Membrane Potential Distribution Research Articles

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks

The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretically and experimentally prove that the binary spike activation map cannot carry enough information, thus causing information loss and resulting in accuracy decreasing. To handle the problem, we propose a ternary spike neuron to transmit information. The ternary spike neuron can also enjoy the event-driven and multiplication-free operation advantages of the binary spike neuron but will boost the information capacity. Furthermore, we also embed a trainable factor in the ternary spike neuron to learn the suitable spike amplitude, thus our SNN will adopt different spike amplitudes along layers, which can better suit the phenomenon that the membrane potential distributions are different along layers. To retain the efficiency of the vanilla ternary spike, the trainable ternary spike SNN will be converted to a standard one again via a re-parameterization technique in the inference. Extensive experiments with several popular network structures over static and dynamic datasets show that the ternary spike can consistently outperform state-of-the-art methods. Our code is open-sourced at https://github.com/yfguo91/Ternary-Spike.

Mean-field method for generic conductance-based integrate-and-fire neurons with finite timescales.

The construction of transfer functions in theoretical neuroscience plays an important role in determining the spiking rate behavior of neurons in networks. These functions can be obtained through various fitting methods, but the biological relevance of the parameters is not always clear. However, for stationary inputs, such functions can be obtained without the adjustment of free parameters by using mean-field methods. In this work, we expand current Fokker-Planck approaches to account for the concurrent influence of colored and multiplicative noise terms on generic conductance-based integrate-and-fire neurons. We reduce the resulting stochastic system through the application of the diffusion approximation to a one-dimensional Langevin equation. An effective Fokker-Planck is then constructed using Fox Theory, which is solved numerically using a newly developed double integration procedure to obtain the transfer function and the membrane potential distribution. The solution is capable of reproducing the transfer function and the stationary voltage distribution of simulated neurons across a wide range of parameters. The method can also be easily extended to account for different sources of noise with various multiplicative terms, and it can be used in other types of problems in principle.

Enhanced Methodologies forInvestigating the Electrophysiological Characteristics of Endogenous Pannexin 1 Intercellular Cell-Cell Channels.

Gap junctions, pivotal intercellular conduits, serve as communication channels betweenadjacent cells, playing a critical role in modulating membrane potential distribution across cellular networks. The family ofPannexin (Panx) proteins, in particular Pannexin1 (Panx1), are widelyexpressed in vertebrate cells and exhibit sequence homology with innexins, the invertebrate gap junction channelconstituents. Despite being ubiquitously expressed, detailed functional and pharmacological properties ofPanx1 intercellular cell-cell channelsrequire further investigation. In this chapter, we introduce optimized cell culture methodologies and electrophysiology protocols to expedite the exploration of endogenous Panx1 cell-cell channels in TC620 cells, a human oligodendroglioma cell line that naturally expresses Panx1. We anticipate theserefined protocols will significantly contributeto future characterizations of Panx1-based intercellularcell-cell channelsacross diverse cell types andoffer valuable insights into both normalcellular physiology and pathophysiology.

Three-dimensional glass scaffolds improve the In Vitro maturation of porcine cumulus-oocyte complexes and subsequent embryonic development after parthenogenetic activation

In vitro maturation (IVM) methods for porcine oocytes are still deficient in achieving full developmental capacity, as the currently available oocyte in vitro culture systems still have limitations. In vitro embryo production must also improve the porcine oocyte IVM system to acquire oocytes with good developmental potential. Herein, we tested a three-dimensional (3D) glass scaffold culture system for porcine oocyte maturation. After 42 h, we matured porcine cumulus-oocyte complexes (COCs) on either two-dimensional glass dishes (2D-B), two-dimensional microdrops (2D-W), or 3D glass scaffolds. The 3D glass scaffolds were tested for porcine oocyte maturation and embryonic development. Among these culture methods, the extended morphology of the 3D group maintained a 3D structure better than the 2D-B and 2D-W groups, which had flat COCs that grew close to the bottom of the culture vessel. The COCs of the 3D group had a higher cumulus expansion index and higher first polar body extrusion rate, cleavage rate, and blastocyst rate of parthenogenetic embryos than the 2D-B group. In the 3D group, the cumulus-expansion-related gene HAS2 and anti-apoptotic gene Bcl-2 were significantly upregulated (p < 0.05), while the pro-apoptotic gene Caspase3 was significantly downregulated (p < 0.05). The blastocysts of the 3D group had a higher relative expression of Bcl-2, Oct4, and Nanog than the other two groups (p < 0.05). The 3D group also had a more uniform distribution of mitochondrial membrane potential and mitochondria (p < 0.05), and its cytoplasmic active oxygen species content was much lower than that in the 2D-B group (p < 0.05). These results show that 3D glass scaffolds dramatically increased porcine oocyte maturation and embryonic development after parthenogenetic activation, providing a suitable culture model for porcine oocytes.

Corilagin Induces ROS-mediated Apoptosis and Triggers Cell-Cycle Arrest at G0/G1 Stage in Osteosarcoma Cells

Background Osteosarcoma (OS) is an extremely aggressive primary bone cancer (BC) malignancy. A variety of malignancies can develop in the bones, including BC. Primary BCs are tumors that start in the bone. Bones can also become affected by tumors that start in the body’s organs or other tissues. Objectives Several reports suggested that corilagin (CL) exerts anticancer properties on various kinds of tumor cells; however, its molecular action on OS cells remains undefined. Materials and Methods The CL activity of OS cells’ cytotoxicity, intracellular reactive oxygen species (ROS), mitochondrial membrane potential (MMP), apoptosis, and cell-cycle distribution was evaluated by 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay, dichloro-dihydro-fluorescein diacetate (DCFH-DA), Rh-123, acridine orange and ethidium bromide (AO/EB), 4′,6-diamidino-2-phenylindole (DAPI), and flow cytometry analysis. Results Results revealed that CL could suppress OS cell proliferation via enhanced intracellular ROS, and MMP loss, and triggered apoptosis in a dose-dependent has an inferior manner. Our findings demonstrated that CL alleviates U2OS and MG-63 cell proliferation by the ROS-mediated apoptosis, which triggers G0/G1 cell-cycle arrest. Conclusion Thus, CL might be a protective therapeutic agent against BC.

Memristor initial-offset boosting and its bifurcation mechanism in a memristive FitzHugh-Nagumo neuron model with hidden dynamics

Electromagnetic induction plays a vital role in impacting the neuron dynamics, since the electromagnetic induction is triggered alongside with the complex interaction of membrane potential and various ions transport. In other words, the electromagnetic induction is reversely subjected to the membrane potential fluctuation and ions distribution. Flux-controlled memristor described by flux and voltage has been employed to restrict the electromagnetic induction effect. This paper presents a flux-controlled memristor with sinusoidal mem-conductance function and hyperbolic tangent function modulated input. The memristor can availably reflect the non-uniform ions distribution inside and outside neuron membrane. Then, an improved memristive FitzHugh-Nagumo (mFHN) neuron model is built to explore the dynamical effect of the memristive electromagnetic induction. Numerical simulations and theoretical analysis are performed, which reveal that the mFHN neuron model possesses no equilibrium point and can generate abundant hidden dynamics. Interestingly, hidden coexisting behavior is triggered by memristor initial-offset boosting, which evokes the emergence of infinitely hidden coexisting firing patterns. Besides, bifurcation mechanism of the memristor initial-offset boosting behavior is theoretically analyzed. Furthermore, PowerSIM-based (PSIM-based) analog circuit simulations and microcontroller unit based (MCU-based) digital experiments are executed, the captured results perfectly verify the correctness of numerical results and the feasibility of analog/digital hardware experiments.

Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings.

Although electrophysiologists have been recording intracellular neural activity routinely ever since the ground-breaking work of Hodgkin and Huxley, and extracellular multichannel electrodes have also been used frequently and extensively, a practical experimental method to track changes in membrane potential along a complete single neuron is still lacking. Instead of obtaining multiple intracellular measurements on the same neuron, we propose an alternative method by combining single-channel somatic patch-clamp and multichannel extracellular potential recordings. In this work, we show that it is possible to reconstruct the complete spatiotemporal distribution of the membrane potential of a single neuron with the spatial resolution of an extracellular probe during action potential generation. Moreover, the reconstruction of the membrane potential allows us to distinguish between the two major but previously hidden components of the current source density (CSD) distribution: the resistive and the capacitive currents. This distinction provides a clue to the clear interpretation of the CSD analysis, because the resistive component corresponds to transmembrane ionic currents (all the synaptic, voltage-sensitive and passive currents), whereas capacitive currents are considered to be the main contributors of counter-currents. We validate our model-based reconstruction approach on simulations and demonstrate its application to experimental data obtained in vitro via paired extracellular and intracellular recordings from a single pyramidal cell of the rat hippocampus. In perspective, the estimation of the spatial distribution of resistive membrane currents makes it possible to distiguish between active and passive sinks and sources of the CSD map and the localization of the synaptic input currents, which make the neuron fire. KEY POINTS: A new computational method is introduced to calculate the unbiased current source density distribution on a single neuron with known morphology. The relationship between extracellular and intracellular electric potential is determined via mathematical formalism, and a new reconstruction method is applied to reveal the full spatiotemporal distribution of the membrane potential and the resistive and capacitive current components. The new reconstruction method was validated on simulations. Simultaneous and colocalized whole-cell patch-clamp and multichannel silicon probe recordings were performed from the same pyramidal neuron in the rat hippocampal CA1 region, in vitro. The method was applied in experimental measurements and returned precise and distinctive characteristics of various intracellular phenomena, such as action potential generation, signal back-propagation and the initial dendritic depolarization preceding the somatic action potential.

P-158 Beneficial effects of platelet rich plasma for human germinal vesicle oocyte in vitro maturation

Abstract Study question Can platelet rich plasma (PRP) supplementation to the medium improve outcomes of in vitro maturation of germinal vesicle (GV) oocytes? Summary answer PRP as a component of in vitro maturation medium benefits the GV oocyte maturation rate, the mitochondrial membrane potential and the distribution of lipid droplets. What is known already The European Society of Human Reproduction and Embryology presents several strategies for maintaining fertility in women, one of which is in vitro maturation (IVM) of oocytes. Despite more than 5,000 children have been born after using the IVM method, the mechanisms involved in meiotic arrest and resumption in oocytes are not fully understood. Hormones and growth factors are components of a complex regulation system which play a mediator role in ovarian function and are involved in meiotic resumption. PRP is a rich source of growth factors and can be effectively used to supplement the medium for in vitro oocyte maturation. Study design, size, duration Oocyte maturity was assessed after denudation, and immature gametes at the GV stage (n = 84) were randomly divided into 4 groups for IVM (24 - 48h): 1- without cumulus cells (CC) and PRP, 2 – with PRP without CC, 3 - with CC without PRP, 4 – with CC and PRP. The maturation rate of the oocytes was evaluated in 24 or 48h. The study was carried out between January 2020 to December 2021. Participants/materials, setting, methods This prospective study included 30 female patients 33±3.6 years old. Women received exogenous gonadotropins as part of a short protocol of superovulation induction. About 15% oocytes were at the germinal vesicle stage after denudation of retrieved gametes. PRP was prepared by two-step centrifugation method and was added (5%) to the maturation media. Primary endpoints were oocyte maturation rate, mitochondrial membrane potential and lipid droplets distribution evaluated by using JC-1 and Nile Red staining respectively. Main results and the role of chance The highest maturation rate was in group 4 (85%) compared with groups 1-3, where the rates were 9.5; 36.4 and 47.6, respectively (p &amp;lt; 0.05). The biggest number of cells with high mitochondrial potential was in oocytes matured with PRP and cumulus cells (4.5 ± 0.7) compared with other groups (1.3 ± 0.3; 3.0 ± 0.5 and 2.7 ± 0.4 for groups 1-3, respectively) (p &amp;lt; 0.05). The distribution of lipid droplets, typical of in vivo matured metaphase II oocytes, was the largest in group 1 (75%) (p &amp;lt; 0.05). There were no differences in this parameter between the other groups. Limitations, reasons for caution Limited number of GV oocytes in the study group. Further studies are recommended and should include group of patients with different age range. Wider implications of the findings PRP injection into ovarian tissue causes its rejuvenation. Our results confirm for the first time that PRP supplementation to the medium of oocyte in vitro maturation can increase the maturation rate, mitochondrial activity, and improve lipid droplets distribution in human oocytes. These finding are valuable for improvement of IVM medium. Trial registration number Not applicable

Discovering and Demonstrating a Novel High-Performing 2D-Patterned Electrode for Proton-Exchange Membrane Water Electrolysis Devices.

Proton-exchange membrane water electrolysis (PEMWE) produces hydrogen with high efficiency and purity but uses high-loading platinum-group metal (PGM) catalysts. Such concerns call for the development of novel electrode architectures to improve catalyst utilization and mass activity, thus promoting PEMWE cost competitiveness for large-scale implementation. In this study, we demonstrated, for the first time, a novel two-dimensional (2D)-patterned electrode with edge effects to address these challenges. The edge effect was induced by membrane properties, potential distribution, and counter electrode coverage and could be optimized by tuning the catalyst layer dimensions. To achieve identical PEMWE performance, the optimal pattern saved the 21% anode PGM catalyst compared with the conventional catalyst fully covered electrode. The PGM catalyst could be further reduced by 61% to boost mass activity with no significant performance loss. The results also indicated that the electrode uniformity in PEMWE cells might not be as critical as that in PEM fuel cells. The novel 2D-patterned electrode could effectively reduce PGM catalyst loading, accelerating affordable and large-scale production of hydrogen and other value-added chemicals via electrolysis.

Local durability optimization of a large-scale direct methanol fuel cell: catalyst layer tuning for homogeneous operation and in-operando detection of localized hydrogen evolution

Lifetime limitations are still penalizing direct methanol fuel cell technology, otherwise outstandingly promising for sustainable portable power generation. Strong heterogeneous performance fading, related to uneven operating conditions, is known to be exacerbated in the upscaling process towards commercial applications, especially at local level. This work applies a localized optimization strategy, previously developed on lab-scale samples, on a commercial 180 cm2 membrane electrode assembly, analysing both current and potential distribution by means of a custom macro-segmented fuel cell provided with array of reference electrodes. Analysis based on local polarization curves and impedance spectroscopy demonstrates that water distribution, leading to local dehydration as well flooded areas, drives uneven operation and fading. Consequently, platinum loading at cathode electrode has been redistributed, sensibly improving current density heterogeneity and stability (voltage decay rate decreased from 148 to 53.8 μV h−1) over a 600 h degradation tests. Then, residual fading identified at outlet regions has been investigated by means of local electrodes potential analysis, demonstrating a highly uneven operation of both electrodes. This phenomenon is discussed as the evidence of localized hydrogen evolution, which is identified for the first time during nominal galvanostatic operation and suspected to contribute to uneven fading of components.

γ‑irradiated prednisolone promotes apoptosis of liver cancer cells via activation of intrinsic apoptosis signaling pathway.

Prednisolone is an anti-inflammatory drug used to treat a number of conditions, including liver disease and cancer. Numerous studies have demonstrated that glucocorticoids such as prednisolone modified by ionizing radiation can promote anticancer activity in cancer cells. To the best of our knowledge, however, the effect of ionizing radiation on prednisolone structure and cancer cells has not yet been identified. The present study created a novel prednisolone derivative using γ-irradiation, and its anticancer properties were investigated in liver cancer cells. The present study confirmed the structure of the new prednisolone derivative using liquid chromatogram-mass spectrometry. MTT assays determined the cytotoxic effects of γ-irradiated (IR)-prednisolone in liver cancer cells. Flow cytometry analysis evaluated apoptosis, mitochondrial membrane potential and cell cycle distribution. Western blotting was used to analyze the proteins associated with apoptosis. The chromatogram profile revealed that IR-prednisolone produced a number of peaks compared with the single peak of the original prednisolone. In contrast to prednisolone, the MTT results showed that IR-prednisolone significantly prevented the growth of liver cancer cells. IR-prednisolone promoted apoptosis and arrested the cell cycle at the G0/G1 stage in Huh7 cells. IR-prednisolone also altered the mitochondrial membrane potential and activated caspase-associated proteins, which activated the intrinsic apoptotic signaling pathway. In conclusion, IR-prednisolone promoted anticancer effects in liver cancer cells via apoptosis activation. The present study demonstrated that IR-prednisolone may be a potential anticancer agent against liver cancer, although specific molecules have yet to be identified.

Local CPG Self Growing Network Model with Multiple Physical Properties

Compared with traditional control methods, the advantage of CPG (Central Pattern Generator) network control is that it can significantly reduce the size of the control variable without losing the complexity of its motion mode output. Therefore, it has been widely used in the motion control of robots. To date, the research into CPG network has been polarized: one direction has focused on the function of CPG control rather than biological rationality, which leads to the poor functional adaptability of the control network and means that the control network can only be used under fixed conditions and cannot adapt to new control requirements. This is because, when there are new control requirements, it is difficult to develop a control network with poor biological rationality into a new, qualified network based on previous research; instead, it must be explored again from the basic link. The other direction has focused on the rationality of biology instead of the function of CPG control, which means that the form of the control network is only similar to a real neural network, without practical use. In this paper, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. Universal gravitation is used to achieve the targeted guidance of axon growth, Brownian random motion is used to simulate the random turning of axon self-growth, and the signal of a single neuron is established by the Rall Cable Model that simplifies the axon membrane potential distribution. The transfer model, which makes the key parameters of the CPG control network—the delay time constant and the connection weight between the synapses—correspond to the axon length and axon diameter in the growth model and the growth and development of the neuron processes and control functions are combined. By coordinating the growth and development process and control function of neurons, we aim to realize the control function of the CPG network as much as possible under the conditions of biological reality. In this way, the complexity of the control model we develop will be close to that of a biological neural network, and the control network will have more control functions. Finally, the effectiveness of the established CPG self-growth control network is verified through the experiments of the simulation prototype and experimental prototype.

Energy features in spontaneous up and down oscillations.

Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.

Relationship between inorganic ion distribution, resting membrane potential, and the ΔG' of ATP hydrolysis: a new paradigm.

Cell membrane potential and inorganic ion distributions are currently viewed from a kinetic electric paradigm, which ignores thermodynamics. The resting membrane potential is viewed as a diffusion potential. The 9 major inorganic ions found in blood plasma (Ca2+, Na+, Mg2+, K+, H+, Cl-, HCO3-, H2PO4-, and HPO42-) are distributed unequally across the plasma membrane. This unequal distribution requires the energy of ATP hydrolysis through the action of the Na+-K+ ATPase. The cell resting membrane potential in each of 3 different tissues with widely different resting membrane potentials has been shown to be equal to the Nernst equilibrium potential of the most permeant inorganic ion. The energy of the measured distribution of the 9 major inorganic ions between extra- and intracellular phases was essentially equal to the independently measured energy of ATP hydrolysis, showing that the distribution of these 9 major ions was in near-equilibrium with the ΔG' of ATP. Therefore, thermodynamics does appear to play an essential role in the determination of the cell resting membrane potential and the inorganic ion distribution across the plasma membrane.-Veech, R. L., King, M. T., Pawlosky, R., Bradshaw, P. C., Curtis, W. Relationship between inorganic ion distribution, resting membrane potential, and the ΔG' of ATP hydrolysis: a new paradigm.

Information Processing in GLIF Neuron Model with Noisy Conductance

In this article, we investigate the generalized leaky integrate-and-fire (GLIF) neuron model with stochastic synaptic conductance. A neuron remains connected with other neuron via dendrites and axons at synapse, which can be treated as an electrical capacitor. Dendrites carry electro-chemical signals from input neuron to synapse whereas axons are responsible for their transmission form synapse to other neurons. Concentration of these electro-chemicals in synapse varies during entire time period. We investigate the effect of varying concentration of electro-chemicals at synapse in a single neuron model. Concentration variation of electro-chemicals at synapse is incorporated as noise in GLIF model. Excitatory and inhibitory synaptic conductance of neuron in GLIF is assumed as stochastic entities driven by Gaussian White noise. Stationary state membrane potential distribution for the proposed model is computed with reflecting boundary conditions, which is noticed as geometrically distributed. In order to investigate spiking activity and information encoding mechanism, an extensive simulation based study has been carried out. Temporal encoding technique is used to analyze the encoding mechanism. It is noticed that ISI distribution has higher variance with respect to excitatory input than inhibitory input.

Aloperine induces apoptosis and G2/M cell cycle arrest in hepatocellular carcinoma cells through the PI3K/Akt signaling pathway

BackgroundHepatocellular carcinoma (HCC) ranks third among the most common causes of cancer-related deaths worldwide. The chemotherapy for HCC is still insufficient, so far. In searching for effective anti-HCC agents from traditional Chinese medicine, we discovered that aloperine (ALO), a quinolizidine alkaloid from Sophora alopecuroides L., exerts anti-HCC activities. However, the effects of ALO on HCC have been rarely studied, and its underlying mechanisms remain unknown. PurposeThis study aims to evaluate the anti-HCC activities of ALO and explore its underlying mechanisms. MethodsMTT assay and colony formation assay were used to investigate the anti-proliferative effects of ALO on human HCC Hep3B and Huh7 cells. Hoechst 33258 staining was used to observe the morphological changes of cells after ALO treatment. Flow cytometry was used to analyze apoptosis induction, the collapse of the mitochondrial membrane potential and cell cycle distribution. Western blotting was used to examine the expression levels of proteins associated with apoptosis and cell cycle arrest, and key proteins in the PI3K/Akt signaling pathway. Small interfering RNA (siRNA) transfection was used to investigate the role of Akt in ALO-induced apoptosis and cell cycle arrest. Zebrafish tumor model was used to evaluate the anti-HCC effects of ALO in vivo. ResultsALO inhibited the proliferation of Hep3B and Huh7 cells. ALO induced apoptosis in HCC cells, which was accompanied by the loss of mitochondrial potential, the release of cytochrome c into cytosol, as well as the increased cleavages of caspase-9, caspase-3 and PARP. Moreover, ALO induced G2/M cell cycle arrest by downregulating the expression levels of cdc25C, cdc2 and cyclin B1. In addition, ALO inhibited activation of the PI3K/Akt signaling pathway by decreasing the expression levels of p110α, p85, Akt and p-Akt (Ser473). Further study showed that inhibition of Akt by siRNA augmented ALO-mediated apoptosis and G2/M cell cycle arrest in HCC cells. Critically, ALO inhibited the growth of Huh7 cells in vivo. ConclusionWe first demonstrated that ALO induced apoptosis and G2/M cell cycle arrest in HCC cells through inhibition of the PI3K/Akt signaling pathway. This study provides a rationale for ALO as a potential chemotherapeutic agent for HCC.

Ionic mechanisms underlying tonic and burst firing behavior in subfornical organ neurons: a combined experimental and modeling study.

Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO. NEW & NOTEWORTHY Our understanding of how the subfornical organ (SFO) selectively influences autonomic nervous system function remains incomplete but theoretically results from the electrical responses of SFO neurons to physiologically important signals. We have built a computational model of SFO neurons, derived from and supported by experimental data, which explains how SFO neurons produce different electrical patterns. The model provides an efficient system to theoretically and experimentally explore how changes in the essential features of SFO neurons affect their electrical activity.

Information Processing in Neuron with Exponential Distributed Delay

Artificial intelligence (AI) has been become the primary need in nearly all sectors namely engineering, services, banking, finance, defense, space etc [3], [33]. Artificial intelligence in these sectors can be implemented in two ways: (i) hardware level implementation (ii) software level implementation. Both kinds of AI implementation require neuron models which mimic the minimal set of real neuron functionality. To this end, Leaky Integrate-and-Fire (LIF) model is performing as the backbone for both kinds of AI implementation. At hardware level implementation, it’s a variant, called as neuristors, is used at chip level implementation, whereas a number of variants LIF model are used to implement AI at software level. In this work, the extended LIF model in distributed delay kernel regime is analyzed. The impact of exponentially distributed delay (EDD) memory kernel on spiking activity and steady state membrane potential distribution (SVD) of LIF neuron is investigated. Fokker-Planck equation associated with the considered model is solved to investigate SVD of the neuron in sub-threshold regime, which results Gaussian distribution. In order to study the information processing, spiking activity of the model is investigated, which is further extended to neuronal rate-code scheme. These finding have been compared with simple LIF model with stochastic input. It is evident that steady state membrane potential distribution of the LIF neuron is invariant due to the presence of EDD. Such kinds of neuron models are useful to implement artificial neural networks. To this end, the proposed model can used to implement recurrent neural networks (RNN) with comparatively more accuracy. Similarly, this model can also be investigated in term of chip level implementation of AI.

Elimination of spiral waves in excitable media by magnetic induction

The formation of spiral waves in excitable media is a fascinating example of the beauty of nonlinear dynamics in spatiotemporal systems. Apart from the beauty of the patterns, the subject also has many practical application. For example, the emergence of spiral waves in cardiac tissue can lead to arrhythmias. Cortical spiral waves are also involved in epileptic seizures. Motivated by this, we here study the effects of magnetic induction on the formation of spiral waves in excitable media. An external sinusoidal magnetic induction with different amplitudes and angular frequencies is applied in order to study whether spiral waves could be eliminated. We use a network of coupled neurons as a model for the excitable medium. The four-variable magnetic Hindmarsh–Rose model is used for the local dynamics of each isolated neuron. The distribution of the cell membrane potential over time, affected by magnetic induction, is determined and the results are depicted as snapshots of the 2D network. Our research reveals that the continuance of rotating spiral seeds is impaired by high-amplitude magnetic induction. Moreover, we show that low-frequency induction is not capable of breaking the reorganizing rhythm of the spiral seeds, while much higher frequencies can be too fast to overcome this special rhythm.