Search Results (2,364)

Despite advances in medicine, cancer remains one of the foremost global health concerns. Conventional treatments like surgery, radiotherapy, and chemotherapy have advanced with the emergence of targeted and immunotherapy approaches. However, therapeutic resistance and relapse remain major barriers to long-term success in cancer treatment, often driven by cancer stem cells (CSCs). These rare, resilient cells can survive therapy and drive tumour regrowth, urging deeper investigation into the mechanisms underlying their persistence. CSCs express ion channels typical of excitable tissues, which, beyond electrophysiology, critically regulate CSC fate. However, the underlying regulatory mechanisms of these channels in CSCs remain largely unexplored and poorly understood. Nevertheless, the therapeutic potential of targeting CSC ion channels is immense, as it offers a powerful strategy to disrupt vital signalling pathways involved in numerous pathological conditions. In this review, we explore the diverse repertoire of ion channels expressed in CSCs and highlight recent mechanistic insights into how these channels modulate CSC behaviours, dynamics, and functions. We present a concise overview of ion channel-mediated CSC regulation, emphasizing their potential as novel diagnostic markers and therapeutic targets, and identifying key areas for future research. Full article

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) signals play a vital role in the diagnosis and analysis of epileptic seizures. However, traditional machine learning techniques often rely on handcrafted features, limiting their robustness and generalizability across diverse EEG acquisition settings, seizure types, and patients. To address these limitations, we propose RDPNet, a multi-scale residual dilated pyramid network with entropy-guided feature fusion for automated epileptic EEG classification. RDPNet combines residual convolution modules to extract local features and a dilated convolutional pyramid to capture long-range temporal dependencies. A dual-pathway fusion strategy integrates pooled and entropy-based features from both shallow and deep branches, enabling robust representation of spatial saliency and statistical complexity. We evaluate RDPNet on two benchmark datasets: the University of Bonn and TUSZ. On the Bonn dataset, RDPNet achieves 99.56–100% accuracy in binary classification, 99.29–99.79% in ternary tasks, and 95.10% in five-class classification. On the clinically realistic TUSZ dataset, it reaches a weighted F₁-score of 95.72% across seven seizure types. Compared with several baselines, RDPNet consistently outperforms existing approaches, demonstrating superior robustness, generalizability, and clinical potential for epileptic EEG analysis. Full article

Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and multi-task learning paradigms. We systematically categorize and analyze existing technologies based on sensing modalities (vision-based, radar-based, thermal imaging, and ambient sensing), integration strategies, and application domains. The paper examines how artificial intelligence has revolutionized this domain, transitioning from early single-modality, single-parameter approaches to sophisticated systems that combine complementary sensing technologies and simultaneously extract multiple vital sign parameters. We discuss the theoretical foundations and practical implementations of multi-modal fusion, analyzing signal-level, feature-level, decision-level, and deep learning approaches to sensor integration. Similarly, we explore multi-task learning frameworks that leverage the inherent relationships between vital sign parameters to enhance measurement accuracy and efficiency. The review also critically addresses persisting technical challenges, clinical limitations, and ethical considerations, including environmental robustness, cross-subject variability, sensor fusion complexities, and privacy concerns. Finally, we outline promising future directions, from emerging sensing technologies and advanced fusion architectures to novel application domains and privacy-preserving methodologies. This review provides a holistic perspective on contactless vital sign monitoring, serving as a reference for researchers and practitioners in this rapidly advancing field. Full article

Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics. Full article

Cucumbers (Cucumis sativus L.) are highly sensitive to cold, but grafting onto cold-tolerant rootstocks can enhance their low-temperature resilience. This study investigates the physiological and molecular mechanisms by which exogenous vitamin C (Vc) mitigates cold stress in grafted cucumber seedlings. Using cucumber ‘Chiyu 505’ as the scion and pumpkin ‘Chuangfan No.1’ as the rootstock, seedlings were grafted using the whip grafting method. In the third true leaf expansion stage, seedlings were foliar sprayed with Vc at concentrations of 50, 100, 150, and 200 mg L⁻¹. Three days after initial spraying, seedlings were subjected to cold stress (8 °C) for 3 days, with continued spraying. After that, morphological and physiological parameters were assessed. Results showed that 150 mg L⁻¹ Vc treatment was most impactive, significantly reducing the cold damage index while increasing the root-to-shoot ratio, root vitality, chlorophyll content, and activities of antioxidant enzymes (SOD, POD, CAT). Moreover, this treatment enhanced levels of soluble sugars, soluble proteins, and proline compared to control. However, 200 mg L⁻¹ treatment elevated malondialdehyde (MDA) content, indicating potential oxidative stress. For transcriptomic analysis, leaves from the 150 mg L⁻¹ Vc and CK treatments were sampled at 0, 1, 2, and 3 days of cold stress. Differential gene expression revealed that genes associated with photosynthesis (LHCA1), stress signal transduction (MYC2-1, MYC2-2, WRKY22, WRKY2), and antioxidant defense (SOD-1, SOD-2) were initially up-regulated and subsequently down-regulated, as validated by qRT-PCR. Overall, we found that the application of 150 mg L⁻¹ Vc enhanced cold tolerance in grafted cucumber seedlings by modulating gene expression networks related to photosynthesis, stress response, and the antioxidant defense system. This study provides a way for developing Vc biostimulants to enhance cold tolerance in grafted cucumbers, improving sustainable cultivation in low-temperature regions. Full article

Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa⁻¹ over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Background: Signaling pathways like those depending on cAMP/PKA, calcium/calmodulin/CaMK, MEK-1/MAPK or PI3K/Akt have been described to modulate suprachiasmatic nucleus (SCN) neuronal signaling via influencing transcription factors like CREB. Here, we analyzed the effect of cyclic nucleotide phosphodiesterase inhibitors and structurally similar substances commonly used as autophagy modulators on a cell line stably expressing a cyclic nucleotide element-driven luciferase reporter. Methods: We used an SCN cell line stably transfected with a CRE-luciferase reporter (SCNCRE) to evaluate signaling and vitality responses to various isoform-selective PDE inhibitors and autophagy modulators to evaluate the mechanism of action of the latter. Results: In this study the different impacts of common PDE inhibitors and autophagy modulators on CRE-luciferase activity applied alone and in combination with known CRE-luciferase activating agents showed that (1) PDE3, 4 and 5 are present in SCNCRE cells, with (2) PDE3 being the most active and (3) the autophagy inhibitor 3-Methyladenin (3-MA) displaying PDE inhibitor-like behavior. Conclusions: Experiments provide evidence that, in addition to the extracellular signaling pathways components shown before to be involved in CRE-luciferase activity regulation like cAMP analogs, adenylate cyclase activators and beta-adrenoceptor agonists, cyclic nucleotide metabolism as realized by phosphodiesterase activity, or molecule/agents influencing processes like autophagy or inflammation, modulate transcriptional CRE-dependent activity in these cells. Specifically, we provide evidence that the autophagy inhibitor 3-MA, given that PDEs are expressed, may also act as a PDE inhibitor and inducer of CRE-mediated transcriptional activity. Full article

Miller–Dieker Syndrome (MDS) is a rare neurodevelopmental disorder caused by a heterozygous deletion of approximately 26 genes within the MDS locus of human chromosome 17. MDS, which affects 1 in 100,000 babies, can lead to a range of phenotypes, including lissencephaly, severe neurological defects, distinctive facial abnormalities, cognitive impairments, seizures, growth retardation, and congenital heart and liver abnormalities. One hallmark feature of MDS is an unusually smooth brain surface due to abnormal neuronal migration during early brain development. Several genes located within the MDS locus have been implicated in the pathogenesis of MDS, including PAFAH1B1, YWHAE, CRK, and METTL16. These genes play a role in the molecular and cellular pathways that are vital for neuronal migration, the proper development of the cerebral cortex, and protein translation in MDS. Improved model systems, such as MDS patient-derived organoids and multi-omics analyses indicate that WNT/β-catenin signaling, calcium signaling, S-adenosyl methionine (SAM) homeostasis, mammalian target of rapamycin (mTOR) signaling, Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling, and others are dysfunctional in MDS. This review of MDS integrates details at the clinical level alongside newly emerging details at the molecular and cellular levels, which may inform the development of novel therapeutic strategies for MDS. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

The transition from conchocelis to conchosporangia in Pyropia haitanensis represents a pivotal stage in its life cycle. As a commercially vital red alga, P. haitanensis plays a dominant role in global nori production. The transition governing its sporulation efficiency is pivotal for aquaculture success, yet the underlying regulatory mechanisms, especially their integration with metabolic cues such as polyamines, remain poorly understood. This study uncovered a critical role for the polyamine spermine (SPM) in promoting conchosporangial formation, mediated through the signaling activity of superoxide anions (O₂·⁻). Treatment with SPM markedly elevated O₂·⁻ levels, an effect that was effectively inhibited by the NADPH oxidase inhibitor diphenyliodonium chloride (DPI), underscoring the role of O₂·⁻ as a key signaling molecule. Transcriptomic analysis revealed that SPM enhanced photosynthesis, carbon assimilation, and respiratory metabolism, while simultaneously activating antioxidant enzymes, such as superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT), to regulate hydrogen peroxide (H₂O₂) levels and maintain redox homeostasis. Furthermore, SPM upregulated genes associated with photosynthetic carbon fixation and the C₂ oxidative photorespiration pathway, supplying the energy and metabolic resources necessary for this developmental transition. These findings suggested that SPM orchestrated O₂·⁻ signaling, photosynthetic activity, and antioxidant defenses to facilitate the transition from conchocelis to conchosporangia in P. haitanensis. Full article

Railway axles are among the most safety-critical components in rolling stock, as their failure can lead to catastrophic consequences. One of the most subtle damage mechanisms affecting these components is fretting fatigue, which is a particularly challenging damage mechanism in these components, as it can initiate cracks under real service conditions and is difficult to detect in its early stages, which is vital to ensure operational safety and to optimize maintenance strategies. This paper focuses on the development of fretting fatigue damage in solid railway axles under realistic service-like conditions. Full-scale axle specimens with artificially induced notches were subjected to loading conditions that promote fretting fatigue crack initiation and growth. Acoustic emission techniques were used to monitor the damage progression, and post-processing of the emitted signals, by using wavelet-based tools, was conducted to identify early indicators of crack formation. The experimental findings demonstrate that the proposed approach allows for reliable identification of fretting-induced crack initiation, contributing valuable insights into the in-service behavior of railway axles under this damage mechanism. Full article

Background: Complete environmental adaptation requires both survival and reproductive success. The hypoxic Qinghai-Tibet Plateau (>3000 m) challenges reproduction in indigenous species. Tibetan sheep, a key plateau-adapted breed, possess remarkable hypoxic tolerance, yet the genetic basis of their reproductive success remains poorly understood. Methods: We integrated transcriptomic and genomic data from Tibetan sheep and two lowland breeds (Small-tailed Han sheep and Hu sheep) to identify Tibetan sheep reproduction-associated genes (TSRGs). Results: We identified 165 TSRGs: four genes were differentially expressed (DEGs) versus Small-tailed Han sheep, 77 DEGs versus Hu sheep were found, and 73 genes were annotated in reproductive pathways. Functional analyses revealed enrichment for spermatogenesis, embryonic development, and transcriptional regulation. Notably, three top-ranked selection signals (VEPH1, HBB, and MEIKIN) showed differential expression. Murine Gene Informatics (MGI) confirmed that knockout orthologs exhibit significant phenotypes including male infertility, abnormal meiosis (male/female), oligozoospermia, and reduced neonatal weight. Conclusions: Tibetan sheep utilize an evolved suite of genes underpinning gametogenesis and embryogenesis under chronic hypoxia, ensuring high reproductive fitness—a vital component of their adaptation to plateaus. These genes provide valuable genetic markers for the selection, breeding, and conservation of Tibetan sheep as a critical genetic resource. Full article

Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. Full article

The combined effect of non-thermal plasma (NTP) and hydropriming on the germination performance and seedling characteristics of specific varieties of cucumber (Cucumis sativus L.) and broccoli (Brassica oleracea var. italica Plenck.) seeds after short-term storage is reported. Seeds were treated with NTP for 10 and 15 min, followed by hydropriming in distilled water for 24 h, and then stored for six months in the dark before evaluation. The treated cucumber seeds demonstrated a statistically significant enhancement in seed germination and seedling vitality indices. In contrast, broccoli seeds showed no significant improvement. The stimulatory effects observed in cucumber may be attributed to reactive oxygen and nitrogen species, which act as signaling molecules to promote stress tolerance and early growth. This study also highlights the potential of combined NTP treatment and hydropriming as a pre-sowing treatment for select crops, underscoring the need for species-specific optimization. The used, portable, and relatively inexpensive NTP device offers practical advantages for agricultural applications. Full article