Search Results (191)

Sphingolipid metabolism has emerged as a regulatory interface between lipid homeostasis, organelle stress, and genome maintenance. Although sphingolipids are essential structural components of cellular membranes, specific metabolites also function as bioactive mediators that shape cellular responses to genotoxic stress. In this review, we examine how canonical and atypical sphingolipid pathways influence the DNA damage response through three mechanistic axes. First, ceramide-centered stress signaling links radiation, chemotherapy, and inflammatory injury to kinase and phosphatase pathways, mitochondrial apoptosis, and checkpoint-associated cell-fate decisions. Second, nuclear sphingolipid metabolism, particularly sphingosine kinase 2-dependent production of sphingosine-1-phosphate, regulates chromatin-associated transcriptional programs through modulation of histone deacetylase activity. Third, persistent sphingolipid imbalance promotes metabolic stress by disrupting lysosomal turnover, mitochondrial function, endoplasmic reticulum homeostasis, and redox balance, thereby increasing endogenous oxidative DNA damage. We also discuss atypical sphingolipids, including 1-deoxysphingolipids generated through altered serine palmitoyltransferase substrate utilization, as emerging mediators of mitochondrial dysfunction and genome instability. Finally, we consider the relevance of these mechanisms to cancer, lysosomal storage disorders, and neurodegenerative diseases, where sphingolipid dysregulation may influence therapeutic responses and disease progression. Together, these position sphingolipid metabolism as an integrated regulatory network connecting cellular stress signaling, chromatin regulation, organelle dysfunction, and genome stability. Full article

Accurate pavement distress identification is essential for infrastructure maintenance. However, prevailing models frequently underperform in complicated environments due to extreme scale variations, atypical defect geometries, and severe background noise. To mitigate these limitations, this study presents BDC-YOLO, an upgraded detection network built upon the YOLOv8 baseline. The proposed architecture structurally incorporates three specialized mechanisms: Bi-level Routing Attention (BRA) to isolate relevant target features from background artifacts; Dynamic Snake Convolution (DySnakeConv) to capture the topological characteristics of elongated and irregularly shaped cracks; and Content-Aware ReAssembly of FEatures (CARAFE) to minimize information degradation during upsampling and refine multi-scale feature fusion. Evaluated on the RDDChina dataset, BDC-YOLO demonstrates superior accuracy over the baseline and comparative state-of-the-art methods. Specifically, the framework yields a mAP_0.5 of 88.9%, representing an absolute gain of 4.7% against the standard YOLOv8 model while achieving an inference speed of 175.4 FPS. Full article

Background: Sensorimotor (mu) rhythms reflect the functional state of sensorimotor cortical networks and are of increasing interest for understanding typical and atypical neurodevelopment. However, the developmental trajectories of mu rhythm modulation in preschool and early school-age children remain poorly characterized. Objectives: We studied age-related changes in alpha (8–13 Hz) and beta (13–30 Hz) sensorimotor rhythms in children aged 3 to 10 years using a mixed longitudinal design and investigated their relationship with language development. Methods: EEG was recorded twice (interval ~1 year) in 44 typically developing children during three conditions: passive hand movement (PHM), video hand movement observation (VHM), and a control condition (video fractal movement, VFM). Language was assessed with the Preschool Language Scales—Fifth Edition (PLS-5) in a subset of 32 of the 44 participants at the first time point. Modulation indices (log10(experimental/control)) were computed, and repeated-measures ANOVAs and Spearman correlations were performed. Results: PHM elicited desynchronization in both alpha and beta bands, while VHM induced alpha synchronization only. Alpha desynchronization showed contralateral lateralization during right- and left-hand movements, without age-related changes. Beta desynchronization showed no lateralization. Beta desynchronization during PHM correlated negatively with Auditory Comprehension and Total Language scores (ρ up to −0.62, FDR-corrected p < 0.05), indicating that more pronounced desynchronization of sensorimotor rhythms relates to better language abilities. Conclusions: These findings support the involvement of sensorimotor networks in auditory language comprehension and suggest that beta mu rhythm may serve as a sensitive marker of individual differences in language development, though replication in larger samples is warranted. Full article

Background and Clinical Significance: This report investigates the complex intersection of macrostructural neuroprotection and cortical hyperexcitability during long-acting atypical antipsychotic maintenance. We present a novel clinical case demonstrating an apparent absence of detectable cerebellar vermis atrophy progression during long-acting injectable (LAI) aripiprazole maintenance, which temporally coincided with the emergence of a potential epileptogenic risk in an adolescent with bipolar disorder (BD) and cannabis use disorder. Beyond motor precision, the vermis modulates emotional-cognitive networks; deficits in these circuits impair impulse control, frequently driving comorbid substance use in youth. Case Presentation: A 16-year-old female with BD and cannabis use disorder presented with pronounced cerebellar and vermis atrophy on brain CT during an acute behavioral crisis. Following diagnostic reformulation, maintenance therapy was initiated via off-label monthly LAI aripiprazole (400 mg) alongside lithium. At 9-month follow-up, psychiatric relapses and cannabis use remitted completely. Repeat CT suggested macrostructural stability with zero apparent atrophy progression. However, during the 9th month, she abruptly developed daily generalized myoclonus. An awake electroencephalogram (EEG) revealed intense cortical hyperexcitability, documenting frequent interictal and ictal epileptiform discharges with generalized 3–4 Hz spike-wave complexes synchronized with the clinical myoclonus. Introduction of levetiracetam (500 mg BID) and cessation of adjunct methylphenidate led to complete clinical and neurophysiological remission. Conclusions: LAI aripiprazole may favorably influence the macrostructural trajectory of the cerebellum/vermis in adolescent BD, suggesting volume stabilization. However, clinicians must monitor for a drug-induced lowering of the seizure threshold, where macrostructural volume preservation coexists with microstructural electrical destabilization. Full article

Children with autism spectrum disorder (ASD) often exhibit atypical patterns of visual attention allocation and social-cue processing. Eye-tracking scanpath (ETSP) retains information about fixation points, saccade paths and their temporal changes in the form of images, providing an intuitive and computable data representation for analyzing ASD-related visual attention patterns. However, in ASD auxiliary identification studies, the same participant often generates multiple eye-tracking recordings or multiple visual representation samples. If participant independence is not properly considered during model evaluation, the training and test sets may share individualized eye-movement patterns from the same child. In such cases, the model may learn subject-specific characteristics rather than stable and transferable ASD-related visual attention features, leading to an overestimation of its recognition ability on unseen participants. To address this issue, we propose a Global–Local Collaborative Fusion Network (GLCF-Net) under a strict participant-independent splitting protocol. Specifically, the proposed method first maps ETSP images into patch token sequences through a shared Patch Embedding layer. A CNN-based local branch is then used to extract local trajectory morphology, path density, and spatial neighborhood structure, while a ViT-based global branch models cross-region gaze transitions and the overall attention distribution. Finally, a gated adaptive fusion module dynamically integrates local and global information to enhance the representation of stable visual attention features. In the primary repeated stratified five-fold participant-level evaluation, averaging the two out-of-fold probabilities for each participant yielded an Accuracy of 87.0% and a ROC-AUC of 93.7%; the original participant split, retained as a secondary analysis, yielded an Accuracy of 83.52% and a ROC-AUC of 90.27%. Under the reported frozen-backbone configurations, the model also showed a balanced pattern across Accuracy, Recall, and F1-score. These results characterize performance for unseen participants within the same dataset and acquisition conditions. Full article

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency management. Full article

Developmental dyslexia is a common neurodevelopmental disorder characterized by persistent difficulties in accurate and fluent word reading despite adequate intelligence, educational opportunity, and intact sensory function. Contemporary neurobiological models have progressively shifted from localization-based explanations toward network-oriented perspectives emphasizing large-scale brain connectivity, developmental maturation, and neuroplasticity. Nevertheless, the developmental mechanisms underlying early right-hemisphere recruitment remain incompletely understood. Although increased right-hemisphere activation has traditionally been interpreted as a compensatory response to left-hemisphere dysfunction, accumulating evidence from longitudinal neuroimaging, intervention, and developmental studies indicates that this explanation alone does not adequately account for the heterogeneity of neurobiological findings observed across individuals with developmental dyslexia. This narrative review synthesizes evidence from developmental neurobiology, network neuroscience, longitudinal neuroimaging, and intervention research to examine the biological processes underlying early right-hemisphere recruitment. Across the reviewed literature, developmental dyslexia is increasingly characterized by atypical maturation of distributed reading networks involving alterations in white-matter development, functional connectivity, hemispheric lateralization, and experience-dependent neuroplasticity. Collectively, these findings suggest that reading networks remain dynamically modifiable throughout literacy acquisition and that multiple developmental pathways may contribute to diverse neurobiological and behavioral outcomes. Building on this evidence, we propose a Developmental Neuroplasticity Framework for Reading Network Reorganization that integrates existing neurobiological models within a unified developmental perspective. Rather than proposing a new neurobiological mechanism, the framework seeks to explain the heterogeneous patterns of early right-hemisphere recruitment that are not fully accounted for by compensation-based interpretations alone. Specifically, it conceptualizes early right-hemisphere recruitment as one possible adaptive developmental outcome emerging from interactions among early neurodevelopmental vulnerability, distributed network connectivity, developmental neuroplasticity, and environmental experience. By integrating evidence that has largely been considered within separate theoretical perspectives, the framework generates empirically testable predictions regarding developmental trajectories, network reorganization, and intervention-related variability, while providing a conceptual basis for earlier identification of children at risk and the development of more targeted, developmentally informed intervention strategies. Full article

Autism spectrum disorder (ASD) is characterized by persistent impairments in social communication together with restricted and repetitive patterns of behavior. Although ASD has traditionally been viewed primarily as a disorder of neural circuitry, increasing evidence indicates that interactions between the immune and nervous systems contribute substantially to brain development and cognitive function. This review develops a neuroimmune–cognitive–account of ASD by examining how immune signaling may influence neural organization and, in turn, cognitive function. Evidence from neuroimmunology, systems neuroscience, and experimental studies of neuromodulation is synthesized to examine relationships among immune signaling, neural network organization, and cognition. Disturbances in these processes have been associated with alterations in excitation–inhibition –balance and atypical large-scale connectivity, especially within networks supporting social cognition. We also examine the role of neuromodulatory systems, with particular emphasis on oxytocin and vasopressin, as intermediaries between biological regulation and cognitive processing. Experimental findings indicate that oxytocin can transiently modulate activity within social brain networks and increase the salience of socially relevant stimuli, although effects across clinical studies remain modest and inconsistent. The reviewed evidence suggests that disturbances in neuroimmune regulation may contribute to altered communication among distributed neural systems, providing one possible account of cognitive dysfunction in ASD. Social deficits, repetitive behaviors, and sensory differences reflect disturbances in the coordination of distributed neural systems rather than isolated impairments within single regions or pathways. This view has important implications for intervention, suggesting that approaches directed at individual molecular or neural targets alone are unlikely to produce broad or lasting effects. More effective strategies may require interventions that address interactions among immune function, neural dynamics, and cognitive processes. The resulting neuroimmune–cognitive account generates testable hypotheses concerning how immune processes may influence neural organization and cognition in ASD while providing a conceptual basis for future experimental and clinical research. Full article

Human pose detection is fundamental to quantitative sports training analysis in college general education courses, enabling an objective assessment of college students’ movement quality and the early identification of sports injury risks among non-professional athletes. At present, those detectors based on YOLO have encountered difficulties in capturing the continuous movement patterns of college athletes in routine training, maintaining the regression accuracy of different size posture targets, and maintaining the real-time calculation speed in the campus sports environment. Furthermore, most existing pose-estimation frameworks are optimized for general scenes and fail to address the unique challenges of college physical education settings, including non-standard student movements, diverse skill levels, and strict cost constraints for large-scale deployment. In order to solve these problems, we put forward PPLCNet-YOLOv11, which is a simplified human posture-estimation framework designed for college physical education. This model is optimized by three key improvements: (1) replacing the original backbone network with PPLCNet to enhance feature extraction, while strictly observing the strict FLOPs and parameter restrictions; (2) an enhanced Multi-Scale Attention Mechanism (MSAM) that combines adaptive scale perception, hierarchical channel attention, and pose-sensitive spatial attention to better represent elongated anatomical structures and multi-scale pose cues; and (3) an improved enhanced IoU loss function that incorporates scale-aware and aspect-ratio-aware penalty terms to refine the bounding box adjustment for atypical and sports-specific gestures. Experiments on both a dedicated college student sports pose dataset and two public benchmark datasets (COCO Keypoints 2017 and MPII Human Pose) demonstrate that PPLCNet-YOLOv11 achieves 77.8% mAP@0.5 and 37.09% mAP@0.95 based on the campus dataset, with 82.34% precision and 75.00% recall, while requiring only 2.62 M parameters and 6.38 GFLOPs. Extensive inference speed tests show that the model achieves 127 FPS on an NVIDIA RTX 4090 GPU, 38 FPS on an Intel i7-12700 CPU, and 16 FPS on a Jetson Nano edge device, meeting the real-time requirements of campus sports monitoring. Compared with mainstream lightweight YOLO variants and state-of-the-art specialized pose-estimation models, our proposed method improves mAP@0.5 by 4.93–12.6 percentage points based on the campus dataset. All experiments were repeated five times with different random seeds, and we report mean values with standard deviations and statistical significance tests to ensure result reliability. These results indicate that PPLCNet-YOLOv11 provides an accurate and resource-efficient solution for real-time pose evaluation in college physical training. Full article

Dynamic community detection seeks to identify changing structural groups in temporal graphs; however, current neural methodologies are susceptible to misinterpreting transient edges, noisy temporal variations, or unusual spectral disturbances as authentic structural changes. This research introduces TriMeta-BFNet, a tri-meta stacked atypical-frequency Bayesian Fourier neural network designed for hallucination-resistant community discovery. The proposed system presents a three-dimensional meta-counterbalance mechanism that includes topological consistency, Fourier-domain atypical frequency modeling, and Bayesian posterior uncertainty estimation. Initially, temporal graph signals are converted into the Fourier domain to distinguish stable low-frequency community patterns from erratic high-frequency disturbances. Secondly, unusual frequency points are detected by spectral energy deviation and integrated into a stacked neural representation module, enabling the model to differentiate significant structural alterations from extraneous oscillations. Third, Bayesian inference is employed to assess posterior uncertainty regarding community assignments, therefore mitigating overconfident predictions in the presence of ambiguous or noisy graph evolution. The three components are simultaneously optimized via a cohesive objective function that integrates community detection loss, structural consistency regularization, atypical-frequency penalty, temporal stability management, and Bayesian calibration loss. The resultant structure offers both resilient community divisions and comprehensible hallucination-risk assessments. TriMeta-BFNet theoretically conceptualizes hallucination in dynamic community detection as an imbalance of structural, spectral, and uncertainty factors, and it develops a mathematically rigorous counterbalance mechanism to mitigate erroneous community evolution. The suggested model presents a novel approach to uncertainty-aware, frequency-sensitive, and interpretable dynamic graph learning. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

The oncocytic variant of adrenocortical carcinoma (OACC) represents an exceptional type of adrenal malignancy, with heterogenous presentation. Currently, the genetic and molecular spectrum remains an open matter. A 20-year-old adult was accidentally found with a 7.2 cm adrenal tumor and underwent an open right adrenalectomy with OACC confirmation. Post-adrenalectomy positron emission tomography/computed tomography was negative. Immunohistochemistry was positive for calretin, inhibin, steroidogenic factor 1; Ki67 of 20%. Microsatellite instability was 7.61. Lin–Weiss–Bisceglia score showed 2 major criteria [mitoses 6/50 HPF + positive atypical mitoses], the reticuline algorithm (disrupted reticuline network + mitoses 6/50 HPF) was consistent for a malignant behavior, the Helsinki score was of 48. Next generation sequencing identified a likely pathogenic variant of CEL gene (heterozygote, c.539-2A>G) in peripheral blood and two pathogenic variants in the tumor: exon 48, NF1 gene [c.7159_7164del p.(N2387_F2388del)] and exon 6, TP53 gene [c.596delG p.(G199Efs*48)]. Polycystic ovary syndrome type A has been diagnosed as teenager with no phenotype change before the tumor detection. After surgery, oocyte retrieval and cryopreservation upon ovarian stimulation protocol (OSP) was performed before starting mitotane therapy. To the best of our knowledge, this is a novel genetic configuration in OACC with an impact on prognosis to be determined. Hyperandrogenemia stands on a dual source (potential CEL-driven insulin resistance for the ovary and OACC-originating for the adrenal glands). Also, this is the first case to receive OSP in OACC, noting that a tailored multidisciplinary management is mandatory. Full article

Background/Objectives: Type II endoleaks (T2ELs) remain one of the most frequent causes of aneurysm sac enlargement following endovascular abdominal aortic aneurysm repair (EVAR). While embolization may be effective in typical T2ELs with a clearly identifiable feeding vessel, management becomes more challenging when no visible communication with a side branch can be demonstrated. Emerging evidence suggests that hypertrophic vasa vasorum may contribute to aneurysm sac expansion in these atypical cases. We present a case of refractory atypical T2EL treated by open conversion and discuss the potential role of the vasa vasorum network in its pathophysiology. Case Presentation: A 77-year-old man presented with lumbar pain ten years after EVAR for a symptomatic abdominal aortic aneurysm. Computed tomography angiography demonstrated progressive aneurysm sac enlargement to 8.5 cm despite three previous translumbar embolization procedures. Multiple areas of contrast pooling were identified within the aneurysm sac, but no clear communication with a feeding side branch was observed. Owing to persistent sac expansion and symptoms, open conversion was performed with partial endograft explantation and reconstruction using a bifurcated PTFE graft. Results: After opening the aneurysm sac and evacuating the thrombus, diffuse bleeding was observed from numerous small vascular orifices distributed throughout the inner sac surface. These findings were considered consistent with a prominent vasa vasorum network. Hemostasis was achieved using a combination of figure-of-eight sutures and electrocautery. The postoperative course was uneventful, and the patient was discharged on postoperative day five. Follow-up imaging demonstrated normal graft patency without complications. Conclusions: This case supports the hypothesis that an extensive vasa vasorum network may contribute to aneurysm sac expansion in atypical T2ELs and possibly endotension after EVAR. In patients with refractory sac enlargement, open conversion remains a definitive treatment option. Further research is needed to clarify the underlying mechanisms and to explore targeted therapeutic strategies aimed at modulating angiogenesis and vascular remodeling. Full article

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability. Full article

Environmental datasets are often characterized by complex spatial structures and the presence of atypical observations that may influence the interpretation of landscape patterns. This study proposes a comparative framework for identifying spatial outliers in landscape structure using two complementary approaches: K-means clustering and multivariate visual exploration based on Chernoff faces. The analysis is conducted on two temporal snapshots (1956 and 2019) representing long-term changes in land use and land cover in the Zemplínska Šírava region, Eastern Slovakia. Outlier detection results from both approaches are systematically compared to assess their consistency and robustness. The two methods show substantial correspondence in the identification of anomalous landscape units. The number of land-cover classes increases from 19 in 1956 to 25 in 2019, reflecting increased landscape heterogeneity over time. Persistent spatial outliers across both methods and time periods include road networks and associated land and broad-leaved forest with continuous canopy, indicating the structural stability of these landscape elements despite long-term transformation. The results demonstrate that combining clustering-based approaches with multivariate visual analytics can improve the interpretation of complex spatial patterns in environmental data. However, the study is exploratory in nature, and the interpretation of Chernoff faces involves inherent visual subjectivity, which should be considered when evaluating the results. The proposed framework should therefore be regarded as a complementary exploratory tool rather than standalone analytical evidence. Future research may extend this framework by integrating identified spatial outliers into environmental assessment models focused on biodiversity patterns, ecological connectivity, and sustainable landscape planning. Full article