Search Results (109,680)

National parks are key tools for safeguarding ecosystem health, yet their conservation performance remains unclear. Comprehensive evaluations are crucial for guiding targeted and effective conservation strategies. This study employed the Vigor–Service–Resilience (VSR) framework together with causal inference models to assess the role of Huangshan National Park (HNP) in promoting ecosystem health and to examine the heterogeneity of its ecological outcomes from 2010 to 2020. The results indicate that (1) ecosystem health improved significantly across the region, with 69.5% of pixels showing positive change, particularly in ecosystem services and vigor; (2) compared with matched unprotected sites, HNP enhanced EH by 5.7% in 2010, 3.4% in 2015, and 6.5% in 2020, and also generating positive spillover effects within 30 km of its boundaries; (3) conservation impacts differed notably across socio-ecological conditions, with greater benefits observed in areas of lower elevation, gentle slopes, and reduced precipitation. These findings provide robust causal evidence of the protective value of HNP and underscore the importance of targeted and cost-efficient management strategies to optimize conservation outcomes and support sustainable regional development. Full article

The early detection of diabetes risk and effective management of patient data are critical for avoiding serious consequences and improving treatment success. This research describes a two-part architecture that combines an energy-efficient wireless communication technology with an interpretable deep learning model for diabetes categorization. In Phase 1, a unique wireless communication model is created to assure the accurate transfer of real-time patient data from wearable devices to medical centers. Using Lagrange optimization, the model identifies the best transmission distance and power needs, lowering energy usage while preserving communication dependability. This contribution is especially essential since effective data transport is a necessary condition for continuous monitoring in large-scale healthcare systems. In Phase 2, the transmitted multimodal clinical, genetic, and lifestyle data are evaluated using a one-dimensional Convolutional Neural Network (1D-CNN) with Bayesian hyperparameter tuning. The model beat traditional deep learning architectures like LSTM and GRU. To improve interpretability and clinical acceptance, SHAP and LIME were used to find global and patient-specific predictors. This approach tackles technological and medicinal difficulties by integrating energy-efficient wireless communication with interpretable predictive modeling. The system ensures dependable data transfer, strong predictive performance, and transparent decision support, boosting trust in AI-assisted healthcare and enabling individualized diabetes control. Full article

Particulate matter (PM) exposure is linked to chronic kidney disease; however, its effect on immunoglobulin A (IgA) nephropathy (IgAN) remains unclear. We investigated whether PM exposure exacerbates IgAN in a mouse model. HIGA mice (IgAN model) and BALB/c controls were exposed to PM in a sealed chamber for 13 weeks. Lung Toll-like receptor 9 (TLR9) expression, serum aberrantly glycosylated IgA, A proliferation-inducing ligand (APRIL) levels, mesangial IgA deposition, and kidney pathology were assessed. RNA sequencing of splenic B cells was performed to evaluate immune-related gene expression. PM exposure increased lung TLR9 expression in both strains, particularly around pigment-laden macrophages. HIGA mice showed elevated aberrant IgA and APRIL levels, with aggravated mesangial expansion and IgA deposition. Transcriptomic analysis revealed immune dysregulation in splenic B cells of PM-exposed HIGA mice. Our findings provide experimental evidence that PM exposure aggravates IgAN via TLR9-mediated mucosal immune activation, leading to aberrant IgA glycosylation and mesangial deposition. These findings emphasize that reducing PM exposure may benefit patients with IgAN. Full article

Background: Human immunodeficiency virus and hypertension increasingly co-occur in South Africa. Despite publicly funded care, patients with multimorbidity face high out-of-pocket costs, yet limited evidence exists from the patient perspective. Purpose: To quantify the economic burden of comorbid HIV and hypertension, assess predictors of monthly out-of-pocket costs, and explore coping mechanisms. Methods: We conducted a cross-sectional analysis using patient-level data from the Mobile Phone Text Messages to Improve Hypertension Medication Adherence in Adults with HIV (MOPHADHIV trial) [Trial number: PACTR201811878799717], a randomized controlled trial evaluating short messages services adherence support for hypertension care in people with HIV. We calculated the monthly direct non-medical, indirect, and coping costs from a patient perspective, valuing indirect costs using both actual income and minimum wage assumptions. Generalized linear models with a gamma distribution and log link were used to identify cost determinants. Catastrophic expenditure thresholds (10–40% of monthly income) were assessed. Results: Among 683 participants, mean monthly total costs were ZAR 105.81 (USD 5.72) using actual income and ZAR 182.3 (USD 9.9) when valuing indirect costs by minimum wage. These time-related productivity losses constituted the largest share of overall expenses. Regression models revealed a strong income gradient: participants in the richest quintile incurred ZAR 131.9 (95% CI: 63.6–200.1) more per month than the poorest. However, this gradient diminished or reversed under standardized wage assumptions, suggesting a heavier proportional burden on middle-income groups. Other socio-demographic factors (gender, employment, education) not significantly associated with total costs, likely reflecting the broad reach of South Africa’s primary health system. Nearly half of the participants also reported resorting to coping mechanisms such as borrowing or asset sales. Conclusions: Comorbid HIV and hypertension impose substantial patient costs, predominantly indirect. Income disparities drive variation, raising equity concerns. Strengthening integrated human immunodeficiency virus—non-communicable diseases care and targeting financial support are key to advancing South Africa’s Universal Health Coverage reforms. Full article

Background/Objectives: Natural products exhibit significant immunomodulatory potential but face severe efficacy loss in three-dimensional (3D) tumor models. This review comprehensively examines the penetration–activity trade-off and proposes integrated strategies for developing effective natural product-based cancer immunotherapies. Methods: We analyzed formulation strategies across three natural product categories (hydrophobic, macromolecular, stability-sensitive), evaluating penetration enhancement versus activity preservation in spheroids, organoids, and advanced 3D platforms. Results: Tumor spheroids present formidable barriers: dense extracellular matrix (33-fold increased fibronectin), pH gradients (7.4 → 6.5), and extreme cell density (6 × 10⁷ cells/cm³). While nanoparticles, liposomes, and cyclodextrins achieve 3–20-fold penetration improvements, biological activity frequently declines through conformational changes, incomplete release (10–75%), and surface modification interference. Critically, immune cells remain peripheral (30–50 μm), questioning deep penetration pursuit. Patient-derived organoids display 68% predictive accuracy, while emerging vascularized models unveil additional complexity. Food and Drug Administration (FDA) Modernization Act 2.0 enables regulatory acceptance of these advanced models. Conclusions: Effective therapeutic outcomes depend on maintaining immunomodulatory activity in peripherally-located immune cell populations rather than achieving maximum tissue penetration depth. Our five-stage evaluation framework and standardization protocols guide development. Future priorities include artificial intelligence-driven optimization, personalized formulation strategies, and integration of multi-organ platforms to bridge the critical gap between enhanced delivery and therapeutic efficacy. Full article

Ischemic stroke remains a leading cause of mortality and disability worldwide, with current therapies such as intravenous thrombolysis and mechanical thrombectomy benefiting only a limited proportion of patients. Neuroinflammation is a key contributor to secondary brain injury, creating a strong rationale for adjunctive therapies targeting immune modulation. Fingolimod, a sphingosine-1-phosphate receptor (S1PR) modulator originally approved for multiple sclerosis, has shown promising effects in both preclinical and early clinical studies of acute ischemic stroke. Methods: We conducted a structured narrative review of preclinical and clinical studies published between 2015 and 2024, using PubMed, Scopus, and Web of Science databases. Inclusion criteria were original studies evaluating fingolimod in ischemic stroke models or human patients, either as monotherapy or in combination with reperfusion therapies. Exclusion criteria included conference abstracts without peer review, studies lacking mechanistic insight, and non-English publications. Results: Preclinical evidence demonstrates that fingolimod reduces infarct size, preserves blood–brain barrier integrity, and modulates neuroinflammation through multiple mechanisms, including T cell sequestration, microglial polarization, and mitochondrial protection. Clinical trials, though limited in size, suggest improved short- and long-term outcomes when fingolimod is used in combination with intravenous thrombolysis or endovascular therapy, with a manageable safety profile. Novel nanotechnology-based delivery systems further enhance central nervous system (CNS) targeting and reduce systemic side effects. Conclusions: Fingolimod represents a promising multi-targeted adjunctive strategy for ischemic stroke, acting at the intersection of immune modulation, vascular protection, and neuroprotection. While current findings are encouraging, larger randomized controlled trials and biomarker-driven patient selection are needed to validate its clinical utility. This review highlights the translational potential of fingolimod and outlines key directions for future research. Full article

Background/Objectives: The rise in methicillin-resistant Staphylococcus aureus (MRSA) demands new therapeutic strategies. In this study, a series of 2-(amino)quinazolin-4(3H)-one derivatives were synthesized and evaluated for antistaphylococcal activity. Methods/Results: Through screening against S. aureus ATCC25923 and USA300 JE2, several submicromolar inhibitors were identified. Among them, compound 6l, which contains a 7-chloro substituent on the key parental scaffold, exhibited strong overall antibacterial activity (MIC₅₀: 1.0 µM, ATCC25923; 0.6 µM, JE2) and served as a lead for further structural optimization. Structure–activity relationship analysis showed that substitution at the 2-position was critical, with its optimized analog 6y (3,4-difluorobenzylamine) exhibiting the highest potency (MIC₅₀: 0.36 µM, ATCC25923; 0.02 µM, JE2). Cytotoxicity assays in HepG2 cells revealed six compounds with IC₅₀ values above 20 µM, yielding efficacy windows greater than 10. Compound 6y exhibited an exceptional index (~885). Consistently, in an H460 lung epithelial infection model mimicking MRSA pneumonia, 6y significantly reduced intracellular bacterial loads with minimal host cell damage, outperforming comparator compounds. Conclusions: These findings highlight 2-(amino)quinazolin-4(3H)-one derivatives, particularly 6y, as promising leads for the development of new antistaphylococcal agents. Full article

This research investigates the statistical correlation between Vickers hardness and tensile properties of helical submerged arc welded high-strength low-alloy (HSLA) API X70 pipeline steel. Tensile tests were performed on cross-weld joints from 138 pipe specimens. Vickers hardness measurements were also conducted on 138 samples to evaluate the hardness distribution across the base metal, fusion zone, and heat-affected zone. Results show that the fusion zone exhibits the highest hardness, correlating with enhanced tensile strength (R² = 82%). Linear regression models indicate that base metal hardness significantly influences yield strength (R² = 71%), while moderate negative correlations exist with elongation (R² = 54%). These findings suggest that hardness measurements can serve as a non-destructive predictive tool for tensile properties, improving weld quality and mechanical performance. This research provides empirical models that enhance the application of API X70 in critical engineering applications, improving pipeline safety and reliability. Full article

Mpox has re-emerged as a global public health threat, particularly in endemic regions such as Nigeria, where human, animal, and environmental health sectors intersect. To inform surveillance and control strategies, this study assessed the knowledge, attitudes, and practices (KAP) toward Mpox among One Health stakeholders in Nigeria. A cross-sectional survey was conducted among 492 participants from human, veterinary, and environmental health sectors using a structured questionnaire. Descriptive statistics, ordinal logistic regression, and margins analysis were used to evaluate levels and predictors of KAP. Results showed that 33.7% of respondents had low knowledge, 43.5% moderate, and 22.8% high. While 62.6% demonstrated high attitude scores, only 48.2% reported moderate preventive practices. Gender was significantly associated with attitudes, with females having lower odds of expressing higher attitudes than males (OR = 0.70, 95% CI: 0.49–1.00, p = 0.052), and margins analysis revealed a predicted probability of high attitude at 56% for females and 64% for males. Multivariable modeling for practice was not pursued because model fit did not improve compared to univariable results, and sparse data led to unstable estimates, thus offering no added explanatory power. These findings underscore persistent knowledge gaps and gender-related disparities that may hinder effective Mpox response. Targeted risk communication and capacity building are recommended to strengthen One Health preparedness in Nigeria. Full article

In recent years, the proliferation of forged videos, known as deepfakes, has escalated significantly, primarily due to advancements in technologies such as Generative Adversarial Networks (GANs), diffusion models, and Vision Language Models (VLMs). These deepfakes present substantial risks, threatening political stability, facilitating celebrity impersonation, and enabling tampering with evidence. As the sophistication of deepfake technology increases, detecting these manipulated videos becomes increasingly challenging. Most of the existing deepfake detection methods use Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Vision Transformers (ViTs), achieving strong accuracy but exhibiting high computational demands. This highlights the need for a lightweight yet effective pipeline for real-time and resource-limited scenarios. This study introduces a lightweight deep learning model for deepfake detection in order to address this emerging threat. The model incorporates three integral components: MobileNet for feature extraction, a Convolutional Block Attention Module (CBAM) for feature enhancement, and a Gated Recurrent Unit (GRU) for temporal analysis. Additionally, a pre-trained Multi-Task Cascaded Convolutional Network (MTCNN) is utilized for face detection and cropping. The model is evaluated using the Deepfake Detection Challenge (DFDC) and Celeb-DF v2 datasets, demonstrating impressive performance, with 98.2% accuracy and a 99.0% F1-score on Celeb-DF v2 and 95.0% accuracy and a 97.2% F1-score on DFDC, achieving a commendable balance between simplicity and effectiveness. Full article

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

Background: The Gender Variance Scale (GVS) was developed to assess self-perceived masculinity and femininity across diverse gender identities, including binary and non-binary experiences. To date, no validated Italian version was available. Methods: A total of 356 participants (192 transgender and gender-diverse [TGD], 164 cisgender) completed the Italian GVS and the SF-12 Health Survey. Translation and cultural adaptation followed international guidelines. Psychometric evaluation included confirmatory factor analysis (CFA), internal consistency, test–retest reliability (n = 63), convergent validity with health-related quality of life, and group comparisons across gender identity categories. Results: CFA supported the original two-factor model (CFI = 0.916, TLI = 0.905, RMSEA = 0.076, SRMR = 0.053). Internal consistency was high (α = 0.89). The GVS distinguished between gender identity groups: TGD participants scored higher than cisgender peers, and non-binary individuals reported significantly lower scores than both binary groups. Test–retest reliability was strong (r = 0.87–0.99; ICC = 0.992–0.996). Conclusions: The Italian GVS is a valid and reliable measure of gender variance. It provides clinicians, researchers, and educators with a culturally appropriate tool to assess gender expression and support inclusive practices in both community and clinical contexts. Full article

Schatzker type V bicondylar tibial plateau fractures present a major challenge due to the difficulty of achieving stable fixation with minimally invasive strategies. This study introduces a dual-thread lag and locking plate (DLLP) design that integrates lag screw compression with unilateral locking plate fixation. A custom-built compression evaluation platform and standardized 3D-printed fracture models were employed to assess biomechanical performance. DLLP produced measurable interfragmentary compression during screw insertion, with a mean displacement of 1.22 ± 0.11 mm compared with 0.02 ± 0.04 mm for conventional single lateral locking plates (SLLPs) (p < 0.05). In static testing, DLLP demonstrated a significantly greater maximum failure force (7801.51 ± 358.95 N) than SLLP (6224.84 ± 411.20 N, p < 0.05) and improved resistance to lateral displacement at 2 mm (3394.85 ± 392.81 N vs. 2766.36 ± 64.51 N, p = 0.03). Under dynamic fatigue loading simulating one year of functional use, all DLLP constructs survived 1 million cycles with <2 mm displacement, while all SLLP constructs failed prematurely (mean fatigue life: 408,679 ± 128,286 cycles). These findings highlight the critical role of lag screw compression in maintaining fracture stability and demonstrate that DLLP provides superior biomechanical performance compared with SLLP, supporting its potential as a less invasive alternative to dual plating in the treatment of complex tibial plateau fractures. Full article

Transient receptor potential canonical channel type 6 (TRPC6), a non-selective cation channel that mediates Ca²⁺ influx, is expressed in the heart and implicated in pathological cardiac hypertrophy. However, the role of TRPC6 in regulating cardiac mitochondrial metabolism and contributing to development of HFpEF remains unclear. We examined whether TRPC6 deficiency prevents mitochondrial dysfunction and offers cardiac protection in a mouse model of HFpEF induced by high-fat diet (HFD) for 12 weeks combined with L-NAME administration during the final 8 weeks in TRPC6 knockout (KO) and wild-type (WT) control mice. Cardiac systolic and diastolic functions were assessed at baseline, 4 and 8 weeks after HFD+L-NAME. Dobutamine-induced stress test and treadmill exercise test were performed at the end of the protocol to evaluate cardiac reserve capacity and exercise tolerance. Mitochondrial oxygen consumption rate (OCR) and mitochondrial-derived reactive oxygen species (ROS) generation were examined in isolated cardiac fibers. WT mice subjected to HFD+L-NAME developed cardiac hypertrophy, diastolic dysfunction, and exercise intolerance, whereas TRPC6 KO mice, under the same conditions, maintained preserved diastolic function, exercise tolerance, and cardiac reserve. We also observed increased TRPC6 in mitochondria, as well as caspase-9 activation and impaired mitochondrial respiration in WT mice. In contrast, TRPC6 KO mice exhibited preserved mitochondrial OCR and attenuated mitochondrial ROS generation. In summary, TRPC6 deficiency prevents the development of HFpEF by mitigating diastolic dysfunction, preserving cardiac reserve capacity, and attenuating mitochondrial dysfunction. Full article