Search Results (219)

Motor neuron diseases (MNDs) encompass a clinically heterogeneous group of neurodegenerative conditions with varying impact on dexterity, mobility, decision making, respiratory and bulbar function. While consensus best-practice recommendations exist for genetic screening, diagnostic work-up, pharmacological and respiratory management, disease-specific facets of driving safety, assessment approaches and intervention strategies to support patients for safe driving have not been comprehensively reviewed. MNDs have unique, phenotype-specific clinical features, and therefore require a careful, thorough and systematic approach to evaluate driving safety. While MNDs are primarily associated with progressive motor impairment, extrapyramidal, cerebellar, cognitive, behavioural, and respiratory manifestations of the disease also affect driving safety and necessitate comprehensive driving assessments and individualised strategies to enable patients to continue to drive. The majority of existing papers focus on amyotrophic lateral sclerosis, and low-incidence MND phenotypes, such as PLS, SBMA, PPS, are glaringly understudied from a driving safety perspective despite the relatively slower progression of these conditions. Beyond the review of specific aspects of driving in MNDs, the main objective of this review paper is to raise awareness of non-motor aspects of MNDs with regard to driving safety and to explore viable strategies to support patients to maintain their independence. Despite the considerable differences in driving regulations around the globe, there are core, disease-specific aspects of MND which are universal. The careful consideration of these clinical factors, comprehensive domain-by-domain assessments, and the implementation of practical, individualised adaptations may enable patients to continue driving safely, maintain their independence and enhance their quality of life. In this paper, we propose a systematic, multidomain driving safety assessment scheme for MND, and outline viable intervention strategies to enhance driving safety. Full article

Alzheimer’s (AD) and Parkinson’s disease (PD) are prominent neurodegenerative disorders characterized by early synaptic loss, which correlates more closely with clinical symptoms than neuronal death. This synaptic impairment is primarily driven by disruptions in synaptic vesicle (SV) trafficking, a critical process for maintaining synaptic integrity through a tightly regulated cycle involving clustering, docking-priming, Ca²⁺-triggered fusion, and endocytosis. In AD, amyloid-β (Aβ) oligomers interfere with SNARE-mediated fusion and endocytosis, while hyperphosphorylated tau obstructs vesicle mobility and docking, resulting in cumulative toxicity that aggravates SV defects. Conversely, in PD, α-synuclein (α-syn) aggregation alters vesicle clustering, membrane fusion, and recycling, and these effects are further influenced by Leucine-rich repeat kinase 2 (LRRK2)-Rab-related trafficking defects and the selective vulnerability of dopaminergic terminals. Different from previous reviews that address synaptic dysfunction in a broader manner, the present review is specifically organized around the SV trafficking cycle and compares both shared presynaptic endpoints and disease-specific upstream mechanisms in AD and PD. In addition, recent mechanism-oriented therapeutic strategies are summarized. This vesicle-cycle-centered perspective may provide a clearer framework for understanding presynaptic pathology and for guiding the development of earlier and more targeted interventions. Full article

Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, their heavy reliance on dense matrix multiplication makes them unsuitable for real-time applications on vehicular edge devices. Spiking neural networks offer a potential solution due to their asynchronous event-driven characteristics and low power consumption. However, existing spiking neural networks face three fundamental challenges: (1) handling heterogeneous inter-event intervals; (2) mitigating quantization errors in regression tasks under limited simulation steps; and (3) efficiently regulating information flow based on external contexts. To address these challenges, we propose an event-driven spiking neural network for private vehicle parking prediction called Spark. First, we design a Time-Adaptive Leaky Integrate-and-Fire neuron with a lookup table-based decay mechanism to efficiently model variable inter-event intervals. Second, an accumulate-based readout strategy is introduced to mitigate quantization errors by integrating discrete spike trains into continuous output values for high-precision regression. Third, a Spiking Contextual Gating module is proposed to selectively regulate spiking information flow across channels based on environmental context. These components are integrated into a unified architecture that maintains high prediction accuracy while remaining computationally efficient. Extensive experiments on real-world datasets demonstrate that Spark achieves an effective balance between prediction accuracy and computational efficiency compared to baselines. Full article

Accurate identification of Alzheimer’s disease (AD)-related cellular characteristics from microscopy images is essential for understanding neurodegenerative mechanisms at the cellular level. While most computational approaches focus on macroscopic neuroimaging modalities, cell type classification from microscopy remains relatively underexplored. In this study, we propose a hybrid vision transformer–convolutional neural network (ViT–CNN) framework that integrates DeiT-Small and EfficientNet-B7 to classify three AD-related cell types—astrocytes, cortical neurons, and SH-SY5Y neuroblastoma cells—from phase-contrast microscopy images. We perform a comparative evaluation against conventional CNN architectures (DenseNet, ResNet, InceptionNet, and MobileNet) and prompt-based multimodal vision–language models (GPT-5, GPT-4o, and Gemini 2.5-Flash) using zero-shot, few-shot, and chain-of-thought prompting. Experiments conducted with stratified fivefold cross-validation show that the proposed hybrid model achieves a test accuracy of 61.03% and a macro F1 score of 61.85, outperforming standalone CNN baselines and prompt-only LLM approaches under data-limited conditions. These results suggest that combining convolutional inductive biases with transformer-based global context modeling can improve generalization for cellular microscopy classification. While constrained by dataset size and scope, this work serves as a proof of concept and highlights promising directions for future research in domain-specific pretraining, multimodal data integration, and explainable AI for AD-related cellular analysis. Full article

Non-long terminal repeat (Non-LTR) retrotransposons are mobile genetic elements that replicate through a “copy-and-paste” mechanism, enabling their expansion within the genome. Aberrant activation of these elements can induce genomic instability, elicit cellular stress responses, and activate inflammasome signaling, leading to tissue injury and disease. The central process of sterile inflammation involves the release and recognition of damage-associated molecular patterns (DAMPs), endogenous molecules that initiate inflammatory responses and form a common basis for many sterile inflammatory disorders. Recent studies have identified non-LTR retrotransposons as key endogenous triggers of DAMP-like signaling that drive sterile inflammation in both neuronal and non-neuronal tissues, contributing to the development of neurodegenerative and other chronic inflammatory diseases. In this review, we summarize recent advances in understanding how non-LTR retrotransposons, particularly LINE and SINE elements, influence sterile inflammation and disease pathogenesis. We highlight how their mobilization reshapes genomic architecture and gene regulation, and how the resulting signaling cascades promote chronic inflammation, immune dysregulation, and tissue injury. We also discuss emerging therapeutic strategies aimed at suppressing retrotransposon activity or interrupting downstream inflammatory signaling for treating sterile inflammation-related diseases. Full article

Calcium (Ca²⁺)signaling dysfunction is a central contributor to neuronal hyperexcitability and seizure propagation in epilepsy, yet the intracellular mechanisms underlying the actions of valproic acid (VPA) remain incompletely understood. In this study, we investigated whether VPA modulates Ca²⁺ homeostasis at the level of the endoplasmic reticulum (ER) and how this action influences cytosolic Ca²⁺ dynamics associated with epileptiform activity. ER Ca²⁺ levels were directly measured using ER-targeted aequorin in HeLa and PC12 cells, while cytosolic Ca²⁺ signals were monitored by fura-2 fluorescence imaging in bovine chromaffin cells exposed to veratridine, a model of sustained sodium channel activation and Ca²⁺ oscillations. VPA induced a concentration-dependent release of Ca²⁺ from the ER, with an IC₅₀ of approximately 17 µM. This effect was preserved in permeabilized cells and exhibited activation kinetics comparable to those elicited by inositol 1,4,5-trisphosphate (InsP₃). Pharmacological inhibition of InsP₃ receptors (InsP₃Rs), but not ryanodine receptors or SERCA, abolished VPA-induced ER Ca²⁺ release, supporting a selective InsP₃R-mediated mechanism. Functionally, VPA suppressed the repetitive cytosolic Ca²⁺ oscillations induced by veratridine, while simultaneously producing a sustained elevation of cytosolic Ca²⁺ originating from ER stores and facilitating depolarization-evoked catecholamine secretion. Together, these results support the conclusion that VPA induces InsP₃R-mediated Ca²⁺ mobilization from the endoplasmic reticulum and identify ER Ca²⁺ release as a previously unrecognized intracellular mechanism contributing to its modulatory effects on Ca²⁺ signaling and excitability in epilepsy. Full article

Background/Objectives: Cancer pain affects 55–95% of patients with advanced malignancy, representing a complex syndrome involving nociceptive, neuropathic and nociplastic mechanisms. Despite therapeutic advances, two-thirds of patients with metastatic cancer experience inadequate pain control. This scoping review synthesizes recent advances in cancer pain pathophysiology and management, focusing on molecular and cellular mechanisms, emerging pharmacological, interventional and technological therapies and key evidence gaps to inform future precision-based pain management strategies. Methods: Following PRISMA-ScR methodology, we searched PubMed, Embase, Scopus, and Web of Science for studies published between January 2022 and September 2025. After screening 3412 records, 278 studies were included and analyzed across different domains: biological mechanisms, pharmacological management, interventional and neuromodulatory approaches, radiotherapy developments, and digital health innovations. Results: Recent mechanistic research reveals cancer pain arises from tumor–neuron–immune crosstalk, with malignant cells secreting neurotrophic factors that promote axonal sprouting and nociceptor sensitization. Genetic polymorphisms and epigenetic modifications contribute to inter-individual pain variability. Management strategies are evolving toward multimodal precision medicine: NSAIDs and opioids remain foundational, complemented by adjuvant agents and interventional procedures including nerve blocks, intrathecal delivery, and neuromodulation (spinal cord and dorsal root ganglion stimulation). Stereotactic body radiotherapy demonstrates superior analgesic durability versus conventional approaches. Digital health innovations, such as mobile applications, remote monitoring, wearables, and AI-enabled predictive models, enable continuous assessment and personalized treatment optimization. Conclusions: Cancer pain management is transitioning toward mechanism-based precision medicine integrating biological insights, advanced interventional techniques, and digital technologies. However, implementation challenges persist, including limited randomized trials for interventional approaches, the incomplete external validation of AI tools, and digital health equity concerns. Future research must prioritize prospective controlled studies and equitable integration into routine care. Full article

The human lifespan has increased dramatically over the last few decades; however, reaching older age increases the risk of age-related diseases and ailments. To extend the healthspan, many have turned to supplements, including plant-based remedies used in traditional medicine, to promote healthy aging. One of these is Scutellaria lateriflora L. (S. lateriflora), native to North America, which has traditionally been used to treat anxiety, stress, and insomnia. However, clinical trials addressing its effects are very limited. Furthermore, plant material is intrinsically complex, and the preparation method affects the composition of extracts. We therefore used Drosophila to test whether S. lateriflora can confer resilience against age-related sleep and mobility deficits, using aqueous (SLAq) and ethanol extracts (SLE). Whereas both SLE and SLAq improved mobility, only SLE reduced sleep fragmentation in older males. By testing several flavonoids present in S. lateriflora, we found that the beneficial effects on mobility were mainly due to baicalin, whereas sleep was improved by a wogonin mix. Since neither the extracts nor the compounds extend the lifespan, this suggests that they improve neuronal health and function and do not generally slow down the aging process. This was supported by our finding that neuronal degeneration was reduced by S. lateriflora (SL) supplementation. Full article

This work develops and systematically evaluates a physics-informed neural network (PINN) solver for the fully coupled, time-dependent Muskat–Leverett system with capillarity modeled in the pressure equation. A single shallow–wide multilayer perceptron jointly predicts wetting pressure and water saturation; physical capillary pressure regularizes the saturation front, while a small numerical diffusion term in the saturation residual acts as a training stabilizer rather than a shock-capturing device. To guarantee admissible states in stiff regimes, we introduce a saturation soft-clamping head enforcing $0<{\mathrm{S}}_{\mathrm{w}}<1$ and activate it selectively for stiff mobility ratios. Using IMPES solutions as reference, we perform a sensitivity study over network depth and width, interior collocation and boundary data density, mobility ratio, and injection pressure. Shallow-wide networks (10 layers × 50 neurons) consistently outperform deeper architectures, and increasing interior collocation points from 5000 to 50,000 reduces mean saturation error by about half, whereas additional boundary data have a much weaker effect. Accuracy is highest at an intermediate mobility ratio and improves monotonically with higher injection pressure, which sharpens yet better conditions the front. Across all regimes, pressure trains easily while saturation determines model selection, and the PINN serves as a physics-consistent surrogate for what-if studies in two-phase porous-media flow. Full article

Epilepsy is increasingly conceptualized as a disorder of large-scale network instability, involving impairments in interhemispheric connectivity, prefrontal inhibitory control, and slow-frequency temporal processing. Rhythmic sensory stimulation—auditory, vibrotactile, or multisensory—can entrain neuronal oscillations and modulate attentional and sensorimotor networks, yet its mechanistic relevance to epileptic network physiology remains insufficiently explored. This conceptual and mechanistic article integrates empirical findings from entrainment research, prefrontal timing theories, multisensory integration, and network-based models of seizure dynamics and uses them to formulate a hypothesis-driven framework for multisensory exogenous rhythmic stimulation (ERS) in focal epilepsy. Rather than presenting a tested intervention, we propose a set of speculative mechanistic pathways through which low-frequency rhythmic cues might serve as an external temporal reference, engage fronto-parietal control systems, facilitate multisensory-driven sensorimotor coupling, and potentially modulate interhemispheric frontal coherence. These putative mechanisms are illustrated by exploratory neurophysiological observations, including a small pilot study reporting frontal coherence changes during mobile ERS exposure, but they have not yet been validated in controlled experimental settings. The framework does not imply therapeutic benefit; instead, it identifies theoretical pathways through which rhythmic sensory cues may transiently interact with epileptic networks. The proposed model is intended as a conceptual foundation for future neurophysiological validation, computational simulations, and early feasibility research in the emerging field of digital neuromodulation, rather than as evidence of clinical efficacy. This Hypothesis article formulates explicitly testable predictions regarding how multisensory ERS may transiently modulate candidate physiological markers of prefrontal network stability in focal epilepsy. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative condition characterized by the degeneration of upper and lower motor neurons. This degeneration leads to a gradual muscle weakness, dysarthria, dysphagia, respiratory insufficiency, and, in some patients, alterations in cognitive and behavioral performance. Regardless of advancements made in pharmacological and gene-targeted interventions, a definitive curative treatment remains elusive. Consequently, rehabilitation plays a pivotal role in preserving autonomy, participation, and overall quality of life. This review outlines the current evidence and clinical approaches related to multidisciplinary rehabilitation in ALS. It covers physical and occupational therapy, respiratory, speech and language, psychological, and palliative care domains. Evidence supports moderate tailored exercise programs, early respiratory therapy, and structured management of mobility deficits, spasticity, pain, dysphagia, and communication impairments as key elements of symptomatic treatment. Psychological and social support, which includes the involvement of caregivers and relatives, enhances emotional well-being and coping resilience. Even with progressive development of gene-targeted and disease-modifying therapies, rehabilitation will stay relevant for maintaining long-term motor function. This review highlights the need for standardized, evidence-based rehabilitation protocols and intensified neurorehabilitation research to strengthen clinical outcomes and quality of life as key therapeutic goals in ALS management. Full article

Activation functions are fundamental to the representational capacity and optimization dynamics of deep neural networks. Although numerous nonlinearities have been proposed, ranging from classical sigmoid and tanh to modern smooth and trainable functions, no single activation is universally optimal, as each involves trade-offs among gradient flow, stability, computational cost, and expressiveness. This study introduces TRex, a novel activation function that combines the efficiency and linear growth of rectified units with the smooth gradient propagation of saturating functions. TRex features a non-zero, smoothed negative region inspired by tanh while maintaining near-linear behavior for positive inputs, preserving gradients and reducing neuron inactivation. We evaluate TRex against five widely used activation functions (ReLU, ELU, Swish, Mish, and GELU) across eight convolutional architectures (AlexNet, DenseNet-121, EfficientNet-B0, GoogLeNet, LeNet, MobileNet-V2, ResNet-18, and VGGNet) on two benchmark datasets (MNIST and Fashion-MNIST) and a real-world medical imaging dataset (SkinCancer). The results show that TRex achieves competitive accuracy, AUC, and convergence stability across most deep, connectivity-rich architectures while maintaining computational efficiency comparable to those of other smooth activations. These findings highlight TRex as a contextually efficient activation function that enhances gradient flow, generalization, and training stability, particularly in deeper or densely connected architectures, while offering comparable performance in lightweight and mobile-optimized models. Full article

Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article

We study motif-based optimization for sparse multilayer perceptrons (MLPs), where weights are shared and updated at the level of small neuron groups (‘motifs’) rather than individual connections. Building on Sparse Evolutionary Training (SET), our approach reduces the number of unique parameters and redundant multiply–accumulate operations by exploiting block-structured sparsity. Across Fashion-MNIST and a lung X-ray dataset, our Motif-SET improves training/inference efficiency with modest accuracy trade-offs, and we provide a principled recipe to choose motif size based on accuracy and efficiency budgets. We further compare against representative modern sparse training and compression methods, analyze failure modes such as overly large motifs, and outline real-world constraints on mobile/embedded targets. Our results and ablations indicate that motif size $m=2$ often offers a strong balance between compute and accuracy under resource constraints. Full article

The Ataxin-2-like (ATXN2L) protein is required to survive embryonic development, as documented in mice with the constitutive absence of the ATXN2L Lsm, LsmAD, and PAM2 domains due to knock-out (KO) of exons 5–8 with a frameshift. Its less abundant paralog, Ataxin-2 (ATXN2), has an extended N-terminus, where a polyglutamine domain is prone to expansions, mediating vulnerability to the polygenic adult motor neuron disease ALS (Amyotrophic Lateral Sclerosis) or causing the monogenic neurodegenerative processes of Spinocerebellar Ataxia Type 2 (SCA2), depending on larger mutation sizes. Here, we elucidated the physiological function of ATXN2L by deleting the LsmAD and PAM2 motifs via loxP-mediated KO of exons 10–17 with a frameshift. Crossing heterozygous floxed mice with constitutive Cre-deleter animals confirmed embryonic lethality among offspring. Crossing with CamK2a-CreERT2 mice and injecting tamoxifen for conditional deletion achieved chimeric ATXN2L absence in CamK2a-positive frontal cortex neurons and reduced spontaneous horizontal movement. Global proteome profiling of frontal cortex homogenate showed ATXN2L levels decreased to 75% and dysregulations enriched in the alternative splicing pathway. Nuclear proteins with Sm domains are critical to performing splicing; therefore, our data suggest that the Like-Sm (Lsm, LsmAD) domains in ATXN2L serve a role in splice regulation, despite their perinuclear location. Full article