Search Results (2,081)

Vanadium carbide (V₂C) MXene shows great potential for addressing challenging implant-associated infections in bone regeneration due to its strong photothermal conversion efficiency. However, its photothermal efficacy is restricted to the near-infrared I (NIR-I) region due to a limited absorption range. To address this, we designed platinum nanoparticle-decorated V₂C heterostructures (Pt@V₂C) via an in situ growth method, leveraging Pt’s plasmonic and catalytic properties to extend the photoresponse to the NIR-II window. Subsequently, Pt@V₂C was integrated into poly-L-lactic acid (PLLA) to fabricate PLLA-Pt@V₂C scaffolds with photothermal antibacterial function by selective laser sintering. The optimized PLLA-Pt@V₂C scaffold achieves a record photothermal conversion efficiency (56.03% at 1064 nm), triggering simultaneous hyperthermia (>52 °C) and catalytic ·OH radical generation. In vitro studies demonstrate exceptional antibacterial efficacy against Staphylococcus aureus and Escherichia coli, achieving over 99% killing rates upon 1064 nm near-infrared irradiation. Furthermore, the scaffold demonstrated significant inhibition of biofilm formation, achieving over 90% reduction in biofilm biomass. Moreover, the scaffold demonstrated high cell viability, confirming its dual functionality of potent bactericidal activity and biocompatibility that supports tissue regeneration. This work provides a feasible strategy for combating implant-associated infections. Full article

Background/Objectives: Accurate sleep stage classification is essential for evaluating sleep quality and diagnosing sleep disorders. Despite recent advances in deep learning, existing models inadequately represent complex brain dynamics, particularly the time-lag effects inherent in neural signal propagation and regional variations in cortical activation patterns. Methods: We propose the MFST-GCN, a graph-based deep learning framework that models these neurobiological phenomena through three complementary modules. The Dynamic Dual-Scale Functional Connectivity Modeling (DDFCM) module constructs time-varying adjacency matrices using Pearson correlation across 1 s and 5 s windows, capturing both transient signal transmission and sustained connectivity states. This dual-scale approach reflects the biological reality that neural information propagates with measurable delays across brain regions. The Multi-Scale Morphological Feature Extraction Network (MMFEN) employs parallel convolutional branches with varying kernel sizes to extract frequency-specific features corresponding to different EEG rhythms, addressing regional heterogeneity in neural activation. The Adaptive Spatio-Temporal Graph Convolutional Network (ASTGCN) integrates spatial and temporal features through Chebyshev graph convolutions with attention mechanisms, encoding evolving functional dependencies across sleep stages. Results: Evaluation on ISRUC-S1 and ISRUC-S3 datasets demonstrates F1-scores of 0.823 and 0.835, respectively, outperforming state-of-the-art methods. Conclusions: Ablation studies confirm that explicit time-lag modeling contributes substantially to performance gains, particularly in discriminating transitional sleep stages. Full article

Choledochal cyst (CC), a congenital biliary anomaly, is associated with recurrent infections, chronic inflammation, and an increased risk of malignancy. Although emerging evidence implicates the biliary microbiome in disease pathophysiology, its developmental dynamics in pediatric CC remain unclear. Using deep metagenomic sequencing and comprehensive functional annotation, this study characterized age-dependent changes in the biliary microbiome of 201 pediatric CC patients stratified into infancy (<1 year), early childhood (1–5 years), and later childhood (5–12 years). We found that while the taxonomic composition and alpha diversity of the microbiota remained conserved across age groups, profound functional remodeling occurred with host development. A core set of microbial species(Bacteroidota, Actinomycetota, Bacillota, and Pseudomonadota) and functional pathways was shared across all ages; however, early childhood (1–5 years) exhibited the greatest number of unique functional genes, metabolic pathways, and carbohydrate-active enzymes, identifying this period as a critical window for microbial metabolic adaptation. Age-specific patterns were also evident in clinically relevant traits: infants (<1 year) harbored the most unique antibiotic resistance and virulence factor genes, whereas the resistome and virulome became more streamlined in older children. These findings establish a paradigm of “taxonomic conservation coupled with functional remodeling” in the CC microbiome and highlight age as a key determinant of microbial community function. This study offers novel insights into the microbial dynamics underlying CC progression and suggests potential age-specific targets for future therapeutic strategies. Full article

In autonomous manufacturing systems, the performance of time-series-based anomaly detection and fault diagnosis is highly sensitive to window size selection. Conventional approaches rely on empirical rules or fixed window settings, which often fail to capture the diverse temporal characteristics of anomalies and lead to performance degradation. This study systematically addresses the window size selection problem by categorizing anomaly patterns into three representative types: variability, cycle, and local spike. Each pattern is associated with a distinct temporal scale and underlying physical mechanism. Based on this insight, an Anomaly Deviation-Based Window Size Selection (ADW) method is proposed, which quantitatively evaluates anomaly deviation as a function of window size. Unlike traditional preprocessing-oriented approaches, the proposed method redefines window size as a core design variable that directly governs anomaly representation and diagnostic sensitivity. The effectiveness of the ADW method is validated using tension data from a roll-to-roll continuous manufacturing process and vibration data from a rotating bearing fault dataset. Experimental results demonstrate that the proposed approach consistently identifies optimized window sizes tailored to different anomaly types, leading to improved fault classification accuracy and diagnostic robustness. The proposed framework provides a physically interpretable and data-driven guideline for adaptive window size selection in long-term autonomous manufacturing systems. Full article

Fluorinated greenhouse gases (FGGs) are classified as worldwide pollutants and have a high global warming potential compared to other greenhouse gases. Detecting the existence and concentration of new and older refrigerant gases is crucial for assessing system functionality and determining whether they can be recycled or need to be disposed of. Additional justifications for the necessity of quantitative measurements of these gases include the manufacturing of air conditioning components; leak detection is conducted to ensure they are free of leaks. Classical laboratory Fast Fourier transform spectrometers enable the detection and measurement of substances while being delicate, unwieldy, and costly, and typically requiring a skilled technician to operate them. For the estimation of refrigerants in the field, a portable, user-friendly, and cost-effective detection device must be deployed. This article provides an in-depth analysis of the categorization of refrigerant gases using an Internet of Things (IoT) gas detection device. The functionality in effectively differentiating between important refrigerant gases, like R-32 and R-134a, with low delay, is demonstrated through practical tests. With the portable device, this study utilizes Fourier-Transformed infrared spectra measured from the refrigerants R-32 and R-134a, collected using a custom-made 3D-printed tubular reactor equipped with two BaF₂ windows, suitable for use in the beamline of a Bruker IR Spectrometer. Calibration was performed by exposing the infrared sensor to controlled gas environments with varying amounts of refrigerant gases using accurately produced gas mixtures. Following the on-field analysis of the reclaimed refrigerants, the obtained data was immediately processed, and both the data and the results were uploaded to an IoT platform, making them available to business-to-business (B2B) clients. The functionality of the device is demonstrated. Full article

Background: Neonatal hypoxic–ischemic encephalopathy remains a leading cause of neonatal mortality and long-term neurodevelopmental disability worldwide. Despite the widespread adoption of therapeutic hypothermia, a substantial proportion of affected infants experience death or significant neurological impairment. Given their metabolic vulnerability, ketogenic diet strategies and ketone bodies have emerged as potential adjunctive neuroprotective interventions. This scoping review aims to critically evaluate the mechanistic rationale, preclinical evidence, and clinical feasibility of ketogenic approaches. Methods: A scoping review of the literature was conducted, including experimental and clinical studies investigating ketogenic diets, endogenous ketosis, and exogenous ketone supplementation in neonatal hypoxia–ischemia. Evidence was synthesized across mechanistic, preclinical, nutritional, and clinical domains, with particular attention to developmental context, timing of intervention, safety considerations, and translational relevance in the contest of therapeutic hypothermia. Results: Preclinical studies consistently demonstrate that ketone bodies enhance cerebral energy metabolism, support mitochondrial function, reduce excitotoxic signaling, and attenuate oxidative stress and neuroinflammation in the immature brain. Neonatal models show preferential utilization of β-hydroxybutyrate over glucose during hypoxic–ischemic stress, suggesting intrinsic metabolic advantages. Emerging evidence also supports potential long-term effects on epigenetic regulation and white matter development, although direct causal validation in neonatal HIE remains limited. Nutritional studies indicate that carefully monitored enteral and parenteral feeding is feasible in critically ill neonates, identifying a potential window for metabolic interventions. Conclusions: Ketogenic strategies represent a plausible, multimodal approach to targeting the metabolic and inflammatory sequelae of neonatal HIE. While current evidence is insufficient to support clinical implementation, this scoping review provides a hypothesis-generating framework to guide future translational research and the design of carefully controlled clinical trials in neonatal neurocritical care. Full article

Background/Objectives: In Japan, cancer-related anemia (CRA) is common, and erythropoiesis-stimulating agents (ESAs) are not approved for chemotherapy-induced anemia. Modern intravenous (IV) iron formulations, such as ferric carboxymaltose (FCM) and ferric derisomaltose (FDI), enable high-dose repletion; however, real-world evidence in ESA-free oncology settings remains limited. Methods: This single-center retrospective study included patients with CRA (N = 55) who received high-dose IV iron (FCM or FDI). Iron phenotypes were classified as absolute iron deficiency (ID), functional ID, or non-ID. The primary endpoint was hemoglobin (Hb) change from baseline to approximately 1 month (21–45 days) in the non-transfused patients. Secondary endpoints included responder rate (ΔHb ≥ 1.0 g/dL), transfusion avoidance rate, dosing adequacy relative to Ganzoni-calculated iron deficit, and safety, particularly hypophosphatemia. Results: Among the non-transfused patients, mean Hb increased from 8.76 ± 1.34 g/dL to 9.73 ± 1.75 g/dL (mean ΔHb +0.92 ± 1.44 g/dL; p < 0.001). The responder and transfusion avoidance rates were 48.9% and 81.8%, respectively. Functional ID was most prevalent (52.7%), with clinically meaningful Hb responses. A total of 38.2% achieved approximately 1000 mg dosing. The safety profile was excellent, and no infusion reactions or symptomatic hypophosphatemia was observed (median serum phosphate changed from 3.4 [3.0–3.9] to 3.2 [2.7–3.8] mg/dL). Conclusions: In this real-world Japanese oncology setting where ESAs were not available for chemotherapy-induced anemia, high-dose IV iron monotherapy (FCM or FDI) was well tolerated and was associated with modest short-term Hb increases and a high observed rate of transfusion avoidance within a 21–45-day assessment window. These findings suggest that a proactive, TSAT-guided IV iron therapy approach may be a pragmatic option for selected patients; however, durability beyond 1 month, optimal re-dosing, and generalizability require confirmation in larger, longer prospective studies. Full article

Against the backdrop of China’s “dual-carbon” goals, accurate analysis and prediction of subway passenger flows are crucial for optimizing operational efficiency and advancing low-carbon urban transportation. Beijing’s subway network exhibits pronounced spatiotemporal heterogeneity across workdays, weekends, and holidays, yet existing studies often rely on static networks or single-scale temporal analyses, failing to capture dynamic flow evolution. To address this gap, this study develops a dynamic time-varying network framework with a 15 min temporal granularity, integrating sliding time-window analysis, node strength evaluation, and betweenness centrality for bottleneck identification. A Temporal–Spatial Fusion Gated Recurrent Unit (TSF-GRU) model is proposed to fuse temporal dependencies, spatial correlations, and network topology for short-term passenger flow forecasting. Results show distinct flow patterns: workdays feature a “concentrated commuting” dual peak, holidays a “steady continuous” leisure pattern, and weekends an “extended flexible” hybrid pattern. Station functions and bottleneck evolution vary dynamically across date types, with transportation hubs central on holidays/weekends and business nodes dominating workday peaks. The TSF-GRU model achieves a test-set MAPE of 7.62% and bottleneck prediction accuracy of 92.3%, outperforming traditional methods. This study provides a feasible pathway for refined, low-carbon subway operations in megacities and methodological support for achieving dual-carbon goals. Full article

Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 to 2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches were developed and tested under operationally realistic conditions—Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), XGBoost—using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional major-hurricane targets due to lower event frequencies and stronger predictability limits. Full article

There is increasing evidence that exposure to environmental toxicants may impact fertility, especially during critical windows of reproductive axis development. Hypothalamic gonadotropin-releasing hormone (GnRH) neurons, essential for puberty onset and fertility, originate from the olfactory placode and migrate toward the hypothalamus during development, making them particularly vulnerable to environmental insults. Cadmium (Cd), a widespread heavy metal, is well known for its gonadotoxicity, but its impact on human hypothalamic neuron development remains unclear. Using human fetal GnRH neuroblasts (FNCB4) we investigated the effects of Cd exposure on their morpho-functional and developmental features. Cd induced oxidative stress and COX2 mRNA upregulation, indicative of inflammatory pathway activation, which was accompanied by reduced cell migration and downregulation of motility-related genes. These effects were associated with F-actin disassembly and altered expression of adhesion molecules. Electrophysiological analyses showed that Cd altered membrane potential, increased capacitance and permeability, and disrupted gap junctional communication, as also confirmed by connexin-43 delocalization. Moreover, Cd significantly reduced the expression of specific GnRH neuronal markers, suggesting impaired functional maturation. Overall, our findings provide the first evidence that Cd may interfere with mechanisms crucially involved in human GnRH neuron development, adding new mechanistic insights into the comprehension of how early-life exposure to Cd may contribute to fertility concerns. Full article

A femtosecond laser serves as an excellent tool for efficiently fabricating large-area anti-reflective microhole arrays on infrared windows. The impact of the arrangement of the microholes during processing on final performance, however, remains unclear. Here, microhole arrays were fabricated on MgF₂ windows using a femtosecond laser. The optical performance was analyzed by the finite-difference time-domain method, focusing on the effects of in-plane arrangement deviation and double-sided alignment error. Simulation results indicate that the arrangement variations alter the average transmittance by less than 0.02%. Analysis via effective medium theory revealed that, within the target band, the microstructure array collectively functions as a thin film with a gradient refractive index. Its macroscopic properties show little sensitivity to minor misalignments at the microscopic scale. As a proof of concept, a large-area (20 mm × 20 mm) double-sided antireflection window was rapidly fabricated by employing a zigzag scanning strategy, which achieved an average transmittance exceeding 97.5% and exhibited a high degree of consistency between the simulated and experimental results. Upon final integration into the infrared thermal imaging system, this window not only enhanced the richness of detail in captured images but also improved target contrast. Full article

Acid mine drainage (AMD) and acid-generating mine wastes exhibit low pH, high sulfate levels, and complex multi-metal loads that strain conventional treatment. Thermoacidophilic red algae of the order Cyanidiales, particularly Galdieria sulphuraria (G. sulphuraria), have attracted interest as a biological option because they tolerate extreme acidity and elevated temperatures, grow under low light in mixotrophic or heterotrophic modes, and display rapid metal binding at the cell surface. This review synthesizes about two decades of peer-reviewed work to clarify how G. sulphuraria can be deployed as a practical module within mine water treatment trains. We examine the mechanisms of biosorption and bioaccumulation and show how they map onto two distinct configurations. Processed freeze-dried biomass functions as a regenerable sorbent for rare earth elements (REEs) and selected transition metals in packed beds with acid elution for recovery. Living cultures serve as polishing units for divalent metals and, when present, nutrients or dissolved organics under low light. We define realistic operating windows centered on pH 2–5 and temperatures of approximately 25–45 °C, and we identify matrix effects that govern success, including competition from ferric iron and aluminum, turbidity and fouling risks, ionic strength from sulfate, and suppression of REE uptake by phosphate in living systems. Building on laboratory studies, industrial leachate tests, and ecosystem observations, we propose placing G. sulphuraria upstream of bulk neutralization and outline reporting practices that enable cross-site comparison. The goal is an actionable framework that reduces reagent use and sludge generation while enabling metal capture and potential recovery of valuable metals from mine-influenced waters. Full article

In complex maritime environments, the safety of path planning for Unmanned Surface Vehicles (USVs) remains a significant challenge. Existing methods for handling dynamic obstacles often suffer from inadequate predictability and generate non-smooth trajectories. To address these issues, this paper proposes a reliable hybrid path planning approach that integrates a reinforcement learning-enhanced A* algorithm with an improved Dynamic Window Approach (DWA). Specifically, the A* algorithm is augmented by incorporating a dynamic five-neighborhood search mechanism, a reinforcement learning-based adaptive weighting strategy, and a path post-optimization procedure. These enhancements collectively shorten the path length and significantly improve trajectory smoothness. While ensuring that the global path avoids dynamic obstacles smoothly, a Kalman Filter (KF) is integrated into the Long Short-Term Memory (LSTM) network to preprocess historical data. This mechanism suppresses transient outliers and stabilizes the trajectory prediction of dynamic obstacles. Moreover, the evaluation function of the DWA is refined by incorporating the International Regulations for Preventing Collisions at Sea (COLREGs) constraints, enabling compliant navigation behaviors. Simulation results in MATLAB demonstrate that the enhanced A* algorithm better conforms to the kinematic model of the USVs. The improved DWA significantly reduces collision risks, thereby ensuring safer navigation in dynamic marine environments. Full article

The global prevalence of obesity continues to rise and is a significant risk factor for the onset and progression of cardiovascular diseases. Despite the development of new pharmacological therapies, novel strategies are being explored to mitigate the impact of this disease. Intermittent fasting (IF) is a nutritional intervention that has gained popularity and shows potential as an innovative approach to weight management. This study aims to compile scientific evidence on various aspects of fasting, including its physiological effects, the molecular and thermogenic mechanisms involved, and recommendations regarding nutritional strategies during the refeeding period within the eating window. We conducted a narrative review, analyzing evidence available from PubMed/MEDLINE based on studies related to intermittent fasting, thermogenesis, and their associated outcomes. Our results demonstrate the existence of three commonly used IF protocols: alternate day fasting (ADF), periodic fasting (PF), and time-restricted eating (TRE). In addition to its effects on weight loss, IF has demonstrated notable benefits for cardiovascular health, oxidative stress, and metabolic function. Moreover, the interaction between the central nervous system and brown adipose tissue provides an alternative mechanism for the molecular regulation of thermogenesis. Nutritional patterns adopted during intermittent fasting play a crucial role in optimizing outcomes, with particular emphasis on the intake of proteins, fiber, bioactive compounds, and essential fatty acids during the feeding window. In summary, current evidence indicates that intermittent fasting provides a biologically robust framework for studying energy balance and holds promise for developing targeted nutritional interventions. Full article

The placenta is often described as the “window to the brain” due to its crucial role in fetal neurological development. In this study, we investigated a family where the older male offspring exhibited severe neurodevelopmental and mild motor coordination disorders. His brother displayed emotional and behavioral dysregulation along with mild motor coordination disorders. The father was asymptomatic, while the mother and daughter showed mild learning disabilities. Whole exome sequencing (WES) identified a disruptive X-linked pathogenic variant, c.1088_1089del p.Asp363GlyfsTer2, within the calpain-6 (CAPN6) gene. We have submitted this variant to the ClinVar database (RCV005234146.2). The variant was found in hemizygous condition in the affected male offspring and in heterozygous condition in both the mother and daughter. As predicted, the variant undergoes nonsense-mediated mRNA decay (NMD), preventing the translation of the CAPN6 gene into a functional protein. CAPN6 is a critical gene predominantly expressed in placental and trophoblast tissues. Although its function is not well characterized, CAPN6 is also expressed in several regions of the developing brain. Recent studies have shown that genetic variants in CAPN6 significantly influence vascular endothelial growth factor (VEGF) activity, thereby affecting angiogenesis and the blood supply essential for fetal growth and development. Although CAPN6 lacks an MIM phenotype code, we hypothesize that it might be enumerated as a novel candidate gene contributing to neurodevelopmental disorders. Functional studies are imperative to elucidate the role of CAPN6 in placental function and its potential implications for neurodevelopmental processes. This work aims to inspire further research into the role of CAPN6 in placental biology and its relevance to neurodevelopmental disorders. Full article