Specific Channel Research Articles

Category-Sensitive Age-Related Shifts Between Prosodic and Semantic Dominance in Emotion Perception Linked to Cognitive Capacities.

Prior research extensively documented challenges in recognizing verbal and nonverbal emotion among older individuals when compared with younger counterparts. However, the nature of these age-related changes remains unclear. The present study investigated how older and younger adults comprehend four basic emotions (i.e., anger, happiness, neutrality, and sadness) conveyed through verbal (semantic) and nonverbal (facial and prosodic) channels. A total of 73 older adults (43 women, Mage = 70.18 years) and 74 younger adults (37 women, Mage = 22.01 years) partook in a fixed-choice test for recognizing emotions presented visually via facial expressions or auditorily through prosody or semantics. The results confirmed age-related decline in recognizing emotions across all channels except for identifying happy facial expressions. Furthermore, the two age groups demonstrated both commonalities and disparities in their inclinations toward specific channels. While both groups displayed a shared dominance of visual facial cues over auditory emotional signals, older adults indicated a preference for semantics, whereas younger adults displayed a preference for prosody in auditory emotion perception. Notably, the dominance effects observed in older adults for visual and semantic cues were less pronounced for sadness and anger compared to other emotions. These challenges in emotion recognition and the shifts in channel preferences among older adults were correlated with their general cognitive capabilities. Together, the findings underscore that age-related obstacles in perceiving emotions and alterations in channel dominance, which vary by emotional category, are significantly intertwined with overall cognitive functioning. https://doi.org/10.23641/asha.27307251.

Electrochemical Performance of MoB/Si3N4 Heterojunction as a Potential Anode Material for Li Ion Batteries.

In response to the current policy of high storage capacity, two-dimensional (2D) materials have revealed promising prospects as high-performance electrode materials. MoB, as a type of such material, is widely regarded as an anode candidate for Li-ion batteries due to its large specific surface area and abundant ion diffusion channels; the long-term cycling stability, however, is poor owing to material pulverization during the cycle. Therefore, MoB/Si3N4 heterojunction in this work is proposed as an anode material, with Si3N4 acting as a skeleton, maintaining the stability of the structure, while retaining the high energy storage properties of MoB as well. In addition, a certain built-in electric field is formed between them, which can play a role in regulating charge transfer, improving the ion transport channel, and accelerating the migration rate. Herein, the structural, electronic, and electrochemical properties are systematically investigated by first-principles calculations; the final results indicate that the heterojunction anode material does indeed have built-in electric fields, which promote the anode material to possess excellent electrical conductivity and outstanding electrochemical property. Meanwhile, the introduction of vacancy defects can bolster the diffusion kinetic performance of ion transport and greatly reduce the diffusion energy barrier of Li ions, which is conducive to the realization of rapid charge and discharge for the Li ion battery. Based on the synergistic effect of two single-component materials, the synthesized anode material displays a high theoretical capacity of 461 mAh/g, and the calculated open-circuit voltage is 0.66 V, within the range of the negative electrode criterion of 0-1 V, which can effectively play a role in preventing the formation of Li dendrites; these properties are comparable to other 2D anode materials as well. Given these intriguing properties, the MoB/Si3N4 heterojunction is an exceptional candidate for advanced LIB high-performance anode materials.

Exploring characteristic features for effective HCN1 channel inhibition using integrated analytical approaches: 3D QSAR, molecular docking, homology modelling, ADME and molecular dynamics.

Neuropathic pain (NP) is characterized by hyperalgesia, allodynia, and spontaneous pain. Hyperpolarization-activated cyclic nucleotide-gated (HCN) channel involved in neuronal hyperexcitability, has emerged as an important target for the drug development of NP. HCN channels exist in four different isoforms, where HCN1 is majorly expressed in dorsal root ganglion having an imperative role in NP pathophysiology. A specific HCN1 channel inhibitor will hold the better potential to treat NP without disturbing the physiological roles of other HCN isoforms. The main objective is to identify and analyze the chemical properties of scaffolds with higher HCN1 channel specificity. The 3D-QSAR studies highlight the hydrophobic & hydrogen bond donor groups enhance specificity towards the HCN1 channel. Further, the molecular interaction of the scaffolds with the HCN1 pore was studied by generating an open-pore model of the HCN1 channel using homology modelling and then docking the molecules with it. In addition, the important residues involved in the interaction between HCN1 pore and scaffolds were also identified. Moreover, ADME predictions revealed that compounds had good oral bioavailability and solubility characteristics. Subsequently, molecular dynamics simulation studies revealed the better stability of the lead molecules A7 and A9 during interactions and ascertained them as potential drug candidates. Cumulative studies provided the important structural features for enhancing HCN1 channel-specific inhibition, paving the way to design and develop novel specific HCN1 channel inhibitors.

Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor's input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor's outputs are PM mass in three size bins, specified as 100-300 nm, 300-600 nm, and 600-1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81-87% for PM mass in three size bins. Given the sensor's straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users' puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness.

A comprehensive review of effective parameters to improve the performance of the Savonius turbine using a computational model and comparison with practical results

Amidst growing global concerns over climate change and escalating greenhouse gas emissions from fossil fuels, the pursuit of renewable energy sources has become critical. This study focuses on harnessing hydropower using Savonius turbines, which are known for their efficiency in generating energy at lower flow rates. However, the intrinsic low efficiency of these turbines necessitates precise optimization tailored to specific river or channel conditions. In this research, we optimized the performance of Savonius turbines by analyzing key parameters such as the height-to-diameter ratio, blade twist, and the integration of multi-stage configurations with deflectors. Our findings reveal significant efficiency improvements through strategic modifications. Specifically, by reducing the height-to-diameter ratio from 1.2 to 0.8 and maintaining a Tip Speed Ratio (TSR) of 0.6, the power coefficient increased by 15%, from 0.33 to 0.38. Further optimization was achieved by adjusting the blade twist angle, with an increase in power coefficient up to an optimal angle of 45°, beyond which efficiency declined. Implementing a two-stage turbine setup with a 90-degree phase difference between stages further improved the power coefficient to 0.51 at the same TSR. Additionally, the use of deflectors, particularly at a 90° angle, significantly boosted the power coefficient, highlighting their effectiveness in optimizing water flow impact on the turbine. This comprehensive study not only advances the understanding of Savonius turbine optimization but also contributes to broader renewable energy applications. The research offers critical insights into sustainable hydroelectric power generation, providing practical solutions to enhance turbine performance for real-world applications.

Neurophysiological correlates of trait and behavioral impulsivity across methamphetamine and gambling Addiction

Investigating neurophysiological markers linked to impulsivity in individuals with gambling and methamphetamine addiction using resting-state EEG data offers valuable insights into the underlying neurophysiological mechanisms associated with impulsivity in individuals with addiction. This study aims to use resting-state EEG to explore the connection between various types of addiction and different aspects of impulsivity. Participants from the methamphetamine, gambling, and healthy control groups (abbreviation: MA, GB, HC) underwent EEG recordings and completed measures of impulsivity. Group differences in trait scores and behavioral tendencies were analyzed. Abnormal connections with node linkage and importance changes were analyzed through the resting-state EEG power spectral and network analyses. Further, relationships between impulsivity scores and connectivity differences in groups were explored through correlation analysis. Finally, these abnormal connections related to impulsivity were tested for their effect of distinguishing individuals with addiction from healthy controls through the ROC analysis. Results revealed that GB displayed the highest trait impulsivity on the overall score, while MA exhibited greater attentional impulsivity. Variations in behavioral impulsivity were reflected in response times. Resting-state EEG analysis showed higher beta power in GB. Specific channel pairs demonstrated abnormal connections and altered connectivity patterns in the beta band, with MA displaying a less efficient network compared to GB. Correlation analyses uncovered associations between impulsivity scores and connectivity, which were influenced by group differences. Furthermore, resting-state EEG connections effectively differentiated individuals with addiction from healthy controls. Overall, this study contributes valuable insights into the neural mechanisms of addiction-related impulsivity, emphasizing the potential of resting-state EEG connections as an important neurophysiological correlate.

Development of an Intravenously Stable Disulfide-Rich Peptide for the Treatment of Chemotherapy-Induced Neuropathic Pain.

α-conotoxins (α-Ctxs), a class of disulfide-rich conopetides, are excellent drug leads due to their small size, high selectivity, and potency for specific membrane receptors and ion channels involved in pain transmission. However, their high susceptibility to proteolytic degradation limits their therapeutic potential. In this study, we designed and synthesized a series of conformationally stable analogues of α-Ctx Mr1.1[S4Dap] using various structural optimization strategies. The Mr1.1[S4Dap, C16Pen] analogue maintained potency at human α9α10 nicotinic acetylcholine receptors, with a half-maximal inhibitory concentration (IC50) of 4 nM. It exhibited over a 5-fold increase in serum stability compared to Mr1.1[S4Dap], without disrupting its overall conformation. Furthermore, intravenous application of Mr1.1[S4Dap, C16Pen] showed potent analgesic activity in oxaliplatin-induced cold allodynia, indicating a high potential for drug development. Overall, the results from this study provide valuable insights for optimizing the serum stability of disulfide-rich peptides in future therapeutic applications.

The spillover and contagion effects of sovereign risk on stock markets

This study examines the spillover effects of sovereign risk on international stock markets using a dynamic spatial Durbin model (SDM) on a panel of forty countries from 2009Q1 to 2024Q2. The findings show that an increase in sovereign risk, as measured by CDS spreads, leads to a significant decrease in both local and foreign stock prices, with 0.027 percent reductions across foreign markets on average. These effects are transmitted through geographical, and economic and financial channels, with exchange rates, gross fixed capital formation, and industrial production playing crucial roles in influencing stock market performance. Additionally, the negative (positive) spillover effects from rising gross fixed capital formation and sovereign risk (industrial production) demonstrate how foreign markets are more impacted than domestic ones. The study highlights the interconnected nature of global financial markets, reinforcing the need for coordinated international policy responses to mitigate the transmission of sovereign risk across borders. These findings provide new insights into how global economic factors influence stock market volatility, particularly through specific transmission channels, underscoring the importance of multilateral collaboration in managing sovereign risk.

How does green manufacturing enhance corporate ESG performance? — Empirical evidence from machine learning and text analysis

Green manufacturing, widely recognized as a crucial avenue for companies to achieve sustainable competitive advantages, exerts significant spillover effects on both environmental protection and social responsibility. Accordingly, this can significantly enhance corporate Environmental, Social, and Governance (ESG) practices and enhance overall ESG performance. This study focuses on Chinese A-share listed firms between 2009 and 2022, characterizing their ESG performance. Employing quasi-natural experimental methods, this research evaluates the causal relationships and specific channels through which green manufacturing enhances corporate ESG performance. The findings demonstrate that green manufacturing significantly empowers companies to strengthen their ESG performance. Channel analysis indicates that enhanced green innovation capabilities and the reduction of financing constraints constitute two critical channels through which green manufacturing facilitates ESG performance improvement. Further analysis indicates that all three subsystems in the green manufacturing system significantly empower companies to enhance ESG performance to varying degrees, with the most significant empowerment effect observed in the environmental dimension of ESG performance. The analysis below indicates that the enhancement of corporate ESG performance yields dual benefits, reflected in both economic gains and reputational enhancements.

A novel trimesoyl chloride/hyper branched polyethyleneimine/MOF (MIL-303)/P84 co-polyimide nanocomposite mixed matrix membranes with an ultra-thin surface cross linking layer for removing toxic heavy metal ions from wastewater

In this study, a positively charged nanofiltration (NF) nanocomposite mixed matrix membrane (MMM) was developed by incorporating metal-organic frameworks (MOFs) (MIL-303) into P84 co-polyimide and cross-linking with hyperbranched polyethyleneimine (HPEI). A very thin selective layer was subsequently formed on the cross-linked membrane surface using trimesoyl chloride (TMC). The incorporation of MIL-303 introduced specific water channels, enhancing the permeance of the nanocomposite MMMs. Additionally, it improved hydrophilicity and influenced the diffusion of the TMC monomer through the channels. The cross-linker HPEI resulted in NF membranes with increased electro-positivity and a reduced mean pore diameter. The very thin crosslinked TMC layer further improved permeance and heavy metal ions rejection of the membrane. This optimized membrane exhibited excellent rejection for both bivalent and monovalent ions, as well as heavy metal ions, effectively overcoming the common trade-off between permeance and rejection in NF membranes. The membrane demonstrated a remarkable permeance of 13.0 LMH/bar, coupled with exceptional rejection for heavy metal ions (96.8 % for Zn²⁺, 95.2 % for Ni²⁺, 95.7 % for Cu²⁺, 93.2 % for Pb²⁺, and 92.9 % for Cd²⁺). The TMC/HPEI/MIL-303/P84 system presented in this study holds significant promise for customizing high-performance positively charged NF membranes for the removal of heavy metal ions from wastewater.

Real-Time Imaging of Calcium Dynamics in Human Sperm After Precise Single-Cell Stimulation.

Calcium signaling is a critical regulator of sperm activation and function during the processes of capacitation and fertilization. Here, we describe a combined method for calcium imaging of single, live human sperm in response to stimuli administered with a precisely targeted delivery technique. This protocol is an adaptation of techniques developed for studies of murine sperm [1, 2], and enables real-time monitoring of human sperm calcium dynamics with high spatiotemporal resolution and concurrent detection of acrosome exocytosis (AE), a functional endpoint of sperm capacitation and requirement for physiological fertilization.The described imaging technique provides a valuable tool for exploration of calcium regulation in human sperm, which is essential to answer important questions and knowledge gaps regarding the link between calcium dynamics, AE, and fertilization. The versatility of this technique can be amplified through use of various indicator dyes or integration with pharmacological strategies such as pre-treating sperm with inhibitors or activators targeting specific receptors, channels, or intracellular signaling pathways of interest. Beyond fundamental inquiries into sperm physiology, this method can also be applied to assess the impact of potential contraceptive compounds on calcium signaling, AE, and membrane integrity.

Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.

Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

Voltage-gated potassium channels and genetic epilepsy.

Recent advances in exome and targeted sequencing have significantly improved the aetiological diagnosis of epilepsy, revealing an increasing number of epilepsy-related pathogenic genes. As a result, the diagnosis and treatment of epilepsy have become more accessible and more traceable. Voltage-gated potassium channels (Kv) regulate electrical excitability in neuron systems. Mutate Kv channels have been implicated in epilepsy as demonstrated in case reports and researches using gene-knockout mouse models. Both gain and loss-of-function of Kv channels lead to epilepsy with similar phenotypes through different mechanisms, bringing new challenges to the diagnosis and treatment of epilepsy. Research on genetic epilepsy is progressing rapidly, with several drug candidates targeting mutated genes or channels emerging. This article provides a brief overview of the symptoms and pathogenesis of epilepsy associated with voltage-gated potassium ion channels dysfunction and highlights recent progress in treatments. Here, we reviewed case reports of gene mutations related to epilepsy in recent years and summarized the proportion of Kv genes. Our focus is on the progress in precise treatments for specific voltage-gated potassium channel genes linked to epilepsy, including KCNA1, KCNA2, KCNB1, KCNC1, KCND2, KCNQ2, KCNQ3, KCNH1, and KCNH5.

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Natural volatiles preventing mosquito biting: an integrated screening platform for accelerated discovery of ORco antagonists

Insect olfactory receptors are heteromeric ligand-gated cation channels composed of an obligatory receptor subunit, ORco, and one of many variable subunits, ORx, in as yet undefined molar ratios. When expressed alone ex vivo, ORco forms homotetrameric channels gated by ORco-specific ligands acting as channel agonists. Using an insect cell-based system as a functional platform for expressing mosquito odorant receptors ex vivo, we identified small molecules of natural origin acting as specific ORco channel antagonists, orthosteric or allosteric relative to a postulated ORco agonist binding site, which cause severe inhibition of olfactory function in mosquitoes. In the present communication, we have compiled common structural features of such orthosteric antagonists and developed a ligand-based pharmacophore whose properties are deemed necessary for binding to the agonist binding site and causing inhibition of ORco's biological function. In silico screening of an available collection of natural volatile compounds with the pharmacophore resulted in identification of several ORco antagonist hits. Cell-based functional screening of the same compound collection resulted in the identification of several compounds acting as orthosteric and allosteric antagonists of ORco channel function ex vivo and inducing anosmic behaviors to Aedes albopictus mosquitoes in vivo. Comparison of the in silico screening results with those of the functional assays revealed that the pharmacophore predicted correctly 7 out of the 8 confirmed orthosteric antagonists and none of the allosteric ones. Because the pharmacophore screen produced additional hits that did not cause inhibition of the ORco channel function, we also generated a Support Vector Machine (SVM) model based on two descriptors of all pharmacophore hits. Training of the SVM on the ex vivo validated compound collection resulted in the selection of the confirmed orthosteric antagonists with a very low cross-validation out-of-sample misclassification rate. Employment of the combined pharmacophore-SVM platform for in silico screening of a larger collection of olfaction-relevant volatiles produced several new hits. Functional validation of randomly selected hits and rejected compounds from this screen confirmed the power of this virtual screening platform as a convenient tool for accelerating the pace of discovery of novel vector control agents. To the best of our knowledge, this study is the first one that combines a pharmacophore with a SVM model for identification of AgamORco antagonists and specifically orthosteric ones.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Voltage Gated Ion Channels and Sleep.

Ion channels are integral components of the nervous system, playing a pivotal role in shaping membrane potential, neuronal excitability, synaptic transmission and plasticity. Dysfunction in these channels, such as improper expression or localization, can lead to irregular neuronal excitability and synaptic communication, which may manifest as various behavioral abnormalities, including disrupted rest-activity cycles. Research has highlighted the significant impact of voltage gated ion channels on sleep parameters, influencing sleep latency, duration and waveforms. Furthermore, these ion channels have been implicated in the vulnerability to, and the pathogenesis of, several neurological and psychiatric disorders, including epilepsy, autism, schizophrenia, and Alzheimer's disease (AD). In this comprehensive review, we aim to provide a summary of the regulatory role of three predominant types of voltage-gated ion channels-calcium (Ca2+), sodium (Na+), and potassium (K+)-in sleep across species, from flies to mammals. We will also discuss the association of sleep disorders with various human diseases that may arise from the dysfunction of these ion channels, thereby underscoring the potential therapeutic benefits of targeting specific ion channel subtypes for sleep disturbance treatment.

Utilization of substrate specific membrane channels in liposome based biosensors: A nitrite biosensor with NirC nitrite channels

A liposome-based nitrite biosensor was prepared by encapsulating the nitrite reductase NirB enzyme and embedding the nitrite-specific NirC channels in the liposome structure. The proteoliposomes were coated on the glassy carbon electrode modified with the multi-walled carbon nanotube/chitosan matrix. The absence of channels in the liposome structure inhibits the generation of typical reduction peaks upon the addition of nitrite suggesting that ion channels are necessary for the passage of charged ions into the liposome. The response of the NirB and NirC incorporated liposome-based biosensor (CBLip/MWCNTs/CHIT/GC electrode) to the nitrite is shown to be fast and reproducible. The electrode shows a linear response between 1–500 µM nitrite concentrations. The sensitivity of the CBLip/MWCNTs/CHIT/GC electrode was 707 mA.M−1.cm−2, lower than that of the unentrapped NirB-loaded electrode (1131 mA.M−1.cm−2). While the electrode prepared with the unentrapped enzyme lost its activity in 30 days,entrapment of the enzyme in the liposome preserved the electrode activity by 55 %. The electrodes showed unexpected oxidation responses against high concentrations of sulfate and formate, theincorporation of NirC channels contributed to increasing the specificity of the electrode towards nitrite in the presence of these ions. The use of substrate-specific membrane channels in liposome-based biosensors was achieved for the first time in the present study.

Acacetin is a Promising Drug Candidate for Cardiovascular Diseases.

Phytochemical flavonoids have been proven to be effective in treating various disorders, including cardiovascular diseases. Acacetin is a natural flavone with diverse pharmacological effects, uniquely including atrial-selective anti-atrial fibrillation (AF) via the inhibition of the atrial specific potassium channel currents [Formula: see text] (ultra-rapidly delayed rectifier potassium current), [Formula: see text] (acetylcholine-activated potassium current), [Formula: see text] (calcium-activated small conductance potassium current), and [Formula: see text] (transient outward potassium current). [Formula: see text] inhibition by acacetin, notably, suppresses experimental J-wave syndromes. In addition, acacetin provides extensive cardiovascular protection against ischemia/reperfusion injury, cardiomyopathies/heart failure, autoimmune myocarditis, pulmonary artery hypertension, vascular remodeling, and atherosclerosis by restoring the downregulated intracellular signaling pathway of Sirt1/AMPK/PGC-1[Formula: see text] followed by increasing Nrf2/HO-1/SOD thereby inhibiting oxidation, inflammation, and apoptosis. This review provides an integrated insight into the capabilities of acacetin as a drug candidate for treating cardiovascular diseases, especially atrial fibrillation and cardiomyopathies/heart failure.