Search Results (181)

Background: Glutamatergic neuron-to-glioma signaling mediated by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) has emerged as an important mechanism in glioma progression. Objectives/Methods: We analyzed the expression of the AMPAR subunit genes GRIA1, GRIA2, GRIA3, and GRIA4 in lower-grade glioma (LGG). Results: Expression of GRIA1–GRIA4 was highest in IDH-mutant/1p19q-codeleted tumors and lowest in IDH-wildtype tumors across both The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts. High expression of each GRIA gene was associated with longer overall survival (OS). Transcriptome-wide analyses identified positive correlations between an AMPAR score and genes involved in synaptic organization, neuronal connectivity, and neurotransmission. Co-expression analyses demonstrated coordinated expression between GRIA1–GRIA4 and genes encoding AMPAR auxiliary proteins. Gene Ontology (GO) enrichment revealed overrepresentation of synaptic signaling, trans-synaptic communication, and synapse organization. Although the AMPAR score was associated with favorable survival in univariate analyses, it did not retain independent prognostic significance after adjustment for key clinicomolecular variables. Elevated expression of AMPAR subunit genes in LGG was associated with favorable molecular subtypes and a synaptic transcriptional program. Conclusions: These findings suggest that GRIA1–GRIA4 expression is associated with a synaptically enriched transcriptional program in LGG, although its cellular origin remains uncertain. Full article

Background: Depression-related liability is frequently accompanied by reduced physical function, yet the shared genetic architecture linking mood-related traits and physical-function decline remains incompletely characterized. Methods: We applied genomic structural equation modeling to European-ancestry GWAS summary statistics for five constituent phenotypes: depressive symptoms, depression diagnosis, grip strength, appendicular lean mass, and walking pace. A Depression–Physical Function shared genetic factor was constructed as a cross-trait genetic covariance dimension and evaluated using LDSC-based validation and leave-one-trait-out sensitivity analyses. We then performed factor GWAS, FUMA locus annotation, Bayesian fine-mapping, MAGMA gene-based analysis, transcriptome-wide association analysis, pathway enrichment, CELLECT/MAGMA cell-type specificity analysis, partitioned heritability analysis, and gsMap spatial transcriptomic mapping. Results: The shared factor showed good model fit and retained 755,397 quality-controlled variants for downstream analysis. The factor was positively genetically correlated with depression-related traits and negatively correlated with physical-function-related traits. FUMA identified 245 genome-wide significant SNPs, 44 lead SNPs, and 38 genomic risk loci, with 127 positional mapped genes. Fine-mapping prioritized one high-confidence locus. MAGMA identified 19 Bonferroni-significant genes and 326 FDR-significant genes, while TWAS identified 322 FDR-significant expression-associated genes. Integrating FUMA positional mapping, MAGMA gene-level association and TWAS expression-level association prioritized eight convergent genes: TMEM106B, CENPW, DRD2, LRFN5, NCAPG, DCAF16, SGIP1, and FAM120A. Functional enrichment highlighted postsynaptic structure, neuron spine, synaptic plasticity, and synapse organization. CELLECT/MAGMA prioritized brain non-myeloid neurons and glial populations, with additional endocrine-metabolic and immune-hematopoietic signals. Spatial transcriptomic mapping localized top signals to brain and spinal cord regions in the embryonic neuro-muscle reference. Partitioned heritability analysis showed enrichment in conserved, intronic, promoter, and chromatin-related genomic annotations. Conclusions: These findings support a shared polygenic covariance dimension linking depression-related liability with reduced physical-function-related genetic propensity. Downstream analyses prioritized candidate loci, genes, and biological contexts, with enrichment patterns consistent with neuronal, synaptic, and regulatory genomic processes. Full article

Transsynaptic bridges are molecular complexes spanning the synaptic cleft that physically couple presynaptic release machinery to postsynaptic receptor fields. That this architecture is the direct target of autoimmune attack in myasthenia gravis and a primary locus of genetic risk in autism spectrum disorder and schizophrenia underscores that transsynaptic bridges are not only important during synapse development but are continuously required organizers of neural function. This review traces the evolution of structural, molecular, and functional evidence that shaped our understanding of the synapse as a single integrated trans-cellular unit. Several defining properties of a synapse emerge as a consequence of transsynaptic bridges—for example, the nanoscale alignment they impose between vesicle fusion sites and receptor nanodomains determines transmission efficacy, the bidirectional communication they coordinate across the cleft drives signaling specificity, and they undergo activity-dependent remodeling, thereby physically encoding synaptic history. Full article

Conventional photodetectors and image sensors deliver high-fidelity digital outputs but face a growing data-movement bottleneck: the energy and latency cost of transferring raw pixel streams to off-chip memory and processors increasingly dominates over both sensing and computation in modern machine-vision pipelines. An emerging response is the neuromorphic photodetector, a class of optoelectronic device that converts incident light into an electrical signal while simultaneously storing, modulating, and pre-processing that signal in a manner inspired by biological synapses and retinas. Over the past decade, demonstrations have spanned at least eight material platforms—organic semiconductors, organic–carbon-nanotube hybrids, perovskite and perovskite hybrids, metal oxides (including ultra-wide-bandgap and printable variants), wide-bandgap III-nitrides and 4H-SiC, two-dimensional materials, photo-memristors, and silicon CMOS in-sensor compute architectures—and have been realised through four distinct architectural families: phototransistor synapses, photo-memristors, heterojunction in-sensor compute, and linear photovoltaic neural networks. Here, we provide a quantitative cross-platform benchmark across forty in-scope articles, identify persistent photoconductivity as a near-universal device-physical substrate underlying synaptic functionality, characterise the responsivity–speed–energy trade-off structure observed across platforms, and present a critical assessment of energy-reporting practice in the field. We further identify three best-practice exemplars from three independent material platforms that converge on operating biases of 0.01–0.1 V and energies of 0.07–0.8 fJ per event, and we propose a unified reporting framework to enable meaningful cross-platform benchmarking of next-generation neuromorphic photodetectors. Full article

The rapid expansion of artificial intelligence has intensified the demand for hardware systems capable of emulating brain-like information processing with high accuracy, energy efficiency, and reliability. Neuromorphic computing based on memristive artificial synapses has emerged as a promising approach to overcome the limitations of conventional von Neumann architectures. Although inorganic and oxide-based synaptic memristors have been widely explored for neuromorphic systems, they often suffer from poor linearity, asymmetric potentiation/depression behavior, limited conductance states, and device variability, which restrict learning accuracy and scalability. In contrast, polymer-based memristors have gained significant attention owing to their intrinsic advantages, including mechanical flexibility, molecular tunability, controllable electronic/ionic transport, low-temperature processability, and compatibility with large-area fabrication. This review critically examines recent advances in polymer—based memristive materials and devices for achieving linear and symmetric artificial synaptic behavior. Polymer synapses are classified into pure polymer, polymer composite, and polymer-hybrid systems through a mechanism to function framework. Rather than providing a general compilation of organic memristor studies, this review analyzes how polymer chemistry, ion-migration control, trap state distribution, redox activity, electrode selection, active layer thickness, and interface engineering govern conductance update linearity, symmetry, and uniformity. Fundamental switching mechanisms, material classifications, device architectures, key synaptic characteristics, and system-level neuromorphic performance, including pattern-recognition applications, are critically discussed. By explicitly linking material and device design to conductance update fidelity, learning accuracy, training convergence, and pattern-recognition reliability, this review provides practical design guidelines and future perspectives for next-generation polymer-based neuromorphic hardware with improved linearity, symmetry, reliability, and scalability. Full article

The core challenge of high-precision medical image segmentation lies in modeling the multi-scale fractal self-similarity of human abdominal organs and cardiac structures, especially the blurred boundaries and weak features of low-contrast tissues. Existing CNNs and Transformers fail to simultaneously capture both global fractal topology and high-frequency fractal details, thereby limiting segmentation performance. To address this, we propose MIS-DFH, a dual-branch CNN–Transformer hybrid model that integrates Hybrid Feature Branches, Multi-Fusion Dense Frequency Skip Connections, and hierarchical Deep Supervision, achieving superior multi-scale feature extraction and segmentation performance. Experiments on the Synapse abdominal CT and ACDC cardiac MRI datasets show that MIS-DFH outperforms all compared state-of-the-art methods. Notably, it achieves 79.90% mean DSC and 20.06 mm HD95 on Synapse, representing a 5.2% DSC improvement and 34.3% HD95 reduction over MSLAU-Net, with consistent gains on ACDC. These results validate the model’s superior segmentation accuracy and clinical application value. Full article

Cisplatin-induced ototoxicity is a permanent, bilateral sensorineural hearing loss occurring in up to 80% of treated patients. Its defining and clinically challenging feature is the progressive worsening of auditory function that continues well after chemotherapy has ended, a trajectory that cannot be explained by cumulative dose alone. This article is a comprehensive review of the present research studies on mechanisms that are responsible for this post-treatment progression. The cochlea, unlike other organs, appears to be unable to eliminate platinum (the active divalent metal ion released from cisplatin and responsible for its cytotoxic and ototoxic effects): traces of it can be found in human temporal bone tissue even more than 18 months after last infusion, and bone might serve as a long-term systemic reservoir. Within the inner ear, platinum accumulates preferentially in the stria vascularis, impairing endocochlear potential and outer hair cell function. Retained platinum sustains cascading effects including sustained NOX3-dependent oxidative stress, mitochondrial dysfunction, ongoing genotoxic injury to non-regenerative cells, and the early loss of ribbon synapses that precipitates delayed spiral ganglion neurodegeneration. Pharmacogenetic variability in platinum transport and antioxidant metabolism further modulates individual susceptibility. These findings support lifelong audiological surveillance and provide a basis for designing strategies that can protect hearing without compromising the essential anticancer efficacy of cisplatin therapy. Full article

Since the first approval of CTLA-4 blockade for melanoma, immune checkpoint inhibitors (ICIs) have expanded into a major class of cancer therapy, with more than 100 FDA-approved oncological indications across metastatic and earlier-stage disease settings, including use as monotherapy and in combination regimens. Preclinical research has largely focused on myocarditis and atherosclerosis, but a wider set of phenotypes, such as non-inflammatory left ventricular dysfunction (NILVD), arrhythmias, and vasculitis, can be observed, and they are rarely connected within a single mechanistic model. We aim to build a systems-oriented, mechanistic framework of the most widely studied biological processes; it will link the main checkpoint pathways to relevant cardiac and vascular cell types, molecular pathways, immune synapses, and candidate biomarkers. We searched PubMed, Scopus, and Web of Science using combinations of terms for immune checkpoint inhibition and cardiovascular-immune-related adverse events that provide mechanistic insight into cardiac-immune-related adverse reactions (irAEs). An AI-assisted semantic clustering approach was used only to organize the included literature. The integrated framework identifies PD-1/PD-L1 as the dominant mechanistic hub linking T-cell activation, endothelial recruitment, myocardial injury, and vascular inflammation. Across phenotypes, a shared immune core involving checkpoint pathways, cytokine signaling, and leukocyte trafficking coexists with phenotype-restricted mediators that may bias injury toward myocarditis, vascular inflammation, conduction-system disease, or NILVD. KEGG analyses support the enrichment of T-cell receptor signaling, Th17 differentiation, JAK-STAT signaling, cytokine–cytokine receptor interaction, and lipid and atherosclerosis pathways. Candidate biomarkers emerging from the reviewed literature include troponin, IL-6, CXCL9/CXCL10/CXCL13, S100A family proteins, ROCK2, HLA-linked susceptibility signals, and T-cell receptor clonality markers. The AI-assisted clustering broadly recapitulated the expert-defined thematic structure while identifying finer semantic neighborhoods within the literature. This framework provides a support map for further hypotheses about toxicity patterns with current and next-generation checkpoint strategies on the cardiac system, while AI-assisted clustering provides a complementary method for organizing the literature rather than an independent source of biological inference. Full article

A myelination is essential for neural function in the vertebrate central nervous system, yet the molecular details of how the oligodendrocyte differentiation program has evolved remain poorly understood. Here, we performed a cross-species single-cell transcriptomic analysis of oligodendrocyte lineage cells in the spinal cord of five vertebrate species: fugu, mudskipper, chicken, mouse, and human. Pseudotime trajectory analysis revealed a shared oligodendrocyte progenitor cell (OPC) to committed oligodendrocyte precursor (COP) to myelin-forming oligodendrocyte (MOL) differentiation trajectory across all species, and CAME-based cross-species mapping confirmed the homology of OPC and MOL identities, while COP showed reduced mapping in teleosts compared with amniotes. Among stage-specific DEGs, highly shared genes (≥4 species) were organized into four co-expression modules encompassing cell projection organization, myelination, synapse assembly, and ribonucleoprotein biogenesis, with evolutionary core genes (all 5 species) enriched for oligodendrocyte differentiation and Wnt signaling. Strikingly, amniote-exclusive genes were enriched for synaptic vesicle transport, cell projection organization, predominantly at the OPC stage. This asymmetry indicates that amniotes have expanded the oligodendrocyte differentiation program at the progenitor stage, potentially linked to the myelination demands of terrestrial locomotor circuits. Our findings provide insights into how the oligodendrocyte differentiation program has been shaped by both deep evolutionary conservation and lineage-specific adaptation. Full article

Acetylcholine (ACh) is the first identified neurotransmitter and an evolutionarily conserved signaling molecule. Although its role in classical synaptic transmission within the central and peripheral nervous systems has been extensively studied, growing evidence indicates that cholinergic signaling extends beyond neuronal synapses and operates in a broad range of non-neuronal cells. Thus, the cholinergic system represents a complex and widely distributed signaling network with both neuronal and non-neuronal components. Within the nervous system, cholinergic neurons display marked molecular heterogeneity, largely driven by the genomic organization and alternative splicing of the choline acetyltransferase (ChAT) gene. Distinct ChAT mRNA splice variants contribute to region- and cell-type specific cholinergic phenotypes in central and peripheral neurons, including the enteric nervous system, which exemplifies a highly autonomous peripheral cholinergic network. Beyond the nervous system, non-neuronal cholinergic signaling has been identified in epithelial, cardiac, immune, and other cell types, where ACh acts as an autocrine and paracrine regulator of key physiological processes. This review summarizes current knowledge on ACh biosynthesis, focusing on ChAT and its splice variants as molecular determinants of cholinergic diversity and function across neuronal and non-neuronal contexts. Full article

Deep neural networks (DNNs) are traditionally analyzed as black-box function approximators, yet their internal structure exhibits phase transitions characteristic of complex physical systems. In this study, we investigate topological–functional decoupling—the phenomenon whereby a network retains full graph connectivity while losing computational function—in trained neural networks through the lens of percolation theory. By subjecting three distinct architectures (Shallow, Deep, and Wide MLPs) to a unified edge-pruning analysis on Fashion-MNIST, we uncover a fundamental divergence between structural integrity and computational capacity in this experimental setting. We report three key phenomena observed in these experiments: (1) the zombie network state under stochastic pruning, where the system retains global connectivity ($P_{\infty }\approx 1.0$) yet suffers a catastrophic functional collapse (accuracy falls below 50% of baseline at prunning ratio $p_f\approx 0.35$–$0.68$ depending on depth), proves that graph reachability does not imply computational capability; (2) depth fragility, where increased network depth triggers multiplicative signal decay (the avalanche effect), rendering deep architectures exponentially more vulnerable to random edge removal than shallow ones ($p_f^{\mathrm{deep}}\approx 0.35$ vs. $p_f^{\mathrm{shallow}}\approx 0.68$); and (3) scale-free universality, observed under magnitude-based pruning, where a robust functional skeleton maintains accuracy near the baseline (∼89%) up to extreme sparsity ($p_f\approx 0.85$–$0.95$) across all three architectures. Robustness stems not from holographic redundancy in the overall connection count but from the emergent heavy-tailed rich-club organization of weight magnitudes—a sparse set of high-magnitude synapses that form the functional backbone of the network, decoupled from the redundant topological mass. These findings offer new physical constraints for the design of resilient neuromorphic hardware. Full article

Background: We investigated whether a water extract of Lilii bulbus (Lilium lancifolium Thunberg; WELB) could modulate inhibitory synaptic organization in a mouse model of pentylenetetrazol (PTZ)-induced kindling. Methods: Starting 14 days prior to the initial PTZ challenge, WELB (500 mg/kg) was delivered via oral gavage once daily. This treatment regimen was maintained for a total of 40 days, spanning the entire period until the animals reached the fully kindled state. Results: Behavioral assessments revealed that WELB treatment significantly reduced seizure severity and Racine scores, prolonged the latency to clonic seizures, and shortened seizure duration, demonstrating potent anticonvulsant activity. Two-photon calcium imaging further showed that WELB markedly suppressed PTZ-induced neuronal hyperexcitability in the posterior parietal cortex, accompanied by decreased expression of neuronal activation markers, including c-fos, phosphorylated-calcium/calmodulin-dependent protein kinase IIα (p-CaMKIIα), and N-methyl-D-aspartate receptor 1 (NR1). In the hippocampus, WELB modulated the expression of GABAergic interneuron markers [glutamate decarboxylase 67 (GAD67), vesicular GABA transporter (VGAT), parvalbumin (PV), somatostatin (SOM)] and upregulated GABAergic gene transcripts [GABA-A receptor α1 subunit (Gabra1), GABA-A receptor α2 subunit (Gabra2), GABA transporter 1 (Gat1), GABA transporter 3 (Gat3), PV, SOM, cholecystokinin (CCK)] that were downregulated by PTZ kindling. Moreover, WELB enhanced the expression of GABAergic synaptic organization-related proteins (gephyrin, collybistin, neurexin-1β, neuroligin-2, and neuropilin-2), indicating its regulatory effect on inhibitory synaptic integrity. Conclusions: Collectively, these findings suggest that WELB may exert its anticonvulsant effects by functionally remodeling GABAergic synaptic organization-related factors, thereby restoring inhibitory circuit integrity and providing a mechanism-based therapeutic strategy for epilepsy and seizure-related neurological disorders. Full article

The rhizosphere is a critical interface linking plants and soil; however, the mechanisms by which parasitic plants affect host growth through rhizosphere microecological changes remain unclear. This study systematically elucidates how Monochasma savatieri, a hemiparasitic plant, promotes blueberry growth by reshaping rhizosphere microecology. Pot experiments showed that parasitism significantly enhanced urease, sucrase, and soil nitrate reductase activities, improving organic matter decomposition and nutrient transformation efficiency. Concurrently, soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK), along with alkali-hydrolyzable nitrogen (AN) and available potassium (AK), decreased, suggesting enhanced nutrient absorption by roots. At the microbial level, parasitism altered community composition and diversity, enriching functional taxa such as Nitrosomonas, OLB5, and Serendipita. Functionally, pathways related to stress resistance (necroptosis and glutamatergic synapses) were activated, whereas those linked to pathogen colonization (Pseudomonas aeruginosa biofilm formation and tryptophan metabolism) were suppressed. These modifications reduced harmful microbial competition, optimized nutrient cycling and signaling networks, and established a favorable rhizosphere microenvironment for root health. By integrating soil enzyme activity, nutrient dynamics, and microbial functions, M. savatieri systemically improves the rhizosphere microenvironment, ultimately enhancing blueberry growth. This study provides theoretical support for intercropping and management of parasitic plants with blueberries. Full article

Background/Objectives: Alzheimer’s disease (AD) is defined by amyloid-β plaques and tau neurofibrillary tangles and is typically associated with progressive cognitive decline. However, a substantial subset of individuals remains cognitively intact despite intermediate-to-high AD pathology, a phenomenon termed cognitive resilience. This review aims to synthesize genetic variants and biological pathways associated with preserved cognition in the presence of AD neuropathology. Methods: We performed a narrative thematic synthesis of human genetic studies (GWAS, sequencing, biomarker-informed cohorts) and extreme resilience case reports. Variants were prioritized by replication, mechanistic plausibility, and relevance to clinicopathologic dissociation, and were organized by shared biological pathways. When applicable, cognitive resilience was operationalized using residual-based approaches modeling cognitive performance after adjustment for neuropathological burden, age, sex, and education or cognitive reserve proxies reported by each cohort. Results: Recurrent resilience-associated variants include APOE ε2, APOE3-Christchurch, RELN-COLBOS, ATP8B1, RAB10, PLCG2, PICALM, CLU, FN1, and synapse-linked markers such as NPTX2. These variants converge on lipid metabolism, synaptic function and neuroplasticity, tau regulation and proteostasis, immune and inflammatory signaling, vascular/BBB resilience, and RNA regulation. Conclusions: Genetic determinants of cognitive resilience highlight mechanisms that preserve neural integrity independent of pathological load. Targeting resilience pathways may enable precision therapies designed to maintain cognitive function in AD. Full article

Electrophysiology, mechanobiology, and the study of soft matter within cells demonstrate increasing amounts of evidence that neuronal signaling arises from interactions between membrane potential, force, and phase. Herein, we have attempted to collect and organize the evidence for each of these areas of study into an approximate structure called the electromechanical connectome: a three-way state–space (membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, including phase-separated liquid–liquid droplets) where membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, and phase-separated liquid–liquid droplets are likely to influence one another, influencing synaptic processing, plasticity and network stability. We will also attempt to illustrate the following: how changes in electrostatic fields can be used to alter the arrangement of lipids, hydration, and dielectric microdomains, and the contact geometry between organelles and activity dependent transcription; how mechanical dynamics associated with spines, axons, and the active zone of synapses may be used to modify the energy landscape of channels, the docking and priming of vesicles, and the transport of cytoskeletons; and how viscosity corridors, along with phase-separated micro-reactors, can be used to regulate the kinetics of signaling, molecular trafficking and metabolic processes in local environments. With these connections in mind, we will propose a multiphysical attractor model in which cognition is the result of navigating through metastable manifolds, while neurodegenerative disease may be a result of the progressive loss of electromechanical coherence, phase boundary control and energetic flexibility. Finally, we will present testable hypotheses and use AI-enabled digital twin methods to potentially quantify the early deformation of manifolds and provide precision biomarkers and therapeutic options. Full article