Search Results (10,134)

Background: Spinal solitary fibrous tumors (SFTs) are a rare oncological entity, almost anecdotal in the pediatric population. They have a high relapse rate and represent an ongoing oncological challenge. Methods: In this article, we conducted a systematic review starting from a case report to highlight the current state of the art in managing these tumors. Results: Spinal solitary fibrous tumors (SFTs) are rare, slow-growing neoplasms that can be either intra- or extramedullary. Only a limited number of studies focus on primary pediatric spinal cord localization. Five pediatric cases of spinal SFT have been documented in the literature. On MRI, they typically present as highly vascularized, contrast-enhancing masses. Histologically, they are composed of spindle-shaped cells within a collagenous stroma featuring staghorn-shaped blood vessels. More aggressive subtypes, such as dedifferentiated SFTs, resemble high-grade sarcomas. The NAB2–STAT6 fusion is a key marker, driving EGFR signaling, collagen production, and fibrosis. Additional diagnostic markers include CD34, CD99, and Bcl-2. Surgical resection remains the primary treatment. In metastatic cases, chemotherapy—mainly with anthracyclines, dacarbazine, or temozolomide—is employed, although no standardized pediatric protocols exist. Anti-angiogenic agents, including tyrosine kinase inhibitors, have shown promise. Radiotherapy is used postoperatively for local disease control, but its impact on survival is still under investigation. Conclusions: Surgery remains the cornerstone of treatment, significantly impacting the natural history of the disease and symptom control. While clinical trials exploring radiotherapy and chemotherapy are ongoing in adults, no specific treatment protocol has been established for pediatric patients. Full article

This work reviews the complex role of the enzyme triosephosphate isomerase (TIM) (EC 5.3.1.1) within the context of diabetes, a prevalent metabolic disorder. It summarizes the main biochemical pathways, cellular mechanisms, and molecular interactions that highlight both the function of TIM and its implications in diabetes pathophysiology, particularly focusing on its regulatory role in glucose metabolism and insulin secretion. TIM’s involvement is detailed from its enzymatic action in glycolysis, influencing the equilibrium between dihydroxyacetone phosphate and glyceraldehyde-3-phosphate, to its broader implications in cellular metabolic processes. The article highlights how mutations in TIM can lead to metabolic inefficiencies that exacerbate diabetic conditions. It discusses the interaction of TIM with various cellular pathways, including its role in the ATP-sensitive potassium channels in pancreatic beta cells, which are crucial for insulin release. Moreover, we indicate the impact of oxidative stress in diabetes, noting how TIM is affected by reactive oxygen species, which can disrupt normal cellular functions and insulin signaling. The enzyme’s function is also tied to broader cellular and systemic processes, such as membrane fluidity and cellular signaling pathways, including the mammalian target of rapamycin, which are critical in the pathogenesis of diabetes and its complications. This review emphasizes the dual role of TIM in normal physiological and pathological states, suggesting that targeting TIM-related pathways could offer novel therapeutic strategies for managing diabetes. It encourages an integrated approach to understanding and treating diabetes, considering the multifaceted roles of biochemical players such as TIM that bridge metabolic, oxidative, and regulatory functions within the body. Full article

Modern distribution networks are increasingly affected by the widespread adoption of photovoltaic (PV) generation and electric vehicles (EVs). The variability of PV output and the fluctuating demand of EVs may cause voltage violations that threaten the safe operation of active distribution networks (ADNs). This paper proposes a voltage control strategy for ADNs to address the voltage violation problem by utilizing the control flexibility of EV charging stations (EVCSs). In the proposed strategy, a state-driven margin algorithm is first employed to generate training data comprising response capability (RC) of EVs and state parameters, which are used to train a multi-layer perceptron (MLP) model for real-time estimation of EVCS response capability. To account for uncertainties in EV departure times, a relevance vector machine (RVM) model is applied to refine the estimated RC of EVCSs. Then, based on the estimated RC of EVCSs, a second-order cone programming (SOCP)-based voltage regulation problem is formulated to obtain the optimal dispatch signal of EVCSs. Finally, a broadcast control scheme is adopted to distribute the dispatch signal across individual charging piles and the energy storage system (ESS) within each EVCS to realize the voltage regulation. Simulation results on the IEEE 34-bus feeder validate the effectiveness and advantages of the proposed approach. Full article

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The goal was to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity (EDA), heart rate variability, skin temperature, and motion. Interpretable machine learning models, including linear classifiers, were trained to distinguish physiological states and rank feature relevance. The results revealed that even minor postural adjustments led to significant shifts in ANS markers, with phasic EDA and RR interval features showing heightened sensitivity. Surprisingly, micro-movements captured via accelerometry and transient electrodermal reactivity, specifically EDA peak-to-RMS ratios, emerged as dominant contributors to classification performance. These findings suggest that small-scale kinematic and autonomic shifts, which are often overlooked, play a central role in the physiological effects of yoga. The study demonstrates that wearable sensor analytics can decode a more nuanced and granular physiological profile of mind–body practices than traditionally appreciated, offering a foundation for precision-tailored biofeedback systems and advancing objective approaches to yoga-based interventions. Full article

Anomaly detection is a pressing issue, particularly in industrial images. Detecting weld defects in radiographic images is a challenge due to the small signal-to-noise ratio (SNR) and the limited availability of data. In this paper, we propose an automated weld defect detection method using Normalizing Flows (NFs). We employed various state-of-the-art NF architectures with different feature extractors to detect defects in radiographic images of welds, comprehensively comparing the results with radiographic images of welded steel pipes collected from industrial sites. The results show that the combination of CFlow-AD with a wide residual network-50-2 (WRN-50-2) outperformed the other methods, indicating its effectiveness in anomaly detection. Full article

Next-point-of-interest (POI) recommendation aims to model user preferences based on historical information to predict future mobility behavior, which has significant application value in fields such as urban planning, traffic management, and optimizing business decisions. However, existing methods often overlook the differences in location, time, and category information features, fail to fully utilize information from various modalities, and lack effective solutions for addressing users’ incidental behavior. Additionally, existing methods are somewhat lacking in capturing users’ personalized preferences. To address these issues, we propose a new method called Multi-Branch Spectral Network with Contrastive Learning (MBSCL-Net) for next-POI recommendation. We use a multihead attention mechanism to separately capture the distinct features of location, time, and category information, and then fuse the captured features to effectively integrate cross-modal features, avoid feature confusion, and achieve effective modeling of multi-modal information. We propose converting the time-domain information of user check-ins into frequency-domain information through Fourier transformation, directly enhancing the low-frequency signals of users’ periodic behavior and suppressing occasional high-frequency noise, thereby greatly alleviating noise interference caused by the introduction of too much information. Additionally, we introduced contrastive learning loss to distinguish user behavior patterns and better model personalized preferences. Extensive experiments on two real-world datasets demonstrate that MBSCL-Net outperforms state-of-the-art (SOTA) methods. Full article

To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries. Full article

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

We determine the efficiency of generating singlet oxygen molecules through Raman excitation in distilled water. Focused nanosecond light pulses in the spectral blue region induce a Raman transition toward the singlet oxygen state, generating a Stokes signal in the red spectral region. The signal is proportional to the number of photons corresponding to the number of excited oxygen molecules. We calculate the efficiency by dividing the number of generated singlet oxygen molecules by the number of incoming pump photons, determining an efficiency of (8 ± 2) × 10⁻⁵ for water when pumping at 410 nm with a pulse energy of 13 mJ. We demonstrate that the Raman method results in no photobleaching, a phenomenon typically observed when photosensitizers are used. Thanks to this property, Raman excitation can continue for as long as the sample is irradiated, generating more singlet oxygen molecules over time than the photosensitization method. Full article

Anabolic–androgenic steroids (AASs) are synthetic derivatives of testosterone and are increasingly misused to enhance muscle growth and physical performance, particularly among athletes and recreational bodybuilders. Although AASs affect multiple organ systems, their severe and potentially life-threatening complications involve the cardiovascular system. This review summarizes current knowledge on the pathophysiological mechanisms and clinical manifestations of AAS-induced cardiomyopathy. Chronic supraphysiologic AAS use promotes cardiac injury and adverse cardiac remodeling via oxidative stress, androgen receptor overactivation, RAAS dysregulation, and pro-apoptotic signaling. These changes could lead to hypertension, dyslipidemia and atherosclerosis, myocardial fibrosis and hypertrophy, arrhythmias, heart failure, and kidney injury. Vascular dysfunction, increased arterial stiffness, and a prothrombotic state further compound the cardiovascular risks. Diagnostic approaches involve biomarker evaluation, echocardiography, and cardiac magnetic resonance imaging, revealing structural and functional cardiac abnormalities such as reduced ejection fraction, concentric hypertrophy, myocardial fibrosis, and impaired diastolic function. Although cessation of AAS use may lead to partial or complete reversal of cardiac dysfunction in some individuals, others may experience irreversible myocardial damage. The reversibility appears to depend on dosage, duration of exposure, and early intervention. This review explores the cardiovascular consequences of AAS use, with a focus on the mechanisms, diagnosis, and management of AAS-induced cardiomyopathy, and underlines the importance of education and early detection. Full article

Compressive Learning (CL) is an emerging paradigm that allows machine learning models to perform inference directly from compressed measurements, significantly reducing sensing and computational costs. While existing CL approaches have achieved competitive accuracy compared to traditional image-domain methods, they typically rely on reconstruction to address information loss and often neglect uncertainty arising from ambiguous or insufficient data. In this work, we propose MCS-TCL, a novel and trustworthy CL framework based on Multi-functional Compressive Sensing Sampling. Our approach unifies sampling, compression, and feature extraction into a single operation by leveraging the compatibility between compressive sensing and convolutional feature learning. This joint design enables efficient signal acquisition while preserving discriminative information, leading to feature representations that remain robust across varying sampling ratios. To enhance the model’s reliability, we incorporate evidential deep learning (EDL) during training. EDL estimates the distribution of evidence over output classes, enabling the model to quantify predictive uncertainty and assign higher confidence to well-supported predictions. Extensive experiments on image classification tasks show that MCS-TCL outperforms existing CL methods, achieving state-of-the-art accuracy at a low sampling rate of 6%. Additionally, our framework reduces model size by 85.76% while providing meaningful uncertainty estimates, demonstrating its effectiveness in resource-constrained learning scenarios. Full article

In the era of artificial intelligence, neuromorphic devices that simulate brain functions have received increasingly widespread attention. In this paper, an artificial neural synapse device based on ZnAlSnO thin-film transistors was fabricated, and its electrical properties were tested: the current-switching ratio was 1.18 × 10⁷, the subthreshold oscillation was 1.48 V/decade, the mobility was 2.51 cm²V⁻¹s⁻¹, and the threshold voltage was −9.40 V. Stimulating artificial synaptic devices with optical signals has the advantages of fast response speed and good anti-interference ability. The basic biological synaptic characteristics of the devices were tested under 365 nm light stimulation, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). This device shows good synaptic plasticity. In addition, by changing the gate voltage, the excitatory postsynaptic current of the device at different gate voltages was tested, two different logical operations of “AND” and “OR” were achieved, and the influence of different synaptic states on memory was simulated. This work verifies the application potential of the device in the integrated memory and computing architecture, which is of great significance for promoting the high-quality development of neuromorphic computing hardware. Full article

This paper investigates modulation recognition technology for high-order modulated signals. Addressing the issue that existing deep learning-based modulation recognition methods struggle to effectively capture the features of long sequence signals in high-order modulation, we propose a ConvMamba model that integrates convolutional neural networks (CNNs) with the Mamba2 architecture. By employing a selective state-space model, the ConvMamba effectively captures the temporal dependencies in long sequence signals. It also combines the local feature extraction capability of CNNs with a soft-thresholding denoising module, forming a hybrid structure that possesses both global modeling and noise resistance capabilities. The evaluation results on the Sig53 dataset, which contains a rich variety of high-order modulations, demonstrate that compared to traditional CNN- or Transformer-based architectures, ConvMamba achieves a better balance between computational efficiency and recognition accuracy. Compared to Transformer models with similar performance, ConvMamba reduces computational complexity by over 60%. Compared to CNN models with comparable computational resource consumption, ConvMamba significantly improves recognition accuracy. Therefore, ConvMamba shows a distinct advantage in processing high-order modulated signals with long sequences. Full article

Mutations in the CFTR genes causing cystic fibrosis (CF) are associated with the presence of thick, viscous mucus and the formation of biofilms in the gastrointestinal tract (GI) that impair intestinal homeostasis, triggering chronic inflammation, epithelial barrier dysfunction, and changes in the composition and activity of the gut microbiota. CFTR protein modulators represent a promising approach to enhancing lower GI function in patients with CF. The aim of the review is to present the complex relationships between the presence of CFTR gene mutations and the gut microbiota dysbiosis in patients with cystic fibrosis. Mutations in the CFTR gene, the molecular basis of cystic fibrosis (CF), disrupt epithelial ion transport and profoundly alter the gastrointestinal environment. Defective chloride and bicarbonate secretion leads to dehydration of the mucosal layer, increased mucus viscosity, and the formation of biofilms that favour microbial persistence, which together promote gut microbiota dysbiosis. This dysbiotic state contributes to impaired epithelial barrier function, chronic intestinal inflammation, and abnormal immune activation, thereby reinforcing disease progression. The interplay between CFTR dysfunction and microbial imbalance appears to be bidirectional, as dysbiosis may further exacerbate epithelial stress and inflammatory signalling. Therapeutic interventions with CFTR protein modulators offer the potential to partially restore epithelial physiology, improve mucus hydration, and foster a microbial milieu more consistent with intestinal homeostasis. The aim of this review is to elucidate the complex relationships between CFTR gene mutations and gut microbiota dysbiosis in patients with cystic fibrosis, with a particular emphasis on the clinical implications of these interactions and their potential to inform novel therapeutic strategies. Full article