Search Results (870)

Approximately 185,000 people in the United states experience limb loss each year. There is a need for an intuitive neural interface that can offer high-fidelity control signals to optimize the advanced functionality of prosthetic devices. Regenerative peripheral nerve interface (RPNI) is a pioneering advancement in neuroengineering that combines surgical techniques with biocompatible materials to create an interface for individuals with limb loss. RPNIs are surgically constructed from autologous muscle grafts that are neurotized by the residual peripheral nerves of an individual with limb loss. RPNIs amplify neural signals and demonstrate long term stability. In this narrative review, the terms “Regenerative Peripheral Nerve Interface (RPNI)” and “RPNI surgery” are used interchangeably to refer to the same surgical and biological construct. This narrative review specifically focuses on RPNIs as a targeted approach to enhance prosthetic control through surgically created nerve–muscle interfaces. This area of research offers a promising solution to overcome the limitations of existing prosthetic control systems and could help improve the quality of life for people suffering from limb loss. It allows for multi-channel control and bidirectional communication, while enhancing the functionality of prosthetics through improved sensory feedback. RPNI surgery holds significant promise for improving the quality of life for individuals with limb loss by providing a more intuitive and responsive prosthetic experience. Full article

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

Microglia, the brain’s resident innate immune cells, play a fundamental role in maintaining neural homeostasis and mediating responses to injury or infection. Upon activation, microglia undergo morphological and functional changes, including phenotypic switching between pro- and anti-inflammatory types and the release of different inflammatory mediators. These processes contribute to neuroprotection and the pathogenesis of various central nervous system (CNS) disorders. Mast cells, although sparsely located in the brain, exert a significant influence on neuroinflammation through their interactions with microglia. Through degranulation and secretion of different mediators, mast cells disrupt the blood–brain barrier and modulate microglial responses, including alteration of microglial phenotypes. Notably, mast cell-derived factors, such as histamine, interleukins, and tryptase, activate microglia through various pathways including protease-activated receptor 2 and purinergic receptors. These interactions amplify inflammatory cascades via various signaling pathways. Previous studies have revealed an exceedingly complex crosstalk between mast cells and microglia suggesting a bidirectional regulation of CNS immunity, implicating their cooperation in both neurodegenerative progression and repair mechanisms. Here, we review some of the diverse communication pathways involved in this complex interplay. Understanding this crosstalk may offer novel insights into the cellular dynamics of neuroinflammation and highlight potential therapeutic targets for a variety of CNS disorders. Full article

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article

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

Cardiorenal syndrome (CRS) is a multifactorial clinical condition characterized by the bidirectional deterioration of cardiac and renal function, driven by mechanisms such as renin–angiotensin–aldosterone system (RAAS) overactivation, systemic inflammation, oxidative stress, endothelial dysfunction, and fibrosis. The aim of this narrative review is to explore the key molecular pathways involved in CRS and to highlight emerging therapeutic approaches, with a special emphasis on nutritional interventions. We examined recent evidence on the contribution of mitochondrial dysfunction, uremic toxins, and immune activation to CRS progression and assessed the role of dietary and micronutrient factors. Results indicate that a high dietary intake of sodium, phosphorus additives, and processed foods is associated with volume overload, vascular damage, and inflammation, whereas deficiencies in potassium, magnesium, and vitamin D correlate with worse clinical outcomes. Anti-inflammatory and antioxidant bioactives, such as omega-3 PUFAs, curcumin, and anthocyanins from maqui, demonstrate potential to modulate key CRS mechanisms, including the nuclear factor kappa B (NF-κB) pathway and the NLRP3 inflammasome. Gene therapy approaches targeting endothelial nitric oxide synthase (eNOS) and transforming growth factor-beta (TGF-β) signaling are also discussed. An integrative approach combining pharmacological RAAS modulation with personalized medical nutrition therapy and anti-inflammatory nutrients may offer a promising strategy to prevent or delay CRS progression and improve patient outcomes. Full article

This study investigates a modified five-dimensional chaotic system by incorporating structural term adjustments and Caputo fractional-order differential operators. The modified system exhibits significantly enriched dynamic behaviors, including offset boosting, phase trajectory rotation, phase trajectory reversal, and contraction phenomena. Additionally, the system exhibits bidirectional transitions—conservative-to-dissipative transitions governed by initial conditions and dissipative-to-conservative transitions controlled by fractional order variations—along with a unique chaotic-to-quasiperiodic transition observed exclusively at low fractional orders. To validate the system’s physical realizability, a signal processing platform based on Digital Signal Processing (DSP) is implemented. Experimental measurements closely align with numerical simulations, confirming the system’s feasibility for practical applications. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

Recent breakthroughs in cancer immunotherapy have shown remarkable success, yet treatment efficacy varies significantly among individuals. Emerging evidence highlights the gut microbiota as a key modulator of immunotherapy response, while vitamin D (VD), an immunomodulatory hormone, has garnered increasing attention for its potential interactions with gut microbiota and immunotherapy outcomes. However, the precise mechanisms and clinical applications of VD in this context remain controversial. This study systematically analyzed peer-reviewed evidence from PubMed, Scopus, Web of Science, PsycINFO, and MEDLINE (January 2000–May 2025) to investigate the complex interplay among VD, gut microbiota, and cancer immunotherapy. This review demonstrates that VD exerts dual immunomodulatory effects by directly activating immune cells through vitamin D receptor (VDR) signaling while simultaneously reshaping gut microbial composition to enhance antitumor immunity. Clinical data reveal paradoxical outcomes: optimal VD levels correlate with improved immunotherapy responses and reduced toxicity in some studies yet are associated with immunosuppression and poorer survival in others. The bidirectional VD–microbiota interaction further complicates this relationship: VD supplementation enriches beneficial bacteria, which reciprocally regulate VD metabolism and amplify immune responses, whereas excessive VD intake may disrupt this balance, leading to dysbiosis and compromised therapeutic efficacy. These findings underscore the need to elucidate VD’s dose-dependent and microbiota-mediated mechanisms to optimize its clinical application in immunotherapy regimens. Future research should prioritize mechanistic studies of VD’s immunoregulatory pathways, personalized strategies accounting for host–microbiota variability, and large-scale clinical trials to validate VD’s role as an adjuvant in precision immunotherapy. Full article

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article

To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article

Accurate sleep staging and sleep-disordered breathing (SDB) severity prediction are critical for the early diagnosis and management of sleep disorders. However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. We evaluate the proposed method across three public datasets (APPLES, SHHS, and HMC) treated as independent clients. For sleep staging, the model achieves accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with Cohen’s Kappa scores exceeding 0.71. For SDB severity classification, it obtains macro-F1 scores of 77.6%, 76.4%, and 79.1% on APPLES, SHHS_rest, and HMC, respectively. These results demonstrate that our unified FMTL framework effectively leverages multimodal PSG signals and federated training to deliver accurate and scalable sleep disorder assessment, paving the way for the development of a privacy-preserving, generalizable, and clinically applicable digital sleep monitoring system. Full article

Preeclampsia (PE) is a pregnancy-specific hypertensive disorder characterized by systemic inflammation, endothelial dysfunction, and placental insufficiency. While inadequate trophoblast invasion and impaired spiral artery remodeling have long been recognized as central to its pathogenesis, emerging evidence underscores the critical roles of dysregulated iron metabolism and its crosstalk with immune responses, particularly macrophage-mediated inflammation, in driving PE development. This review systematically explores the dynamic changes in iron metabolism during pregnancy, including increased maternal iron demand, placental iron transport mechanisms, and the molecular regulation of placental iron homeostasis. We further explore the contribution of ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, to trophoblast dysfunction and pregnancy-related diseases, including PE. Macrophages, pivotal immune regulators at the maternal–fetal interface, exhibit distinct polarization states that shape tissue remodeling and immune tolerance. We outline their origin, distribution, and polarization in pregnancy, and emphasize their aberrant phenotype and function in PE. The bidirectional crosstalk between iron and macrophages is also dissected: iron shapes macrophage polarization and function, while macrophages reciprocally modulate iron homeostasis. Notably, excessive reactive oxygen species (ROS) and pro-inflammatory cytokines secreted by M1-polarized macrophages may exacerbate trophoblast ferroptosis, amplifying placental injury. Within the context of PE, we delineate how iron overload and macrophage dysfunction synergize to potentiate placental inflammation and oxidative stress. Key iron-responsive immune pathways, such as the HO-1/hepcidin axis and IL-6/TNF-α signaling, are discussed in relation to disease severity. Finally, we highlight promising therapeutic strategies targeting the iron–immune axis, encompassing three key modalities—iron chelation therapy, precision immunomodulation, and metabolic reprogramming interventions—which may offer novel avenues for PE prevention and treatment. Full article

Wear state prediction based on oil monitoring technology enables the early identification of potential wear and failure risks of friction pairs, facilitating optimized equipment maintenance and extended service life. However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative features, limiting the accuracy of wear state prediction. To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. The proposed architecture implements a three-stage computational pipeline. Initially, the CNN performs hierarchical extraction of localized patterns from multi-sensor tribological signals. Subsequently, the self-attention mechanism conducts adaptive recalibration of feature saliency, prioritizing diagnostically critical feature channels. Ultimately, bidirectional LSTM establishes cross-cyclic temporal dependencies, enabling cascaded fully connected layers with Gaussian activation to generate probabilistic wear state estimations. Experimental results demonstrate that the proposed model not only achieves superior predictive accuracy but also exhibits robust stability, offering a reliable solution for condition monitoring and predictive maintenance in industrial applications. Full article

Pear (Pyrus pyrifolia) is an important deciduous fruit tree that requires a specific period of low-temperature accumulation to trigger spring flowering. The warmer winter caused by global warming has led to insufficient winter chilling, disrupting floral initiation and significantly reducing pear yields in Southern China. In this study, we integrated targeted phytohormone metabolomics, full-length transcriptomics, and proteomics to explore the regulatory mechanisms of dormancy in ‘Mixue’, a pear cultivar with an extremely low chilling requirement. Comparative analyses across the multi-omics datasets revealed 30 differentially abundant phytohormone metabolites (DPMs), 2597 differentially expressed proteins (DEPs), and 7722 differentially expressed genes (DEGs). Integrated proteomic and transcriptomic expression clustering analysis identified five members of the dormancy-associated MADS-box (DAM) gene family among dormancy-specific differentially expressed proteins (DEPs) and differentially expressed genes (DEGs). Phytohormone correlation analysis and cis-regulatory element analysis suggest that DAM genes may mediate dormancy progression by responding to abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). A dormancy-associated transcriptional regulatory network centered on DAM genes and phytohormone signaling revealed 35 transcription factors (TFs): 19 TFs appear to directly regulate the expression of DAM genes, 18 TFs are transcriptionally regulated by DAM genes, and two TFs exhibit bidirectional regulatory interactions with DAM. Within this regulatory network, we identified a novel pathway involving REVEILLE 6 (RVE6), DAM, and CONSTANS-LIKE 8 (COL8), which might play a critical role in regulating bud dormancy in the ‘Mixue’ low-chilling pear cultivar. Furthermore, lncRNAs ONT.19912.1 and ONT.20662.7 exhibit potential cis-regulatory interactions with DAM1/2/3. This study expands the DAM-mediated transcriptional regulatory network associated with bud dormancy, providing new insights into its molecular regulatory mechanisms in pear and establishing a theoretical framework for future investigations into bud dormancy control. Full article