Search Results (312)

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder involving the progressive degeneration of upper and lower motor neurons. While oxidative stress, RNA-binding protein (RBP) pathology, mitochondrial dysfunction, and glial–neuronal dysregulation is involved in ALS pathogenesis, current therapies provide limited benefit, underscoring the need for multi-target disease-modifying strategies. Nuclear factor erythroid 2-related factor 2 (Nrf2), classically regarded as a master regulator of redox homeostasis, has recently emerged as a central integrator of cellular stress responses relevant to ALS. Beyond its canonical antioxidant function, Nrf2 regulates critical pathways involved in mitochondrial quality control, proteostasis, nucleocytoplasmic transport, RNA surveillance, and glial reactivity. Experimental models demonstrate that astrocyte-specific Nrf2 activation enhances glutathione metabolism, suppresses neuroinflammation, promotes stress granule disassembly, and reduces RBP aggregation. In C9orf72-linked ALS, Nrf2 activation mitigates dipeptide repeat protein toxicity and restores RNA processing fidelity via modulation of nonsense-mediated decay and R-loop resolution. Recent advances in Nrf2-targeted interventions including Keap1–Nrf2 protein–protein interaction inhibitors, dual Nrf2/HSF1 activators, and cell-type-selective Adeno-associated virus 9 (AAV9) vectors show promise in preclinical ALS models. These multimodal approaches highlight Nrf2’s therapeutic versatility and potential to address the upstream convergence points of ALS pathogenesis. Taken together, positioning Nrf2 as a systems-level regulator offers a novel framework for developing precision-based therapies in ALS. Integrating Nrf2 activation with RNA- and glia-directed strategies may enable comprehensive modulation of disease progression at its molecular roots. Full article

Spinal cord injury (SCI) often results in long-term locomotor impairments, and strategies to enhance functional recovery remain limited. While epidural electrical stimulation (EES) has shown clinical promise, our understanding of the mechanisms by which it improves function remains incomplete. Here, we use genetic tools in an animal model to perform neuromodulation and treadmill rehabilitation in a manner similar to EES, but with the benefit of the genetic tools and animal model allowing for targeted manipulation, precise quantification of the cells and circuits that were manipulated, and the gathering of extensive kinematic data. We used a viral construct that selectively transduces large diameter afferent fibers (LDAFs) with a designer receptor exclusively activated by a designer drug (hM3Dq DREADD; a chemogenetic construct) to increase the excitability of large fibers specifically, in the rat contusion SCI model. As changes in locomotion with afferent stimulation can be subtle, we carried out a detailed characterization of the kinematics of locomotor recovery over time. Adult Long-Evans rats received contusion injuries and direct intraganglionic injections containing AAV2-hSyn-hM3Dq-mCherry, a viral vector that has been shown to preferentially transduce LDAFs, or a control with tracer only (AAV2-hSyn-mCherry). These neurons then had their activity increased by application of the designer drug Clozapine-N-oxide (CNO), inducing tonic excitation during treadmill training in the recovery phase. Kinematic data were collected during treadmill locomotion across a range of speeds over nine weeks post-injury. Data were analyzed using a mixed effects model chosen from amongst several models using information criteria. That model included fixed effects for treatment (DREADDs vs. control injection), time (weeks post injury), and speed, with random intercepts for rat and time point nested within rat. Significant effects of treatment and treatment interactions were found in many parameters, with a sometimes complicated dependence on speed. Generally, DREADDs activation resulted in shorter stance duration, but less reduction in swing duration with speed, yielding lower duty factors. Interestingly, our finding of shorter stance durations with DREADDs activation mimics a past study in the hemi-section injury model, but other changes, including the variability of anterior superior iliac spine (ASIS) height, showed an opposite trend. These may reflect differences in injury severity and laterality (i.e., in the hemi-section injury the contralateral limb is expected to be largely functional). Furthermore, as with that study, withdrawal of DREADDs activation in week seven did not cause significant changes in kinematics, suggesting that activation may have dwindling effects at this later stage. This study highlights the utility of high-resolution kinematics for detecting subtle changes during recovery, and will enable the refinement of neuromechanical models that predict how locomotion changes with afferent neuromodulation, injury, and recovery, suggesting new directions for treatment of SCI. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

iPSCs and their derivatives are used to investigate the molecular genetic mechanisms of human diseases, to identify therapeutic targets, and to screen for small molecules. Combining technologies for generating patient-specific iPSC lines and genome editing allows us to create cell models with unique characteristics. We obtained and characterized three iPSC lines by reprogramming peripheral blood mononuclear cells of a patient with Huntington’s disease (HD) using episomal vectors encoding Yamanaka factors. iPSC lines expressed pluripotency marker genes, had normal karyotypes and were capable of differentiating into all three germ layers. The obtained iPSC lines are useful for modeling disease progression in vitro and studying pathological mechanisms of HD, such as ER stress. A transgene of genetically encoded biosensor XBP1-TagRFP was introduced into the iPSCs to visualize ER stress state of cells. The study demonstrated that iPSC-derived medium spiny neurons develop ER stress, though the IRE1-mediated pathway does not seem to be involved in the process. Full article

The aggregation of α-synuclein (αSyn) is a central feature of Parkinson’s disease (PD) and other synucleinopathies. The efficient clearance of αSyn depends largely on the autophagy–lysosomal pathway. Emerging genetic evidence highlights the role of pleckstrin homology and RUN domain-containing M1 protein (PLEKHM1), a critical regulator of autophagosome–lysosome fusion, in the pathogenesis of multiple neurodegenerative diseases. This study investigates the possible effects of increased PLEKHM1 expression on αSyn pathology and neurodegeneration in mice. We utilized a mouse model of PD that is based on A53T-αSyn overexpression, achieved by the stereotactic injection of recombinant adeno-associated viral vectors (rAAV) into the substantia nigra. Additionally, this study explores the effect of PLEKHM1 overexpression on the autophagy–lysosomal pathway under physiological conditions, using transgenic autophagy reporter mice. PLEKHM1 overexpression facilitated the αSyn-induced degeneration of dopaminergic somata in the substantia nigra and degeneration of dopaminergic axon terminals in the striatum. In concert with αSyn expression, PLEKHM1 also potentiated microglial activation. The extent of αSyn pathology, as reported by staining for phosphorylated αSyn, was not affected by PLEKHM1. Using RFP-EGFP-LC3 autophagy reporter mice, rAAV-mediated PLEKHM1 overexpression reduced lysosomal and autolysosomal area, increased LAMP1-LC3 colocalization, and decreased the autolysosome-to-autophagosome ratio. Concurrently, PLEKHM1 overexpression in both genotypes caused p62 accumulation, accompanied by reduced overlap with lysosomal and autophagosomal markers but increased colocalization with autolysosomal markers, indicating impaired cargo degradation during late-stage autophagy. Taken together, elevated PLEKHM1 levels exacerbate neurodegeneration in αSyn-overexpressing mice, possibly by impairing autophagic flux. Now, with in vivo evidence complementing genetic data, alterations in PLEKHM1 expression appear to compromise autophagy, potentially enhancing neuronal vulnerability to secondary insults like αSyn pathology. Full article

The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuronal networks, predict connectivity patterns, and examine how morphological changes impact neural network function. In this study, we aimed to explore the role of the actin cytoskeleton in neuronal signaling via primary cilia and to elucidate the role of the actin network in conjunction with neuronal electrical activity in shaping spatial neuronal formation and organization, as demonstrated by relevant mathematical models. Our proposed model is based on the polygamma function, a mathematical application of ramification, and a geometrical definition of the actin cytoskeleton via complex numbers, ring polynomials, homogeneous polynomials, characteristic polynomials, gradients, the Dirac delta function, the vector Laplacian, the Goldman equation, and the Lie bracket of vector fields. We were able to reflect the effects of neuronal electrical activity, as modeled by the Van der Pol equation in combination with the actin cytoskeleton, on neuronal morphology in a 2D model. In the next step, we converted the 2D model into a 3D model of neuronal electrical activity, known as a core-shell model, in which our generated membrane potential is compatible with the neuronal membrane potential (in millivolts, mV). The generated neurons can grow and develop like an organoid brain based on the developed mathematical equations. Furthermore, we mathematically introduced the signal transduction of primary cilia in neurons. Additionally, we proposed a geometrical model of the neuronal branching pattern, which we described as ramification, that could serve as an alternative mathematical explanation for the branching pattern emanating from the neuronal soma. In conclusion, we highlighted the relationship between the actin cytoskeleton and the signaling processes of primary cilia. We also developed a 3D model that integrates the geometric organization unique to neurons, which contains soma and branches, such that the mathematical model represents the interaction between the actin cytoskeleton and neuronal electrical activity in generating action potentials. Next, we could generalize the model into a cluster of neurons, similar to an organoid brain model. This mathematical framework offers promising applications in artificial intelligence and advancements in neural networks. Full article

Astrocytes, which proliferate after brain injury, represent a promising target for cellular reprogramming due to their abundance and ability to support brain repair. In this study, we investigated the in vitro reprogramming of primary cortical astrocytes from neonatal rats into neuronal precursor cells (NPCs) using the transcription factors Oct4, Sox2, and Klf4 (OSK), delivered via lentiviral vectors. We designed a reporter system to trace the conversion of astrocytes to NPCs and neurons by using GFAP-driven iCre and Nestin- or Synapsin1-driven fluorescent reporters. After transduction, we observed morphological changes and the expression of neuronal markers in some cells, while many cells remained in a transitional state, expressing both astrocytic and neuronal features. Importantly, the study was not designed to quantify reprogramming efficiency or demonstrate full astrocyte-to-neuron conversion but rather to establish and evaluate a traceable reporter system. Our data suggest that OSK-mediated reprogramming in this in vitro model can initiate conversion of astrocytes to neuronal precursor-like cells, although the process is complex and incomplete within the one-week timeframe. We also highlight limitations in co-transduction efficiency and potential silencing of the reporter system during reprogramming. These findings provide an initial technical platform to explore astrocyte reprogramming in vitro and inform future studies aiming to refine these methods and apply them in vivo. Full article

Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN). An improved population-based metaheuristic optimization algorithm, the artificial lemming algorithm (ALA), is employed to select the hyperparameters of these machine learning models, enhancing their performance. All developed models are trained and tested using experimental data from multiple studies. The performance of the models is evaluated using various statistical metrics, complemented by regression plots, error analysis, and Taylor graphs to further identify the most effective predictive model. The results show that the ALA-RF model obtains the best performance in predicting hydrogen storage, with optimal values of coefficient of determination (R²), root mean square error (RMSE), Willmott’s index (WI), and weighted average percentage error (WAPE) in both training and testing phases (0.9845 and 0.9840, 0.2719 and 0.2828, 0.9961 and 0.9959, and 0.0667 and 0.0714, respectively). Additionally, pressure is identified as the most significant feature for predicting hydrogen storage in MOFs. These findings provide an intelligent solution for the selection of MOFs and optimization of operational conditions in hydrogen storage processes. Full article

In order to address the problem of error accumulation in long-duration autonomous navigation using Strapdown Inertial Navigation Systems (SINS), this paper proposes an error prediction and correction method based on Deep Neural Networks (DNN). A 12-dimensional feature vector is constructed using angular increments, velocity increments, and real-time attitude and velocity states from the inertial navigation system, while a 9-dimensional response vector is composed of attitude, velocity, and position errors. The proposed DNN adopts a feedforward architecture with two hidden layers containing 10 and 5 neurons, respectively, using ReLU activation functions and trained with the Levenberg–Marquardt algorithm. The model is trained and validated on a comprehensive dataset comprising 5 × 10³ seconds of real vehicle motion data collected at 100 Hz sampling frequency, totaling 5 × 10⁵ sample points with a 7:3 train-test split. Experimental results demonstrate that the DNN effectively captures the nonlinear propagation characteristics of inertial errors and significantly outperforms traditional SINS and LSTM-based methods across all dimensions. Compared to pure SINS calculations, the proposed method achieves substantial error reductions: yaw angle errors decrease from 2.42 × 10⁻² to 1.10 × 10⁻⁴ radians, eastward velocity errors reduce from 455 to 4.71 m/s, northward velocity errors decrease from 26.8 to 4.16 m/s, latitude errors reduce from 3.83 × 10⁻³ to 7.45 × 10⁻⁴ radians, and longitude errors reduce dramatically from 3.82 × 10⁻² to 1.5 × 10⁻⁴ radians. The method also demonstrates superior performance over LSTM-based approaches, with yaw errors being an order of magnitude smaller and having significantly better trajectory tracking accuracy. The proposed method exhibits strong robustness even in the absence of external signals, showing high potential for engineering applications in complex or GPS-denied environments. Full article

Bovine alphaherpesvirus 1 (BoAHV-1) is a promising oncolytic virus that can infect the human lung carcinoma cell line A549. In an effort to adapt the virus to grow more rapidly in these cells through the serial passaging of viral progeny, we were unsuccessful. Here, we found that extracellular vesicles (EVs) secreted by BoAHV-1-infected A549 cells (referred to as EDVs) contain 59 viral proteins, including both viral structure proteins (such as gC and gD) and viral regulatory proteins (such as bICP4 and bICP22), as identified via a proteomic analysis. These EDVs can bind to and enter target cells, inhibit viral particles binding to cells, and stimulate the production of IFN-α and IFN-β in A549 cells. When EDVs are inoculated into rabbits via either the conjunctival sacs or intravenously, they can be readily detected in neurons within the trigeminal ganglia (TG), where they reduce viral replication and promote the transcription of IFN-γ. Furthermore, incorporation of the known anti-herpesvirus drug Acyclovir (ACY) into the EDVs leads to synergistically enhanced antiviral efficacy. Collectively, the EDVs exhibit antiviral effects by blocking viral binding to target cells and stimulating the innate immune response, thereby leading to the failure of the serial passaging of viral progeny in these cells, and these EDVs may serve as a promising vector for delivering drugs targeting TG tissues for antiviral purposes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by the degeneration of dopaminergic neurons in the substantia nigra, leading to decreased dopamine levels in the striatum and causing a range of motor and non-motor impairments. Although the molecular mechanisms driving PD progression remain incompletely understood, emerging evidence suggests that the buildup of nuclear DNA damage, especially DNA double-strand breaks (DDSBs), plays a key role in contributing neurodegeneration, promoting senescence and neuroinflammation. Despite the pathogenic role for DDSB in neurodegenerative disease, targeting DNA repair mechanisms in PD is largely unexplored as a therapeutic approach. Ataxia telangiectasia mutated (ATM), a key kinase in the DNA damage response (DDR), plays a crucial role in neurodegeneration. In this study, we evaluated the therapeutic potential of AZD1390, a highly selective and brain-penetrant ATM inhibitor, in reducing neuroinflammation and improving behavioral outcomes in a mouse model of α-synucleinopathy. Four-month-old C57BL/6J mice were unilaterally injected with either an empty AAV1/2 vector (control) or AAV1/2 expressing human A53T α-synuclein to the substantia nigra, followed by daily AZD1390 treatment for six weeks. In AZD1390-treated α-synuclein mice, we observed a significant reduction in the protein level of γ-H2AX, a DDSB marker, along with downregulation of senescence-associated markers, such as p53, Cdkn1a, and NF-κB, suggesting improved genomic integrity and attenuation of cellular senescence, indicating enhanced genomic stability and reduced cellular aging. AZD1390 also significantly dampened neuroinflammatory responses, evidenced by decreased expression of key pro-inflammatory cytokines and chemokines. Interestingly, mice treated with AZD1390 showed significant improvements in behavioral asymmetry and motor deficits, indicating functional recovery. Overall, these results suggest that targeting the DDR via ATM inhibition reduces genotoxic stress, suppresses neuroinflammation, and improves behavioral outcomes in a mouse model of α-synucleinopathy. These findings underscore the therapeutic potential of DDR modulation in PD and related synucleinopathy. Full article

Olfaction has been extensively studied in the yellow fever mosquito, Aedes aegypti. This species uses its sense of smell to find blood hosts and other resources, contributing to its impact as a vector for human pathogens. Two major families of protein-coding genes, the odorant receptors (Ors) and the ionotropic receptors (Irs), provide the mosquito with sensitivities to distinct classes of volatile compounds in the antennae. Individual tuning receptors in both families require co-receptors for functionality: Orco for all Ors, and Ir8a for many Irs, especially ones that are involved in carboxylic acid detection. In Drosophila melanogaster, disruptions of Orco or Ir8a impair receptor function, tuning receptor expression, and membrane localization, leading to general anosmia. We reasoned that Orco and Ir8a might also be important for coordinated chemosensory receptor expression in the antennal sensory neurons of Ae. aegypti. To test this, we performed RNAseq and differential expression analysis in wildtype versus Orco^−/− and Ir8a^−/− mutant adult female antennae. Our analyses revealed Or and Ir tuning receptors are broadly under-expressed in Orco^−/− mutants, while a subset of tuning Irs are under-expressed in Ir8a mutants. Other chemosensory and non-chemosensory genes are also dysregulated in these mutants. Furthermore, we identify differentially expressed transcription factors including homologs of the Drosophila melanogaster Mip120 gene. These data suggest a previously unknown pleiotropic role for the Orco and Ir8a co-receptors in the coordination of expression of chemosensory receptors within the antennae of Ae. aegypti by participating in a feedback loop involving amos and members of the MMB/dREAM complex. Full article

Background/Objectives: Glycogen synthase kinase-3 beta (GSK3β) is a key enzyme involved in neurodegenerative diseases such as Alzheimer’s and Parkinson’s, contributing to tau hyperphosphorylation, amyloid-beta (Aβ) aggregation, and neuronal dysfunction. Methods: This study applied a machine learning-driven virtual screening approach to identify potent natural inhibitors of GSK3β. A dataset of 3092 natural compounds was analyzed using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), with feature selection focusing on key molecular descriptors, including lipophilicity (ALogP: −0.5 to 5.0), hydrogen bond acceptors (0–10), and McGowan volume (0.5–2.5). RF outperformed SVM and KNN, achieving the highest test accuracy (83.6%), specificity (87%), and lowest RMSE (0.3214). Results: Virtual screening using AutoDock Vina and molecular dynamics simulations (100 ns, GROMACS 2022) identified artemisinin as the top GSK3β inhibitor, with a binding affinity of −8.6 kcal/mol, interacting with key residues ASP200, CYS199, and LEU188. Dihydroartemisinin exhibited a binding affinity of −8.3 kcal/mol, reinforcing its neuroprotective potential. Pharmacokinetic predictions confirmed favorable drug-likeness (TPSA: 26.3–70.67 Ų) and non-toxicity. Conclusions: While these findings highlight artemisinin-based inhibitors as promising candidates, experimental validation and structural optimization are needed for clinical application. This study demonstrates the effectiveness of machine learning and computational screening in accelerating neurodegenerative drug discovery. Full article

Federated learning is a distributed machine learning approach where end users train local models with their own data and combine model updates on a reliable server to create a global model. Despite its advantages, this distributed structure is vulnerable to attacks as end users keep their data and training process private. Current defense mechanisms often fail when facing different attack types or high percentages of malicious participants. This paper proposes a new defense algorithm called Neuron-Centric Federated Learning Defense (NC-FLD), a novel approach that dynamically identifies and analyzes the most significant neurons across model layers rather than examining entire gradient spaces. Unlike existing methods that analyze all parameters equally, NC-FLD creates feature vectors from specifically selected neurons that show the highest training impact, then applies dimensionality reduction to enhance their discriminative features. We conduct experiments with various attack scenarios and different malicious participant rates across multiple datasets (CIFAR-10, F-MNIST, and MNIST). Additionally, we perform simulations on the GTSR dataset as a real-world application. Experimental results demonstrate that NC-FLD successfully defends against diverse attack scenarios in both IID and non-IID dataset distributions, maintaining accuracy above 70% with 40% malicious participation, a 5–15% improvement over the state-of-the-art method, showing enhanced robustness across diverse data distributions while effectively mitigating the impacts of both data and model poisoning attacks. Full article

Previous studies have demonstrated neuronal and microglial Fyn, a Src family kinase (SFK), and how its interactions with tau contribute to epileptogenesis. Saracatinib, a Fyn/SFK inhibitor, modifies disease progression in rat kainate (KA) epilepsy models. In this study, we investigated neuronal-specific fyn knockdown effects on Fyn–tau signaling, neurodegeneration, and gliosis using a calcium/calmodulin-dependent protein kinase II (CaMKII)-promoter-driven adeno-associated viral vector (AAV9)-mediated fyn-shRNA injection in the rat hippocampus. Eight days following AAV administration, rats received repeated low-dose KA injections intraperitoneally to induce status epilepticus (SE). Both fyn-shRNA and control groups showed comparable SE severity, indicating inadequate neuronal fyn knockdown at this timepoint. Two weeks post fyn-shRNA injection, hippocampal Fyn significantly decreased, alongside reductions in NR2B, pNR2B^Y1472, PSD95, and total tau. There was also a compensatory activation of SFK (pSFK^Y416:Fyn) and tau hyperphosphorylation (AT8:total tau), negatively correlating with NeuN expression. Proximity ligation assay indicated unchanged Fyn–tau interactions, suggesting tau interactions with alternative SH3 domain proteins. Persistent neuronal loss, astrogliosis, and microgliosis suggested limited effectiveness of neuronal-specific fyn knockdown at this timepoint. An extended-duration fyn knockdown study, or using broad SFK inhibitors such as saracatinib or tau-SH3 blocking peptides, may effectively prevent SE-induced epileptogenesis. Full article