivatives were determined with neuroprotection effect using the select biomarkers of oxidative stress on induced hypoglycemic male rat brain and liver. The enzyme kinetics was established by the inhibition of the 5α-reductase enzyme on the brain myelin. Results: We used novel chemical structures to order the information of a Swiss data bank that allow target predictions. Biological assays suggest that steroid derivatives with an electrophilic center can interact more efficiently with the 5α-reductase enzyme, and by this way, induce neuroprotection in hypoglycemia model. All compounds were synthesized with a yield of 30–80% and evaluated with tool target prediction to understand the molecular mechanisms underlying a given phenotype or bioactivity and to rationalize possible favorable or unfavorable side effects, as well as to predict off-targets of known molecules and to clear the way for drug repurposing. Apart, they turned out to be good inhibitors for the 5α-reductase enzyme. Conclusions: The probed efficacy of these novel steroids with respect to spironolactone control appears to be a promising compound for future hormonal therapy with neuroprotection activity in glucose disorder status. However, further research with clinically meaningful endpoints is needed to optimize the use of androgen antagonists in these hormonal therapies in COVID-19 progression. Full article

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain’s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen’s kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment. Full article

Background: Although orbital fractures are common in trauma care, age-specific mechanisms and admission physiology-based risk stratification have not been systematically characterized. This study aimed to identify age–mechanism interaction patterns and develop an admission-based physiological risk score for orbital fracture patients. Methods: This retrospective cohort study analyzed 41,464 adult orbital fracture patients from the National Trauma Data Bank (2018–2020). A three-component physiological risk score was developed using admission vital signs: severe hypotension (<90 mmHg, 2 points), tachycardia (>100 bpm, 1 point), and severe traumatic brain injury (GCS ≤ 8, 1 point). Risk stratification performance was validated against composite adverse outcomes. Results: Distinct age–mechanism patterns emerged: 74.0% of elderly patients (≥65 years) sustained falls, while young adults demonstrated a bimodal distribution with motor vehicle crashes (31.2%) and violence (28.4%). Violence-related injuries occurred in younger patients (40.3 vs. 55.0 years) but had lower injury severity scores (10.0 vs. 14.4) and mortality (2.8% vs. 5.2%) than accidental mechanisms. High-/critical-risk patients (8.4% of the cohort) had 16.2% mortality versus 2.1% in stable patients. Complex facial injuries demonstrated 11-fold higher mortality (7.7% vs. 0.7%). The physiologic risk score achieved AUC 0.79 (95% CI: 0.78–0.80). Conclusions: Age–mechanism interactions revealed distinct bimodal injury patterns in young adults. Admission physiologic parameters effectively identify 8.4% of patients requiring intensive resources, while violence-related injuries paradoxically demonstrate better outcomes than accidental mechanisms. Full article