Search Results (198,268)

Background/Objectives: Aging is associated with declines in cognitive function and neurotrophic support. Brain-derived neurotrophic factor (BDNF) and insulin-like growth factor-1 (IGF-1) are peripheral biomarkers discussed in relation to brain health and aging. This study investigated changes in serum BDNF, IGF-1, and cognitive screening scores after a 16-week Hatha Yoga program performed twice or four times per week in older women. Methods: Fifty-one community-dwelling women aged 70–79 years were allocated to a twice-per-week yoga group (2YG; n = 17), a four-times-per-week yoga group (4YG; n = 17), or a non-exercise control group (CON; n = 17) based on availability and participant preference; forty-three participants completed the study. Serum BDNF and IGF-1 were analyzed using enzyme-linked immunosorbent assay and chemiluminescent immunoassay, and cognitive status was evaluated using the Cognitive Impairment Screening Test (CIST). Outcomes were analyzed using two-way repeated-measures ANOVA and additional ANCOVA models adjusting for corresponding baseline values. Exploratory correlations were examined between biomarker changes and CIST changes. Effect sizes and 95% confidence intervals were reported. Results: BDNF showed a significant main effect of time (p < 0.05) without a significant group × time interaction; ANCOVA adjusting for baseline BDNF showed no significant group effect (p = 0.270). IGF-1 showed a significant group × time interaction (p < 0.01) with increases in both yoga groups; ANCOVA adjusting for baseline IGF-1 showed a significant group effect (p = 0.001). CIST showed a significant main effect of time (p < 0.01), but changes were small and the group × time interaction was not significant; ANCOVA adjusting for baseline CIST showed no significant group effect (p = 0.114). Biomarker changes were not clearly correlated with CIST changes (ΔBDNF–ΔCIST: r = −0.244, p = 0.115; ΔIGF-1–ΔCIST: r = −0.050, p = 0.750). Conclusions: In this quasi-experimental study with non-random allocation and limited covariate information, changes in peripheral neurotrophic factors and only small changes in cognitive screening scores were observed after participation in a 16-week Hatha Yoga program. However, frequency-dependent conclusions are limited, and findings should be interpreted cautiously as screening-level, hypothesis-generating reference data. Nevertheless, the program is considered a feasible, low-risk health promotion activity for older women and may inform future randomized or well-controlled studies. Full article

The present study aimed to investigate the effects of polyvinyl chloride microplastics with different sizes on the growth, intestinal and hepatic health of turbot (Scophthalmus maximus L.) at 3 and 9 weeks of exposure. Three diets were formulated: a control diet with no microplastics, a diet containing 2% micrometer-sized plastics (MPs), and a diet containing 2% nanoplastics (NPs), with four replicates (40 fish/tank, 12 tanks total). The results showed that MPs and NPs had no significant effects on the growth performance of turbot. Analyses of intestinal histology and gene expression (intestinal barrier-related and antioxidant-related genes) indicated that the turbot intestine exhibited a certain degree of tolerance and adaptability to MPs and NPs exposure. Observations of liver histology and analyses of gene expression (inflammatory cytokines, apoptosis-related, and antioxidant-related genes) revealed that the liver damage induced by microplastics in turbot exhibited obvious size-dependent and time-cumulative effects, with NPs exerting a stronger impact. Compared with MPs, long-term exposure to NPs can induce obvious intestinal microbiota dysbiosis in turbot. In summary, particle size and exposure duration are important factors regulating the impacts of PVC microplastics on the intestinal and hepatic health of turbot. Full article

Background: Parents serve as the primary decision-makers for childhood vaccination while also making decisions regarding their own vaccination, yet vaccination decision drivers are typically studied separately by vaccine type or target population. Methods: This study investigated parental decision-making processes for two self-paid and non-National Immunization Program vaccines in China, childhood rotavirus vaccine and adult influenza vaccine, by utilizing a structured survey grounded in the World Health Organization Behavioral and Social Drivers of Vaccination framework. Spearman’s rank correlation coefficients were used to assess the consistency of parental attitudes toward the two vaccines across behavioral and social driver domains. Structural equation models were conducted separately for childhood and adult vaccines to examine decision-making pathways. Results: The findings indicated that parental drivers related to awareness, social processes, and practical issues showed a high consistency across adult and childhood vaccination decisions (r > 0.7), whereas the consistency in vaccination behaviors remained low (r = 0.21). Compared with adult vaccination, childhood vaccination decisions were more strongly influenced by vaccine safety concerns and healthcare practitioners’ recommendations, which emerged as key drivers. Furthermore, family norms emerged as an effectively shared driver of vaccination decisions for both adult and childhood vaccines (adult: β = 0.784; childhood: β = 0.970). Conclusions: By jointly synthesizing adult and childhood vaccination decisions from a parental perspective, this study provides crucial evidence to support the development of integrated, family-centered strategies to improve vaccine uptake. Full article

Glioblastomas represent the most aggressive and lethal form of primary brain cancer and continue to pose a major challenge to global health. MicroRNAs (miRNAs), as central regulators of gene expression, are intimately involved in the initiation, progression, and therapeutic resistance of numerous malignancies, including glioblastoma. Therefore, this class of non-coding RNAs are considered to be valuable candidates for innovative therapeutic strategies. However, despite many promising preclinical studies, miRNA-based therapies have yet to be translated into routine clinical practice. In the context of glioblastoma, one of the principal obstacles to the effective delivery of synthetic miRNA therapy is their limited ability to cross the blood–brain barrier (BBB). To address this challenge, a variety of locoregional delivery strategies have been developed in recent years. In this review, we provide a detailed discussion and a state-of-the-art overview of these local delivery methods in the context of glioblastoma treatment, with a specific emphasis on their application for delivering miRNA-based therapeutic oligonucleotides, formulated either with or without synthetic nanoparticles. Full article

The glass industry contributes to long-term soil contamination. This study assesses the impact of over 150 years of industrial activity and over a century of glassmaking processes in the Serbian Glass Factory in Paraćin. Focusing on potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs), ecological and human health risks were evaluated. Sampling was conducted at 34 locations within the factory area, including 33 soil samples (0–30 cm) and one industrial waste (IW) sample. Soil analyses indicate that Zn, Ni, Cu, and Cd exceeded both the maximum permissible concentrations (MPCs) and remediation values (RVs) in many samples, while As and Hg showed fewer exceedances. Based on the Potential Ecological Risk Index (RI), about 33% of soil samples were moderately to highly polluted, and Cd, Pb, As, and Hg were identified as the main contributors. High levels of PAHs and PTEs reflect the cumulative impact of long-term industrial operations, a historical fire, and secondary sources, including traffic-related emissions from nearby highways. These factors resulted in elevated total carcinogenic risk (TCR) for Ni, Cr, and As. This study highlights soil contamination and associated health risks at the glass factory, emphasizing the need for environmental monitoring and management. Full article

Sensitivity and effective sensing range are core performance metrics of flexible pressure sensors, directly dictating their practical applicability. A key challenge in sensor design is sensitivity degradation with elevated pressure, hindering synergistic optimization of high sensitivity and broad sensing range, while cumbersome electrode fabrication further impedes facile preparation and large-scale deployment of high-performance devices. Herein, this work proposes a novel fabrication strategy for flexible iontronic pressure sensors via direct laser writing (DLW) technology. A controllable ultraviolet laser patterns polyimide substrates to fabricate hierarchical stepped conoid-like microstructural templates, which are transferred to ion gels through reverse molding. The DLW-enabled precise geometric control and hierarchical conical architectures efficiently amplify interfacial contact area variation under pressure, significantly boosting sensitivity. The resultant sensor achieves a high sensitivity of 118.4 kPa⁻¹ and a broad detection range up to 2000 kPa, with fast response/recovery times of 38.4 ms and 47 ms and excellent mechanical stability enduring 2000 loading–unloading cycles at 850 kPa. Multi-scenario physiological signal monitoring validates its accurate capture of laryngeal vibrations and joint movements. This work establishes a straightforward, efficient microfabrication route for high-performance flexible iontronic sensors, accelerating their practical application in wearable health monitoring and related fields. Full article

Efficient crop health monitoring is crucial for global food security. Supervised deep learning approaches are often impractical due to the scarcity of large, labeled datasets. To address this limitation, this study adapts EfficientAD, an unsupervised, label-free anomaly detection framework originally designed for industrial inspection, for agricultural imagery on small datasets. The method utilizes a Patch Description Network (PDN) for localized feature extraction, a student network for local anomalies, and an autoencoder for global structural constraints. Benchmarked against AnoGAN, Pix2Pix, InTra, and Teacher–Student models, the framework demonstrated superior performance on the MVTec AD, PlantVillage, Coffee Leaf, and a custom real-world Sweet Potato dataset. The model achieved perfect area under the receiver operating characteristic curve (AUROC) scores of up to 100% in categories like “Pongamia”, “Potato”, and “Coffee Leaf”. While image-level classification was exceptionally robust, pixel-level localization (AUPRO) proved sensitive to complex agricultural backgrounds. To overcome this, a background interference analysis was conducted using Background Removed (BGRM) and out-of-distribution Background Replaced-Green (BGRP-G) strategies on the custom dataset. Notably, the BGRP-G strategy remarkably improved the image-level AUROC from 88.9% to 99.5% and substantially boosted the pixel-level AUPRO from 47.1% to 61.9%, successfully preserving the boundary integrity of severe structural defects. Achieving millisecond-level latency without complex data augmentation, this adapted label-free framework offers a versatile, highly efficient solution for real-time crop health diagnostics on resource-constrained Edge AI devices. Full article

Nepetalactone (NL) is a volatile iridoid monoterpene widely used in biopesticidal and repellent applications, yet its toxicokinetic behavior and metabolic fate as a pure compound remain poorly characterized. This study aimed to provide an integrated toxicokinetic evaluation of NL by combining in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) modeling with in vitro metabolism assays using rat and human liver microsomes, supported by UHPLC–MS/MS analysis for metabolite identification. The in silico biotransformation predicted extensive phase I oxidation followed by phase II conjugation, while ADMET predictions indicated low systemic persistence and limited toxicological concern for most metabolites. The performed in vitro microsomal assays confirmed the in silico prediction by a rapid and time-dependent NL metabolism via both oxidative (86% reduction in NL concentration after 120 min) and conjugative (89% reduction in NL concentration after 120 min) pathways in rat and human systems, with comparable depletion kinetics between species. UHPLC–MS/MS enabled the identification of multiple phase I and phase II metabolites, pointing to pronounced interspecies differences in conjugative metabolism. In this sense, while oxidoreduction and hydrolysis reactions were consistent with previously reported iridoid metabolism. This study suggests the possible formation of previously unreported amino acid-related derivatives, although these require further confirmation. Overall, these findings advance the understanding of NL biotransformation, propose a new, previously unknown, metabolic pathway for iridoids, and provide relevant data to support human health and environmental risk assessment frameworks. Full article

Nile tilapia (Oreochromis niloticus) is a major aquaculture species worldwide, yet bacterial infections remain a critical constraint to production sustainability. Although pathogen-associated dysbiosis has been widely described, most studies have focused exclusively on bacterial communities, overlooking the multi-kingdom nature of the intestinal microbiota. This study evaluated the impact of experimental Providencia vermicola infection on both prokaryotic and microeukaryotic intestinal communities under controlled conditions. Using 16S (V3–V4) and 18S (V9) rRNA amplicon sequencing, we compared healthy and infected fish and assessed taxonomic, structural, and predicted functional changes. Infection was associated with significant compositional shifts, including increased relative abundances of Bacteroidota and Proteobacteria and decreased relative abundances of Fusobacteriota and Patescibacteria. Concomitantly, microeukaryotic groups such as Protalveolata, Nematozoa, and Phragmoplastophyta were significantly reduced. Functional prediction revealed metabolic pathway reconfiguration consistent with infection-associated ecological disturbance. Together, these results suggest that the pathogen challenge is associated with coordinated changes in the intestinal microbiota as an integrated system across multiple microbial kingdoms rather than as isolated bacterial shifts. This study supports ecosystem-level interpretations of dysbiosis and highlights the importance of incorporating cross-domain analyses into health assessment strategies in aquaculture species. Full article

Background/Objectives: The use of physical and mechanical restraints and seclusion in psychiatric facilities to manage violent and aggressive behaviours has long been a contentious issue, balancing patient safety with ethical considerations. With advancements in psychiatry and increased understanding of mental illness, there have been expectations that such interventions would no longer be required; however, their use persists in clinical practice. Management policies differ across countries and are largely influenced by legal frameworks. This study aimed to identify the factors influencing the incidence of these interventions across two psychiatric facilities in Saudi Arabia and to examine associations among inpatient variables. Methods: A prospective cohort study was conducted over six months (September 2021–March 2022) across two psychiatric facilities in Saudi Arabia (Eradah Complex, n = 1120; King Fahd University Hospital (KFUH), n = 268). Data from 333 restriction events were analysed using descriptive statistics, chi-square tests, and negative binomial regression to calculate incidence rates and explore associated factors. Results: The findings revealed a complex interplay of factors related to patient characteristics and clinical and environmental conditions within the facilities. Key contributing variables included symptom deterioration and the duration of observation required. Longer observation periods were associated with certain diagnoses, particularly schizophrenia and mood disorders. Conclusions: Restraints and seclusion remain influenced by multiple interacting factors within psychiatric settings. These findings highlight the need to reduce their use and ensure they are applied cautiously, with greater emphasis on minimising patient trauma and promoting safer, person-centred care. Full article

Individuals in the southeastern United States of America (USA) have an increasing risk of contracting a tick-borne disease. Land use changes, changing climate, and redistribution of both ticks and their hosts make systematic tick and tick-borne pathogen investigation crucial for public health protection. Prior to 2020, South Carolina had limited data on tick species distribution and tick infection rates. In this work, we describe establishment of a sustainable tick and tick-borne pathogen collaborative network for South Carolina. A major determinant of program success was sharing work effort between the University of South Carolina, the South Carolina Department of Public Health, and key partners including state park employees, local veterinarians, students, and volunteers. The program collected questing ticks from public lands and host-attached ticks from animal shelters. Amblyomma americanum was the most commonly collected tick, with highest density in South Carolina’s southern coastal region. A greater tick species diversity was seen in animal shelter collected versus questing ticks. Pathogen testing results yielded a high presence of Rickettsia amblyommatis among Am. americanum ticks with several other Rickettsia spp. detected including Rickettsia parkeri, Candidatus R. andeanae, R. montanensis, and R. asembonensis. Additional Rickettsiales detected included multiple Ehrlichia and Anaplasma species, with higher presence in the state’s northern region. Borrelia burgdorferi sensu stricto was detected in one questing Ixodes keiransi from the southern coastal region. The current report presents the initial steps for pathogen and tick species surveillance in South Carolina, providing successes and pitfalls as a model for other states and regions to establish similar efforts to improve national tick surveillance. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article