Search Results (617)

The ability of the neuropeptide oxytocin to affect glial cell function is receiving increasing attention. We previously reported that oxytocin at a low nanomolar concentration could inhibit both astrocytic Ca²⁺ signals and glutamate release. Here, we investigate the ability of oxytocin receptors to couple both inhibitory and stimulatory pathways in astrocytes, as already reported in neurons. We assessed the effects of oxytocin at concentrations ranging from low to high in the nanomolar range on intracellular Ca²⁺ signals and on the glutamate release in astrocyte processes freshly prepared from the striatum of adult rats. Our main findings are as follows: oxytocin could induce dual responses in astrocyte processes, namely the inhibition and facilitation of both Ca²⁺ signals and glutamate release; the inhibitory and the facilitatory response appeared dependent on activation of the G_i and the G_q pathway, respectively; both inhibitory and facilitatory responses were evoked at the same nanomolar oxytocin concentrations; and the biased agonists atosiban and carbetocin could duplicate oxytocin’s inhibitory and facilitatory response, respectively. In conclusion, due to the coupling of striatal astrocytic oxytocin receptors to different transduction pathways and the dual effects on Ca²⁺ signals and glutamate release, oxytocin could also play a crucial role in neuron–astrocyte bi-directional communication through a subtle regulation of striatal glutamatergic synapses. Therefore, astrocytic oxytocin receptors may offer pharmacological targets to regulate glutamatergic striatal transmission, which is potentially useful in neuropsychiatric disorders and in neurodegenerative diseases. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

Diabetic sensorimotor polyneuropathy (DSPN) is the most prevalent complication of diabetes, affecting nearly half of all persons with diabetes. It is characterized by nerve degeneration, progressive sensory loss and pain, with increased risk of ulceration and amputation. Despite its high prevalence, disease-modifying treatments for DSPN do not exist. Mitochondrial dysfunction and Ca²⁺ dyshomeostasis are key contributors to the pathophysiology of DSPN, disrupting neuronal energy homeostasis and initiating axonal degeneration. Recent findings have demonstrated that antagonism of the muscarinic acetylcholine type 1 receptor (M₁R) promotes restoration of mitochondrial function and axon repair in various neuropathies, including DSPN, chemotherapy-induced peripheral neuropathy (CIPN) and HIV-associated neuropathy. Pirenzepine, a selective M₁R antagonist with a well-established safety profile, is currently under clinical investigation for its potential to reverse neuropathy. The transient receptor potential melastatin-3 (TRPM3) channel, a Ca²⁺-permeable ion channel, has recently emerged as a downstream effector of G protein-coupled receptor (GPCR) pathways, including M₁R. TRPM3 activation enhanced mitochondrial Ca²⁺ uptake and bioenergetics, promoting axonal sprouting. This review highlights mitochondrial and Ca²⁺ signaling imbalances in DSPN and presents M₁R antagonism and TRPM3 activation as promising neuro-regenerative strategies that shift treatment from symptom control to nerve restoration in diabetic and other peripheral neuropathies. Full article

Microglia, as the immune guardians of the central nervous system (CNS), have the ability to maintain neural homeostasis, respond to environmental changes, and remodel the synaptic landscape. However, persistent microglial activation can lead to chronic neuroinflammation, which can alter neuronal signaling pathways, resulting in accelerated cognitive decline. Phosphoinositol 3-kinase (PI3K) has emerged as a critical driver, connecting inflammation to neurodegeneration, serving as the nexus of numerous intracellular processes that govern microglial activation. This review focuses on the relationship between PI3K signaling and microglial activation, which might lead to cognitive impairment, inflammation, or even neurodegeneration. The review delves into the components of the PI3K signaling cascade, isoforms, and receptors of PI3K, as well as the downstream effects of PI3K signaling, including its effectors such as protein kinase B (Akt) and mammalian target of rapamycin (mTOR) and the negative regulator phosphatase and tensin homolog (PTEN). Experiments have shown that the overproduction of certain cytokines, coupled with abnormal oxidative stress, is a consequence of poor PI3K regulation, resulting in excessive synapse pruning and, consequently, impacting learning and memory functions. The review also highlights the implications of autonomously activated microglia exhibiting M1/M2 polarization driven by PI3K on hippocampal, cortical, and subcortical circuits. Conclusions from behavioral studies, electrophysiology, and neuroimaging linking cognitive performance and PI3K activity were evaluated, along with new approaches to therapy using selective inhibitors or gene editing. The review concludes by highlighting important knowledge gaps, including the specific effects of different isoforms, the risks associated with long-term pathway modulation, and the limitations of translational potential, underscoring the crucial role of PI3K in mitigating cognitive impairment driven by neuroinflammation. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-β plaques, tau hyperphosphorylation, and chronic neuroinflammation. Emerging evidence suggests a crucial role of lipid signaling pathways in AD pathogenesis, particularly those mediated by autotaxin (ATX) and lysophosphatidic acid (LPA). ATX, an enzyme responsible for LPA production, has been implicated in neuroinflammatory processes, blood–brain barrier dysfunction, and neuronal degeneration. LPA signaling, through its interaction with specific G-protein-coupled receptors, influences neuroinflammation, synaptic plasticity, and tau pathology, all of which contribute to AD progression. This review synthesizes recent findings on the ATX/LPA axis in AD, exploring its potential as a biomarker and therapeutic target. Understanding the mechanistic links between ATX, LPA, and AD pathology may open new avenues for disease-modifying strategies. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. The dopamine active transporter (DAT), a key protein involved in dopamine reuptake, serves as a selective biomarker for dopaminergic terminals in the striatum. DAT binding has been extensively studied using in vivo imaging techniques such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). To support the design of new radiotracers targeting DAT, we employ Quantitative Structure–Activity Relationship (QSAR) analysis on a structurally diverse dataset composed of 57 compounds with known affinity constants for DAT. The best-performing QSAR model includes four molecular descriptors and demonstrates robust statistical performance: R² = 0.7554, Q²_LOO = 0.6800, and external R² = 0.7090. These values indicate strong predictive capability and model stability. The predicted compounds are evaluated using a docking methodology to check the correct coupling and interactions with the DAT. The proposed approach—combining QSAR modeling and docking—offers a valuable strategy for screening and optimizing potential PET/SPECT radiotracers, ultimately aiding in the neuroimaging and early diagnosis of Parkinson’s disease. Full article

α-Latrotoxin stimulates neurotransmitter release by binding to a presynaptic receptor and then forming ion-permeable membrane pores and/or stimulating the receptor, latrophilin-1, or Adhesion G-protein-coupled receptor type L1 (ADGRL1). To avoid pore formation, we use the mutant α-latrotoxin (LTX^N4C), which does not form pores and only acts through ADGRL1. ADGRL1 is cleaved into an N-terminal fragment (NTF) and a C-terminal fragment (CTF), which behave as independent cell-surface proteins, reassociating upon binding LTX^N4C. We investigated the role of the NTF-CTF association in LTX^N4C action, using perfluorooctanoic acid (PFOA). We demonstrate that at low concentrations (≤100 μM) PFOA does not adversely affect ADGRL1-expressing neuroblastoma cells or inhibit LTX^N4C binding. However, it causes the dissociation of the NTF-CTF complexes, independent redistribution of the fragments on the cell surface, and their separate internalization. PFOA also promotes the dissociation of NTF-CTF complexes induced by LTX^N4C binding. When applied to mouse neuromuscular junctions, PFOA inhibits LTX^N4C-induced neurotransmitter release in a concentration-dependent manner. Our results indicate that ADGRL1 can mediate LTX^N4C signaling only while its fragments remain associated. These findings explain some aspects of receptor-dependent toxin action and contribute to a mechanistic understanding of ADGRL1 functions in neurons. Full article

Background: The Nerve Growth Factor (NGF) is essential for neuronal survival and function and represents a key therapeutic target for pain and inflammation-related disorders, as well as for neurodegenerative diseases. Small-molecule antagonists of human NGF (hNGF) offer advantages over monoclonal antibodies, including oral availability and reduced immunogenicity. However, their development is often hindered by solubility challenges, necessitating the use of solvents like dimethyl sulfoxide (DMSO). This study investigates whether DMSO directly interacts with hNGF and affects its receptor-binding properties. Methods: Integrative/hybrid computational and experimental biophysical approaches were used to assess DMSO-NGF interaction by combining machine-learning tools and Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared (FT-IR) spectroscopy, Differential Scanning Fluorimetry (DSF) and Grating-Coupled Interferometry (GCI). These techniques evaluated binding affinity, conformational stability, and receptor-binding dynamics. Results: Our findings demonstrate that DMSO binds hNGF with low affinity in a specific yet non-disruptive manner. Importantly, DMSO does not induce significant conformational changes in hNGF nor affect its interactions with its receptors. Conclusions: These results highlight the importance of considering solvent–protein interactions in drug discovery, as these low-affinity yet specific interactions can affect experimental outcomes and potentially alter the small molecules binding to the target proteins. By characterizing DMSO-NGF interactions, this study provides valuable insights for the development of NGF-targeting small molecules, supporting their potential as effective alternatives to monoclonal antibodies for treating pain, inflammation, and neurodegenerative diseases. Full article

This scoping review explores the expression patterns and molecular features of sarcoglycans (SGs) in non-muscle organs, challenging the long-standing assumption that their function is confined to skeletal and cardiac muscle. By analyzing evidence from both animal models and human studies, the review highlights the widespread presence of SG subunits in organs, including the nervous system, glands, adipose tissue, oral mucosa, retina, and other structures, with distinct regional and cell-type-specific patterns. Studies on the central nervous system demonstrate a widespread “spot-like” distribution of SG subunits in neurons and glial cells, implicating their involvement in synaptic organization and neurotransmission. Similarly, SGs maintain cellular integrity and homeostasis in glands and adipose tissue. At the same time, the altered expression of SGs is associated with pathological conditions in the gingival epithelium of the oral mucosa. These findings underscore the multifaceted roles of SGs beyond muscle, suggesting that they may contribute to cellular signaling, membrane stability, and neurovascular coupling. However, significant gaps remain regarding SG post-translational modifications and functional implications in non-muscle organs. Future research integrating molecular, cellular, and functional approaches in animal models and human tissues is essential to fully elucidate these roles and explore their potential as therapeutic targets in various diseases. Full article

Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin–adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to growth cone, and (iv) orientation dynamics guided by substrate anisotropy. Dynamical systems analysis reveals that a saddle–node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

In recent years, there has been a surge in the extrusion-based 3D printing of materials for various biomedical applications. This work presents a novel methodology for optimizing extrusion-based 3D bioprinting of a gelatin/siloxane hybrid material for biomedical applications. A systematic approach integrating rheological characterization, computational fluid dynamics simulation (CFD), and machine-learning-based image analysis, was employed. Rheological tests revealed a shear stress of 50 Pa, a maximum viscosity of 3 × 10⁵ Pa·s, a minimum viscosity of 0.089 Pa·s, and a shear rate of 15 rad/s (27G nozzle, 180 kPa pressure, 32 °C temperature, 30 mm/s velocity) for a BIO X bioprinter. While these parameters yielded constructs with 54.5% similarity to the CAD design, a multi-faceted optimization strategy was implemented to enhance fidelity, computational fluid dynamics simulations in SolidWorks, coupled with a custom-develop a binary classifier convolutional neuronal network for post-printing image analysis, facilitated targeted parameter refinement. Subsequent printing optimized parameters (25G nozzle, 170 kPa, 32 °C, 20 mm/s) achieved a significantly improved similarity of 92.35% CAD, demonstrating efficacy. The synergistic combination of simulation and machine learning ultimately enabled the fabrication of complex 3D constructs with a high fidelity of 94.13% CAD similarity, demonstrating the efficacy and potential of this integrated approach for advanced biofabrication. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive dysfunction and working memory impairment, with early hippocampal damage being a prominent feature. Transcranial magneto-acoustic stimulation (TMAS) has been shown to target specific brain regions for neuroregulation. Methods: This study investigated the effects of TMAS on cognitive function, working memory, and hippocampal CA3 neural rhythms in AD rats by specifically stimulating the hippocampal region. Results: The novel object recognition test and T-maze test were employed to assess behavioral performance, while time-frequency analyses were conducted to evaluate memory-related activity, neural synchronization, and cross-frequency phase-amplitude coupling. TMAS significantly improved cognitive and working memory deficits in AD rats, enhancing long-term memory performance. Additionally, the abnormal energy levels observed in the θ and γ rhythm power spectra of the CA3 region were markedly restored, suggesting the recovery of normal neural function. This improvement was accompanied by a partial resurgence of neural activity, indicating enhanced inter-neuronal communication. Furthermore, the previously damaged coupling between the θ-fast γ and θ-slow γ rhythms was successfully improved, resulting in a notable enhancement of synchronized activity. Conclusions: These findings suggest that TMAS effectively alleviates cognitive and working memory impairments in AD rats and may provide experimental support for developing new treatments for AD. Full article

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures. Full article