Search Results (8,840)

Foodborne diseases pose a severe threat to human health. As a critical meat-borne pathogen, Escherichia coli O157:H7 (E. coli O157:H7) can trigger severe illnesses such as hemorrhagic enteritis, threatening public safety and causing a huge economic impact on the meat industry. However, previous detection methods have struggled to strike an optimal balance among accuracy, reliability, and efficiency, with various traditional techniques exhibiting obvious technical shortcomings. To overcome these limitations, this study developed a novel biosensor for the rapid detection of E. coli O157:H7 based on bimetallic carbon-based nanozymes. This sensor leverages the superior peroxidase-like activity and excellent intrinsic fluorescence of bimetallic carbon dots, providing a solid foundation for constructing a dual-modal platform combining colorimetry and fluorescence. Ultimately, this collaborative approach enables the rapid and accurate detection of the pathogen, substantially enhancing the reliability and stability of the results with a limit of detection (LOD) of 10 cfu/mL. This research presents significant practical application value and offers an innovative design strategy for developing advanced diagnostic sensors for E. coli O157:H7 and other meat-borne pathogens. Full article

Objective: This study aims to provide a global landscape of research into oral hygiene products with antimicrobial or microbiome-modulating activity through a comprehensive bibliometric analysis to identify trends and hotspots that may influence future research frontiers. Methods: A structured bibliographic search was conducted within the Web of Science Core Collection database from 2006 to 2025. Manual screening was performed to exclude duplicate records, studies that did not align with the core topic, and those failing to meet the predefined inclusion criteria. Bibliometric and visual analyses were performed using VOSviewer, CiteSpace, and the R package ‘bibliometrix’ to evaluate production metrics, citation networks, and multi-level collaboration patterns. Results: The analysis included 1007 publications. Sreenivasan PK was the most productive author, and Lundberg JO was the most cited. The United States, followed by India, Brazil, and China, led global research volume, while the United Kingdom and the Netherlands led in total citations. The International Journal of Dental Hygiene was the most productive journal (n = 48), and the Journal of Dentistry was the most cited (n = 1356). Burgeoning research hotspots include the impact of mouthwashes on the oral microbiome and systemic disorders, the controlled clinical use of chlorhexidine, and alternative formulations incorporating probiotics, herbal extracts, or hyaluronic acid. Conclusions: This study underscores a global shift in dental research priorities from traditional bacterial elimination toward preserving oral microbiota eubiosis. While chlorhexidine remains a subject of research due to its widespread use for therapeutic benefits, bibliometric research highlights its potential systemic consequences as a hotspot. Therefore, future research should focus on innovative antimicrobial formulations for mouthwashes and toothpastes that maintain oral health without causing dysbiosis. Full article

Background: Timely public reimbursement of innovative medicines remains a challenge in Canada despite expedited regulatory review pathways. This study evaluated whether an accelerated reimbursement pathway, similar to Ontario’s Funding Accelerated for Specific Treatments (FAST) for oncology drugs approved through Project Orbis could improve access for therapies approved through Health Canada’s Priority Review (PR) pathway, extending to indications beyond oncology. Methods: Health Canada drug submissions completed between 2021 and 2025 were reviewed to characterize PR, Notice of Compliance with conditions (NOC/c), and Project Orbis. Drug submissions completed in 2022 were selected for detailed analysis. Drug review and approval process data were compiled from Health Canada (HC), Canada’s Drug Agency (CDA-AMC), the pan-Canadian Pharmaceutical Alliance (pCPA), Ontario government, and manufacturer sources. Time from Health Canada Notice of Compliance (NOC) to Ontario public listing was compared for Orbis, PR non-Orbis, and FAST therapies. Results: Among drugs approved in 2022, mean time from NOC to Ontario listing was 625.29 ± 365.80 days for PR non-Orbis and 604.18 ± 283.92 days for Orbis. FAST therapies were listed in 229.22 ± 140.50 days, approximately 60% faster than Orbis and PR non-Orbis. Half of PR approvals in 2022 were not associated with Project Orbis and were therefore ineligible for existing accelerated reimbursement pathways. Conclusions: The Ontario FAST program is associated with substantially shorter times to public listing for novel oncology medicines. Extending a similar accelerated access pathway to therapies approved through PR could improve timely and equitable patient access in Canada. Full article

This review examines how smart city technologies have influenced health equity in urban settings between 2019 and 2026, guided by a dual-lens framework that assesses both equity-related outcomes and the systemic factors enabling or constraining their realization. As cities increasingly turn to digital tools to strengthen healthcare delivery, it remains unclear whether these interventions narrow or widen existing health disparities. Drawing on interdisciplinary literature spanning public health, urban informatics, and digital governance, this synthesis finds that smart health initiatives have improved healthcare access for some underserved populations, but their overall effect on health equity remains modest, inconsistent, and highly dependent on local context. Persistent structural obstacles, including digital divides, socio-economic inequality, and fragmented governance, continue to limit equitable implementation, while institutional barriers emerge as the most widespread challenge. Conversely, people-centered design, participatory governance, inclusive digital infrastructure, and equity-sensitive policy frameworks stand out as critical enablers of more just outcomes. The review further identifies an underexplored link between environmental sustainability and smart city technology, showing that AI-driven tools addressing emissions and urban environmental quality can indirectly support more equitable health systems. Overall, the findings underscore that technological innovation alone is insufficient; realizing the equity potential of smart cities requires deliberate integration of social justice, environmental sustainability, and collaborative governance into digital health strategy and practice. Full article

Accurate state-of-health (SOH) prediction of lithium batteries is critical for guaranteeing the long endurance and system safety of autonomous underwater vehicles (AUVs) in marine operations. However, owing to complex underwater-operation conditions, data acquisition in AUVs is typically restricted to rest stages in the communication period, which would be characterized by incomplete data with high-frequency sensor noise. As a result, existing battery SOH prediction approaches would struggle to ensure estimation accuracy and model robustness for within-cell degradation trajectories in AUV applications. This paper proposes a novel heterogeneous dynamic fusion model based on a data-mechanism dual-driven (DMDD) framework for the SOH prediction of lithium batteries in AUV applications, innovatively utilizing features extracted from the rest stage after discharge. At first, a dual-filter strategy based on the interquartile range interception and the Savitzky–Golay algorithms is designed to effectively eliminate transient spikes and high-frequency artifacts of raw data. Furthermore, a two-stage feature-screening architecture is developed, which can not only filter out statistical redundancies but also elucidate the electrochemical mechanisms between extracted features and battery degradation. Moreover, a heterogeneous fusion model comprising random forest, support vector regression, and gated recurrent unit networks is constructed. On this basis, an adaptive dynamic fusion strategy based on the K-nearest neighbor and the minimum-variance unbiased estimation (MVUE) is proposed, which enables locally optimal credit assignments tailored to the specific characteristics of different aging stages. Experimental validations comprehensively demonstrate the superior performance of the heterogeneous dynamic fusion model based on the DMDD framework across the entire battery lifecycle. Specifically, the proposed heterogeneous dynamic fusion model can achieve a coefficient of determination ($R^2$) over 0.99907, while the MAE and the RMSE can be maintained within 0.33958% and 0.56211%, respectively, showing satisfactory accuracy for battery SOH prediction in AUV applications. Full article

Artificial intelligence (AI) is transforming lithium-ion battery (LIB) research by linking heterogeneous data, predictive modeling, mechanistic interpretation, and experimental validation within a closed-loop paradigm. AI does not uniquely enable nonlinear representation; rather, it complements electrochemical, statistical, and control models by learning high-dimensional relationships among composition, structure, processing, interfacial chemistry, operating history, and performance. This review critically examines three connected domains: AI-assisted materials discovery, battery state prediction, and trustworthy intelligent management. To distinguish this contribution from recent topic-specific surveys, we organize methods along four complementary axes—model architecture, learning strategy, physics integration, and deployment strategy—and compare landmark studies using quantitative evidence such as independent cell count, validation design, reported error, experimental budget, and closed-loop acceleration. Materials applications include cathodes, anodes, liquid and solid electrolytes, and electrode-electrolyte interphases; operational applications include state of charge, state of health, remaining useful life, degradation diagnosis, safety warning, and digital twin-enabled management. The critical synthesis identifies data leakage, inconsistent metadata, domain shift, uncalibrated uncertainty, black-box reasoning, computational constraints, and limited external validation as recurring barriers. Future progress will depend less on model complexity alone than on FAIR data, cell-independent evaluation, physics-informed learning, uncertainty-aware decisions, autonomous experimentation, human oversight, and deployment-aware design. These elements define an evidence chain for moving LIB AI from proof-of-concept prediction toward reproducible, closed-loop, and trustworthy battery innovation. Full article

Baijiu aroma arises from the combined effects of diverse volatile and semivolatile trace components within the Baijiu matrix. A single concentration value or odor activity value (OAV) cannot fully characterize the relative importance of a compound in a specific sample. A knowledge base containing 5572 Baijiu aroma-related compounds was used to develop a comprehensive contribution ranking model and a sample-level intensity prediction model. Odor evidence, molecular structure, physicochemical properties, volatility and partitioning characteristics, matrix information, and evidence quality were integrated. Comprehensive contribution is defined here as a within-sample relative ranking index obtained from multi-source molecular features, concentration information, and matrix conditions under a specified model setting. It is not a direct measurement of the true sensory contribution of a compound. After data standardization and screening for feature computability, 5568 compounds were included in the scorer. The R², RMSE, and MAE obtained from 9000 records in the formal independent test set were 0.952018, 0.045684, and 0.036464, respectively. Light-aroma, strong-aroma, and sauce-aroma Baijiu were then analyzed by GC×GC–TOFMS. A total of 732 trace components were obtained, of which 191, 474, and 510 were detected in the three sample groups, respectively. The aroma-related compounds in each sample group were ranked within the group. The top 20 compounds were selected and recombined at their semiquantitative concentrations in the original Baijiu. Sensory evaluation by 10 assessors showed overall similarity scores of 8.70 ± 1.16 and 9.20 ± 1.03 for the light-aroma and strong-aroma recombination samples, respectively. The correlation coefficients between their 10-dimensional sensory profiles and the mean profiles of the original samples were 0.988 and 0.992. The overall similarity score of the sauce-aroma recombination sample was 5.40 ± 1.26. Significant proportional deviations were observed among acidic aroma, sauce-like and roasted aroma, and floral and fruity aroma (p < 0.05). These results indicate that multi-source feature fusion can provide an interpretable basis for the relative screening and experimental prioritization of candidate aroma compounds in Baijiu. The rankings for the light-aroma and strong-aroma samples received strong sensory support. However, they should not be interpreted as absolute contributions across aroma types. Evaluation of sauce-aroma Baijiu requires further incorporation of aroma-type-specific prior information, low-OAV components, component interactions, and effects of the authentic Baijiu matrix. Full article

Background/Objectives: High-risk human papillomavirus (HR-HPV) causes cervical cancer, but other sexually transmitted infections (STIs) act as possible cofactors. We herein evaluated the feasibility, logistics, and epidemiology of a community-based screening program in Romania using an innovative veil-based self-sampling device and a digital platform. Methods: Adult women self-collected genital secretions using the Vaginal Veil Collector V-Veil UP2™ device (V-Veil-Up Production SRL, Pitesti, Romania). A digital platform managed registration and results. Dry impregnated veils were transported at ambient temperature via standard courier to an accredited French laboratory for molecular testing using in parallel the Allplex™ HPV HR Detection assay (Seegene, Seoul, Republic of Korea), detecting 14 HR-HPVs, and the Allplex™ STI Essential Assay (Seegene), detecting 7 major pathogens causing STIs [Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis, Mycoplasma hominis, Ureaplasma urealyticum, Ureaplasma parvum]. Results: Among 960 included women (mean age 41.3 years), technical success was high: 97.7% of samples yielded valid results for HR-HPV and 97.4% for STIs. Sample stability at ambient temperature eliminated cold-chain requirements. Overall, 17.2% of women were positive for HR-HPV and 26.7% for an STI. The most frequent genotypes were HPV-68 (3.0%), HPV-16 (2.9%), and HPV-31 (2.3%); Ureaplasma parvum (24.0%) was the most prevalent bacterial pathogen. The 18–29 age group exhibited the highest risk for HR-HPV (22.2%) and STIs (36.8%). Multivariate analysis revealed strong biological associations: Chlamydia trachomatis was independently associated with overall HR-HPV (aOR: 4.52; 95% CI: 1.26–16.11) and multiple HR-HPV infections (aOR: 13.96; 95% CI: 3.70–52.64). Mycoplasma hominis and Ureaplasma species were also independently associated with HR-HPV (aOR: 1.83 and 1.67, respectively) or nonvaccine types (aOR: 2.75 and 5.03, respectively). Conclusions: Veil-based self-sampling combined with digital logistics is highly feasible, scalable, and overcomes geographical barriers. The marked association between Chlamydia trachomatis, Mycoplasma hominis, Ureaplasma species, and HR-HPV strongly advocates for integrated molecular co-testing models in public health screening programs. Full article

Background: Iodine is an essential micronutrient required for foetal development, cognitive function, and metabolic regulation; however, suboptimal iodine status remains a public health concern in Europe and other regions. Improving food literacy related to iodine may support healthier dietary choices during adolescence, a critical life stage for establishing long-term habits. This intervention development study describes the development of The ABC of Iodine Teaching Programme within the EUthyroid2 project, designed to enhance iodine-related knowledge and awareness among adolescents aged 13–17 years across six regions (UK, Republic of Cyprus, Slovenia, Germany, Bangladesh, and Pakistan). Methods: The intervention was developed according to the Behaviour Change Wheel, targeting capability, opportunity, and motivation, and informed by guidance for complex interventions. The programme was comprised of three flexible, culturally adapted modules integrating lectures on iodine physiology, deficiency risks, WHO recommendations, and locally relevant dietary sources. Active learning strategies, including collaborative tasks and personalised feedback through an Iodine Feedback Tool, were included. Materials were translated and adapted to local contexts and implemented in a hybrid format combining printed booklets with QR-linked digital resources. Results: The primary outcome of the intervention development process was The ABC of Iodine Teaching Programme, a multi-component, scalable educational intervention aligned with principles of food literacy, active learning and behaviour change theory. It incorporated behaviour change techniques and context-specific adaptations to facilitate engagement and knowledge acquisition in school settings. The programme is currently under evaluation in participating regions. Conclusions: This study presents the systematic development of a complex teaching programme. By combining behaviour change theory with innovative and context-sensitive educational strategies, The ABC of Iodine Teaching Programme provides a flexible and scalable framework for improving iodine-related food literacy among adolescents. Its effectiveness will be determined through the ongoing evaluation studies. Full article

This study analyzed the relationships among objectively monitored physical activity, motor competence, and physical fitness in primary-school children. It examined whether physical activity predicts motor competence and cardiorespiratory fitness. A total of 164 children participated (86 boys; aged 6.42–10.36 years). Physical activity was assessed with waist-worn ActiGraph wGT3X-BT accelerometers on five consecutive school days. It was quantified as the daily time in moderate-to-vigorous physical activity accumulated within a common school-hours window (09:00–17:00; school-hours MVPA). Motor competence was assessed with the Motor Competence Assessment (MCA). Cardiorespiratory fitness was assessed with the 20 m shuttle-run (Luc Léger) test of the PréFITescola^® platform, and body mass index (BMI) was computed. Pearson correlations were computed, and two multiple linear regression models with heteroscedasticity-robust standard errors were estimated. Physical activity was the focal predictor; sex, age, BMI, and recorded time within the window were covariates. School-hours MVPA (M = 55.8 min/day) was positively associated with motor competence (r = 0.16; p = 0.041). In the fully adjusted model, the estimate remained positive but was attenuated (β = 0.15; p = 0.077). MVPA did not predict cardiorespiratory fitness (β = 0.03; p = 0.765). BMI was the strongest predictor of motor competence (β = −0.31; p < 0.001). Measured with independent instruments, motor competence and cardiorespiratory fitness were only weakly associated (r = 0.19; p = 0.019). Weight status, rather than the volume of school-hours activity, emerges as the strongest correlate of motor competence at these ages. Full article

Cities create their own local climate, and numerical weather models can reproduce it if given an accurate picture of the urban surface. The local climate zone (LCZ) framework classifies neighborhoods by building height, density, and materials and provides this information to fine-scale weather models. This approach has mostly been evaluated in warm seasons, leaving open how it performs in the cold, air-stagnant winters of high-latitude cities like Ulaanbaatar, Mongolia. We ran nine model versions over one cold week (22–29 February 2024) across three nested domains (12.5, 2.5, and 0.5 km) with LCZ data resolved to 100 m. Run 8 achieved the highest aggregate validation skill, whereas Run 9 was retained as the configuration most suitable for the LCZ-based analysis rather than as the best model overall. Run 9 reproduced near-surface temperature at the urban Bayanzurkh station (R = 0.89) and gave the smallest wind-speed error (1.5 m s⁻¹), while uniquely resolving the inter-class morphological contrasts required here. The dense urban core proved warmer than the non-urban area by 4.1 °C on average and up to 9.3 °C at night in the compact high-rise zone. Yet this warming barely relieves cold stress: the Universal Thermal Climate Index (UTCI) averaged −15.5 °C across the LCZ classes, within the strong-cold-stress range, with only the compact high-rise zone reaching a milder category. In the low-rise “ger districts”, wind speed rather than air temperature governs perceived cold, and compact high-rise form offers the strongest wind shelter. These findings provide a baseline for future scenario testing of winter thermal exposure in Ulaanbaatar. Full article

Background/Objectives: Healthcare burnout is a pervasive problem affecting both professionals and patients. Resilience training may be especially important for healthcare teams practicing in underserved areas. This article describes the development and pilot testing of a curriculum innovation developed as part of California Area Health Education Centers’ (CA AHEC) resilience training program. Methods: A team of experts from CA AHEC and Hennepin Healthcare Systems led the development of a whole-person approach to resilience for health professionals, trainees, and other healthcare workers in medically underserved areas. A curriculum was piloted with 7 two-hour Train-the-Trainer video conferences and 1 six-hour in-person workshop. Participants (n = 44) included representatives from 12 CA AHEC centers. Attendance was tracked, and pre/post Knowledge, Skills, and Attitudes (KSA) and Mini Z Burnout Reduction surveys were administered. An engagement survey was fielded after training. Results: Attendance averaged 26 participants per session. Engagement surveys reflected high engagement, utility, and program appreciation, with KSA surveys reflecting large gains in understanding burnout mechanisms/prevention strategies (13.5% high pre- vs. 94.7% post-, p < 0.001; Absolute difference of 81.22, 95% CI 66.87–95.58), large Cohen’s h Effect Size (ES) of 1.9) and the perceived ability to use the training to cope with stress (8.11% high pre- vs. 94.7% post, p < 0.001; Absolute Difference of 86.63%, 95% CI 73.55–99.71, large Cohen’s h ES of 2.1). Mini-Z burnout scores remained relatively unchanged. Mindfulness, mind/body approaches to resilience, community support, and system change were rated as the most impactful curricular aspects. Conclusions: This whole-person resilience curriculum represents an emerging approach to healthcare burnout. While the curriculum was greatly appreciated by attendees, working conditions and burnout remained unchanged, suggesting a need for programs that address underlying systemic stressors. Full article

Individuals with lower-limb mobility impairment often experience fear of falling and difficulty coordinating with caregivers during transfer tasks. Therefore, safe, stable, and easy-to-operate collaborative transfer assistive equipment is urgently needed. This study proposes a Health Belief Model-driven design method for transfer assistive equipment, aiming to translate users’ risk perceptions and behavioral needs into implementable structural design strategies. First, transfer-related behavioral characteristics were analyzed according to the six constructs of the Health Belief Model. Second, the KJ method was used to classify original user requirements and establish a hierarchical requirement model, while the Fuzzy Analytic Hierarchy Process was applied to calculate requirement weights. Third, Quality Function Deployment and the House of Quality were used to transform user requirements into technical characteristics, through which four key technical contradictions were identified: anti-tipping stability versus lightweight design, status feedback versus process simplification, assistance and effort reduction versus lightweight design, and support-bearing capacity versus human–machine adaptability. Finally, structural optimization strategies were generated using TRIZ, and the proposed concept was preliminarily assessed through JACK digital human simulation and multi-stakeholder assessment at the conceptual design stage. The multi-stakeholder assessment further indicated that the proposed concept responded to requirements related to fall risk mitigation, injury protection, and perceived benefits, while adaptability for small-sized users, distal lower-limb support, and status-feedback mechanisms require further optimization. This study provides a traceable design process for incorporating users’ psychological and behavioral factors into structural innovation for assistive devices, thereby offering a methodological reference for the design of transfer assistive equipment. Full article

Objectives: To synthesize current evidence regarding advances in periodontal diagnosis and therapy, with emphasis on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence-based analytical models that support the transition toward precision periodontology. Methods: This narrative review examines contemporary evidence on emerging molecular, microbiological, and digital technologies applied to periodontal diagnosis, prognostic assessment, and therapeutic planning. The review includes studies addressing salivary and gingival crevicular fluid biomarkers, microbiome characterization, omics approaches, cone-beam computed tomography, three-dimensional imaging, machine-learning algorithms, and personalized periodontal therapies. Relevant literature was identified through searches in major biomedical databases, including PubMed/MEDLINE, Scopus, and Web of Science, focusing on studies published on periodontal diagnostics, biomarkers, digital technologies, artificial intelligence, and precision medicine approaches in periodontology. Results: Peer-reviewed articles addressing innovative diagnostic and therapeutic approaches in periodontology were considered. Priority was given to studies evaluating clinical applicability, diagnostic performance, prognostic utility, and personalized treatment strategies integrating molecular and digital technologies. Conclusions: Emerging molecular and digital technologies are reshaping periodontal diagnosis and therapy by improving disease detection, risk prediction, and individualized treatment planning. Biomarkers, omics technologies, microbiome profiling, and artificial intelligence-assisted imaging may enhance diagnostic precision and clinical decision-making. These developments support the implementation of precision periodontology; however, challenges related to biomarker validation, algorithm standardization, cost, and accessibility remain barriers to routine clinical adoption. Further research is necessary to validate these approaches and facilitate their integration into periodontal practice. The integration of biomarkers, omics technologies, advanced imaging, and artificial intelligence may improve early periodontal diagnosis, prognostic assessment, and personalized treatment planning. These innovations support the transition toward precision periodontology and have the potential to enhance clinical decision-making, treatment outcomes, and long-term periodontal health in routine dental practice. Full article

With extremely low odor thresholds and potent flavor activity, sulfur-containing aroma compounds (SACs) constitute the molecular cornerstone of characteristic flavors in meat, coffee, and fermented foods. Research has advanced from early component identification to the elucidation of structure–activity relationships, olfactory receptor recognition mechanisms, and food-flavor improvement. This review first summarizes the detection and quantification methods for SACs, their distribution in foods, key odor contributions, and major formation pathways. It then highlights progress in understanding molecular structural parameters, olfactory receptor recognition, and computational simulations that decode flavor perception mechanisms. From a translational perspective, we further discuss flavor retention in real food matrices, off-flavor regulation, cross-modal perceptual enhancement, and functional applications. Current challenges include food matrix complexity, high compound reactivity, and nonlinear olfactory combinatorial coding. Future directions involve constructing a multiscale predictive framework integrating neuroscience, developing explainable artificial intelligence to decode olfactory coding, and advancing closed-loop green biomanufacturing for the precise design and sustainable production of SACs. Full article