Search Results (406)

Modern healthcare systems are under growing strain from aging populations, urbanization, and rising chronic disease burdens, creating an urgent need for real-time monitoring and informed decision-making. This survey examines how the convergence of Integrated Sensing and Communication (ISAC) and digital-twin technologies can meet that need by analyzing how ISAC unifies sensing and communication to gather and transmit data with high timeliness and reliability and how digital-twin platforms use these streams to maintain continuously updated virtual replicas of patients, devices, and care environments. Our synthesis compares ISAC frequency options across sub-6 GHz, millimeter-wave, and terahertz bandswith respect to resolution, penetration depth, exposure compliance, maturity, and cost, and it discusses joint waveform design and emerging 6G architectures. It also presents reference architecture patterns that connect heterogeneous clinical sensors to ISAC links, data ingestion, semantic interoperability pipelines using Fast Healthcare Interoperability Resources (FHIR) and IEEE 11073, and digital-twin synchronization, and it catalogs clinical and operational applications, together with validation and integration requirements. We conduct a targeted scoping review of peer-reviewed literature indexed in major scholarly databases between January 2015 and July 2025, with inclusion restricted to English-language, peer-reviewed studies already cited by this survey, and we apply a transparent screening and data extraction procedure to support reproducibility. The survey further reviews clinical opportunities enabled by data-synchronized twins, including personalized therapy planning, proactive early-warning systems, and virtual intervention testing, while outlining the technical, clinical, and organizational hurdles that must be addressed. Finally, we examine workflow adaptation; governance and ethics; provider training; and outcome measurement frameworks such as length of stay, complication rates, and patient satisfaction, and we conclude that by highlighting both the integration challenges and the operational upside, this survey offers a foundation for the development of safe, ethical, and scalable data-driven healthcare models. Full article

Prenatal maternal stress (PNMS) predicts risk for autism spectrum disorders (ASD), although the mechanisms are unknown. Because ASD and autistic-like traits have been associated with both prenatal stress and DNA methylation differences, it is important to examine whether epigenetic mechanisms mediate the pathway from PNMS to later autistic-like outcomes. This study aimed to determine the extent to which DNA methylation mediates the association between PNMS from a natural disaster and autistic-like traits in offspring assessed during adolescence. Five months following the 1998 ice storm in Quebec, we recruited women who had been pregnant during the crisis and assessed their PNMS: objective hardship, subjective distress, and cognitive appraisal. At age 13, their children provided blood samples for DNA. At ages 15, 16 and 19, the youth self-reported their own autistic-like traits using the Broad Autism Phenotype Questionnaire. This longitudinal design allowed us to track the developmental pathway from prenatal exposure, through adolescent DNA methylation, to later behavioral outcomes. Analyses included youth with data on PNMS, DNA methylation, and the BAPQ (n = 27 at age 15; 22 at age 16; and 13 at age 19). Results showed that mothers’ disaster-related objective hardship and their negative cognitive appraisal of the disaster were associated with DNA methylation at age 13, which then were associated with the severity of their children’s Aloof Personality and Pragmatic Language Deficits, but not Rigid Personality, at ages 15, 16 and 19. Mediation was significant particularly through genes within the PI3K/AKT/mTOR pathway, which has been implicated in various neurodevelopmental disorders, including ASD. Interestingly, while greater PNMS predicted more severe ASD traits, the epigenetics effects were for less severe traits. Although other interpretations are possible, these results could suggest that DNA methylation, assessed in early adolescence, may protect against ASD traits at later ages, particularly when there is a mismatch between the prenatal environment (disaster) and the postnatal environment (absence of disaster). The interpretation of these findings benefits from the longitudinal design and is discussed in the context of fetal programming and the predictive adaptive response. Full article

This study interrogates whether Augmented Reality (AR) enhances vocabulary and content acquisition within Content and Language Integrated Learning (CLIL), situating the question in the broader debate on how immersive, multimodal technologies shape achievement and engagement. This study’s novelty lies in its direct AR-versus-print comparison in a real CLIL classroom using markerless, smartphone-based technology. Using a mixed-methods, classroom-based experiment, we drew on a convenience sample of 129 secondary students (ages 16–18), assigning them to an AR intervention (n = 64) or a print-based control (n = 65). Both cohorts received parallel instruction covering identical objectives and materials; vocabulary attainment was gauged using matched pretest and post-test measures, while engagement, attitudes, and perceived usefulness were captured through paired pre- and post-surveys and open-ended prompts. Quantitative analyses compared change scores across conditions and were complemented by qualitative summaries of learner comments. Results indicate that exposure to AR exerted a positive influence on learners’ engagement and supported learning processes, with perceptible shifts in students’ views of AR between baseline and post-intervention; nevertheless, effects were heterogeneous across instruments, items, and subgroups, suggesting that benefits accrued in a targeted rather than uniform fashion. Compared to the print-based group, students using AR demonstrated greater gains on visually supported vocabulary and content items, while other items showed no significant differences between groups. We conclude that AR constitutes a promising pedagogical resource for CLIL, capable of scaffolding vocabulary/content development and motivating participation, while the observed variability underscores the need for principled, context-sensitive integration. Future work should specify boundary conditions—such as task type, prior proficiency, cognitive load, and technology familiarity—and employ robust mixed-methods designs to determine for whom, and under which instructional circumstances, AR yields the greatest and most sustainable gains. Full article

Background: Engineered nanoparticles (NPs)—titanium dioxide, silver, zinc oxide and silica—are widely used in cosmetics for UV protection, antimicrobial activity and texturising effects. Chronic consumer-level exposure may impair skin-barrier integrity, disturb microbiome composition and dysregulate immune signalling via the gut–skin axis. Current regulatory frameworks typically omit chronic- or microbiome-focused safety assessments, leaving potential gaps. Objectives: This study aimed to evaluate the long-term effects of cosmetic-relevant NPs (titanium dioxide, silver, zinc oxide, silica) on skin and gut microbiota, epithelial-barrier integrity and immune signalling—including telocyte- and exosome-mediated pathways—and to identify regulatory shortcomings, particularly the absence of microbiome endpoints, validated chronic models and consideration of vulnerable populations. Methods: Following PRISMA 2020, PubMed, Scopus and Web of Science were searched for English-language in vivo animal or human studies (December 2014–April 2025) meeting chronic-exposure criteria (≥90 days in rodents or >10% of lifespan in other species; for humans, prolonged, repetitive application over months to years consistent with cosmetic use). Although not registered in PROSPERO, the review adhered to a pre-specified protocol. Two independent reviewers screened studies; risk of bias was assessed using a modified SYRCLE tool (animal) or adapted NIH guidance (zebrafish). Owing to heterogeneity, findings were synthesised narratively. Results: Of 600 records, 450 unique articles were screened, 50 full texts were assessed and 12 studies were included. Oral exposure predominated and was associated with dysbiosis, barrier impairment, immune modulation and metabolic effects. Dermal models showed outcomes from minimal change to pronounced immune activation, contingent on host susceptibility. Comparative human–animal findings are summarised; telocyte and exosome pathways were largely unexplored. Regulatory reviews (EU SCCS, US FDA and selected Asian frameworks) revealed no requirements for chronic microbiome endpoints. Limitations: Evidence is limited by the small number of eligible studies, heterogeneity in NP characteristics and exposure routes, predominance of animal models and a scarcity of longitudinal human data. Conclusions: Cosmetic nanoparticles may disrupt the microbiome, compromise barrier integrity and trigger immune dysregulation—risks amplified in vulnerable users. Existing regulations lack requirements for chronic exposure, microbiome endpoints and testing in vulnerable groups, and neglect mechanistic pathways involving telocytes and exosomes. Long-term, real-world exposure studies integrating gut–skin microbiome and immune outcomes, and harmonised global nanomaterial-safety standards, are needed to ensure safer cosmetic innovation. Full article

Given the exposure to stressors in their home countries, during migration and after arrival, refugees are vulnerable to mental health problems. Their access to adequate health care and other social infrastructures, however, is hampered. This reduces, in addition to other factors, their ability to take part in social and economic activities. We examine the prevalence of mental disorders among the refugee population that arrived in Austria mainly between 2013 and 2018, drawing on data from a refugee survey. We found a high share of refugees (32%) to have moderate or severe mental health problems. When investigating the effects of stressors on the mental health situation, we found a positive association with experienced discrimination in Austria and the fear for partners and children left behind, and a negative correlation with proficiency in the German language, being employed (including volunteer work), having more supportive relationships, and satisfaction with the housing situation. Full article

Background. Bilingual novel word learning is shaped by both semantic context and the language in which learning occurs. According to the context variability hypothesis and instance-based learning frameworks, varied semantic contexts promote the formation of flexible lexical-semantic representations. However, the extent to which these benefits generalize across languages and transfer to novel contexts remains unclear. Method. Two experiments examined the effects of study language (L1, L2, or both) and semantic variability (repeated vs. varied contexts) on novel word learning in English–Spanish bilinguals. Participants studied rare words embedded in sentences and were tested via a word-stem completion task. In Experiment 1, test sentences were identical to those seen during the study. In Experiment 2, half of the test sentences were novel, requiring generalization beyond previously encountered contexts. Orthographic overlap across languages was also assessed. Results. In Experiment 1, varied semantic contexts improved recall accuracy, supporting the context variability hypothesis. Unexpectedly, words studied in L2 were recalled more accurately than those studied in L1, consistent with desirable difficulty effects. Additionally, orthographic overlap moderated learning, with greater benefits observed in mixed-language conditions. In Experiment 2, overall accuracy declined, and no main effects of language or context were observed. However, a three-way interaction showed that orthographic overlap improved recall only when words were studied in L1 and tested in novel contexts. Conclusions. Semantic and linguistic variability can enhance bilingual word learning when test conditions are consistent with the learning context. However, generalization to novel contexts may require deeper processing, extended exposure, or additional retrieval cues. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

The internet’s evolution as a global communication nexus has enabled unprecedented connectivity, allowing users to share information, media, and personal updates across social platforms. However, these platforms also amplify risks such as cyberbullying, cyberstalking, and other forms of online abuse. Cyberbullying, in particular, causes significant psychological harm, disproportionately affecting young users and females. This work leverages recent advances in Natural Language Processing (NLP) to design a robust and privacy-preserving framework for detecting abusive language on social media. The proposed approach integrates ensemble federated learning (EFL) and transfer learning (TL), combined with differential privacy (DP), to safeguard user data by enabling decentralized training without direct exposure of raw content. To enhance transparency, Explainable AI (XAI) methods, such as Local Interpretable Model-agnostic Explanations (LIME), are employed to clarify model decisions and build stakeholder trust. Experiments on a balanced benchmark dataset demonstrate strong performance, achieving 98.19% baseline accuracy and 96.37% with FL and DP respectively. While these results confirm the promise of the framework, we acknowledge that performance may differ under naturally imbalanced, noisy, and large-scale real-world settings. Overall, this study introduces a comprehensive framework that balances accuracy, privacy, and interpretability, offering a step toward safer and more accountable social networks. Full article

This paper examines the quotative system of Sri Lankan English. Quotatives are identified in face-to-face conversations in the Sri Lankan component of the International Corpus of English. The use of kiyala indicating and following quoted material has been transferred from Sinhala, one of the indigenous languages of the country, into Sri Lankan English. Together with the occurrence of complementising that, the localisation of the Sri Lankan English quotative system is evident. Special emphasis is given to the choice between BE like and SAY, the by far most frequent quotative forms in the informal spoken data analysed. They are annotated with established structural (e.g., content of the quote or tense) and sociobiographic variables (e.g., age and gender of the speaker) apparent from earlier quotative research, but also with new ones (e.g., quote length or speakers’ stays abroad or media exposure to particular varieties of English). Via a generalised linear mixed-effects model tree implementing the latest methodological suggestions for classification trees, it is found that BE like is favoured over SAY in Sri Lankan English with younger speakers—particularly when the conversation took place after 2015 and events are narrated using the historical present. Full article

Background: Children with neurodevelopmental disorders (NDDs) commonly experience executive function (EF) impairments that impact daily life and academics. While bilingualism has generally been associated with cognitive advantages in typically developing (TD) children, its relationship with EF in children with NDDs remains unclear and represents a critical knowledge gap for families and clinicians considering bilingual exposure in these populations. Methods: For this scoping review, we searched PubMed, ProQuest, CogNet, PsycINFO, Scopus, ERIC, Embase, CINAHL, Linguistics Abstracts Online, and Google Scholar for studies published between database inception and December 2024, without language restrictions. We included quantitative, qualitative, and mixed-methods studies that (i) involved participants aged 4–12 years with diagnosed NDDs; (ii) examined children with bilingual language exposure; (iii) employed validated instruments for measuring cognitive or executive function; (iv) presented original empirical findings; and (v) were published in English. We excluded studies lacking comparisons between groups and longitudinal studies. Data on study characteristics, participants, EF assessments, and main findings were extracted. This study is registered with OSF Registries. Findings: Fifteen cross-sectional studies met the inclusion criteria, all of which focused exclusively on children with autism spectrum disorder (ASD), with no studies examining other NDDs. The studies involved 982 children with ASD (463 monolingual; 404 bilingual) and 644 TD children. Most studies (n = 11) revealed that, compared with monolingual children with ASD, bilingual children with ASD demonstrated advantages in working memory, cognitive flexibility, and inhibitory control on performance-based tasks. However, findings were inconsistent for spatial inhibition tasks, and parent-reported measures sometimes failed to detect bilingual-related differences. Interpretation: Bilingualism is associated with specific EF benefits for children with ASD, adding to evidence that questions longstanding concerns about the negative impacts of bilingual exposure in NDD populations. The evidence suggests that bilingual exposure could potentially serve as a complementary approach to traditional interventions for addressing EF impairments in children with ASD, although this evidence is limited to cross-sectional designs and requires further studies. However, the exclusive focus on ASD limits generalisability across the broader spectrum of NDDs. Further research is needed across diverse NDD populations employing comprehensive, multi-method EF assessments that combine performance-based tasks with parent-reported measures to better inform parenting, clinical, and educational practices. Full article

Introduction: Little is known about the effects of intrauterine exposure to SARS-CoV-2, especially on growth and neurodevelopment in children. Objective: We wished to verify the effect of intrauterine exposure to SARS-CoV-2 on neurological development in children. Methods: Infants born to mothers presenting with SARS-CoV-2 infection during pregnancy were enrolled in a prospective descriptive–analytical study involving outpatient appointments performed 6 and 12 months after birth. Their neurological development was assessed using the Bayley-III Scale, using a score of >85 as the cutoff threshold for identifying developmental delay. Differences between groups were assessed through an ANOVA, using Bonferroni correction for multiple comparisons. Regression models were employed to examine the associations between the Bayley-III scores and maternal features. Results: Two hundred and sixty-nine infants were evaluated, most of whom were born full-term and with birth weights appropriate for gestational age at delivery. Developmental delays were observed in 26% of the infants in at least one of the Bayley-III domains. The language domain was particularly affected, with impairments observed in children exposed to SARS-CoV-2 closer to the time of delivery. These findings were statistically significant (p < 0.05). Conclusions: Infants born to mothers presenting with SARS-CoV-2 infection during pregnancy presented developmental delays at 6 and 12 months, particularly in the language domain. These findings reinforce the relevance of long-term clinical follow-ups of newborns exposed to SARS-CoV-2 infection during pregnancy. Full article

This study is about Palestinian university students’ attitudes toward English as a foreign language (EFL) learning, paying special attention to variables such as gender, parents’ knowledge of a foreign language, academic year, and travel to English-speaking countries. The researchers implemented a descriptive–analytical design to collect data. The sample of the study is 570 university students across various higher education institutions in Palestine. The researchers used several statistical tests, such as an Independent Sample t-test and one-way ANOVA, to analyse data. The findings suggest that Palestinian university students’ attitudes toward learning English are mainly neutral. However, there are positive inclinations in specific aspects such as travel, academic content, and reading and writing, with no statistically significant differences due to variables such as gender, academic year, or exposure to English-speaking countries. These results suggest that student attitudes are shaped less by personal background and more by broader sociopolitical and educational conditions. This study contributes to the limited body of localised research on affective variables in second language acquisition (SLA) within conflict-affected regions. It highlights the need for contextually responsive pedagogies that promote student engagement and linguistic resilience. Implications are offered for educators, curriculum developers, and policymakers seeking to enhance EFL instruction in Palestine and similar settings. Full article

Large language models (LLMs) often fail to correctly associate biomedical terms with their standardized ontology identifiers, posing challenges for downstream applications that rely on accurate, machine-readable codes. These linking failures can compromise the integrity of data used in precision medicine, clinical decision support, and population health. Fine-tuning can partially remedy these issues, but the degree of improvement varies across terms and terminologies. Focusing on the Human Phenotype Ontology (HPO), we show that a model’s prior knowledge of term–identifier pairs, acquired during pre-training, strongly predicts whether fine-tuning will enhance its linking accuracy. We evaluate prior knowledge in three complementary ways: (1) latent probabilistic knowledge, revealed through stochastic prompting, captures hidden associations not evident in deterministic output; (2) partial subtoken knowledge, reflected in incomplete but non-random generation of identifier components; and (3) term familiarity, inferred from annotation frequencies in the biomedical literature, which serve as a proxy for training exposure. We then assess how these forms of prior knowledge influence the accuracy of deterministic identifier linking. Fine-tuning performance varies most for terms in what we call the reactive middle zone of the ontology—terms with intermediate levels of prior knowledge that are neither absent nor fully consolidated. Fine-tuning was most successful when prior knowledge as measured by partial subtoken knowledge, was ‘weak’ or ‘medium’ or when prior knowledge as measured by latent probabilistic knowledge was ‘unknown’ or ‘weak’ ($p<0.001$). These terms from the ‘reactive middle’ exhibited the largest gains or losses in accuracy during fine-tuning, suggesting that the success of knowledge injection critically depends on the level of term–identifier pair knowledge in the LLM before fine-tuning. Full article

Environmental pollution poses an increasing threat to public health, particularly in urban areas with high levels of pollutant exposure. To address this challenge, this study proposes a model based on fine-tuning the LLaMA 3 large language model for the classification of pollution-related diseases using user-reported symptoms. A balanced dataset was employed, with examples evenly distributed across 10 common diseases, and several preprocessing techniques were applied, including tokenization, normalization, noise removal, and data augmentation. The model was fine-tuned using the QLoRA technique, which integrates quantization with low-rank adaptation, enabling both training and inference on resource-constrained hardware. During training, a consistent reduction in loss and a progressive improvement in validation accuracy were observed. Moreover, the confusion matrix demonstrated a high classification success rate with minimal misclassification across classes. The findings suggest that optimized large language models can be effectively applied in settings with limited computational infrastructure, supporting the early diagnosis of diseases associated with environmental factors. Full article

Prenatal exposure to air pollution is a major public health concern due to its potential to impair fetal brain development. This study examined whether maternal inflammatory and oxidative stress biomarkers mediate the association between trimester-specific air pollutant exposure during pregnancy and infant neurodevelopment at one year. We analyzed 87 mother–infant pairs from the OBESO perinatal cohort in Mexico City. Trimester-specific exposure to CO, PM₁₀, PM_2.5, SO₂, and O₃ was estimated using residential geolocation. Biomarkers were measured in the first and third trimesters by protocol, and intra-pregnancy change was calculated as Δ(3T–1T) for cytokines (IL-1β, IL-6, TNFα) and oxidative stress markers (malondialdehyde (MDA), protein carbonyls (PC), and total antioxidant capacity (TAC). Infant neurodevelopment at 12 months was assessed using Bayley-III. Exploratory mediation analyses were conducted, adjusting for gestational age at birth, pre-eclampsia, gestational diabetes, fetal growth restriction, marital status, mode of delivery, and infant sex; bootstrapping was applied to obtain robust estimates. Third-trimester CO exposure was associated with poorer receptive language (coef = 0.754, p = 0.02). PM_2.5 exposure showed direct effects on expressive language in the first (coef = 0.01, p = 0.04) and third trimesters (coef = 0.007, p = 0.015) in models including IL-1β. Third-trimester O₃ and SO₂ exposures were linked to lower expressive scores in models including TNFα (coef = 0.007, p = 0.02), MDA (coef = 0.008, p = 0.04), and PC (coef = 0.007, 95% p = 0.04). Meanwhile PM₁₀ exposure was associated with socio-emotional outcomes in models with IL-6 and TAC (coef = 0.003, p = 0.04). These findings indicate that maternal inflammation and oxidative stress biomarkers did not mediate the associations between prenatal air pollution exposure and infant neurodevelopment, and this study cannot elucidate their specific biological role in neurodevelopment. Full article