Search Results (1,050)

The rapid adoption of generative artificial intelligence (AI) systems has transformed health information seeking, raising questions about their role as intermediaries in non-professional health self-consultation. This study compares Google Search and ChatGPT as paradigmatic models of algorithmic mediation of health information, focusing on accuracy, biases, information quality and potential harms. A scoping review was conducted following the PRISMA-ScR framework. Empirical studies published between 2023 and 2025 were retrieved from PubMed/MEDLINE, Web of Science (WoS) and Scopus. After screening and eligibility assessment, 63 original empirical studies were included. The results indicate that ChatGPT consistently outperforms Google Search in terms of factual accuracy and information quality, achieving moderate to high DISCERN scores (4–5 out of 5) and showing moderate to strong correlations with expert clinical evaluations. Users also tend to value ChatGPT responses positively due to their clarity, coherence and perceived empathy. However, these advantages coexist with significant structural limitations. Hallucinations are reported in an estimated 31–45% of references, source provenance remains opaque, linguistic complexity is high, and actionability is limited, with only around 40% of responses providing clearly actionable guidance. In contrast, Google Search offers greater source traceability and verifiability, but at the cost of fragmented information and higher exposure to commercial content. The review identifies critical research gaps related to behavioural impacts, critical health literacy, equity of access, professional integration and vulnerable contexts. Overall, the findings highlight the need for hybrid human–AI models, professional mediation and critical AI literacy to ensure safe, equitable and trustworthy use of generative AI in public health communication. Full article

The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive approach to determine the maturity of the Hass avocado crop based on machine learning techniques. The approach consists of a low-cost, non-invasive bioimpedance spectroscopy system operating in the 1–10 kHz range, featuring a custom Analog Front End (AFE) and a tetrapolar surface probe to mitigate skin contact resistance, which collects data for predictive models of avocado maturity. To evaluate the quality of the approach, a longitudinal field study (n = 100) was conducted in a commercial orchard in Cundinamarca, Colombia, tracking complex impedance features—Magnitude, Phase Angle, Resistance, and Reactance—of tagged fruits over 8 weeks across four measurement timepoints. The predictive performance of a classical chemometric model (PLS-DA), non-linear classifiers (SVM, Random Forest), and a temporal Deep Learning (LSTM) architecture was compared using a Stratified Group K-Fold Cross-Validation scheme to prevent data leakage across fruits from the same tree. The 4-electrode configuration successfully isolated mesocarp impedance, identifying the 5–7.2 kHz band as the most sensitive to physiological maturation. In turn, the LSTM model achieved a mean accuracy of 92.0% and an AUC of 0.94, outperforming the other models by 4.0% in mean accuracy. The results demonstrate that modeling the temporal trajectory of impedance, rather than single-point measurements, improves harvest maturity classification in Hass avocados, providing a scalable, low-cost alternative to destructive testing. Full article

Objective: The aim of this study was to investigate the association between dental caries and multidimensional poverty in Colombia using data from the National Oral Health Survey (ENSAB IV, Spanish acronym). Methods: A cross-sectional analytical study was conducted using data from 20,534 individuals in six regions of the country. Dental caries was assessed using the ICDAS system, and multidimensional poverty was measured using a proxy Multidimensional Poverty Index (MPI) adapted from the method adjusted for Colombia. Descriptive analyses and bivariate comparisons were carried out, and Poisson regression models adjusted for sociodemographic variables were applied. Results: Households containing at least one member with caries had a higher incidence (59.9%) and intensity (46.7%) of multidimensional poverty compared to those without caries (52.6% and 45.6%, respectively). Significant associations were identified between caries and deprivation in education (low educational attainment: RR = 1.27), child labor (RR = 1.16), unemployment (RR = 1.04), lack of health insurance (RR = 1.09), and inadequate housing conditions (RR = 1.19). The model that analyzed the presence of caries in a household and multidimensional poverty, when controlled for housing conditions, confirmed a positive association between the MPI and the presence of caries (IRR = 1.08; 95% CI: 1.050–1.107; p-value < 0.001). A female head of household and rural residence were also identified as variables associated with the presence of caries in a household. Conclusions: The presence of a household member with dental caries is significantly associated with multidimensional poverty in Colombia. This study highlights the need to consider oral health as a sensitive indicator of structural inequality and proposes its inclusion in social progress metrics. The findings support the design of comprehensive public health strategies that address the social determinants of oral health, especially in vulnerable populations. Full article

Background: In the last few decades, endoscopic endonasal approaches (EEA) have revolutionized surgical access to the sellar region and anterior cranial fossa (ACF). One technique, the endoscopic endonasal transethmoidal transcribriform approach (EETTA), offers distinct advantages over traditional open transcranial approaches, such as reduced morbidity, shorter hospital stays, faster recovery, and a reduced risk of neurological deficit due to less brain tissue manipulation. Methods: We present a comprehensive step-by-step description of the EETTA surgical technique, illustrated through four representative cases of varying pathologies treated at our institution. The anatomical boundaries—including the lamina papyracea, anterior and posterior ethmoidal arteries, and frontal sinus—and the surgical corridor are detailed alongside indications, technical nuances, limitations, and operative recommendations. Results: Four cases demonstrate the versatility of EETTA across diverse pathologies: two olfactory groove meningiomas (including one WHO grade 2 and one recurrent case with invasive skull base involvement), an esthesioneuroblastoma (ENB), and a recurrent inverted papilloma requiring combined transcranial and endoscopic resection. Near-total or gross-total resection was achieved in all cases. The indications, nuances, and limitations of this approach are discussed, along with tips for successful surgery. Conclusions: The EETTA represents an important minimally invasive option for ACF tumors extending into the nasal cavity, with midline involvement limited medially by the lamina papyracea. Success requires a thorough understanding of skull base anatomy, meticulous multilayer reconstruction techniques, and appropriate patient selection, based on the tumor location and lateral extension. While cerebrospinal fluid (CSF) leak remains the primary concern, contemporary techniques have substantially reduced this complication rate. Full article

Administrative strategies are essential for ensuring efficiency and effectiveness in public institutions, particularly in the context of low- and middle-income countries where governance challenges and resource constraints persist. This study analyzes the relationship between organizational commitment and administrative management in a local public financial institution in Peru. Drawing on Meyer and Allen’s three-component model of commitment (affective, continuance, and normative) and classical administrative theory (planning, organizing, directing, and controlling), the research explores how psychosocial drivers influence perceptions of administrative practices. A cross-sectional, quantitative, non-experimental design was applied, surveying 31 employees using validated Likert-scale questionnaires. Fieldwork was conducted from January to June 2024. Non-parametric correlation analysis revealed a strong and statistically significant positive association between organizational commitment and administrative management (Spearman’s rho = 0.661, p < 0.01). Normative commitment was the most influential dimension, underscoring the role of loyalty and ethical obligation in sustaining perceived administrative management. These findings highlight the importance of strengthening human capital and organizational commitment as part of administrative strategies for institutional development. The study contributes to debates on governance and public sector reform by emphasizing how organizational dynamics in local institutions can shape broader trajectories of economic growth and development in emerging contexts. Full article

Hypoxia and nutrient-deprived microenvironments pose significant challenges to the survival of transplanted human umbilical cord mesenchymal stem cells (UC-MSCs), necessitating the development of controllable oxygen delivery strategies. In this study, we engineered fluorosurfactant-coated oxygen nanobubbles (Tivida^®-stabilized; TONBs) and assessed their cytoprotective effects in a two-dimensional (2D) ischemia-mimetic model (1% O₂ and 1% FBS). The TONBs were characterized by nanoparticle tracking analysis and zeta potential, while dissolved oxygen (DO) release was quantified in DMEM culture media. TONBs formed stable sub-200 nm populations with high colloidal stability (−58 mV) and demonstrated elevated DO levels up to ~18 ppm, compared to DMEM control (~ 8 ppm). Under hypoxic stress, TONB treatment preserved metabolic activity and viability, reduced mitochondrial ROS levels by ~20% and resulted in an ~8–9 fold downregulation of HIF-1α expression relative to untreated hypoxic controls. These results indicate that TONBs provide oxygen buffering to mitigate hypoxia-driven metabolic stress, supporting their potential as an oxygen delivery adjunct for regenerative medicine applications and tissue engineering applications. Full article

Metal–organic frameworks (MOFs) are among the most structurally diverse classes of crystalline nanomaterials, offering exceptional tunability, porosity, and chemical modularity. These characteristics have positioned MOFs as promising platforms for nanomedicine, bioimaging, and integrated nanotheranostic applications. However, the rational design of MOFs that satisfy stringent biomedical requirements, including high drug loading capacity, controlled and stimuli responsive release, selective targeting, physiological stability, biodegradability, and multimodal imaging capability, remains challenging due to the vast combinatorial design space and the complex interplay between physicochemical properties and biological responses. The objective of this review is to critically examine recent advances in artificial intelligence approaches based on Transformer architectures for the design and optimization of MOFs aimed at next-generation nanotheranostics. In contrast to prior reviews that broadly survey machine learning methods for MOF research, this article focuses specifically on Transformer-based models and their ability to capture long-range, hierarchical, and multiscale relationships governing MOF structure, chemistry, and functional behavior. We review state-of-the-art models, including MOFormer, MOFNet, MOFTransformer, and Uni MOF, and discuss graph-based and sequence-based representations used to encode MOF topology and composition. This review highlights how Transformer-based models enable predictive assessment of properties directly relevant to nanotheranostic performance, such as adsorption energetics, framework stability, diffusion pathways, pore accessibility, and surface functionality. By explicitly linking these predictive capabilities to drug delivery efficiency, imaging performance, targeted therapeutic action, and combined diagnostic and therapeutic applications, this work delineates the specific contribution of Transformer-based artificial intelligence to biomedical translation. Finally, we discuss emerging opportunities and remaining challenges, including generative Transformer models for inverse MOF design, self-supervised learning on hybrid experimental and computational datasets, and integration with autonomous synthesis and screening workflows. By defining the scope, novelty, and contribution of Transformer-based design strategies, this review provides a focused roadmap for accelerating the development of MOF-based platforms for next-generation nanotheranostics. Full article

The efficient storage of strategic gases—CH₄, CO₂, and H₂—remains a critical challenge due to the need for high pressures or cryogenic temperatures to achieve sufficient storage densities, often resulting in energy- and cost-intensive processes. Adsorption-based storage using porous materials offers a promising alternative. In particular, ordered mesoporous carbons, such as CMK-8 and CMK-9, are attractive due to their mechanical, thermal, and chemical stability, as well as their highly tunable textural properties. Surface functionalization can further enhance gas uptake, though the effect is often gas-specific. This study investigates the adsorption performance of four carbon materials: pristine CMK-8 and CMK-9, and their oxygen-functionalized counterparts produced via HNO₃ treatment. The adsorption capacities for CH₄, CO₂, and H₂ were evaluated through a combination of experimental gas adsorption measurements and molecular simulations. The results reveal structure–property relationships between surface chemistry and gas-specific adsorption behavior, with implications for the rational design of carbon-based materials for gas storage. Full article

The COVID-19 pandemic profoundly disrupted the lives of older adults, yet their experiences have remained underexplored. This paper draws on empirical evidence from a two-wave (W1 April–July 2020, W2 January–April 2021) qualitative study in the UK (n = 62) and a companion study in Colombia (n = 32), focusing on participants aged 60 and above. Data was analysed using constructivist grounded theory principles, leading to the development of an ecological theory of older adults’ well-being within the context of a health crisis at three interconnected levels: individual, community, and societal. Individual resources, such as adaptability and support systems, contributed to enhancing and maintaining their well-being. Community support and a sense of belonging were essential to meet the needs of people in later life, whilst necessary social health protection measures during the pandemic restricted social activities, further impacting well-being, mostly perceived as challenging. Cultural differences and societal support systems shaped participants’ experiences. The study emphasises the interdependence of the different levels in impacting older adults’ well-being, offers strategies for policy and practice, and advocates and contributes for the development of gerontological theories in the context of health crises. Full article

Exogenous toxic compounds in foods, arising from agricultural practices, environmental contamination, industrial processing, and packaging migration, remain a major global concern for food safety. These contaminants include mycotoxins, veterinary drug residues, antibiotics, pesticides, per- and polyfluoroalkyl substances, heterocyclic aromatic amines, and polycyclic aromatic hydrocarbons, which have multiple adverse effects on human and animal health. The continued presence of these substances highlights the need for reliable exposure assessment, strengthened regulatory frameworks, and advanced analytical methodologies. Food matrices introduce variability in analytical performance, making sample preparation a critical and often limiting step. Conventional extraction techniques such as solid-phase extraction, liquid–liquid extraction, and Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) are still widely applied. Moreover, recent advances have highlighted sustainable alternatives aligned with the principles of green analytical chemistry. In this context, this review provides a comprehensive overview of recent advances (2020–2025) in environmentally friendly extraction techniques for determining exogenous toxic compounds in food samples analyzed by liquid chromatography coupled with mass spectrometry (LC–MS), including their sustainability. Special attention is given to the chemical nature and toxicological relevance of major exogenous organic contaminant families (specialized categories such as hormones and packaging-derived bisphenols were excluded due to distinct migration and metabolic pathways; however, these topics exceed the scope of this manuscript), the analytical challenges associated with different food matrices, and the evolution of extraction and cleanup techniques. Overall, this review integrates analytical robustness, matrix effects, and green metrics to support the development of reliable and more sustainable sample preparation strategies. Full article

Melanoma-associated antigens (MAGEs) are cancer-testis antigens (CTAs) aberrantly expressed in multiple cancer types, including hepatocellular carcinoma (HCC), and associated with aggressive phenotypes. Although MAGE proteins are widely studied as cancer immunotherapy targets, their roles in HCC and the regulation of their expression during liver pathogenesis in mouse models, including dietary effects, remain poorly understood. We analyzed Mage gene expression in liver tissues from 78 C3H/HeJ mice with chronic diet-induced obesity. While type I MAGE genes are frequently expressed in human HCC, we found no evidence of their expression in mouse liver tumors, suggesting species-specific regulation. In contrast, type II Maged2, previously reported to be upregulated in human HCC, was significantly increased in mouse liver tumors. Analysis of human HCC samples from The Cancer Genome Atlas (TCGA) database confirmed MAGED2 upregulation and its association with patient prognosis. Together, these findings identify MAGED2 as a conserved marker of liver cancer in both humans and mice and emphasize the importance of cross-species comparative approaches for selecting appropriate models and accurately interpreting results, particularly for CTAs, which often evolved recently and in a species-specific manner. Full article

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there is a lack of studies that assess its impact on students’ learning outcomes. Seemingly, Education 4.0 features are taken for granted, as if the technology in itself were enough to guarantee students’ learning, self-efficacy, and engagement. Seeking to address this lack, this study describes the implications of tailoring Education 4.0 tenets and digital learning in an engineering curriculum. Four case studies conducted in the last four years with 119 students are presented, in which technologies such as digital twins, a Modular Production System (MPS), low-cost robotics, 3D printing, generative AI, machine learning, and mobile learning were integrated. With these case studies, an educational methodology with active learning, hands-on activities, and continuous teacher support was designed and deployed to foster cognitive and affective learning outcomes. A mixed-methods study was conducted, utilizing students’ grades, surveys, and semi-structured interviews to assess the approach’s impact. The outcomes suggest that including Education 4.0 tenets and digital learning can enhance discipline-based skills, creativity, self-efficacy, collaboration, and self-directed learning. These results were obtained not only via the technological features but also through the incorporation of reflective teaching that provided several educational resources and oriented the methodology for students’ learning and engagement. The results of this study can help complement the concept of Education 4.0, helping to find a student-centered approach and conceiving a balance between technology, teaching practices, and cognitive and affective learning outcomes. Full article

The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed several challenges, contributions remain fragmented and difficult for non-technical users to apply. This work addresses the following research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems. Full article

Background: Plants represent an important source of photoprotective compounds that are capable of protecting human skin from solar-induced damage. In this study we investigated the suitability of a murine model for estimating the Erythema Protection Efficacy (EPE) of natural compound. Methods: UVB-induced skin erythema in albino BALB/c mice was quantified using a Mexameter MX18 MDD colorimeter. The ARRIVE principle was followed. The Minimum Erythema Dose (MED) was determined based on Log₁₀ dose–erythema response curves. EPE values for UV filters (e.g., titanium dioxide or zinc oxide) and selected plant-derived compounds (apigenin, caffeic acid, epigallocatechin gallate, kaempferol, and pinocembrin) were calculated as the ratio between the MED of protected skin and that of unprotected skin. Results: The UVB-induced erythema in both female and male mouse skin followed a linear response. Erythema intensity varied by sex and by the dorsal skin area examined. MED values ranged from 39 to 57 mJ/cm² in female mice and from 71 to 80 mJ/cm² in male mice. In both sexes, MED increased linearly with the logarithm of the radiation dose. All tested compounds (apigenin, caffeic acid, epigallocatechin gallate, kaempferol, and pinocembrin) provided protection against UV-radiation-induced erythema in mouse skin. Among them, apigenin, caffeic acid, and kaempferol exhibited the highest EPE values, indicating strong potential for incorporation into sunscreen formulations. Conclusions: The murine EPE metric proved to be a useful tool for identifying plant-derived compounds with potential relevance for the photoprotection of human skin. Full article

Background: Trypanosoma cruzi, the causative agent of Chagas disease, remains a major public health concern, and there is a continued need for new antitrypanosomal agents. Thiosemicarbazone (TSC) derivatives have emerged as a promising class of compounds with potential antiparasitic activity. Objectives: This study aimed to report the synthesis, characterization, and biological profiling of a novel series of thiosemicarbazone derivatives as antitrypanosomal agents against Trypanosoma cruzi. Methods: Fourteen new compounds and six previously described analogues were prepared and characterized by ¹H/¹³C nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). As a preliminary in vitro screen, activity was assessed by direct parasite counting in epimastigote and bloodstream trypomastigote forms, as tractable models of replicative and infective stages sharing core metabolic targets with intracellular amastigotes. Epimastigote potency was quantified as half-maximal effective concentrations (EC₅₀) derived from dose–response curves, whereas trypomastigote response was evaluated as percent viability after treatment at a fixed concentration of 20 µM. Mechanistic profiling included inhibition assays against the cysteine protease cruzipain (CZP) and selected redox defense enzymes, complemented by in silico similarity clustering and binding-pose affinity scoring. Results: A nitro-methoxy-substituted TSC showed potent CZP inhibition but limited trypomastigote efficacy, whereas brominated analogues displayed dual-stage activity independent of CZP inhibition. Tanimoto similarity analysis identified distinct structure–activity clusters, linking nitro-methoxy substitution to epimastigote selectivity and brominated scaffolds to broader antiparasitic profiles, with hydrophobicity and steric complementarity as key determinants. Enzymatic assays revealed no significant inhibition of cytosolic tryparedoxin peroxidase (cTXNPx) or glutathione peroxidase type I (TcGPx-I), suggesting redox disruption is not a primary mode of action. In vitro and in silico analyses showed low or no non-specific cytotoxicity under the tested conditions, supporting further optimization of these derivatives as antitrypanosomal preliminary hits. Key hits included derivative 3e (epimastigote EC₅₀ = 0.36 ± 0.02 µM) and brominated analogues 2c and 2e (epimastigote EC₅₀ = 3.92 ± 0.13 and 4.36 ± 0.10 µM, respectively), while docking supported favorable binding-pose affinity (e.g., ΔG_S-pose = −20.78 ± 2.47 kcal/mol for 3e). Conclusions: These results support further optimization of the identified thiosemicarbazone derivatives as preliminary antitrypanosomal hits and provide insight into structure–activity relationships and potential mechanisms of action. Full article