Search Results (15,044)

The growing interest in probiotic bacteria within the food industry is driven by their recognized health benefits for consumers. However, preserving their therapeutic viability and stability during gastrointestinal transit remains a formidable challenge. Hence, this research aimed to enhance the viability of Lactobacillus reuteri through microencapsulation using a binary polysaccharide mixture composed of low acyl gellan gum (LAG), high acyl gellan gum (HAG), and calcium for the microencapsulation of L. reuteri. To achieve this, the Box–Behnken design was applied, targeting the optimization of L. reuteri microencapsulated to withstand simulated gastrointestinal conditions. The microcapsules were crafted using the internal ionic gelation method, and optimization was performed using response surface methodology (RSM) based on the Box–Behnken design. The model demonstrated robust predictive power, with R² values exceeding 95% and a lack of fit greater than p > 0.05. Under optimized conditions—0.88% (w/v) LAG, 0.43% (w/v) HAG, and 24.44 mM Ca—L. reuteri reached a viability of 97.43% following the encapsulation process. After 4 h of exposure to simulated gastric fluid (SGF) and intestinal fluid (SIF), the encapsulated cells maintained a viable count of 8.02 log CFU/mL. These promising results underscore the potential of biopolymer-based microcapsules, such as those containing LAG and HAG, as an innovative approach for safeguarding probiotics during gastrointestinal passage, paving the way for new probiotic-enriched food products. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

Monitoring cardiovascular health enables continuous and real-time risk assessment. This study utilized the Framingham Heart Study dataset to develop and evaluate machine-learning models for predicting mortality risk based on key cardiovascular parameters. Some machine-learning algorithms were applied to multiple machine-learning models. Among these, XGBoost achieved the highest predictive performance, each with an area under the curve (AUC) value of 0.83. Feature importance analysis revealed that coronary artery disease, glucose levels, and diastolic blood pressure (DIABP) were the most significant risk factors associated with mortality. The primary contribution of this research lies in its implications for public health and preventive medicine. By identifying key risk factors, it becomes possible to calculate individual and population-level risk scores and to design targeted early intervention strategies aimed at reducing cardiovascular-related mortality. Full article

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Background: Social and financial services are essential for the inclusion and well-being of people with disabilities (PWDs), who often rely on family caregivers to access these systems. In Saudi Arabia, where disability inclusion is a strategic goal under Vision 2030, understanding caregiver experiences is crucial to identifying service gaps and improving accessibility. Objectives: This study aimed to explore caregivers’ perspectives on awareness, perceived barriers, and accessibility of social and financial services for PWDs in Saudi Arabia. The analysis is grounded in Andersen’s Behavioural Model of Health Service Use and the WHO’s International Classification of Functioning, Disability and Health (ICF) framework. Methods: A cross-sectional survey was conducted with 3353 caregivers of PWDs attending specialised day schools. The survey collected data on demographic characteristics, service awareness, utilisation, and perceived obstacles. Exploratory Factor Analysis (EFA) identified latent constructs, and Structural Equation Modelling (SEM) was used to test relationships between awareness, barriers, and accessibility. Results: Findings reveal that over 70% of caregivers lacked awareness of available services, and only about 3% had accessed them. Key challenges included technological barriers, complex procedures, and non-functional or unclear service provider platforms. Both User Barriers and Service Barriers were negatively associated with Awareness and Accessibility. Awareness, in turn, significantly predicted perceived Accessibility. Caregiver demographics, such as age, education, gender, and geographic location, also influenced awareness and service use. Conclusions: There is a pressing need for targeted awareness campaigns, accessible digital service platforms, and simplified service processes tailored to diverse caregiver profiles. Inclusive communication, decentralised outreach, and policy reforms are necessary to enhance service access and promote the societal inclusion of PWDs in alignment with Saudi Arabia’s Vision 2030. Full article

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article

Alopecia, a prevalent dermatological disorder affecting over half of the global population, is strongly associated with psychological distress. Extracts from Periplaneta americana (L. PA), a medicinal insect resource, exhibit pharmacological activities (e.g., antioxidant, anti-inflammatory, microcirculation improvement) that align with core therapeutic targets for alopecia. This study aimed to systematically investigate the efficacy and mechanisms of PA extracts in promoting hair regeneration. A strategy combining network pharmacology prediction and in vivo experiments was adopted. The efficacy of a Periplaneta americana extract was validated by evaluating hair regrowth status and skin pathological staining in C57BL/6J mice. Transcriptomics, metabolomics, RT-qPCR, and 16s rRNA techniques were integrated to dissect the underlying mechanisms of its hair-growth-promoting effects. PA-011 significantly promoted hair regeneration in depilated mice via multiple mechanisms: enhanced skin superoxide dismutase activity and upregulated vascular endothelial growth factor expression; modulated FOXO/PI3K/AKT signaling pathway and restored skin microbiota homeostasis; and accelerated transition of hair follicles from the telogen to anagen phase. PA-011 exerts hair-promoting effects through synergistic modulation of FOXO/PI3K/AKT signaling and the skin microbiome. As a novel therapeutic candidate, it warrants further systematic investigation for clinical translation. Full article

Cancer is a major global health issue, with rising incidence rates highlighting the urgent need for more effective treatments. Despite advances in cancer therapy, challenges such as adverse effects and limitations of existing treatments remain. Immunotherapy, which harnesses the body’s immune system to target cancer cells, offers promising solutions. Gamma delta (γδ) T cells are noteworthy due to their potent ability to kill various cancer cells without needing conventional antigen presentation. Recent studies have focused on the role of γδ T cells in α-galactosylceramide (α-GalCer)-mediated immunity, opening new possibilities for cancer immunotherapy. We engineered humanized T cell receptor (HuTCR)-T1 γδ mice by replacing mouse sequences with human counterparts. This study investigates the cytotoxic activity of humanized γδ T cells against several human cancer cell lines (A431, HT-29, K562, and Daudi) in vitro, aiming to elucidate mechanisms underlying their anticancer efficacy. Human cancer cells were co-cultured with humanized γδ T cells, with and without α-GalCer, for 24 h. The humanized γδ T cells showed enhanced cytotoxicity across all tested cancer cell lines compared to wild-type γδ T cells. Additionally, γδ T cells from HuTCR-T1 mice exhibited higher levels of anticancer cytokines (IFN-γ, TNF-α, and IL-17) and Granzyme B, indicating their potential as potent mediators of anticancer immune responses. Blocking γδ T cells’ cytotoxicity confirmed their γδ-mediated function. These findings represent a significant step in preclinical development of γδ T cell-based cancer immunotherapies, providing insights into their mechanisms of action, optimization of therapeutic strategies, and identification of predictive biomarkers for clinical application. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

Allicin (ALC), a naturally occurring organosulfur compound derived from garlic (Allium sativum), exhibits potential neuroprotective properties. Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by degeneration of dopaminergic neurons and motor dysfunction. This study utilized bioinformatics and network pharmacology methods to predict the anti-PD mechanism of ALC and established in vivo and in vitro PD models using 6-hydroxydopamine (6-OHDA) for experimental verification. Network pharmacological analysis indicates that apoptosis regulation and the PKA/p-CREB/BDNF signaling pathway are closely related to the anti-PD effect of ALC, and protein kinase A (PKA) and dopamine transporter (DAT) are key molecular targets. The experimental results show that ALC administration can alleviate the cytotoxicity of SH-SY5Y induced by 6-OHDA and simultaneously improve the motor dysfunction and dopaminergic neuron loss in PD mice. In addition, ALC can also activate the PKA/p-CREB/BDNF signaling pathway and increase the DAT level in brain tissue, regulate the expression of BAX and Bcl-2, and reduce neuronal apoptosis. These results indicate that ALC can exert anti-PD effects by up-regulating the PKA/p-CREB/BDNF/DAT signaling pathway and inhibiting neuronal apoptosis, providing theoretical support for the application of ALC in PD. Full article

Accurate evaluation of the operational status of smart meters, as the critical interface between the power grid and its users, is essential for ensuring fairness in power transactions. This highlights the importance of implementing rotation management practices based on meter status. However, the traditional expiration-based rotation method has become inadequate due to the extended service life of modern smart meters, necessitating a shift toward status-driven targeted management. Existing multifactor comprehensive assessment methods often face challenges in balancing accuracy and interpretability. To address these limitations, this study proposes a novel method for analyzing the status of smart meter groups using an interpretable random forest model. The approach incorporates an expert-knowledge-guided grouping assessment strategy, develops a multi-source heterogeneous feature set with strong correlations to meter status, and enhances the random forest model with the SHAP (SHapley Additive exPlanations) interpretability framework. Compared to conventional methods, the proposed approach demonstrates superior efficiency and reliability in predicting the failure rates of smart meter groups within distribution network areas, offering robust support for the maintenance and management of smart meters. Full article

Emerging and persistent contaminants (EPCs) were detected at high concentrations in Chiang Mai’s Mae Kha Canal, identifying urban waterways as important sources of pollution in the Ping River system in northern Thailand. Maximum levels of metformin (20,000 ng/L), fexofenadine (15,900 ng/L), gabapentin (12,300 ng/L), sucralose (38,000 ng/L), and acesulfame (23,000 ng/L) point to inadequately treated wastewater as a plausible contributor. Downstream enrichment patterns relative to upstream sites highlight the cumulative impact of urban runoff. Five compounds—acesulfame, gemfibrozil, fexofenadine, TBEP, and caffeine—consistently emerged as reliable tracers of urban wastewater, forming a distinct chemical fingerprint of the riverine exposome. Median EPC concentrations were highest in Mae Kha, lower in other urban canals, and declined with distance from the city, reflecting spatial gradients in urban density and pollution intensity. Although most detected concentrations fell below predicted no-effect thresholds, ibuprofen frequently approached or exceeded ecotoxicological benchmarks and may represent a compound of ecological concern. Non-targeted analysis revealed a broader “chemical cocktail” of unregulated substances—illustrating a witches’ brew of pollution that likely escapes standard monitoring efforts. These findings demonstrate the utility of wide-scope surveillance for identifying key compounds, contamination hotspots, and spatial gradients in mixed-use watersheds. They also highlight the need for integrated, long-term monitoring strategies that address diffuse, compound mixtures to safeguard freshwater ecosystems in rapidly urbanizing regions. Full article

Today, ‘social sustainability’ is a key feature of many organisations’ environmental, social, and governance strategies, as well as underpinning sustainable development goals. The term refers to the implementation of targets such as reduced societal inequalities, the promotion of social well-being, and the practice of positive community relations. Building a meaningful, accountable, and quantifiable evidence-base from which to translate these high-level concepts into tangible and achievable goals is, however, challenging. The complexities of measuring social capital—often described as a building block of social sustainability—have been documented. The challenge lies in measuring the person, group, or collective in interaction with the context under investigation, whether that be a climate goal, an institution, or a national policy. Social identity theory is a social psychological approach that articulates the processes through which an individual internalises the values, norms, and behaviours of their contexts. Levels of social identification—a concept capturing the state of internalisation—have been shown to be predictive of outcomes as diverse as communication and cognition, trust and citizenship, leadership and compliance, and health and well-being. Applying this perspective to the articulation and measurement of social sustainability provides an opportunity to build an empirical approach with which to reliably translate this high-level concept into achievable outcomes. Full article

Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with disrupted lipid metabolism. This study aimed to uncover novel molecular subtypes and biomarkers by integrating sphingolipid metabolism-related genes (SMGs) with machine learning approaches. Using data from the GEO and GeneCards databases, 29 UC-related SMGs were identified. Consensus clustering was employed to define distinct molecular subtypes of UC, and a diagnostic model was developed through various machine learning algorithms. Further analyses—including functional enrichment, transcription factor prediction, single-cell localization, potential drug screening, molecular docking, and molecular dynamics simulations—were conducted to investigate the underlying mechanisms and therapeutic prospects of the identified genes in UC. The analysis revealed two molecular subtypes of UC: C1 (metabolically dysregulated) and C2 (immune-enriched). A diagnostic model based on three key genes demonstrated high accuracy in both the training and validation cohorts. Moreover, the transcription factor FOXA2 was predicted to regulate the expression of all three genes simultaneously. Notably, mebendazole and NVP-TAE226 emerged as promising therapeutic agents for UC. In conclusion, SMGs are integral to UC molecular subtyping and immune microenvironment modulation, presenting a novel framework for precision diagnosis and targeted treatment of UC. Full article

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article