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Integrating augmented reality virtual patients into healthcare training: A scoping review of learning design and technical requirements
Augmented reality (AR) enables users to view the real world with enhanced digital information, making it a transformative tool in education. Virtual patients (VPs) technology is also defined as “a specific type of computer-based application that simulates real-world clinical scenario. AR and VPs offer interactive and immersive learning experiences, with AR enhancing the understanding of complex concepts and VPs providing hands-on practice in clinical scenario. This scoping review aims to identify an integrated learning design framework and the technical requirements for augmented reality-based virtual patient Simulation in healthcare professions education. This study employed a scoping review methodology that adhered to the PRISMA-ScR checklist and the Joanna Briggs Institute (JBI) guidelines, conducted between September and October 2024. The review covered six reputable databases: MEDLINE (PubMed), Science Direct (Elsevier), Web of Science (Clarivate), Cochrane library, ERIC, Scopus. A comprehensive search yielded 924 potential studies. Articles were selected via a two-stage screening process, involving title/abstract and full-text reviews based on predefined inclusion criteria. Disagreements were resolved through consultation, resulting in 27 studies being included. Eligible studies focused on augmented reality (AR)-based virtual patient (VP) technology in healthcare education, encompassing observational, quasi-experimental, and descriptive designs. Exclusions comprised grey literature, irrelevant studies, non-full-text articles, and non-AR/VP-focused research (e.g., standalone virtual reality). Various design approaches were employed, including situated learning, experiential learning, and the ADDEI model. The technical foundations of these studies were diverse, with Unity, UTTIME and PalpSim being commonly used software platforms. It is recommended that future studies thoroughly investigate each of these design framework and the technical requirements of VPAR, examining them in greater detail from various cultural, economic, social, and emotional perspectives. Tackling these problems will be a crucial stride towards enhancing and optimizing education for healthcare professions education.
Mechanochemical Formation Mechanism of Alloyed AgBi-Elpasolites
Mapping burnt areas using very high-resolution imagery and deep learning algorithms - a case study in Bandipur, India
Burnt area (BA) mapping is crucial for assessing wildfire impact, guiding restoration efforts, and improving fire management strategies. Accurate BA data helps estimate carbon emissions, biodiversity loss, and land surface properties post-fire changes. In this study, we designed and evaluated two deep learning-based architectures, a Custom UNET and a novel UNET-Gated Recurrent Unit (GRU), for burnt area classification using PlanetScope data over Bandipur, India. Both models demonstrated high accuracy in classifying burnt and unburnt areas. Performance metrics, including Precision, Recall, F1-Score, Accuracy, Mean Intersection over Union (IoU), and Dice Coefficient, revealed that the UNET-GRU hybrid consistently outperformed the Custom UNET, particularly in Recall and spatial overlap metrics. The Receiver Operating Characteristic (ROC) curve indicated excellent classification performance for both models, with the UNET-GRU achieving a higher AUC (0.98) compared to the Custom UNET (0.96). These findings highlight the UNET-GRU’s enhanced capacity to handle finer distinctions and capture spatial and contextual features, making it a robust choice for burnt area classification in the study area. While both models avoided overfitting and maintained generalizability, integrating GRU into the UNET architecture proved particularly effective for precise classification and spatial accuracy. Our results highlight the potential of the novel UNET-GRU for burnt area mapping using very high-resolution data.
Synthesis of 1,<i>n</i>-Diamines via Selective Catalytic C–H Diamination
Climate variability and Germanic settlement dynamics in the Middle Danube region during the Roman Period (1st–4th Century CE)
Climatic variability inevitably impacted past societies and acted as a driver of change. The combined analyses of the archaeological record and written documentary sources, together with high-resolution climate reconstructions, remain rare. In this work, we compare evidence of change at the Germanic settlements (residential areas) of Iron Age Germanic societies in the Middle Danube region (the region of Moravia in the Czech Republic, Lower Austria and the Záhorie region in Slovakia) and reconstruct the effect of agroclimatic conditions during the first four centuries CE. Based on data from 773 residential areas with temporal identification, we demonstrate a coherent relationship between spatiotemporal changes in Germanic settlement structures and agroclimatic conditions. A nearly exponential increase in settlement structure during the 1st and half of the 2nd century CE coincided with improved agroclimatic conditions, whereas the subsequent settlement structure decline during the Late Roman Period temporally overlapped with agroclimatic deteriorations. Documented peak in cessations of residential areas in the late 2nd century CE appears unrelated to regional agroclimatic conditions and was instead caused by the Marcomannic Wars. We argue that separating periods of agroclimatic importance and insignificance is the first step towards identifying possible causal environmental drivers of settlement dynamics and societal change in the Middle Danube region.
Reversible Thermosalience as a Result of Cooperative Bond and Molecular Rotations in a Multicomponent Hydrogen-Bonded Solid
Prehospital blood pressure lowering in patients with ultra-acute presumed stroke: A systematic review and meta-analysis
Objective High blood pressure frequently occurs in the setting of acute stroke and is associated with worse prognoses. However, it is still uncertain whether initiating blood pressure-lowering therapy in the prehospital phase after stroke onset can enhance outcomes for patients with undifferentiated acute stroke. Methods We conducted a search of the PubMed, Embase, and Cochrane databases to identify randomized controlled trials investigating prehospital blood pressure lowering interventions for presumed ultra-acute stroke (within <6 hours). The primary outcome analyzed was the 90-day modified Rankin Scale (mRS), while mortality was considered a secondary outcome. Results This meta-analysis included four studies with a total of 3912 patients. The pooled data revealed no significant difference in poor functional outcomes at 90 days (RR = 0.97, 95% CI: 0.92-1.02) or mortality rates (RR = 1.02, 95% CI: 0.90-1.15) between the group receiving blood pressure lowering treatment and the control or placebo group. Conclusions In patients with ultra-acute presumed stroke, prehospital blood pressure lowering treatment within 6 hours of stroke did not improve clinical outcomes (PROSPERO: CRD42024557505).
Orthogonally Functionalizable Redox-Responsive Polymer Brushes: Catch and Release Platform for Proteins and Cells
Dementia risk estimation in persons at risk and the predictive turn in Alzheimer’s disease—The PreTAD project: Study protocol with an ethical, clinical, linguistic, and legal approach
Background Despite progress in the field of Alzheimer’s disease (AD) dementia risk estimation, little is known about its impact at the individual and societal levels. Objective Introducing the explorative tri-national PreTAD project (The Predictive Turn in Alzheimer’s Disease: Ethical, Clinical, Linguistic and Legal Aspects), which aims to (1) learn about attitudes, needs, and perspectives on AD dementia risk estimation of the general population and cognitively unimpaired individuals with and without contact to memory clinics, (2) identify anticipated impacts of AD dementia risk estimation and (3) discuss the implications of the paradigm shift in medicine at individual and societal levels from an ethical, linguistic and legal perspective. Methods Different approaches are used: (1) an assessment of a population without experience with dementia, (2) an assessment in memory clinics, and (3) an online survey of the general population. Participants include cognitively healthy adults (n=2760), first-degree relatives of dementia patients (n=150), and participants with existing (n=150) and newly diagnosed (n=90) subjective cognitive decline (SCD) from Germany, Switzerland, and Spain. Results As part of the PreTAD project, new questionnaires are developed that (1) collect attitudes, needs, and perspectives on AD dementia risk estimation and (2) assess anticipated impacts of dementia risk estimation using hypothetical blood-based biomarker dementia risk scenarios. Conclusion The PreTAD study combines an interdisciplinary approach to develop a framework for predictive medicine in the preclinical stages of AD and supports improving communication of biomarker-based dementia risk estimation in clinical practice. The study was registered in the German Clinical Trials Register (DRKS00029035 on 03/08/2023). Trial registration German clinical trials register (Deutsches Register Klinischer Studien, DRKS): http://www.drks.de/DRKS00029035, DRKS registration number: DRKS00029035, date of registration: 08.03.2023. Protocol version 3.0, date 01.06.2024
Molluscicidal activities of Senna alata silver nanoparticles against adult and egg stages of Lymnaea natalensis
Membrane-Anchored Polyproline Provides Controlled Microdomain Formation and Permeability in Lipid Vesicles
Do metacognitions contribute to pathological health anxiety? A systematic review and meta-analysis
Objective The purpose of this meta-analysis is to give an overview of the relationships between positive and negative metacognitions (PMC, NMC) with health anxiety and pathological safety seeking and avoidant behavior (SSB, AB). Method The preregistered systematic literature screening included following data bases: MEDLINE, PsycINFO, PSYNDEX, PubMed, The Cochrane Library, Web of Science, ProQuest Dissertations & Theses, and The German National Library. The studies were evaluated based on predefined eligibility criteria (i.e., data for PMC/NMC and health anxiety and/ or SSB/AB from adult samples, assessed with validated inventories and presented in English or German language) and risk of bias categories. Correlation coefficients were aggregated with random effect models. Publication biases were estimated with contour enhanced funnel plots and outlier analyses. Results 23 studies (N = 9229) were included in the meta-analysis. Most studies assessed health anxiety in analogue samples. A significant medium effect was found for PMC and health anxiety (r = .36, p < 0.0001, 95% CI:.29 ≤ r ≤ .43), whereas for NMC the effect was large (r = .52, p < 0.0001, 95% CI:.46 ≤ r ≤ .58). For the relationship with SSB the results revealed a moderate effect for PMC (r = 0.31, p = .004; 95%-CI: 0.19 ≤ r ≤ 0.42) and a small effect for NMC (r = .25, p = .02, 95% CI:.05 ≤ r ≤ .43). No study assessed AB. Discussion Metacognitions are a significant pathological factor in health anxiety, with particularly strong association with NMC. PMC might be of special interest for health anxiety and SSB compared to other psychopathologies. Heterogeneity, missing clinical samples and studies on AB limit generalizability. Future research should further explore the role of metacognitions in health anxiety and focus on the relation with pathological SSB and AB.
A study on different methods to change the Rayleigh number in the analysis of heat transfer
Abstract This study provides awareness about natural convection and associated non-dimensional numbers like the Prandtl number, Grashof number, Rayleigh number, and Reynolds number. The main focus of this research is to present the different methods employed to vary the Rayleigh number $$\:\left(Ra\right)$$ in an extensive range. The research concludes that changing the gravity value to obtain the considerable variation in $$\:Ra$$ is also a possible method for conducting the numerical analysis and observing the impact of the Rayleigh number. The validation of the numerical scheme with existing literature is provided here. An attempt is made to show that similar effects could be obtained by changing the value of gravity and the body’s characteristics length. The comparative results obtained by changing length and gravity are presented which gives almost the same result $$\:(\pm\:\:10\%\:$$ error) to get the same $$\:Ra$$ . This presents the beauty of a non-dimensional study. Moreover, it is possible to say that in the non-dimensional analysis of engineering practice, the individual variables that are changed are not important considering the non-dimensional results.
Conformational Selection Mechanism of Rhomboid-Catalyzed Intramembrane Proteolysis Revealed by Solid-State NMR
Quantifying transmission and immunity dynamics of multiple SARS-CoV-2 variants using models and epidemic data from a highly populated area
Identifying temporal patterns in dynamics of acute, immunizing infectious diseases informs our understanding of transmission, epidemic prediction, and disease control. However, for emerging pathogens like SARS-CoV-2, temporal dynamics remain underinformed, even though COVID-19 cases varied greatly over time. Using nested compartmental models, we quantified transmission and immune dynamics in part of Columbus, the capital city of the state of Ohio, United States (US). We parameterized models using state-reported COVID-19 case counts and wastewater-based surveillance (WWS) for SARS-CoV-2. We used the models to reconstruct transmission and the rate of waning immunity in three distinct pandemic phases from April 2020 to August 2022. On average, transmission rates were lowest for the ancestral strain and highest for the Omicron variant. Transmission did not display consistent seasonal changes but did vary through time in ways that might have been influenced by host behavior or viral strain switching. Our findings also indicate that vaccine-induced and infection-induced SARS-CoV-2 immunity wane at similar rates. Gaining a better understanding of population-level transmission and immune dynamics following the emergence of a novel pathogen can inform future public health interventions including vaccine schedules.
Antimicrobial activity of new glycoside derivatives of chloroflavones obtained by fungal biotransformation
Abstract Chlorinated flavonoids represent a unique subclass of flavonoids with chlorine substituents. The incorporation of chlorine atoms and glucosyl moieties may influence their bioavailability, bioactivity, and pharmacological potential. In this study, 2′-chloroflavone, 3′-chloroflavone, 4′-chloroflavone, and 6-chloroflavone were synthesized and biotransformed using entomopathogenic fungi cultures (Isaria fumosorosea KCH J2 and Beauveria bassiana KCH J1.5) to obtain novel glycosylated derivatives. Pharmacokinetic properties and drug-likeness were predicted using cheminformatics tools. Antimicrobial activity was evaluated against several microbial strains. Enterococcus faecalis ATCC 19433 showed complete growth inhibition with 4′-chloroflavone and 6-chloroflavone, while 2′-chloroflavone and 3′-chloroflavone significantly inhibited its growth. Flavonoid glycosides and flavone demonstrated lower efficacy. Staphylococcus aureus ATCC 29213 was completely or strongly inhibited by all tested compounds. Lactobacillus acidophilus ATCC 4356 was moderately inhibited by flavonoid aglycones and slightly inhibited by glycosides. Escherichia coli ATCC 25922 was most effectively inhibited by 4′-chloroflavone and 6-chloroflavone, followed by 2′-chloroflavone and 3′-chloroflavone, with flavone and glycosides showing lower activity. Candida albicans ATCC 1023 exhibited high sensitivity to all compounds. Overall, chlorinated flavones demonstrated greater antimicrobial activity than non-chlorinated counterparts, with aglycones being more effective than glycosylated derivatives. The position of the chlorine atom significantly influences antimicrobial activity.
Organic Carbon Monoxide Prodrugs Activated by Endogenous Reactive Oxygen Species for Targeted Delivery
The intelligent evaluation model of the English humanistic landscape in agricultural industrial parks by the SPEAKING model: From the perspective of fish-vegetable symbiosis in new agriculture
To more accurately capture the expression of the English humanistic landscape in agricultural industrial parks under the emerging agricultural paradigm of fish-vegetable symbiosis, and to address the limitations of unscientific evaluation standards and inadequate adaptability in Chinese-English translation within multimodal contexts, this study proposes an intelligent translation evaluation framework based on the SPEAKING model—comprising Setting, Participants, Ends, Act Sequence, Key, Instrumentalities, Norms, and Genre. The study identifies the core elements essential for articulating the English humanistic landscape of agricultural industrial parks and conducts a comprehensive analysis from the dual perspectives of translation accuracy and adaptability. Fish-vegetable symbiosis, an ecological agricultural system integrating aquaculture and plant cultivation, emphasizes resource recycling and ecological synergy. Internationally referred to as the “aquaponics system,” this model has become a pivotal direction in sustainable ecological agriculture due to its efficiency and environmental compatibility. This study investigates multimodal translation tasks across text, image, and speech data. It addresses two primary challenges: (1) the absence of robust theoretical grounding in existing translation evaluation systems, which leads to partial and insufficiently contextualized assessments in agricultural industrial park translations; and (2) difficulties in maintaining consistency and readability across multimodal translation tasks, particularly in speech and visual modalities. The proposed optimization model integrates linguistic theory with deep learning techniques, providing a detailed analysis of contextual translation elements. Comparative evaluations are conducted against five prominent translation models: Multilingual T5 (mT5), Multilingual Bidirectional and Auto-Regressive Transformers (mBART), Delta Language Model (DeltaLM), Many-to-Many Multilingual Translation Model-100 (M2M-100), and Marian Machine Translation (MarianMT). Experimental results indicate that the proposed model outperforms existing benchmarks across multiple evaluation metrics. For translation accuracy, the Setting score for text data reaches 96.72, exceeding mT5’s 92.35; the Instrumentalities score for image data is 96.11, outperforming DeltaLM’s 93.12; and the Ends score for speech data achieves 94.83, surpassing MarianMT’s 91.67. In terms of translation adaptability, the Genre score for text data is 96.41, compared to mT5’s 93.21; the Key score for image data is 92.78, slightly higher than mBART’s 92.12; and the Norms score for speech data is 91.78, exceeding DeltaLM’s 90.23. These findings offer both theoretical insights and practical implications for enhancing multimodal translation evaluation systems and optimizing cross-modal translation tasks. The proposed model significantly contributes to improving the accuracy and adaptability of language expression in the context of agricultural landscapes, advancing research in intelligent translation and natural language processing.