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Voltage-gated sodium channels (VGSCs) initiate action potentials in electrically excitable cells and tissues. Surprisingly, some VGSC genes are aberrantly expressed in a variety of cancers, derived from "non-excitable" tissues that do not generate classic action potentials, showing potential as a promising pharmacological target for cancer. Most of the previous review articles on this topic are limited in scope, and largely unable to provide researchers with a comprehensive understanding of the role of VGSC in cancers. Here, we review the expression patterns of all nine VGSC α-subunit genes (SCN1A-11A) and their four regulatory β-subunit genes (SCN1B-4B). We reviewed data from the Cancer Genome Atlas (TCGA) database, complemented by an extensive search of the published papers. We summarized and reviewed previous independent studies and analyzed the VGSC genes in the TCGA database regarding the potential impact of VGSC on cancers. A comparison between evidence gathered from independent studies and data review was performed to scrutinize potential biases in prior research and provide insights into future research directions. The review supports the view that VGSCs play an important role in diagnostics as well as therapeutics of some cancer types, such as breast, colon, prostate, and lung cancer. This paper provides an overview of the current knowledge on voltage-gated sodium channels in cancer, as well as potential avenues for further research. While further research is required to fully understand the role of VGSCs in cancer, the potential of VGSCs for clinical diagnosis and treatment is promising. [ABSTRACT FROM AUTHOR]

This paper introduces a transformative "living" hypothesis in architecture and engineering, proposing a paradigm shift from conventional design to regenerative, ecologically interconnected resilient systems. At the heart of our hypothesis is the integration of earth-bound materials and bioreceptive surfaces through metabolic exchanges that can be directly monitored via bioelectricity using advanced computational models and cooperative governance structures. This innovative approach that links the living world with natural materials and digital computing, aims to foster sustainable urban development that dynamically and meaningfully responds to ecological shifts, thereby enhancing social sustainability and environmental resilience. Founded on an active relationship with Earth Based Materials (EBMs) our work operationalises the foundational link between organic life and inorganic matter, e.g., minerals, to establish a dynamic relationship between building materials, and ecological systems drawing on the foundational metabolisms of microbes. To enable this ambitious synthesis, our work builds upon and diverges from traditional foundations by operationalizing actor-network theory, new materialism, and regenerative design principles through the application of bioelectrical microbes to "living" materials and digital twins. We propose a novel resilience framework that not only advocates for a symbiotic relationship between human habitats and natural ecosystems but also outlines practical pathways for the creation of adaptive, self-organizing built environments that are informed by data collection and metabolic feedback loops. These environments are fundamentally regenerative, dynamic, and environmentally responsive in ways that can be understood and engaged by human engineers and designers, transcending current sustainability and resilience targets through a methodology rooted in interdisciplinary collaboration. We address challenges such as regulatory barriers, lack of standardization, and perceptions of inferiority compared to conventional materials, proposing a new standardization framework adaptable to the unique properties of these materials. Our vision is supported by advanced predictive digital modelling techniques and sensors, including the integration of biofilms that generate action potentials, enabling the development of Digital Twins that respond to metabolic signals to enhance sustainability, biodiversity, and ultimately generate environmentally positive socio-economic outcomes. This paper reviews existing methodologies to establish an overview of state-of-the-art developments and offers a clear, actionable plan and recommendations for the realization of regenerative and resilient systems in urban development. It contributes a unique perspective on sustainable urban development, emphasizing the need for a holistic approach, which integrates the foundational metabolism of microbes, assisted by big biological data and artificial intelligences that act in concert to respect both the environment and the intricate dynamics of living systems. [ABSTRACT FROM AUTHOR]

Neurons in the locus coeruleus (LC) have been traditionally viewed as a homogenous population. Recent studies begin to reveal their heterogeneity at multiple levels, ranging from molecular compositions to projection targets. To further uncover variations of neuronal properties in the LC, we took a genetic-based tagging approach to identify these neurons. Our data revealed diverse spike waveforms among neurons in the LC region, including a considerable fraction of narrow-spiking units. While all wide-spiking units possessed the regular waveform polarity (negative-positive deflection), the narrow units can be further divided based on opposing waveform polarities. Under anesthesia, wide units emitted action potential at a higher rate than the narrow units. Under wakefulness, only one subtype of narrow units exhibited fast-spiking phenotype. These neurons also had long latencies to optogenetic stimulation. In-situ hybridization further supported the existence of a small population of putative GABAergic neurons in the LC core. Together, our data reveal characteristic differences among neurons in the LC region, and suggest that a fraction of electrophysiologically-identified narrow-spiking neurons can be fast-spiking interneurons, and their fast-spiking feature is masked by anesthesia., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's efficient processing of information. Although biological evidence suggests that precise spike timing is crucial for effective information encoding, contemporary SNN research mainly concentrates on adjusting connection weights. In this work, we introduce Delay Learning based on Temporal Coding (DLTC), an innovative approach that integrates delay learning with a temporal coding strategy to optimize spike timing in SNNs. DLTC utilizes a learnable delay shift, which assigns varying levels of importance to different informational elements. This is complemented by an adjustable threshold that regulates firing times, allowing for earlier or later neuron activation as needed. We have tested DLTC's effectiveness in various contexts, including vision and auditory classification tasks, where it consistently outperformed traditional weight-only SNNs. The results indicate that DLTC achieves remarkable improvements in accuracy and computational efficiency, marking a step forward in advancing SNNs towards real-world applications. Our codes are accessible at https://github.com/sunpengfei1122/DLTC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ "repeat coding" that re-enter all input tokens at each timestep, which fails to fully exploit temporal relationships between the tokens and introduces memory overhead. In this work, we align the number of input tokens with the timestep and refer to this input coding as "individual coding". To cope with the increase in training time for individual encoded SNNs due to the dramatic increase in timesteps, we design a Bidirectional Parallel Spiking Neuron (BPSN) with following features: First, BPSN supports spike parallel computing and effectively avoids the issue of uninterrupted firing; Second, BPSN excels in handling adaptive sequence length tasks, which is a capability that existing work does not have; Third, the fusion of bidirectional information enhances the temporal information modeling capabilities of SNNs; To validate the effectiveness of our BPSN, we present the SNN-BERT, a deep direct training SNN architecture based on the BERT model in NLP. Compared to prior repeat 4-timestep coding baseline, our method achieves a 6.46× reduction in energy consumption and a significant 16.1% improvement, raising the performance upper bound of the SNN domain on the GLUE dataset to 74.4%. Additionally, our method achieves 3.5× training acceleration and 3.8× training memory optimization. Compared with artificial neural networks of similar architecture, we obtain comparable performance but up to 22.5× energy efficiency. We would provide the codes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consumed, the way neurons communicate, or the way they learn. To close the gap between artificial neural networks and biological ones, researchers proposed the spiking neural network. Layered Spiking Neural P systems (LSN P systems) are networks of spiking neurons used to solve various classification problems. In this paper, we study the LSN P systems in the context of a federated learning client-server architecture over horizontally partitioned data. We analyze the privacy implications of pre-trained LSN P systems through membership inference attacks. We also perform experiments to assess the performance of an LSN P system trained in the federated learning setup. Our findings suggest that LSN P systems demonstrate higher accuracy and faster convergence compared to federated algorithms based on either perceptron or spiking neural networks.

Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot's control unit, i.e., as a cyborg's brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Substantial sex-based differences have been reported in atrial fibrillation (AF), but the underlying mechanisms are poorly understood., Objectives: This study sought to gain a mechanistic understanding of Ca 2+ -handling disturbances and Ca 2+ -driven arrhythmogenic events in male vs female atrial cardiomyocytes and establish their responses to Ca 2+ -targeted interventions., Methods: We integrated reported sex differences and AF-associated changes (ie, expression and phosphorylation of Ca 2+ -handling proteins, cardiomyocyte ultrastructural characteristics, and dimensions) into our human atrial cardiomyocyte model that couples electrophysiology with spatially detailed Ca 2+ -handling processes. Sex-specific responses of atrial cardiomyocytes to arrhythmia-provoking protocols and Ca 2+ -targeted interventions were evaluated., Results: Simulated quiescent cardiomyocytes showed increased incidence of Ca 2+ sparks in female vs male myocytes in AF, in agreement with previous experimental reports. Additionally, our female model exhibited elevated propensity to develop pacing-induced spontaneous Ca 2+ releases (SCRs) and augmented beat-to-beat variability in action potential (AP)-elicited Ca 2+ transients compared with the male model. Sensitivity analysis uncovered distinct arrhythmogenic contributions of each component involved in sex and/or AF alterations. Specifically, increased ryanodine receptor phosphorylation emerged as the major SCR contributor in female AF cardiomyocytes, whereas reduced L-type Ca 2+ current was protective against SCRs for male AF cardiomyocytes. Furthermore, simulated Ca 2+ -targeted interventions identified potential strategies (eg, t-tubule restoration, and inhibition of ryanodine receptor and sarcoplasmic/endoplasmic reticulum Ca 2 ⁺-ATPase) to attenuate Ca 2+ -driven arrhythmogenic events in women, and revealed enhanced efficacy when applied in combination., Conclusions: Sex-specific modeling uncovers increased Ca 2+ -driven arrhythmogenic events in female vs male atria in AF, and suggests combined Ca 2+ -targeted interventions are promising therapeutic approaches in women., Competing Interests: Funding Support and Author Disclosures This work was supported by American Heart Association Career Development Award 24CDA1258695 (to Dr Ni), Postdoctoral Fellowships 20POST35120462 (to Dr Ni), 24POST1195229 (to Dr Wu), and Predoctoral Fellowship 20PRE35120465 (to Dr Zhang); National Institutes of Health/National Heart, Lung, and Blood Institute grants R01HL131517 (to Drs Grandi, Louch, and Dobrev), R01HL170521 (to Drs Grandi and Ni), R01HL141214 and P01HL141084 (to Dr Grandi), R01HL136389 (to Dr Dobrev), R01HL089598 (to Dr Dobrev), R01HL163277 (to Dr Dobrev), R01HL160992 (to Dr Dobrev), R01HL165704 (to Dr Dobrev), and R00HL138160 (to Dr Morotti); National Institutes of Health Stimulating Peripheral Activity to Relieve Conditions Grant 1OT2OD026580-01 (to Dr Grandi); the Research Council of Norway (Grant No. 287395 to Dr Louch); UC Davis School of Medicine Dean’s Fellow Award (to Dr Grandi); the European Union (large-scale integrative project MAESTRIA, No. 965286, to Dr Dobrev); and the Burroughs Wellcome Fund–Doris Duke Charitable Foundation "COVID-19 Fund to Retain Clinical Scientists" Award (to Dr Morotti). All authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems.

Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none nature has prevented direct training of SNNs for various applications. The surrogate gradient (SG) algorithm has recently enabled spiking neural networks to shine in neuromorphic hardware. However, introducing surrogate gradients has caused SNNs to lose their original sparsity, thus leading to the potential performance loss. In this paper, we first analyze the current problem of direct training using SGs and then propose Masked Surrogate Gradients (MSGs) to balance the effectiveness of training and the sparseness of the gradient, thereby improving the generalization ability of SNNs. Moreover, we introduce a temporally weighted output (TWO) method to decode the network output, reinforcing the importance of correct timesteps. Extensive experiments on diverse network structures and datasets show that training with MSG and TWO surpasses the SOTA technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Alzheimer's disease (AD) affects both grey and white matter (WM), but considerably more is known about the former. Interestingly, WM disruption has been consistently observed and thoroughly described using imaging modalities, particularly MRI which has shown WM functional disconnections between the hippocampus and other brain regions during AD pathogenesis when early neurodegeneration and synapse loss are also evident. Nonetheless, high-resolution structural and functional analyses of WM during AD pathogenesis remain scarce. Given the importance of the myelinated axons in the WM for conveying information across brain regions, such studies will provide valuable information on the cellular drivers and consequences of WM disruption that contribute to the characteristic cognitive decline of AD. Here, we employed a multi-scale approach to investigate hippocampal WM disruption during AD pathogenesis and determine whether hippocampal WM changes accompany the well-documented grey matter losses. Our data indicate that ultrastructural myelin disruption is elevated in the alveus in human AD cases and increases with age in 5xFAD mice. Unreliable action potential propagation and changes to sodium channel expression at the node of Ranvier co-emerged with this deterioration. These findings provide important insight to the neurobiological substrates and functional consequences of decreased WM integrity and are consistent with the notion that hippocampal disconnection contributes to cognitive changes in AD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Resonance and synchronized rhythm are significant phenomena observed in dynamical systems in nature, particularly in biological contexts. These phenomena can either enhance or disrupt system functioning. Numerous examples illustrate the necessity for organs within the human body to maintain their rhythmic patterns for proper operation. For instance, in the brain, synchronized or desynchronized electrical activities can contribute to neurodegenerative conditions like Huntington's disease. In this paper, we utilize the well-established Hodgkin-Huxley (HH) model, which describes the propagation of action potentials in neurons through conductance-based mechanisms. Employing a "data-driven" approach alongside the outputs of the HH model, we introduce an innovative technique termed "dynamic entrainment." This technique leverages deep learning methodologies to dynamically sustain the system within its entrainment regime. Our findings show that the results of the dynamic entrainment technique match with the outputs of the mechanistic (HH) model., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Dysfunction of the Nav1.5, Cav1.2, and Kv channels could interfere with the AP and result in arrhythmias and even heart failure. We herein present a novel library of nuciferine analogs that target ion channels for the treatment of arrhythmias. Patch clamp measurements of ventricular myocytes revealed that 6a dramatically blocked both the I Na and I Ca without altering the currentvoltage relationship (including the activation potential and peak potential), accelerated the inactivation of Nav and Cav channels and delayed the resurrection of these channels after inactivation. Additionally, 6a significantly decreased the APA and RMP without affecting the APD30 or APD50. The IC 50 values of 6a against Nav1.5 and Cav1.2 were 4.98 μM and 4.62 μM, respectively. Furthermore, 6a (10 μM) blocked I Ks , I K1 , and I to with values of 17.01 %±2.54 %, 9.09 %±2.78 %, and 11.15 %±3.52 %, respectively. Surprisingly, 6a weakly inhibited hERG channels, suggesting a low risk of proarrhythmia. The cytotoxicity evaluation of 6a with the H9c2 cell line indicated that this compound was noncytotoxic. In vivo studies suggested that these novel nuciferine analogs could shorten the time of arrhythmia continuum induced by BaCl 2 and normalize the HR, QRS, QT and QTc interval and the R wave amplitude. Moreover, 6a dose-dependently affected aconitine-induced arrhythmias and notably improved the cumulative dosage of aconitine required to evoke VP, VT, VF and CA in rats with aconitine-induced arrhythmia. In conclusion, nuciferine analogs could be promising ion channel blockers that could be further developed into antiarrhythmic agents., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

The atrioventricular node (AVN) is a crucial component of the cardiac conduction system. Despite its pivotal role in regulating the transmission of electrical signals between atria and ventricles, a comprehensive understanding of the cellular electrophysiological mechanisms governing AVN function has remained elusive. This paper presents a detailed computational model of mouse AVN cell action potential (AP). Our model builds upon previous work and introduces several key refinements, including accurate representation of membrane currents and exchangers, calcium handling, cellular compartmentalization, dynamic update of intracellular ion concentrations, and calcium buffering. We recalibrated and validated the model against existing and unpublished experimental data. In control conditions, our model reproduces the AVN AP experimental features, (e.g. rate = 175 bpm, experimental range [121, 191] bpm). Notably, our study sheds light on the contribution of L-type calcium currents, through both Ca v 1.2 and Ca v 1.3 channels, in AVN cells. The model replicates several experimental observations, including the cessation of firing upon block of Ca v 1.3 or I Na,r current. I f block induces a reduction in beating rate of 11%. In summary, this work presents a comprehensive computational model of mouse AVN cell AP, offering a valuable tool for investigating pacemaking mechanisms and simulating the impact of ionic current blockades. By integrating calcium handling and refining formulation of ionic currents, our model advances understanding of this critical component of the cardiac conduction system, providing a platform for future developments in cardiac electrophysiology. KEY POINTS: This paper introduces a comprehensive computational model of mouse atrioventricular node (AVN) cell action potentials (APs). Our model is based on the electrophysiological data from isolated mouse AVN cells and exhibits an action potential and calcium transient that closely match the experimental records. By simulating the effects of blocking specific ionic currents, the model effectively predicts the roles of L-type Ca v 1.2 and Ca v 1.3 channels, T-type calcium channels, sodium currents (TTX-sensitive and TTX-resistant), and the funny current (I f ) in AVN pacemaking. The study also emphasizes the significance of other ionic currents, including I Kr , I to , I Kur , in regulating AP characteristics and cycle length in AVN cells. The model faithfully reproduces the rate dependence of action potentials under pacing, opening the possibility of use in impulse propagation models. The population-of-models approach showed the robustness of this new AP model in simulating a wide spectrum of cellular pacemaking in AVN., (© 2024 The Author(s). The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our research is based on the hypothesis that an SNN that implements the Hebbian learning paradigm is capable of becoming specialized in the detection of statistically significant word patterns in the presence of adequately tailored sequential input. To support this hypothesis, we propose a novel spiking topic model (STM) that transforms text into a sequence of spikes and uses that sequence to train single-layer SNNs. In STM, each SNN neuron represents one topic, and each of the neuron's weights corresponds to one word. STM synaptic connections are modified according to spike-timing-dependent plasticity; after training, the neurons' strongest weights are interpreted as the words that represent topics. We compare the performance of STM with four other TM methods Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Embedding Topic Model (ETM) and BERTopic on three datasets: 20Newsgroups, BBC news, and AG news. The results demonstrate that STM can discover high-quality topics and successfully compete with comparative classical methods. This sheds new light on the possibility of the adaptation of SNN models in unsupervised natural language processing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias., Objective: With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities., Methods: To solve these two problems, an ECG classification method based on a residual attention neural network is proposed in this paper. The residual network (ResNet) is used to solve the gradient vanishing problem. Moreover, it has fewer model parameters, and its structure is simpler. An attention mechanism is added to focus on key information, integrate channel features, and improve voting methods to alleviate the problem of data imbalance., Results: Experiments and verifications are conducted using the PhysioNet/CinC Challenge 2017 dataset. The average F1 value is 0.817, which is 0.064 higher than that for the ResNet model. Compared with the mainstream methods, the performance is excellent., Competing Interests: Declarations. Conflict of interest: The authors of this article declare no potential conflicts of interest. Informed Consent: The authors declare that informed consent was obtained from all patients involved in the study., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols. [ABSTRACT FROM AUTHOR]

Background: Variants of the SCN5A gene, which encodes the Na V 1.5 cardiac sodium channel, have been linked to arrhythmic disorders associated with dilated cardiomyopathy (DCM). However, the precise pathological mechanisms remain elusive. The present study aimed to elucidate the pathophysiological consequences of the DCM-linked Nav1.5/R219H variant, which is known to generate a gating pore current, using patient-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) cultured in monolayers., Methods: Ventricular- and atrial-like hiPSC-CM monolayers were generated from DCM patients carrying the R219H SCN5A variant as well as from healthy control individuals. CRISPR-corrected hiPSC-CMs served as isogenic controls. Simultaneous optical mapping of action potentials (APs) and calcium transients (CaTs) was employed to measure conduction velocities (CVs) and AP durations (APDs) and served as markers of electrical excitability. Calcium handling was evaluated by assessing CaT uptake (half-time to peak), recapture (tau of decay), and durations (TD 50 and TD 80 ). A multi-electrode array (MEA) analysis was conducted on hiPSC-CM monolayers to measure field potential (FP) parameters, including corrected Fridericia FP durations (FPDc)., Results: Our results revealed that CVs were significantly reduced by more than 50 % in both ventricular- and atrial-like hiPSC-CM monolayers carrying the R219H variant compared to the control group. APDs were also prolonged in the R219H group compared to the control and CRISPR-corrected groups. CaT uptake, reuptake, and duration were also markedly delayed in the R219H group compared to the control and CRISPR-corrected groups in both the ventricular- and the atrial-like hiPSC-CM monolayers. Lastly, the MEA data revealed a notably prolonged FPDc in the ventricular- and atrial-like hiPSC-CMs carrying the R219H variant compared to the control and isogenic control groups., Conclusions: These findings highlight the impact of the gating pore current on AP propagation and calcium homeostasis within a functional syncytium environment and offer valuable insights into the potential mechanisms underlying DCM pathophysiology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Mohamed Chahine reports was provided by Laval University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Non-invasive or minutely invasive and wireless brain stimulation that can target any region of the brain is an open problem in engineering and neuroscience with serious implications for the treatment of numerous neurological diseases. Despite significant recent progress in advancing new methods of neuromodulation, none has successfully replicated the efficacy of traditional wired stimulation and improved on its downsides without introducing new complications. Due to the capability to convert magnetic fields into local electric fields, MagnetoElectric NanoParticle (MENP) neuromodulation is a recently proposed framework based on new materials that can locally sensitize neurons to specific, low-strength alternating current (AC) magnetic fields (50Hz 1.7 kOe field). However, the current research into this neuromodulation concept is at a very early stage, and the theoretically feasible game-changing advantages remain to be proven experimentally. To break this stalemate phase, this study leveraged understanding of the non-linear properties of MENPs and the nanoparticles' field interaction with the cellular microenvironment. Particularly, the applied magnetic field's strength and frequency were tailored to the M - H hysteresis loop of the nanoparticles. Furthermore, rectangular prisms instead of the more traditional "spherical" nanoparticle shapes were used to: (i) maximize the magnetoelectric effect and (ii) improve the nanoparticle-cell-membrane surface interface. Neuromodulation performance was evaluated in a series of exploratory in vitro experiments on 2446 rat hippocampus neurons. Linear mixed effect models were used to ensure the independence of samples by accounting for fixed adjacency effects in synchronized firing. Neural activity was measured over repeated 4-min segments, containing 90 s of baseline measurements, 90 s of stimulation measurements, and 60 s of post stimulation measurements. 87.5 % of stimulation attempts produced statistically significant (P < 0.05) changes in neural activity, with 58.3 % producing large changes (P < 0.01). In negative controls using either zero or 1.7 kOe-strength field without nanoparticles, no experiments produced significant changes in neural activity (P > 0.05 and P > 0.15 respectively). Furthermore, an exploratory analysis of a direct current (DC) magnetic field indicated that the DC field could be used with MENPs to inhibit neuron activity (P < 0.01). These experiments demonstrated the potential for magnetoelectric neuromodulation to offer a near one-to-one functionality match with conventional electrode stimulation without requiring surgical intervention or genetic modification to achieve success, instead relying on physical properties of these nanoparticles as "On/Off" control mechanisms. ONE-SENTENCE SUMMARY: This in vitro neural cell culture study explores how to exploit the non-linear and anisotropic properties of magnetoelectric nanoparticles for wireless neuromodulation, the importance of magnetic field strength and frequency matching for optimization, and demonstrates, for the first time, that magnetoelectric neuromodulation can inhibit neural responses., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The concept of using MENPs for medical applications was for the first time proposed by SK and PL, with its neuromodulation hypothesis described in their 2012 paper [66]. In 2016, PL and SK have co-founded a biotech start-up, Cellular Nanomed, to develop a wireless brain-machine interface (BMI) based on magnetoelectric nanoparticles. PL is the chief scientist at Cellular Nanomed., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This paper presents a method for using atomic force microscopy to probe action potentials of single beating cardiomyocytes at the nanoscale. In this work, the conductive tip of an atomic force microscope (AFM) was used as a nanoelectrode to record the action potentials of self-beating cardiomyocytes in both the non-constant force contact mode and the constant force contact mode. An electrical model of a tip-cell interface was developed and the indentation force effect on the seal of an AFM conductive tip-cell membrane was theoretically analyzed. The force feedback of AFM allowed for the precise control of tip-cell contact, and enabled reliable measurements. The feasibility of simultaneously recording the action potentials and force information during the contraction of the same beating cardiomyocyte was studied. Furthermore, the AFM tip electrode was used to probe the differences of action potentials using different drugs. This method provides a way at the nanoscale for electrophysiological studies on single beating cardiomyocytes, neurons, and ion channels embedded within the cell membrane in relation to disease states, pharmaceutical drug testing and screening.

Antipsychotic drugs often lead to adverse effects, including those related to the cardiovascular system. Of these, quetiapine is known to cause significant changes in the QT interval although the underlying mechanism remains mysterious, prompting us to examine its effects on cardiac electrophysiological properties. Therefore, we investigated the effect of quetiapine on contraction, action potential (AP), and the associated membrane currents such as L-type Ca 2+ and K + using the whole-cell patch clamp method to examine its impacts on isolated rat ventricular myocytes. Our results showed that (1) quetiapine reduces cell contractility in a concentration-dependent manner and (2) leads to a significant prolongation in the duration of AP in isolated ventricular myocytes. This effect was both concentration and frequency-dependent; (3) quetiapine significantly decreased the Ca 2+ , transient outward K + , and steady-state K + currents. However, only high concentration of quetiapine (100 μM) could significantly change the activation and reactivation kinetics of L-type Ca 2+ channels. This study demonstrates that QT extension induced by quetiapine is mainly associated with the prolongation of AP. Moreover, quetiapine caused a significant decrease in contractile force and excitability of ventricular myocytes by suppressing Ca 2+ and K + currents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

An important working hypothesis to investigate brain activity is whether it operates in a critical regime. Recently, maximum-entropy phenomenological models have emerged as an alternative way of identifying critical behavior in neuronal data sets. In the present paper, we investigate the signatures of criticality from a firing rate-based maximum-entropy approach on data sets generated by computational models, and we compare them to experimental results. We found that the maximum entropy approach consistently identifies critical behavior around the phase transition in models and rules out criticality in models without phase transition. The maximum-entropy-model results are compatible with results for cortical data from urethane-anesthetized rats data, providing further support for criticality in the brain.

Voltage-gated ion channels play an important role in generating action potential in neurons. These ion channels are found to be in localized cluster form on the axonal membrane surface and behave cooperatively. However, in Hodgkin & Huxley's model of action potential the ion channels are considered to function independently. According to some recent reports, the activity of an ion channel is influenced by the neighboring ion channels' activities. We have modified the Hodgkin-Huxley's model based on our previous studies on cooperativity among ion channels. Computational analysis of the proposed model shows that the initiation of the action potential, amplitude and hyperpolarization are affected significantly by the cooperative interactions among the voltage-gated ion channels present on the axonal membrane surface. These results are qualitatively supported by the existing experimental facts., Competing Interests: Declaration of competing interest This is to state that none of the authors of our paper (Jitender Kumar, Patrick Das Gupta & Subhendu Ghosh) has any conflict of interest (mutual or with any other scientists). This statement is related to the present submission of our manuscript “Investigating the role of axonal ion channel cooperativity in action potential dynamics: Studies on Hodgkin-Huxley's model” to your esteemed journal., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10 -3 V 2 /Hz to 4.04 × 10 -6 V 2 /Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

Atrial flutter, a prevalent cardiac arrhythmia, is primarily characterized by reentrant circuits in the right atrium. However, atypical forms of atrial flutter present distinct challenges in terms of diagnosis and treatment. In this study, we examine three noteworthy clinical cases of atypical atrial flutter, which offer compelling evidence indicating the implication of the lesser-known Septopulmonary Bundle (SPB). This inference is based on the identification of distinct electrocardiographic patterns observed in these patients and their favorable response to catheter ablation, which is a standard treatment for atrial flutter. Remarkably, in each case, targeted ablation at the anterior portion of the left atrial roof effectively terminated the arrhythmia, thus providing further support for the hypothesis of SPB involvement. These insightful observations shed light on the potential significance of the SPB in the etiology of atypical atrial flutter and introduce a promising therapeutic target. We anticipate that this paper will stimulate further exploration into the role of the SPB in atrial flutter and pave the way for the development of targeted ablation strategies., (© 2024. The Author(s).)

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl−, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks. Author summary: Neurons communicate using electrical signals called action potentials. To create these signals, sodium ions must flow into the cells and potassium ions must flow out. This transmembrane flow requires a concentration difference across the neuronal membrane, which the brain works continuously to maintain. When scientists build mathematical models of neurons, they often apply the simplifying assumption that these ion concentration differences remain constant over time. This assumption works well for many scenarios, but not all. For instance, during events like stroke or epilepsy, the ion concentrations can change dramatically, affecting how neurons behave. Moreover, recent literature suggests that changing ion concentrations also play an important role in normal brain function. To study these scenarios, we need models that can dynamically track changes in ion concentrations. The neuroscience community currently lacks a computational model describing the effects of ion concentration dynamics on neuronal networks, while maintaining a biophysical consistent relationship between ion concentrations and electrical potentials. To address the need for such a model, we have developed a neuronal network model that predicts changes in both intra- and extracellular ion concentrations, electrical potentials, and volumes in a biophysically consistent way. [ABSTRACT FROM AUTHOR]

Action potential generation underlies some of the most consequential dynamical systems on Earth, from brains to hearts. It is therefore interesting to develop synthetic cell-free systems, based on the same molecular mechanisms, which may allow for the exploration of parameter regions and phenomena not attainable, or not apparent, in the live cell. We previously constructed such a synthetic system, based on biological components, which fires action potentials. We call it "Artificial Axon". The system is minimal in that it relies on a single ion channel species for its dynamics. Here we characterize the Artificial Axon as a dynamical system in time, using a simplified Hodgkin-Huxley model adapted to our experimental context. We construct a phase diagram in parameter space identifying regions corresponding to different temporal behavior, such as Action Potential (AP) trains, single shot APs, or damped oscillations. The main new result is the finding that our system with a single ion channel species, with inactivation, is dynamically equivalent to the system of two channel species without inactivation (the Morris-Lecar system), which exists in nature. We discuss the transitions and bifurcations occurring crossing phase boundaries in the phase diagram, and obtain criteria for the channels' properties necessary to obtain the desired dynamical behavior. In the second part of the paper we present new experimental results obtained with a system of two AAs connected by excitatory and/or inhibitory electronic "synapses". We discuss the feasibility of constructing an autonomous oscillator with this system. [ABSTRACT FROM AUTHOR]

The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system's ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes. [ABSTRACT FROM AUTHOR]

We give a characterization of the singular points of the free boundary ∂{u>0} for viscosity solutions of the nonlinear equation F(D²u)=-χ {u>0}, where F is a fully nonlinear elliptic operator and χ is the characteristic function. This equation models the propagation of a nerve impulse along an axon. We analyze the structure of the free boundary ∂{u>0} near the singular points where u and ∇u vanish simultaneously. Our method uses the stratification approach developed in Dipierro and the author's 2018 paper. In particular, when n=2 we show that near a flat singular free boundary point, ∂{u>0} is a union of four C 1 arcs tangential to a pair of crossing lines. [ABSTRACT FROM AUTHOR]

The article features an interview with M. H. D. Fouad Zakaria, the first author of a paper on the role of NaV1.1 in regulating the cell cycle of human mesenchymal stem cells. Zakaria, a PhD student at Kyushu University, discusses his research findings and the significance of his work in cell biology. He credits his mentor, Hiroki Kato, for his scientific development and shares his motivation for pursuing a career in science to impact medical science on a broader scale. Zakaria will continue his research as an assistant professor at Kyushu University Graduate School of Dental Science in Japan. [Extracted from the article]

The networks proposed here show how neurons can be connected to form flip-flops, the basic building blocks in sequential logic systems. The novel neural flip-flops (NFFs) are explicit, dynamic, and can generate known phenomena of short-term memory. For each network design, all neurons, connections, and types of synapses are shown explicitly. The neurons' operation depends only on explicitly stated, minimal properties of excitement and inhibition. This operation is dynamic in the sense that the level of neuron activity is the only cellular change, making the NFFs' operation consistent with the speed of most brain functions. Memory tests have shown that certain neurons fire continuously at a high frequency while information is held in short-term memory. These neurons exhibit seven characteristics associated with memory formation, retention, retrieval, termination, and errors. One of the neurons in each of the NFFs produces all of the characteristics. This neuron and a second neighboring neuron together predict eight unknown phenomena. These predictions can be tested by the same methods that led to the discovery of the first seven phenomena. NFFs, together with a decoder from a previous paper, suggest a resolution to the longstanding controversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts. Two novel NFFs are composed of two and four neurons. Their designs follow directly from a standard electronic flip-flop design by moving each negation symbol from one end of the connection to the other. This does not affect the logic of the network, but it changes the logic of each component to a logic function that can be implemented by a single neuron. This transformation is reversible and is apparently new to engineering as well as neuroscience. [ABSTRACT FROM AUTHOR]

The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level. [ABSTRACT FROM AUTHOR]

Aging is associated with the slowdown of neuronal processing and cognitive performance in the brain; however, the exact cellular mechanisms behind this deterioration in humans are poorly elucidated. Recordings in human acute brain slices prepared from tissue resected during brain surgery enable the investigation of neuronal changes with age. Although neocortical fast-spiking cells are widely implicated in neuronal network activities underlying cognitive processes, they are vulnerable to neurodegeneration. Herein, we analyzed the electrical properties of 147 fast-spiking interneurons in neocortex samples resected in brain surgery from 106 patients aged 11-84 years. By studying the electrophysiological features of action potentials and passive membrane properties, we report that action potential overshoot significantly decreases and spike half-width increases with age. Moreover, the action potential maximum-rise speed (but not the repolarization speed or the afterhyperpolarization amplitude) significantly changed with age, suggesting a particular weakening of the sodium channel current generated in the soma. Cell passive membrane properties measured as the input resistance, membrane time constant, and cell capacitance remained unaffected by senescence. Thus, we conclude that the action potential in fast-spiking interneurons shows a significant weakening in the human neocortex with age. This may contribute to the deterioration of cortical functions by aging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The use of tyrosine kinase inhibitors (TKIs) has resulted in significant occurrence of arrhythmias. However, the precise mechanism of the proarrhythmic effect is not fully understood. In this study, we found that nilotinib (NIL), vandetanib (VAN), and mobocertinib (MOB) induced the development of "cellrhythmia" (arrhythmia-like events) in a concentration-dependent manner in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Continuous administration of NIL, VAN, or MOB in animals significantly prolonged the action potential durations (APD) and increased susceptibility to arrhythmias. Using phosphoproteomic analysis, we identified proteins with altered phosphorylation levels after treatment with 3 μM NIL, VAN, and MOB for 1.5 h. Using these identified proteins as substrates, we performed kinase-substrate enrichment analysis to identify the kinases driving the changes in phosphorylation levels of these proteins. MAPK and WNK were both inhibited by NIL, VAN, and MOB. A selective inhibitor of WNK1, WNK-IN-11, induced concentration- and time-dependent cellrhythmias and prolonged field potential duration (FPD) in hiPSC-CMs in vitro; furthermore, administration in guinea pigs confirmed that WNK-IN-11 prolonged ventricular repolarization and increased susceptibility to arrhythmias. Fingding indicated that WNK1 inhibition had an in vivo and in vitro arrhythmogenic phenotype similar to TKIs. Additionally,three of TKIs reduced hERG and KCNQ1 expression at protein level, not at transcription level. Similarly, the knockdown of WNK1 decreased hERG and KCNQ1 protein expression in hiPSC-CMs. Collectively, our data suggest that the proarrhythmic effects of NIL, VAN, and MOB occur through a kinase inhibition mechanism. NIL, VAN, and MOB inhibit WNK1 kinase, leading to a decrease in hERG and KCNQ1 protein expression, thereby prolonging action potential repolarization and consequently cause arrhythmias., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Mutations in the SCN8A gene, encoding the voltage-gated sodium channel Na V 1.6, are associated with a range of neurodevelopmental syndromes. The p.(Gly1625Arg) (G1625R) mutation was identified in a patient diagnosed with developmental epileptic encephalopathy (DEE). While most of the characterized DEE-associated SCN8A mutations were shown to cause a gain-of-channel function, we show that the G1625R variant, positioned within the S4 segment of domain IV, results in complex effects. Voltage-clamp analyses of Na V 1.6 G1625R demonstrated a mixture of gain- and loss-of-function properties, including reduced current amplitudes, increased time constant of fast voltage-dependent inactivation, a depolarizing shift in the voltage dependence of activation and inactivation, and increased channel availability with high-frequency repeated depolarization. Current-clamp analyses in transfected cultured neurons revealed that these biophysical properties caused a marked reduction in the number of action potentials when firing was driven by the transfected mutant Na V 1.6. Accordingly, computational modeling of mature cortical neurons demonstrated a mild decrease in neuronal firing when mimicking the patients' heterozygous SCN8A expression. Structural modeling of Na V 1.6 G1625R suggested the formation of a cation-π interaction between R1625 and F1588 within domain IV. Double-mutant cycle analysis revealed that this interaction affects the voltage dependence of inactivation in Na V 1.6 G1625R . Together, our studies demonstrate that the G1625R variant leads to a complex combination of gain and loss of function biophysical changes that result in an overall mild reduction in neuronal firing, related to the perturbed interaction network within the voltage sensor domain, necessitating personalized multi-tiered analysis for SCN8A mutations for optimal treatment selection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.

This paper studies the phenomenon of conduction block in model neurons using high-frequency biphasic stimulation (HFBS). The focus is investigating the triggering of undesired onset action potentials when the HFBS is turned on. The approach analyzes the transient behavior of an averaged system corresponding to the FitzHugh--Nagumo neuron model using Lyapunov and quasi-static methods. The first result provides a more comprehensive understanding of the onset activation through a mathematical proof of how to avoid it using a ramp in the amplitude of the oscillatory source. The second result tests the response of the blocked system to a piecewise linear stimulus, providing a quantitative description of how the HFBS strength translates into conduction block robustness. The results of this work can provide insights for the design of electrical neurostimulation therapies. [ABSTRACT FROM AUTHOR]

Living tissues are still far from being used as practical components in biohybrid robots because of limitations in life span, sensitivity to environmental factors, and stringent culture procedures. Here, we introduce fungal mycelia as an easy-to-use and robust living component in biohybrid robots. We constructed two biohybrid robots that use the electrophysiological activity of living mycelia to control their artificial actuators. The mycelia sense their environment and issue action potential–like spiking voltages as control signals to the motors and valves of the robots that we designed and built. The paper highlights two key innovations: first, a vibration- and electromagnetic interference–shielded mycelium electrical interface that allows for stable, long-term electrophysiological bioelectric recordings during untethered, mobile operation; second, a control architecture for robots inspired by neural central pattern generators, incorporating rhythmic patterns of positive and negative spikes from the living mycelia. We used these signals to control a walking soft robot as well as a wheeled hard one. We also demonstrated the use of mycelia to respond to environmental cues by using ultraviolet light stimulation to augment the robots' gaits. Editor's summary: Many biohybrid robots are powered by animal or plant cells, which are sensitive to specific culture procedures and limited to short life spans. In contrast, fungi can be easily cultured and are robust in extreme conditions. Taking advantage of fungal mycelia's natural light sensitivity, Mishra et al. developed an electrical interface to both house the mycelia and measure their electrophysiological action potentials. A control model was then developed to use the rhythmic voltage spikes from the living mycelia to control the locomotion of both a soft starfish-inspired robot and a wheeled robot. Robot trajectories could be altered by stimulating the mycelia with ultraviolet light. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent Plasticity (STDP) benefit from gradient-free and unsupervised local learning, which can be easily implemented on ultra-low-power neuromorphic hardware. However, classification tasks cannot be performed solely with unsupervised STDP. In this paper, we propose Stabilized Supervised STDP (S2-STDP), a supervised STDP learning rule to train the classification layer of an SNN equipped with unsupervised STDP for feature extraction. S2-STDP integrates error-modulated weight updates that align neuron spikes with desired timestamps derived from the average firing time within the layer. Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP. PCN associates each class with paired neurons and encourages neuron specialization toward target or non-target samples through intra-class competition. We evaluate our methods on image recognition datasets, including MNIST, Fashion-MNIST, and CIFAR10. Results show that our methods outperform state-of-the-art supervised STDP learning rules, for comparable architectures and numbers of neurons. Further analysis demonstrates that the use of PCN enhances the performance of S2-STDP, regardless of the hyperparameter set and without introducing any additional hyperparameters. [ABSTRACT FROM AUTHOR]

Walter Eichler (1904–1942) performed the first in situ nerve conduction studies in humans. Eichler's work has been largely overlooked and there have been no biographical accounts written of him. His 1937 paper, Über die Ableitung der Aktionspotentiale vom menschlichen Nerven in situ (On the recording of the action potentials from human nerves in situ) was translated and reviewed. Archival material was obtained on his career that was housed predominantly at the University of Freiburg im Breisgau. He had memberships in Nazi organizations but did not appear to be politically active. During his brief career, he constructed novel equipment and established seminal principles for performing nerve conductions on humans. The authors repeated his experiment in the ulnar nerve, which duplicated Eichler's findings. His recordings were quite remarkable given advances in technology. In summary, the Eichler paper is the first study in the development of in situ clinical electroneurography in humans. Many of his procedural observations are still fundamental in the current practice of electroneurography. As best can be determined, his study in humans did not appear ethically compromised. Although Eichler's personal background remains open to question, his paper is a seminal study in the history and development of clinical electroneurography. Abbreviations: AP: Action potential; C: Capacitor; CNP: Compound nerve potential; DC: Direct current; E1: Preferred term for active electrode; E2: Preferred term for reference electrode; NSDÄB: Nationalsozialistische Deutsche NSD-Ärtzebund (National Socialist German Doctors' League; NSDAP: Nationalsozialistische Deutsche Arbeiterpartei (National Socialist German Workers' Party/ Nazi Party); SS: Schutzstaffel (Protective Echelon or Squad of the Nazi party) [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Excitation properties of computational models of unmyelinated peripheral axons" published in the Journal of Neurophysiology. The authors identified three errors in their implementation of the fiber model used in the study. They corrected these errors and regenerated certain figures in the article. The corrected models exhibited different action potential shapes and durations compared to the original models. The authors also noted some differences between their code and the original Tigerholm paper, which they corrected accordingly. [Extracted from the article]

A sodium current (INa) reduction occurs in the setting of many acquired and inherited conditions and is associated with cardiac conduction slowing and increased arrhythmia risks. The sodium channel blocker mexiletine has been shown to restore the trafficking of mutant sodium channels to the membrane. However, these studies were mostly performed in heterologous expression systems using high mexiletine concentrations. Moreover, the chronic effects on INa in a non-diseased cardiomyocyte environment remain unknown. In this paper, we investigated the chronic and acute effects of a therapeutic dose of mexiletine on INa and the action potential (AP) characteristics in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of a healthy individual. Control hiPSC-CMs were incubated for 48 h with 10 µM mexiletine or vehicle. Following the wash-out of mexiletine, patch clamp analysis and immunocytochemistry experiments were performed. The incubation of hiPSC-CMs for 48 h with mexiletine (followed by wash-out) induced a significant increase in peak INa of ~75%, without any significant change in the voltage dependence of (in)activation. This was accompanied by a significant increase in AP upstroke velocity, without changes in other AP parameters. The immunocytochemistry experiments showed a significant increase in membrane Nav1.5 fluorescence following a 48 h incubation with mexiletine. The acute re-exposure of hiPSC-CMs to 10 µM mexiletine resulted in a small but significant increase in AP duration, without changes in AP upstroke velocity, peak INa density, or the INa voltage dependence of (in)activation. Importantly, the increase in the peak INa density and resulting AP upstroke velocity induced by chronic mexiletine incubation was not counteracted by the acute re-administration of the drug. In conclusion, the chronic administration of a clinically relevant concentration of mexiletine increases INa density in non-diseased hiPSC-CMs, likely by enhancing the membrane trafficking of sodium channels. Our findings identify mexiletine as a potential therapeutic strategy to enhance and/or restore INa and cardiac conduction. [ABSTRACT FROM AUTHOR]

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) signals are the sum of action potentials emitted by many motor units; they contain the information of muscle contraction patterns and intensity, so they can be used as a simple and reliable source for grasping mode recognition. This paper introduces the InRes-ACNet (inception–attention–ACmix-ResNet50) model, a novel deep-learning approach based on ResNet50, incorporating multi-scale modules and self-attention mechanisms. The proposed model aims to improve gesture recognition performance by enhancing its ability to extract channel feature information within sparse sEMG signals. The InRes-ACNet model is evaluated on the NinaPro DB1 and NinaPro DB5 datasets; the recognition accuracy for these datasets can reach 87.94% and 87.04%, respectively, and recognition accuracy can reach 88.37% in the grasping mode prediction of an electromyography manipulator. The results show that the fusion of multi-scale modules and self-attention mechanisms endows a strong ability for the task of gesture recognition based on sparse sEMG signals. [ABSTRACT FROM AUTHOR]

Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health. [ABSTRACT FROM AUTHOR]

The Huxley equation, which is a nonlinear partial differential equation, is used to describe the ionic mechanisms underlying the initiation and propagation of action potentials in the squid giant axon. This equation, just like many other nonlinear equations, is often very difficult to analyze because of the presence of the nonlinearity term, which is always very difficult to approximate. This paper aims to design a reliable scheme that consists of a combination of the nonstandard finite difference in time method, the Galerkin method and the compactness methods in space variables. This method is used to show that the solution of the problem exists uniquely. The a priori estimate from the existence process is applied to the scheme to show that the numerical solution from the scheme converges optimally in the L 2 as well as the H 1 norms. We proceed to show that the scheme preserves the decaying properties of the exact solution. Numerical experiments are introduced with a chosen example to validate the proposed theory. [ABSTRACT FROM AUTHOR]

The thymus is a lymphoid organ involved in the differentiation of T cells, and has a central role in the physiopathogenesis of Myasthenia Gravis (MG). This connection is proved by a series of changes in the level of neuromuscular junctions, which leads to a decrease in the amplitude of the action potential in the post-synaptic membrane. Because of this, the presence of anti-cholinergic receptor antibodies (AChR), characteristic of MG, is found, which causes the progressive regression of the effect of acetylcholine at the level of neuromuscular junctions, with the appearance of muscle weakness. The thymectomy is a surgical variant of drug therapy administered to patients with MG. In the case of patients with nonthymomatous MG, thymectomy has become a therapeutic standard, despite the fact that there is no solid scientific evidence to explain its positive effect. Videothoracoscopic surgery or robotic surgery led to a decrease in the length of hospital stay for these patients. This paper aims to synthesize the information presented in the literature in order to create a background for the perspectives of thymectomy. [ABSTRACT FROM AUTHOR]