Search Results (28,139)

Climate change has intensified issues that undermine children’s health, compromise their well-being, and hinder their ability to develop disaster resilience. Disaster education is essential for building disaster resilience among children. As a disaster-prone country, Japan has been developing new approaches to improve disaster education programs, including those in early childhood education, to equip children with the knowledge and skills needed to mitigate risks and respond effectively to disasters. Basic disaster concepts are introduced through hands-on learning, helping children understand key ideas. This paper examines innovations in disaster education, particularly at the kindergarten level, using Bousai Terakoya as a case study. The study reveals that Bousai Terakoya fosters collaboration among schools, communities, and industries to educate kindergarten students about disasters. It emphasizes that protecting oneself and one’s family is a focus of the program, which aims to strengthen future disaster education efforts. This research adds to the discussion on disaster education for children. Active involvement from schools, communities, and industries can help develop strategies to improve the retention of essential disaster concepts in children’s memories. Full article

(1) Background: The escalating crisis of multidrug-resistant (MDR) bacteria presents a formidable threat to global public health, necessitating the urgent development of alternative antimicrobial strategies. Nanoparticles (NPs) have emerged as a promising frontier in this effort, leveraging their unique physicochemical properties and multi-modal mechanisms of action to combat bacterial infections. This systematic review aims to comprehensively evaluate the current body of evidence on the dynamic interplay between nanoparticles and bacterial resistance. (2) Methods: A comprehensive search of electronic databases, including PubMed, Scopus, and Web of Science, was performed using a combination of keywords and Medical Subject Headings (MeSH) terms to identify relevant primary research articles. Eligibility criteria focused on studies evaluating the antimicrobial effects of nanoparticles on MDR bacterial strains, reporting on mechanisms of action, efficacy, or resistance development. (3) Results: The synthesis of findings revealed that nanoparticles exert their antimicrobial effects through multiple pathways, including the generation of reactive oxygen species (ROS), direct disruption of bacterial membranes, and the release of toxic ions. However, the analysis also confirmed that bacteria have evolved sophisticated defense mechanisms against nanoparticles, including surface modifications that prevent adhesion, upregulation of efflux pumps, and chemical neutralization of toxic ions. (4) Conclusions: Nanoparticles represent a potent and versatile tool in the global effort to combat antimicrobial resistance. Their long-term efficacy is not guaranteed, as bacteria have shown a remarkable capacity for adaptation. The future of this field lies in the development of rationally designed nanoparticle systems that not only possess intrinsic antimicrobial activity but also actively disarm bacterial resistance mechanisms. This includes the strategic use of synergistic combinations with conventional antibiotics and the exploration of resistance-agnostic approaches like nanotoxoid vaccines. Full article

The Australian health care system continues to struggle to meet the needs of people experiencing multiple complex chronic conditions. Australians who report poorer health continue to report poorer access to health care. Inequities in access are attributed to a “mistmatch” between the health care system and individuals’ clinical and social needs. To address this misalignment at the interface of access, innovative approaches that consider both individual and system-level barriers to care need to be examined. Nurse navigation models designed to support people negotiating complex care and bridge systems and service gaps have been touted as a method to enhance access, but how nurse navigators work at the interface of access in practice is unclear. This qualitative study examined the mechanisms by which nurse navigators facilitate access to care for people experiencing complex care needs through an exploration of key stakeholder perspectives: nurse navigators, nurse navigator patients, and care professionals. Data collection involved in-depth semi-structured interviews, and analysis included reflexive thematic analysis and data triangulation processes. A conceptual framework of access to health care was used to explore nurse navigators’ roles at both system and patient levels. Nurse navigators supported both patients and care professionals by building relationships across the interface of access, challenging norms of care, and facilitating empowerment. Nurse navigators acted as intermediaries to negotiate access, work made possible through their knowledge of systems and capacity to identify and respond to multidimensional care needs and systems challenges. This research highlights the importance of holistic and relational approaches to overcome issues of access for all involved. Full article

Accurate inflation forecasting is of central importance for monetary authorities, governments, and businesses, as it shapes economic decisions and policy responses. While most studies focus on headline inflation, this paper analyses the Harmonised Index of Consumer Prices (HICP) and its 12 subcomponents in the euro area over the period 2000–2023, covering episodes of financial crisis, economic stability, and recent inflationary shocks. We apply a broad set of machine learning and deep learning models, systematically optimized through grid search, and evaluate their performance using the Normalized Mean Absolute Error (NMAE). To complement traditional accuracy measures, we introduce the Forecastability Index (FI) and the Interquartile Range (IQR), which jointly capture both the difficulty and robustness of forecasts. Our results show that RNN and LSTM architectures consistently outperform traditional approaches such as SVR and RFR, particularly in volatile environments. Subcomponents such as Health and Education proved easier to forecast, while Recreation and culture and Restaurants and hotels were among the most challenging. The findings demonstrate that macroeconomic stability enhances forecasting accuracy, whereas crises amplify errors and inter-model dispersion. By highlighting the heterogeneous predictability of inflation subcomponents, this study provides novel insights with strong policy relevance, showing which categories can be forecast with greater confidence and where uncertainty requires more cautious intervention. Full article

Facility agriculture is essential for modernizing the production of horticultural plants, while long-standing over-fertilization and improper tillage in some vegetable facilities in northern China have resulted in reduced soil quality, increased greenhouse gas (GHG) emissions, and diminished vegetable yields and quality. This study systematically analyzed the deteriorating health of typical cucumber facility soils in Hebei Province, China, induced by long-term over-fertilization. Based on field surveys, we explored dynamic changes in soil physicochemical properties across different durations of over-fertilization. Subsequently, a series of field trials were conducted to assess whether reducing nitrogen application, either alone or when combined with microbial agents, could ameliorate soil properties, reduce greenhouse gas emissions, and enhance cucumber productivity. The initial field assessment revealed severe topsoil salt and nutrient accumulation, with water-soluble salt content in 5-year-old greenhouses from Yongqing soaring to 3.82 g·kg⁻¹, nearly eight times the level found in 1-year-old plots. Field experiments demonstrated that a 20% reduction in nitrogen application from the conventional rate of 900 kg·hm⁻² effectively mitigated salt accumulation, improved the structure of the microbial community, and maintained cucumber yield at 66,914 kg·hm⁻², an output comparable to conventional practices. More notably, integrating this 20% nitrogen reduction with an inoculation of Bacillus megaterium reduced the overall global warming potential by 26.7% and simultaneously increased cucumber yield to 72,747 kg·hm⁻². The most comprehensive strategy combined deep tillage, soybean straw incorporation, and B. megaterium application under reduced nitrogen, which boosted nitrogen use efficiency by 13.7% and achieved the highest yield among all treatments. In conclusion, our findings demonstrate that a combined approach of nitrogen reduction, microbial amendment, and straw application offers an effective strategy to restore soil health, enhance crop productivity, and mitigate environmental impacts in protective vegetable production systems. Full article

Traffic accidents remain a major global burden, causing mortality, disability, and socio-economic losses that hinder sustainable development. Beyond human suffering, crashes place long-term pressures on health systems, labor markets, and national economies, disproportionately impacting low- and middle-income countries. Estimating the true societal costs of accidents is therefore essential for designing effective, equitable, and sustainable road safety policies. This study applies the Willingness-to-Pay (WTP) method to evaluate the external costs of traffic-related deaths and injuries in Türkiye between 2008 and 2018. By incorporating material and immaterial losses, the WTP framework captures a broader spectrum of impacts than traditional approaches, offering valuable insights into the scale of welfare losses and the value of risk reduction. The findings reveal that external costs rose substantially over the decade, from 1.63% to 2.72% of national Gross Domestic Product (GDP), underscoring that economic losses from road crashes are growing faster than the economy. These results highlight the need for systematic interventions that integrate road safety into national sustainability agendas, including safer infrastructure, behavioral programs, advanced vehicle technologies, and efficient emergency response systems. The evidence presented strengthens the case for prioritizing traffic safety as a fundamental component of sustainable transport and public health strategies. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be a leading cause of morbidity and mortality in Mexico, with more than 20,000 new cases annually and a rising proportion of drug-resistant strains. This work addresses the molecular epidemiology of TB in the Mexican context, emphasizing its role in understanding transmission, genetic diversity, and resistance mechanisms. To achieve this, we reviewed molecular typing approaches including spoligotyping, Mycobacterial Interspersed Repetitive Unit–Variable Number Tandem Repeat (MIRU-VNTR) analysis, and whole-genome sequencing (WGS), which have been applied to characterize circulating lineages and identify drug-resistance-associated mutations. The results indicate that the Euro-American lineage (L4) predominates across the country, although significant regional variation exists, with Haarlem, LAM, T, and X sub lineages dominating in different states, and occasional detection of Asian (L2) and Indo-Oceanic (L1) lineages. Key resistance mutations were identified in katG, rpoB, pncA, and gyrA, contributing to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, particularly in border and marginalized regions. These findings highlight how social factors, such as migration, urban overcrowding, and comorbidities including diabetes and HIV, influence transmission dynamics. We conclude that integrating molecular tools with epidemiological surveillance is crucial for strengthening public health strategies and guiding interventions tailored to Mexico’s heterogeneous TB burden. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

Background/Objectives: Prediabetes (PreDM) is a heterogeneous condition, impacting hundreds of millions worldwide, associated with a substantially high risk of Type 2 Diabetes Mellitus (T2DM) and cardiovascular complications. Early identification of subgroups within the PreDM population may support tailored prevention strategies. Methods: We conducted a cross-sectional study using data from annual health check-ups of 419 university staff (aged 27–69) in Kazakhstan. Latent Class Analysis (LCA) was applied to identify subgroups of individuals with PreDM based on cardiovascular risk factors. Differences in glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) were compared across identified classes. Results: PreDM prevalence was 43.4%. LCA revealed four distinct classes: Class 1: healthy, low-risk individuals; Class 2: overweight with moderate metabolic risk; Class 3: older, overweight individuals with high cardio-metabolic risk; and Class 4: obese, middle-aged to older individuals with very high cardio-metabolic risk. Significant differences were found in glucose metabolism profiles across the classes. IFG predominated in Class 1 (95%), while Classes 3 and 4 had higher rates of β-cell dysfunction and combined IFG/IGT patterns. HOMA-β differed significantly between classes (p  <  0.001), while HOMA-IR did not. Conclusions: PreDM is highly prevalent in this working-age Kazakh population and demonstrates marked heterogeneity. Based on easily obtainable cardiovascular risk factors, we have identified four subgroups with distinct glucose profiles that may inform personalized interventions. These distinct subgroups may require differentiated prevention strategies, moving beyond a one-size-fits-all approach. Full article

COVID-19, caused by SARS-CoV-2, has created unprecedented global health challenges, necessitating rapid and reliable diagnostic strategies. The spike (S) protein, particularly its S1 subunit, plays a critical role in viral entry, making it a prime biomarker for early detection. In this study, we present a disposable, low-cost, and portable electrochemical biosensor employing specifically optimized aptamers (Optimers) for SARS-CoV-2 S1 recognition. The sensing approach is based on aptamer–protein complex formation in solution, followed by immobilization onto pencil graphite electrodes (PGEs). The key parameters, including aptamer concentration, interaction time, redox probe concentration, and immobilization time, were systematically optimized by performing electrochemical measurement in redox probe solution containing ferri/ferrocyanide using differential pulse voltammetry (DPV) technique.Under optimized conditions, the biosensor achieved an ultralow detection limit of 18.80 ag/mL with a wide linear range (10⁻¹–10⁴ fg/mL) in buffer. Importantly, the sensor exhibited excellent selectivity against hemagglutinin antigen and MERS-CoV-S1 protein, while maintaining high performance in artificial saliva with a detection limit of 14.42 ag/mL. Furthermore, its integration with a smartphone-connected portable potentiostat underscores strong potential for point-of-care use. To our knowledge, this is the first voltammetric biosensor utilizing optimized aptamers (Optimers) specific to SARS-CoV-2 S1 on disposable PGEs, providing a robust and field-deployable platform for early COVID-19 diagnostics. Full article

Background/Objectives: Osteopenia is common in postmenopausal women and predisposes to osteoporosis and fracture, representing a population at risk of bone loss but without indication for pharmacologic therapy. Conventional calcium salts offer modest, often transient gains in bone mineral density (BMD). We evaluated whether CalGo^®, a salmon bone complex containing microcrystalline hydroxyapatite within a collagen-rich matrix, preserves BMD versus placebo in post-menopausal women with osteopenia. Methods: In a 24-month, randomized, double-blind, placebo-controlled trial, 80 women (50–80 years) with dual-energy X-ray absorptiometry (DXA)-confirmed femoral-neck osteopenia were assigned to CalGo^® (2 g/day) or placebo. The prespecified primary endpoint was 24-month change in femoral-neck BMD (g/cm²) analyzed by linear regression (unadjusted and baseline-adjusted). Secondary endpoints included lumbar spine and distal radius BMD, serum P1NP and β-CTX-I, health-related quality of life, and safety. Results: The primary analysis included participants with 24-month DXA (CalGo^® n = 29; placebo n = 30). Femoral-neck BMD was maintained with CalGo^® (+0.003 g/cm²; +0.4%) but declined with placebo (−0.017 g/cm²; −2.4%), yielding a significant baseline-adjusted between-group difference of +0.019 g/cm² (95% confidence interval (CI) 0.001–0.038; p = 0.044). Lumbar-spine loss was attenuated with CalGo^® (−0.005 g/cm²; −0.3%) versus placebo (−0.028 g/cm²; −3.4%); the adjusted difference favored CalGo^® (+0.026 g/cm²; p = 0.058). In exploratory responder analysis, ≥1% lumbar-spine gain was more likely with CalGo^® (32.5% vs. 11.4%; OR 3.61; p = 0.043). No treatment effects were observed at the distal radius, in P1NP or β-CTX-I, or in EQ-5D-3L/EQ-VAS. CalGo^® was well tolerated with no hepatic or renal safety signals. Conclusions: CalGo^® maintained femoral-neck bone mineral density and reduced lumbar-spine loss over 24 months in osteopenic women, with good tolerability. These findings support its potential role as a nutritional approach for maintaining bone health. Full article

The treatment of parasitic infections has evolved in terms of effectiveness and the prevention of drug resistance. This is highlighted by the discovery of ivermectin (IVM), a macrocyclic lactone and broad-spectrum antiparasitic agent. IVM garnered scientific attention by presenting a therapeutic alternative in the field of veterinary medicine due to its control of multiple parasite species, including nematodes and soil-transmitted helminths. Shortly after its discovery, IVM was approved for human use by the World Health Organization (WHO) and United States Food and Drug Administration (FDA) for combating head lice, onchocerciasis, rosacea, scabies, and worm infestations within the gastrointestinal tract (GIT). In veterinary medicine, IVM is available in a range of formulations and can be administered via different routes (i.e., oral, topical, and parenteral), whereas for humans, IVM is only approved as a single oral dose and dermal cream. Establishing a comprehensive overview of IVM’s applications in both human and veterinary medicine is necessary, particularly in light of its repurposing potential as a treatment for various conditions and emerging diseases. Given its primary application in veterinary medicine, there is a need to enhance the development of dosage forms suitable for human use. Therefore, this review details the discovery, mechanisms, and applications of IVM, while also examining the challenges of resistance, side-effects, and controversy surrounding its use, to ultimately emphasize the importance of targeted, optimized IVM delivery via tailored dosage form development in animals and humans as part of the One Health approach to interlink innovations across veterinary and human medicine fields. Full article

Background/Objectives: Preterm birth (PTB), defined as birth before 37 completed weeks of gestation, is a major global health challenge and a leading cause of neonatal mortality. PTB is broadly classified into spontaneous and medically indicated (iatrogenic), which have distinct etiologies. While prediction is key to improving outcomes, there is a lack of models that specifically differentiate between spontaneous and iatrogenic PTB subtypes. This study aimed to develop and validate predictive models for the prediction of spontaneous and iatrogenic PTB at <32, <34, and <37 weeks’ gestation using medical history and readily available second-trimester data. Methods: This was a retrospective cohort study on singleton pregnancies from a single tertiary institution (2012–2025). Predictor variables included maternal characteristics, obstetric history, and second-trimester ultrasound markers. Four algorithms, including multivariable Logistic Regression and three machine learning methods (Random Forest, XGBoost, and a Neural Network), were trained and evaluated on a held-out test set (20% of the data). Model performance was primarily assessed by the Area Under the Curve (AUC). Results: In total, 9805 singleton pregnancies were included. The models performed significantly better for iatrogenic PTB than for spontaneous PTB. For delivery <37 weeks, the highest AUC for iatrogenic PTB was 0.764 (Random Forest), while for spontaneous PTB it was 0.609 (Neural Network). Predictive accuracy improved for earlier gestations; for delivery <32 weeks, the best model for iatrogenic PTB achieved an AUC of 0.862 (Neural Network), and the best model for spontaneous PTB achieved an AUC of 0.749 (Random Forest). Model interpretation revealed that iatrogenic PTB was primarily driven by markers of placental dysfunction, such as estimated fetal weight by ultrasound scan and uterine artery pulsatility index, while spontaneous PTB was most associated with a history of PTB and a short cervical length. Conclusions: Models using routine mid-gestation data demonstrate effective prediction for iatrogenic PTB, with accuracy improving for earlier, more severe cases. In contrast, performance for spontaneous PTB was modest. Traditional Logistic Regression performed comparably to complex machine learning algorithms, highlighting that the clinical value is rooted in the subtype-specific modeling approach rather than in algorithmic complexity. Full article

The ecological self is a core competence in social work education. This study aims to deepen the understanding of sustainability for social work students through rural fieldwork in China. Based on home visits with grassland families in Inner Mongolia, the research employed immersive engagement with nature and communities to foster ecological humility and responsibility among social work students. Findings show that students developed a multidimensional view of sustainability, integrating health practices shaped by the environment, women’s roles in maintaining family’ ecological resilience, and kinship metaphors derived from human–animal relations. The study concludes that the ecological self enables a deeper, relational interpretation of sustainability, moving beyond technocratic approaches toward embodied, context-sensitive, and intergenerationally conscious practice. It underscores the need to embed ecological consciousness in social work fieldwork training to strengthen both professional identity and transformative engagement with sustainable development. Full article

Background/Objectives: Prediabetes markedly increases the risk of progression to type 2 diabetes. While exercise and dietary polyphenols independently enhance metabolic health, their combined and synergistic effects remain unclear. This randomized, single-blind, placebo-controlled trial investigated the synergistic effects of concurrent training and a microencapsulated persimmon–karonda polyphenol formulation on glycemic control and inflammatory outcomes in adults with prediabetes and who are overweight/obese. Methods: Forty-three participants completed the intervention and were assigned to placebo, concurrent training (CBT), supplementation (EATME), or the combined intervention (CBT + EATME) for 8 weeks. Primary outcomes included fasting blood glucose (FBG), glycated hemoglobin (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), adiponectin, physical fitness, and quality of life (QoL). Results: All intervention groups (CBT, EATME, and CBT + EATME) showed improvements in glycemic indices, with the greatest reductions in FBG (p < 0.01), HbA1c (p < 0.05), and HOMA-IR (p < 0.01) observed in the CBT + EATME group compared with placebo. All interventions significantly reduced hs-CRP (p < 0.01) and IL-6 (p < 0.01), accompanied by marked increases in adiponectin (p < 0.01), compared with placebo. In the CBT + EATME group, reductions in hs-CRP were positively correlated with improvements in HOMA-IR (r = 0.627, p < 0.05). Both CBT and CBT + EATME improved muscular strength and maximal oxygen consumption (V̇O₂max), with the combined intervention producing greater gains in upper- and lower-body strength (p < 0.05), V̇O₂max (p < 0.05), and the psychological well-being domain of QoL (p < 0.05) compared with placebo. Conclusions: These findings highlight that combining concurrent training with microencapsulated polyphenol supplementation produced the most consistent improvements across metabolic, inflammatory, and fitness outcomes, supporting this combined approach as an integrated and synergistic strategy to reduce diabetes risk and promote overall health in at-risk adults. The trial was registered at the Thai Clinical Trials Registry (TCTR20250512003). Full article