Search Results (38,975)

Background: Inherited epidermolysis bullosa (EB) includes a group of rare genetic disorders affecting the skin and mucous membranes. These disorders are characterized by extreme fragility and blister formation after minimal or no trauma. Oral and systemic manifestations vary by subtype; the more severe forms often present with extensive intra-oral blistering, scarring, microstomia, vestibular obliteration, ankyloglossia, and—in some cases—oral cancer. This study aims to collect data on oral-health practices and challenges in people with EB to inform preventive strategies and dental care. Methods: An international, structured online questionnaire with 31 items was distributed to individuals with a confirmed diagnosis of EB. The survey explored clinical and oral manifestations, home-care routines (oral hygiene and diet), experiences with dental professionals, and the impact of oral health on quality of life. Results: Eighty-two questionnaires were completed. Dystrophic EB was the most often reported subtype (69.5%). Most respondents (67.1%) experienced recurrent oral blisters and/or erosions. Many reported relying exclusively on soft foods and struggling with mechanical plaque removal because of microstomia and pseudo-syndactyly. Severe oral pain hindered effective brushing in 17% of participants. Hand contractures and microstomia interfered with oral hygiene in 74% and 31% of participants, respectively. Nearly 30% sought dental care only when in pain. Among those who did not attend regular check-ups or hygiene sessions (44.6%), the most cited reason was that dental clinics were inadequately equipped or trained to manage EB. Conclusions: Because dental procedures carry significant risks for patients with EB, preventive care should begin in early childhood. Yet many patients are still insufficiently informed about essential preventive measures and lack access to dental professionals trained in EB management. Full article

Background: Bronchiectasis is a chronic respiratory condition characterized by permanent bronchial dilation, recurrent infections, and progressive lung damage. A subset of patients, known as frequent exacerbators, experience multiple exacerbations annually, leading to accelerated lung function decline, hospitalizations, and reduced quality of life. The aim of this study is to identify distinct phenotypes and treatable traits in bronchiectasis frequent exacerbators, since it could be crucial for optimizing patient management. Research question: Could clinically distinct phenotypes and treatable traits be identified among frequent exacerbators with bronchiectasis to guide personalized management strategies? Methods: We analysed a cohort of 56 bronchiectasis frequent exacerbator patients using 21 clinically relevant variables, including pulmonary function tests, radiological patterns, and microbiological data. Hierarchical clustering and k-means algorithms were applied to identify subgroups. Key outcomes included cluster-specific characteristics, treatable traits, and their implications for management. Results: Four distinct clusters were identified: 1. Mild, idiopathic bronchiectasis (Cluster 1): Predominantly mild disease (FACED), idiopathic etiology (93.3%), and cylindrical bronchiectasis with moderate obstruction (60%). 2. Rheumatological and NTM-associated bronchiectasis (Cluster 2): Patients with systemic inflammatory diseases (50%) and NTMever (50%) but minimal infections by Pseudomonas aeruginosa. 3. Mild, post-infective bronchiectasis (Cluster 3): Exclusively mild disease, mixed idiopathic and post-infective etiologies, and preserved lung function. 4. Severe, chronic infection phenotype (Cluster 4): Severe disease with high colonization rates of Pseudomonas aeruginosa (71.4%), advanced structural damage (57.1% varicose, 50% cystic bronchiectasis), and frequent exacerbations. Interpretation: This analysis highlights the heterogeneity of bronchiectasis and its frequent exacerbator phenotype. The treatable traits framework underscores the importance of aggressive infection control and management of airway inflammation in severe cases, while milder clusters may benefit from preventive strategies. These findings support the integration of precision medicine in bronchiectasis care, focusing on phenotype-specific interventions to improve outcomes. Full article

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

Coastal marine ecosystems are facing serious ecological risks from metals pollution, threatening biodiversity and human health. The main objective of this study is to evaluate the spatial distributions and ecological risks of dissolved cadmium (Cd), lead (Pb), and copper (Cu) in the Shandong Peninsula coastal areas, China. Two sampling campaigns were conducted at 21 sites in early spring 2025 to measure the concentrations of the three trace metals in the study area using an electrochemical detection system. The results revealed higher metals concentrations in nearshore areas (e.g., port entrances, aquaculture zones, and estuaries). Specifically, the Cd, Pb, and Cu concentrations in the study area ranged from 0 to 0.079 µg L⁻¹, 0.30 to 0.84 µg L⁻¹, and 2.19 to 4.79 µg L⁻¹, with average concentrations of 0.033, 0.55, and 3.18 µg L⁻¹, respectively. The contamination factors (C_f) of the three metals were below 1, indicating low pollution levels and thus meeting China’s Class I seawater quality standard. However, the ecological risk assessment, employing complementary methods, revealed varying interpretations: the risk quotient (RQ), based on species sensitivity distribution and predicted no-effect concentrations (PNECs), indicated low risks associated with Cd and Pb (RQ < 0.1) but a high risk for Cu (RQ > 1) at all sites, attributable to the exceedance of Cu’s protective threshold (0.46 µg L⁻¹), despite its low C_f. These findings highlight the need for continuous monitoring of Cu due to its high ecological impacts. In contrast, the Hakanson potential ecological risk index (ERI), which incorporates toxicity coefficients, suggested overall low risks (ERI < 150) for the combined metals; however, Cd contributed approximately 70% to the ERI due to its high toxicity coefficient, warranting attention despite the low individual Erⁱ values for Cd across the study area. This study provides valuable recent data on metals pollution dynamics in the Shandong Peninsula coastal areas, offering a scientific basis for developing marine pollution control policies and sustainable marine resource management. Full article

Significant time, money, and energy are invested in Continuing Professional Development (CPD) across Further Education (FE) colleges in England, with the aim of enhancing teaching strategies, sharing “best” practices, and improving educational quality. Despite these intentions, practitioner perceptions of CPD’s value remain mixed, highlighting concerns about the effectiveness of current approaches. CPD managers often face competing financial and operational demands, alongside pressure to comply with external requirements, resulting in CPD that is frequently instrumental, mandatory, and delivered through one-off events. These practices reflect a data-driven, prescriptive management culture that prioritizes measurable outcomes over meaningful educational experiences. Consequently, teachers are compelled to demonstrate compliance within a system where accountability is unevenly distributed. This medium-scale, multi-method practitioner research study investigates how such compliance-driven CPD practices divert attention and resources from genuine educational improvement. This study explores an alternative model of CPD rooted in teacher agency and enriched through engagement with the arts and aesthetic experiences. Drawing on surveys, semi-structured interviews, critical incidents, and narrative accounts, the findings suggest that this approach fosters more democratic, creative, and impactful professional development. In promoting teacher agency and challenging dominant power structures, this study offers a vision of CPD that supports meaningful educational transformation, with practical examples and recommendations for broader implementation. Full article

This study presents the implementation and evaluation of a high-resolution operational forecasting system for the Tagus estuary (Portugal), focusing on inundation and water quality predictions to support estuarine management. Developed using the relocatable Water Information Forecast Framework (WIFF), the system integrates two implementations of SCHISM: a 2D barotropic model including wave–current interactions for flood-prone areas, and a 3D baroclinic model simulating salinity, temperature, and biogeochemical variables. Forecasts were assessed over six months using in situ and satellite near real-time observations. Results show that the operational models represent well water levels, waves, salinity, temperature, and water quality dynamics. Compared to a regional model, the local forecast system generally offers improved accuracy within the estuary due to higher spatial resolution and better representation of local dynamics. Several challenges remain, including uncertainties in oceanic and riverine boundary conditions and limited high-resolution near real-time observations to continuously assess and improve operational models. Furthermore, the absence of operational two-way coupling between regional and local models limits cross-scale integration of physical and biogeochemical processes. The forecasting system for the Tagus estuary demonstrates the potential of local high-resolution operational models as reliable, user-oriented tools for managing transitional water systems, and as core elements for coastal management. Full article

Background: Glaucoma is a progressive optic neuropathy marked by retinal ganglion cells (RGCs), apoptosis, vascular insufficiency, oxidative stress, mitochondrial dysfunction, excitotoxicity, and neuroinflammation. While intraocular pressure (IOP) reduction remains the primary intervention, many patients continue to lose vision despite adequate pressure control. Emerging neuroprotective agents—citicoline, coenzyme Q10 (CoQ10), pyruvate, nicotinamide, pyrroloquinoline quinone (PQQ), homotaurine, berberine, and gamma-aminobutyric acid (GABA)—target complementary pathogenic pathways in experimental and clinical settings. Methods: This literature review synthesizes current evidence on glaucoma neuroprotection, specifically drawing on the most relevant and recent studies identified via PubMed. Results: Citicoline enhances phospholipid synthesis, stabilizes mitochondrial membranes, modulates neurotransmitters, and improves electrophysiological and visual field outcomes. CoQ10 preserves mitochondrial bioenergetics, scavenges reactive oxygen species, and mitigates glutamate-induced excitotoxicity. Pyruvate supports energy metabolism, scavenges reactive oxygen species, and restores metabolic transporter expression. Nicotinamide and its precursor nicotinamide riboside boost NAD⁺ levels, protect against early mitochondrial dysfunction, and enhance photopic negative response amplitudes. PQQ reduces systemic inflammation and enhances mitochondrial metabolites, while homotaurine modulates GABAergic signaling and inhibits β-amyloid aggregation. Berberine attenuates excitotoxicity, inflammation, and apoptosis via the P2X7 and GABA-PKC-α pathways. Preclinical models demonstrate synergy when agents are combined to address multiple targets. Clinical trials of fixed-dose combinations—such as citicoline + CoQ10 ± vitamin B3, citicoline + homotaurine ± vitamin E or PQQ, and nicotinamide + pyruvate—show additive improvements in RGCs’ electrophysiology, visual function, contrast sensitivity, and quality of life without altering IOP. Conclusions: A multi-targeted approach is suitable for glaucoma’s complex neurobiology and may slow progression more effectively than monotherapies. Ongoing randomized controlled trials are essential to establish optimal compound ratios, dosages, long-term safety, and structural outcomes. However, current evidence remains limited by small sample sizes, heterogeneous study designs, and a lack of long-term real-world data. Integrating combination neuroprotection into standard care holds promise for preserving vision and reducing the global burden of irreversible glaucoma-related blindness. Full article

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article

Tuanao, a traditional Northern Thai fermented soybean product, was profiled with an integrated multi-omics workflow to clarify how microbes and metabolites co-evolve during household fermentation. Soybeans were fermented spontaneously for three days; samples from four time points were analyzed by shotgun metagenomics alongside 1H-NMR and UHPLC-ESI-QTOF-MS/MS metabolomics. Bacillus spp. (phylum Bacilliota) quickly supplanted early Enterobacterales and dominated the mature microbiome. The rise of Bacillus coincided with genes for peptide and carbohydrate utilization and with the accumulation of acetate, free amino acids (glutamine, leucine, alanine, valine) and diverse oligopeptides, whereas citrate and glucose-1-phosphate were depleted. This Bacillus-linked metabolic shift indicates that Tuanao is a promising source of probiotics and bioactive compounds. Our study provides the first system-level view of Tuanao fermentation and offers molecular markers to guide starter-culture design and quality control. Full article

Although synthetic aperture radar (SAR) performance and polarimetric data quality are closely related, they represent fundamentally different concepts. This paper delineates their distinctions, investigates their interdependence, and introduces a comprehensive set of technical metrics for evaluating radar system performance and assessing polarimetric data quality. Specifically, radar performance is quantified by seven independent parameters, whereas data quality is characterized by a three-component channel imbalance vector and a twelve-element channel crosstalk matrix. The paper details the measurement methods for these parameters and outlines the associated technical requirements, including calibrator specifications and test-site conditions. To improve operational applicability, an approximate method for data quality assessment is proposed, and its associated errors are analyzed. Special attention is given to the $\gamma$ factor, which is highlighted as a critical and irreplaceable indicator of radar performance. Using field data from the GaoFen-3 (GF-3) satellite, the proposed metrics are applied to evaluate both radar performance and data quality. The results provide insights into the polarimetric characteristics of the system and offer practical guidance for the calibration and application of GF-3 polarimetric SAR data. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are marked by progressive network dysfunction that challenges conventional, protocol-based neurorehabilitation. In parallel, neuromodulation, encompassing deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and artificial intelligence (AI), has matured rapidly, offering complementary levers to tailor therapy in real time. This narrative review synthesizes current evidence at the intersection of AI and neuromodulation in neurorehabilitation, focusing on how data-driven models can personalize stimulation and improve functional outcomes. We conducted a targeted literature synthesis of peer-reviewed studies identified via PubMed, Embase, Scopus, and reference chaining, prioritizing recent clinical and translational reports on adaptive/closed-loop systems, predictive modeling, and biomarker-guided protocols. Across indications, convergent findings show that AI can optimize device programming, enable state-dependent stimulation, and support clinician decision-making through multimodal biomarkers derived from neural, kinematic, and behavioral signals. Key barriers include data quality and interoperability, model interpretability and safety, and ethical and regulatory oversight. Here we argue that AI-enhanced neuromodulation reframes neurorehabilitation from static dosing to adaptive, patient-specific care. Advancing this paradigm will require rigorous external validation, standardized reporting of control policies and artifacts, clinician-in-the-loop governance, and privacy-preserving analytics. Full article

Antimicrobial peptides (AMPs) are small-molecule polypeptides with broad-spectrum antibacterial and immunomodulatory properties. As feed additives, they have demonstrated synergistic effects in aquaculture by enhancing growth performance and maintaining host health. Its negligible drug resistance makes it an ideal additive to replace antibiotics in the “antibiotic-free breeding” system. Antimicrobial peptides were added to the basic diet of the crucian carp (Carassius auratus) to assess their impacts on growth, muscle quality, antioxidant capacity, immune function, and key gene expression in the TLR4/NF-κB signaling pathway. Crucian carp were fed with experimental diets containing antimicrobial peptides for 49 days, namely four treatments: L0 (0 g/kg), L1 (0.2 g/kg), L2 (0.4 g/kg), and L3 (0.6 g/kg), with three repetitions of each treatment. The findings indicated that AMPs had the potential to improve growth performance and muscle quality. The final weight, WGR, and SGR of crucian carp of group L1 significantly increased compared with groups L0 and L3 (p < 0.05). The condition factor of group L2 significantly increased compared with group L0(p < 0.05). The FCR of groups L0, L1, and L2 was significantly reduced compared with group L3 (p < 0.05). The muscle redness of group L1 was significantly higher compared with groups L0, L2, and L3 (p < 0.05). The muscle shear force of groups L0, L1, and L2 was significantly lower compared with group L3 (p < 0.05). The crude protein content of groups L0, L1, and L2 showed significantly higher crude protein content than group L3 (p < 0.05). Conversely, the crude fat content was significantly lower in groups L1, L2, and L3 compared with group L0 (p < 0.05). The superoxide dismutase (SOD) activity of group L1 was significantly higher compared with groups L0, L2, and L3 (p < 0.05). The catalase (CAT) activity of groups L0 and L1 was significantly increased compared with groups L2 and L3 (p < 0.05). The malondialdehyde (MDA) content of groups L1 and L2 was significantly reduced compared with groups L0 and L3 (p < 0.05). The acid phosphatase (ACP) activity of groups L1 and L2 was significantly increased compared with group L0 (p < 0.05). The alkaline phosphatase (AKP) activity of group L1 was significantly increased compared with groups L0 and L3 (p < 0.05). Compared with groups L2 and L3, the lysozyme activity of group L1 was significantly increased (p < 0.05). The C3 content of groups L1, L2, and L3 was significantly higher compared with group L0 (p < 0.05). Similarly, C4 levels of groups L2 and L3 significantly exceeded group L0 (p < 0.05). For inflammatory cytokines, the IL-1 levels of groups L1 and L2 were significantly higher than those of group L0 (p < 0.05). The IL-6 and IL-12 levels of groups L0, L1, and L2 significantly increased compared with group L3 (p < 0.05). Compared with group L0, the levels of TNF and IFN-γ of groups L1, L2, and L3 were significantly higher (p < 0.05). Compared with group L0, the relative expression levels and protein expression levels of key genes TLR4, MyD88, IRAK4, TRAF6, and NF-κB of groups L1, L2, and L3 were significantly upregulated (p < 0.05). In conclusion, supplementation with 0.2–0.4 g/kg antimicrobial peptides promoted the growth of crucian carp, improved muscle quality, enhanced the antioxidant capacity, and boosted immunity through modulation of the TLR4/NF-κB signaling pathway. Full article

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management. Full article