Search Results (112,669)

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered by inherent limitations, including low selectivity and elevated operating temperatures, which increase power consumption. Two-dimensional metal oxysulfides have recently attracted attention as room-temperature sensing materials due to their unique electronic properties and fully reversible sensing performance. Meanwhile, their combination with optoelectronic gas sensing has emerged as a promising solution, combining higher efficiency with minimal energy requirements. In this work, we introduce non-layered 2D indium oxysulfide (In₂S_xO_3−x) synthesized via a two-step process: liquid metal printing of indium followed by thermal annealing of the resulting In₂O₃ in a H₂S atmosphere at 300 °C. The synthesized material is characterized by a micrometer-scale lateral dimension with 6.3 nm thickness and remaining n-type semiconducting behavior with a bandgap of 2.53 eV. It demonstrates a significant response factor of 1.2 toward 10 ppm NO₂ under blue light illumination at room temperature. The sensor exhibits a linear response across a low concentration range of 0.1 to 10 ppm, alongside greatly improved reversibility, selectivity, and sensitivity. This study successfully optimizes the application of 2D metal oxysulfide and presents its potential for the development of energy-efficient NO₂ sensing systems. Full article

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls (CONTROL) using recent advances in InterCriteria Analysis (ICrA). The novelty lies in the (i) comprehensive temporal–kinetic feature set, (ii) use of ICrA to characterize inter-feature coordination patterns at population and disease-group levels and (iii) interpretation in a neuromechanical context. Forty-one temporal/kinetic features were extracted from left/right leg ground reaction force and rate-of-force-development signals, considering laterality, gait phase (stance, swing, double support), magnitudes, waveform correlations, and inter-/intra-limb asymmetries. The analysis included 14,580 steps from 64 recordings in the Gait in Neurodegenerative Disease Database: 16 CONTROL (4054 steps), 13 ALS (2465), 20 HUNT (4730), 15 PARK (3331). Sensitivity analysis identified strict consonance thresholds (μ ≥ 0.75, ν ≤ 0.25), selecting <5% strongest inter-feature relations from 820 feature pairs: population level (16 positive, 14 negative), group-level (15–25 positive, 9–14 negative). ICrA identified group-specific consonances—present in one group but absent in others—highlighting disease-related alterations in gait coordination: ALS (15/11 positive/negative, disrupted bilateral stride coordination, prolonged stance/double-support, decoupled stride/cadence, desynchronized force-generation patterns—reflecting compensatory adaptations to muscle weakness and instability), HUNT (11/7, severe temporal–kinetic breakdown consistent with gait instability—loss of bilateral coordination, reduced swing time, slowed force development), PARK (1/2, subtle localized disruptions—prolonged stance and double-support intervals, reduced force during weight transfer, overall coordination remained largely preserved). Benchmarking vs. Pearson correlation showed strong linear agreement (R² = 0.847, p < 0.001), confirming that ICrA captures dominant dependencies while moderating the correlation via uncertainty. These results demonstrate that ICrA provides a quantitative, interpretable framework for characterizing gait coordination patterns and can guide principled feature selection in future predictive modeling. Full article

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems. Full article

The detection of chiral properties is crucial for pharmacology and biochemistry, yet standard circular dichroism spectroscopy suffers from low sensitivity when probing minute sample volumes. While complex asymmetric chiral nanostructures can enhance these Circular Dichroic (CD) signals, their fabrication is intricate and costly. In this work, we analyzed an alternative based on extrinsic chirality in achiral square arrays of plasmonic circular NHAs realized via Displacement Talbot Lithography (DTL), thus exploring the chiroptical response arising from symmetry breaking induced by oblique illumination. Unlike isolated nanoparticles, nanohole arrays (NHAs) support propagating Surface Plasmon Polaritons (SPPs), allowing for unique light confinement capabilities essential for high-throughput sensing. A careful characterization in terms of Stokes parameters has been performed over a selected range of different optical angles of incidence and sample orientation to disentangle extrinsic chiral contribution from spurious effects related to sample imperfections. By optimizing such extrinsic chiral contributions, enhanced chiroptical response could be engineered by significantly amplifying the interaction between light and chiral biomolecules trapped within the holes. This methodology establishes DTL-fabricated achiral NHAs as an ultrasensitive, cost-effective platform for the detection and discrimination of enantiomers in biosensing applications. Full article

Background and aims: Amid global aging, the role of diet in cognitive health is crucial. The healthful plant-based diet index (hPDI) is linked to cardiometabolic benefits, but its association with cognitive function in older adults, particularly through nutritional status and across different socio-geographic contexts, remains unclear. This study investigated the association between hPDI and multidimensional cognitive function, the mediating role of nutritional status, and potential associated modifications by urban–suburban location and gender. Methods: A community-based cross-sectional study was conducted in Shanghai, China, involving 2079 older adults (aged ≥60). Dietary intake was assessed by a validated food frequency questionnaire (FFQ) to calculate hPDI. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-B), and Clinical Dementia Rating (CDR). Nutritional status was measured by the Mini Nutritional Assessment (MNA). Hierarchical regression, interaction, and mediation analyses were performed, adjusting for comprehensive covariates based on social determinants of health (SDoH). Results: Higher hPDI was significantly associated with better cognitive scores (MMSE: β = 0.083, p < 0.001; MoCA-B: β = 0.069, p < 0.001) and lower odds of worse CDR (OR = 0.944, p < 0.001) in fully adjusted models. In the cross-sectional mediation analysis, MNA statistically mediated a significant proportion of the observed associations (MMSE: 41.25%; MoCA-B: 53.68%; CDR: 38.98%). The protective association was consistent across urban and suburban areas. However, a significant three-way interaction (hPDI × Gender × Area, p < 0.01) was found, with no cognitive benefit observed for males in suburban areas. Conclusions: Adherence to a healthful plant-based diet is associated with better cognitive function in older adults, partly statistically mediated by improved nutritional status. While this association is geographically equitable in Shanghai, suburban males do not appear to benefit, highlighting the need for gender- and context-sensitive dietary interventions. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article

Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the latest advances in this field. It details the gas release characteristics during the TR failure process and identifies H₂, electrolyte vapor, CO, CO₂, and CH₄ as effective TR warning markers. The core of this review lies in an in-depth critical analysis of gas-sensing materials designed for these target gases, systematically summarizing the design, performance, and application research of semiconductor gas-sensing materials for each aforementioned gas in battery monitoring. We further summarize the current challenges of this technology and provide an outlook on future development directions of gas-sensing materials, including improved selectivity, integration, and intelligent advancement. This review aims to provide a roadmap that directs the rational design of next-generation sensing materials and fast-tracks the implementation of gas-sensing technology for enhanced battery safety. Full article

Cancer stem cells (CSCs) represent a small but highly resilient tumor subpopulation responsible for sustained growth, metastasis, therapeutic resistance, and recurrence. Their survival is supported by aberrant activation of developmental and inflammatory pathways, including Wnt/β-catenin, Notch, Hedgehog, PI3K/Akt/mTOR, STAT3, and NF-κB, as well as epithelial–mesenchymal transition (EMT) programs and niche-driven cues. Increasing evidence shows that phytochemicals, naturally occurring bioactive compounds from medicinal plants, can disrupt these networks through multi-targeted mechanisms. This review synthesizes current findings on prominent phytochemicals such as curcumin, sulforaphane, resveratrol, EGCG, genistein, quercetin, parthenolide, berberine, and withaferin A. Collectively, these compounds suppress CSC self-renewal, reduce sphere-forming capacity, diminish ALDH⁺ and CD44⁺/CD24⁻ fractions, reverse EMT features, and interfere with key transcriptional regulators that maintain stemness. Many phytochemicals also sensitize CSCs to chemotherapeutic agents by downregulating drug-efflux transporters (e.g., ABCB1, ABCG2) and lowering survival thresholds, resulting in enhanced apoptosis and reduced tumor-initiating potential. This review further highlights the translational challenges associated with poor solubility, rapid metabolism, and limited bioavailability of free phytochemicals. Emerging nanotechnology-based delivery systems, including polymeric nanoparticles, lipid carriers, hybrid nanocapsules, and ligand-targeted formulations, show promise in improving stability, tumor accumulation, and CSC-specific targeting. These nanoformulations consistently enhance intracellular uptake and amplify anti-CSC effects in preclinical models. Overall, the consolidated evidence supports phytochemicals as potent modulators of CSC biology and underscores the need for optimized delivery strategies and evidence-based combination regimens to achieve meaningful clinical benefit. Full article

Dextrin-based nanosponges (D-NS) are promising candidates for oral drug delivery due to their biocompatibility, mucoadhesive properties, and tunable swelling behavior. In this study, pH-sensitive nanosponges were synthesized using β-cyclodextrin (β-CD), GluciDex^®2 (GLU2), and KLEPTOSE^® Linecaps (LC) as building blocks, crosslinked with pyromellitic dianhydride (PMDA) and citric acid (CA). The nanosponges were mechanically size-reduced via homogenization and ball milling, and characterized by FTIR, TGA, dynamic light scattering (DLS), and zeta potential measurements. Swelling kinetics, cross-linking density (determined using Flory–Rehner theory), rheological behavior, and mucoadhesion were evaluated under simulated gastric and intestinal conditions. The β-CD:PMDA 1:4 NS was selected for drug studies due to its optimal balance of structural stability, swelling capacity (~863% at pH 6.8), and highest apomorphine (APO) loading (8.23%) with 90.58% encapsulation efficiency. All nanosuspensions showed favorable polydispersity index values (0.11–0.30), homogeneous size distribution, and stable zeta potentials, confirming suspension stability. Storage at 4 °C for six months revealed no changes in physicochemical properties or apomorphine (APO) degradation, indicating protection by the nanosponge matrix. D-NS exhibited tunable swelling, pH-responsive behavior, and mucoadhesive properties, with nanoparticle–mucin interactions quantified by the rheological synergism parameter (∆G′ = 53.45, ∆G″ = −36.26 at pH 6.8). In vitro release studies demonstrated slow, sustained release of APO from D-NS in simulated intestinal fluid compared to free drug diffusion, highlighting the potential of D-NS as pH-responsive, mucoadhesive carriers with controlled drug release and defined nanoparticle–mucin interactions. Full article

Neonatal Diabetes Mellitus (NDM) is a rare, heterogeneous monogenic disorder typically presenting within the first six months of life. Unlike type 1 or type 2 diabetes, NDM is caused by single-gene mutations that disrupt pancreatic β-cell function or development. With the advent of next-generation sequencing, the genetic spectrum of NDM has expanded significantly, necessitating a shift from symptomatic management to precision medicine. This narrative review summarizes the genetic basis and pathogenic mechanisms of NDM, categorizing them into three major pathways: (1) ATP-sensitive potassium (K_ATP) channelopathies (e.g., ABCC8, KCNJ11), where gain-of-function mutations inhibit insulin secretion; (2) Transcription factor defects (e.g., GLIS3, PAX6, GATA6), which impair pancreatic development and often present with syndromic features; and (3) Endoplasmic reticulum (ER) stress-mediated β-cell apoptosis, exemplified by WFS1 mutations. Furthermore, we highlight the clinical complexity of these mutations, including the “biphasic phenotype” observed in ABCC8 and HNF1A variants. Understanding these molecular mechanisms is critical for clinical decision-making. We discuss the transformative impact of genetic diagnosis in treatment, particularly the successful transition from insulin to oral sulfonylureas in patients with K_ATP channel mutations, and emphasize the importance of early genetic testing to optimize glycemic control and prevent complications. Full article

Background/Objectives: Whole exome sequencing (WES) is an effective method for detecting disease-causing variants. However, copy number variation (CNV) detection using WES data often has limited sensitivity and high false-positive rates. Methods: In this study, we constructed a reference CNV set using chromosomal microarray analysis (CMA) data from 44 of 180 individuals who underwent WES and CMA and evaluated four WES-based CNV callers (CNVkit, CoNIFER, ExomeDepth, and cn.MOPS) against this benchmark. For each tool, we first defined software-specific problematic genomic regions across the full WES cohort and filtered out the CNVs that overlapped these regions. Results: The four algorithms showed low mutual concordance and distinct distributions in the problematic regions. On average, 2210 sequencing target baits (1.23%) were classified as problematic; these baits had lower mappability scores and higher coefficients of variation in RPKM than the remaining probes. After the supplementary filtration step, all tools demonstrated improved performance. Notably, ExomeDepth achieved gains of 14.4% in sensitivity and 7.9% in positive predictive value. Conclusions: We delineated software-specific problematic regions and demonstrated that targeted filtration markedly reduced false positives in WES-based CNV detection. Full article

Accessible and comfortable public transportation is a cornerstone of sustainable and inclusive urban mobility. However, there is a knowledge gap in how interior layout influences riders’ comfort perception under constant occupancy conditions. We conducted a pilot laboratory experiment in Valparaíso, Chile using a full-scale urban bus mock-up. Twenty-five participants each experienced four seating scenarios (yielding 100 total observations per outcome) that varied seat pitch (20, 30, 45 cm) and seat orientation (forward-facing vs. side-facing). Cumulative link mixed models were used to estimate seat pitch and orientation effects on the comfort outcomes, with participant-specific random intercepts. Increased seat pitch dramatically improved comfort ratings (e.g., virtually no participants felt comfortable at 20 cm, whereas nearly all did at 45 cm). Side-facing bench seating (longitudinal orientation) yielded significantly higher comfort, legroom, and ease-of-movement ratings than the forward-facing configuration at ~30 cm pitch (p < 0.001). Within the tested mock-up conditions, the results suggest that seat pitch is a major driver of perceived comfort and in-vehicle usability, and that a side-facing bench layout (tested at ~30 cm spacing) can improve perceived spaciousness relative to forward-facing seating. Because this is a small, non-probability pilot sample and a partial factorial design, these findings should be considered preliminary design sensitivities that warrant validation in larger, in-service studies before informing fleet-wide standards. Full article

Background/Objectives: Cardiovascular (CV) and cerebrovascular diseases, primarily attributed to atherosclerosis, stand as leading global causes of morbidity and mortality. This study aims to evaluate the impact of preclinical atherosclerosis parameters, including intima-media thickness (IMT) and arterial stiffness, in a seven-year follow-up of 100 patients with CV risk factors but no known history of CV or cerebrovascular diseases. Methods: Between April 2014 and December 2015, 100 patients presenting with suspected ischemic heart disease were enrolled. The study integrates the color Doppler examination of the supra-aortic trunks with the evaluation of preclinical parameters of atherosclerosis, such as intima-media thickness (IMT), βeta index, and pulse wave velocity (PWV), as well as echocardiographic evaluations, including global longitudinal strain (GLS). CV risk factors, metabolic syndrome, and insulin resistance were assessed and measured for each patient using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). Two- and seven-year follow-ups assessed various CV events. Results: The study population comprised 67% males and 33% females. Metabolic syndrome, impaired fasting glycemia and hypertension were prevalent. The mean value of IMT was 1.21 ± 0.26 mm, and PWV was 8.47 ± 2.14 m/s. The 7-year follow-up identified IMT, PWV, and HOMA-IR as strong positive predictors of cardiovascular events, with PWV emerging as a particularly sensitive indicator of early events. Conclusions: Insulin resistance and cardiovascular risk factors may contribute to early alterations in myocardial and vascular function, even in the absence of overt disease. PWV, as a recognized surrogate marker of arterial stiffness, may serve as a sensitive tool for the early prediction of cardiovascular events. A comprehensive screening, including the assessment of markers indicating subclinical vascular alterations, along with the implementation of preventive interventions, is crucial for populations at risk. Full article

Ovarian cancer remains a top mortality contributor within gynecological cancers because patients receive diagnoses late in the disease course and conventional treatment resistance along with high recurrence rates cause poor outcomes. Aberrant regulation of autophagy and apoptosis has a critical role in the development, progression, chemoresistance, and immune escape from ovarian cancer. Recent evidence has demonstrated a complicated and dynamic crosstalk between autophagy and apoptosis, during which autophagy can act as a cytoprotective or cell death-promoting process depending on tumor stage and therapeutic context. In parallel, apoptosis functions as a tightly regulated form of programmed cell death that is essential for eliminating damaged or malignant cells and serves as a major tumor-suppressive mechanism in ovarian cancer. The PI3K/AKT/mTOR signaling pathway is the most active and clinically relevant pathway in the management of ovarian cancer as a master regulator of both autophagy and apoptosis, suppressing apoptotic cell death while promoting cytoprotective autophagy under chemotherapeutic stress. Bioactive natural products derived from plants, marine sources, and dietary intake have emerged as potential modulators of the autophagy-apoptosis crosstalk. Curcumin, resveratrol, quercetin, berberine, and epigallocatechin gallate are known to have the ability to restore apoptotic signaling, block pro-survival autophagy, and sensitize ovarian cancer cells to chemotherapy through the regulation of key pathways including PI3K/AKT/mTOR, AMPK, MAPK, p53, and Bcl-2 family proteins. In this review, we provide an updated understanding of the molecular mechanisms through which bioactive natural products modulate autophagy–apoptosis crosstalk in ovarian cancer. We also highlight the translational challenges, therapeutic potential, and future directions for the integration of natural product-based strategies in precision medicine for ovarian cancer. Full article

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise. Full article