Several risks to ecosystems and human health are increasing day by day and among them, Polychlorinated dibenzofurans (PCDFs) are persistent environmental pollutants which pose significant risks because of its resistance, bioaccumulation, and toxicity. Their effective degradation remains a pressing challenge for sustainable environmental remediation. This study investigates the potential of the laccase enzyme from Bacillus paralicheniformis as an eco-friendly solution to reduce PCDF contamination. The enzyme's structural and functional properties were analyzed using advanced computational tools such as Discovery Studio, Prankweb, and MetaCyc, which identified key binding sites and metabolic pathways involved in aromatic compound degradation. Toxicity assessments using ToxinPred and Toxtree confirmed the non-toxic nature of laccase and the systemic toxicity of PCDFs. Molecular docking studies showed high binding affinities between laccase and PCDFs, particularly 1,2,3,4,7,8-Hexachlorodibenzofuran and 1,2,3,7,8-Pentachlorodibenzofuran, with binding energies of -7.3 and -7.2 kcal/mol, respectively. Interaction analyses highlighted PRO A:433, HIS B:431, and LYS B:466 residues involved in the stabilization of the enzyme-substrate complex. Molecular dynamics simulations for 100 ns further indicated the stability of the docked complex. These findings demonstrate the laccase enzyme's potential as a safe and effective tool for bioremediation, providing a foundation for eco-friendly strategies to mitigate PCDF contamination.
This study combines modern source-separation techniques with interpretable machine learning methods to investigate how binary perceptual annotations of diphthongal and monophthongal /aɪ/ relate to measurable acoustic variation in sung vocal performance. Using a corpus of studio-recorded music processed with vocal-instrumental source separation, we extract F1 and F2 trajectories for 1004 /aɪ/ tokens, each perceptually categorized as either monophthongal or diphthongal. These trajectories were modeled using two approaches: (i) a gradient boosted decision tree trained on engineered acoustic features and (ii) a multilayer perceptron (MLP) trained directly on raw formant trajectories. Both models achieved high accuracy and Area Under the Receiver Operating Characteristic Curve, indicating that perceptual labels can reliably be predicted from both raw and engineered acoustic input. Moreover, a Shapley Additive Explanations-based feature importance analysis showed that features such as ΔF1/ΔF2, cubic spline coefficients, and trajectory derivatives captured systematic differences between perceived monophthongal and diphthongal tokens, highlighting the value of dynamic representations over static distance-based measures. The results indicate that perceptual annotations of diphthongal and monophthongal /aɪ/ correspond with quantifiable acoustic information and demonstrate how explainable machine learning can help map gradient vowel dynamics onto binary perceptual categories. The study further emphasizes how recent advances in source separation make sung performance a valuable new domain for phonetic research.
HIV (Human Immunodeficiency Virus) is a virus that causes the immune system to be damaged, which results in reducing the body's ability to defend against infections and illnesses. This study aims to model new HIV cases among adults in KSA using selected growth, time series, and hybrid models. The study utilized official data on new HIV/AIDS cases among adults (15-49) from the World Bank website for the period 1990-2024. Various growth, conventional time series and hybrid models were proposed and used to model the series. The accuracy measures like RMSE, MAE, MAPE, MASE, and SMAPE were used to compare models, and the DM test was applied to assess the consistency between model predictions. Results were considered statistically significant if p < 0.05 and all the analyses and visualizations were performed in R-Studio. The time-series data of newly infected HIV cases among adults (ages 15-49) from 1990 to 2024 showed the mean number of cases was approximately 502 ± 323. Among the hybrid frameworks evaluated, Exponential-ETS emerged as the superior model, attaining the lowest RMSE value of 91.500, outperforming all competing specifications. The forecast for 2025 using the hybrid model projects new cases at 1,553.91 (95% CI: 1,272.23-1,828.94), rising to 2,157.74 (95% CI: 1,497.40-2,866.45) by 2029. However, the projected annual increment is lower than the surge observed in 2019-2024. This study concluded that the hybrid model provides the most robust forecasting framework for HIV incidence in KSA. The 5-year projections indicated a continued upward trend, but with a decelerated rate of growth compared to the sharp surge observed in 2019-2024. This deceleration suggests progress towards Vision 2030 goals of reducing the burden of infectious diseases through strengthened health infrastructure, expanded screening programs, and improved public health awareness.
This study reports the preparation of potassium hydroxide-impregnated corn-cob-derived activated carbon (AC500-K1.1) as an efficient adsorbent for the rapid removal of the azo dye tartrazine (TZ) from aqueous solutions. The raw corn-cob (CC) powder was chemically activated at various KOH-to-biomass ratios and activation temperatures to optimise surface chemistry and porosity. Under optimal conditions (1.1 ratio at 500 °C), the prepared activated carbon exhibited an amorphous structure, a BET surface area of 276.34 m2 g⁻1, and a well-developed porous morphology. FTIR, XRD, FESEM-EDX, TEM, TGA, and BET characterisation confirmed the formation of abundant oxygen-containing acidic functional groups (which can also significantly enhance dye adsorption through electrostatic attraction and hydrogen-bond interactions). The batch adsorption studies revealed that the optimum adsorbent dosage was 0.04 g, with a contact time of 60 min, an initial dye concentration of 100 mg L-1, a pH of 7, and a temperature range of 15-40 °C. The adsorption of TZ onto AC500-K1.1 was best represented by the Freundlich isotherm model (R2 ≈ 0.9986), suggesting heterogeneous and multilayer adsorption, whereas the kinetic behavior was adequately described by the pseudo-first-order model. Thermodynamic parameters demonstrated that the adsorption process was spontaneous and exothermic (ΔH° =  - 8.157 kJ mol⁻1). Moreover, the prepared adsorbent exhibited excellent performance, achieving a maximum experimental adsorption capacity of 675.88 mg g⁻1 and a removal efficiency of 95.07% under the optimized conditions. All of them were shown to exhibit much higher stability in regeneration experiments with up to 10 adsorption-desorption cycles and in recycling experiments. For the TZ-spiked real water samples (distilled water, tap water, and river water), significant dye uptake (> 73%) was observed with AC500-K1.1 and was marginally affected by ionic strength. This process establishes its practical applicability for actual real effluent decontamination. Molecular dynamics simulations performed using BIOVIA Materials Studio indicated that van der Waals interactions play a major role in the adsorption of TZ on activated carbon, with additional contributions from electrostatic interactions, hydrogen bonding, and π-π stacking. RDF and adsorption energy analyses further supported the spontaneous and stable adsorption configuration of TZ molecules on the carbon surface. These results provide molecular-level evidence supporting the experimentally observed adsorption behavior.
This paper presents a dataset of Lithuanian language comments annotated with emotional manipulation techniques. The source material comprises comments from Lithuanian news portal texts. A total of 1000 comments were selected and manually annotated by four human annotators using Label Studio. The annotators identified text fragments corresponding to fourteen emotional manipulation techniques, producing span-level human annotations that form the primary component of the dataset. In addition to the manual annotations, the dataset includes machine-generated outputs created by the GPT-4.1 model. These outputs were produced by executing prompts designed for the detection and classification of emotional manipulation span. Several prompting strategies were applied, and each prompt was run five times to capture variability in model behaviour. Because GPT-4.1 does not always return verbatim text excerpts, all generated spans were post-processed to extract precise fragments from the original comments and compute their character-level offsets. The resulting dataset encompasses four components: unprocessed source comments, human-annotated spans, prompt templates, and GPT-4.1 generated predictions. The dataset provides the first publicly available Lithuanian resource annotated for emotional manipulation techniques and can support research on manipulation and persuasion phenomena in a morphologically rich, low-resource language. The human annotations may be used as a benchmark for evaluating computational models for span extraction and technique classification. The inclusion of prompts and corresponding GPT-4.1 outputs enables the study of large language model behaviour under different prompting strategies and facilitates prompt engineering research without repeating inference runs. The dataset may also be reused for training or evaluating multilingual and cross-lingual models and offers a foundational framework for the development of annotation schemes and guidelines in related corpus construction efforts.
Healthcare simulation training faces significant barriers due to the "clinician-developer gap," where educators lack programming expertise to create customized digital simulators. Natural Language-Driven Development (NLDD) is an emerging paradigm that enables clinicians to develop educational technology through conversational artificial intelligence interfaces. We implemented NLDD methodology to develop Open Vent Sim, a comprehensive mechanical ventilation simulator designed to replace anesthesia machines and ventilators in educational contexts lacking dedicated equipment. A multidisciplinary team comprising anesthesiologists, residents, a research nurse, IT, and biomedical engineers collaborated using Google AI Studio to iteratively create a web-based application through natural language prompts. Development proceeded through conversational cycles in which clinical requirements were translated into functional code via large language model assistance. Open Vent Sim was successfully developed in about 40 h over two weeks, featuring three simulation environments: anesthesia workstation, ICU ventilator, and high-flow oxygenation systems. The simulator incorporates physiological patient profiles (normal, ARDS, COPD) with dynamic compliance calculations and realistic waveform generation. Clinical validation was achieved through the integration of continuous resident feedback during iterative development. The application was successfully implemented in SimZone 1 as an interactive skill trainer and in SimZone 2 for team-based clinical scenarios during formal anesthesia and critical care education. Significant technical adaptation was required to transform the AI-generated prototype into a production-ready application. NLDD demonstrates the potential to democratize the creation of educational technology by empowering clinical domain experts to develop sophisticated simulation tools without traditional programming expertise. This approach addresses resource limitations while maintaining clinical authenticity, though professional technical oversight remains essential for production-ready implementations.
To investigate the level of medication literacy and explore the conditional associations among medication literacy, beliefs about medicines, self-efficacy for appropriate medication use, and illness perceptions in patients with coronary heart disease and comorbid diabetes mellitus using network analysis. A convenience sample of 417 patients with coronary heart disease and diabetes mellitus was recruited from two Grade A tertiary hospitals and a community health center in Guangdong, China between January and August 2025. Measures included a general information questionnaire, the Self-Assessment Scale for Medication Literacy in Patients with Coronary Heart Disease Comorbidity Diabetes, the Chinese version of the Beliefs about Medicines Questionnaire-Specific, the Chinese version of the Self-Efficacy for Appropriate Medication Use Scale, and the Chinese version of the Brief Illness Perception Questionnaire. Statistical analyses were conducted using SPSS 27.0 and R Studio. The network structure was estimated with the EBICglasso algorithm. Expected influence was used to identify central nodes, and bridge expected influence was used to identify bridge nodes. The stability and accuracy of the network were examined using case-dropping and bootstrap procedures. The average score of medication literacy was 77.09±10.29. The network showed that the edge weight between node S1 (medication use under difficult circumstances) and node M5 (calculation) was 0.24, which was the largest among cross-network edges. The average node predictability was 48.6%. M2 (comprehension) had the largest expected influence index (0.89), and MB1 (necessity of medication) had the largest bridge expected influence index (0.35). The 95% confidence intervals for the edge weights were narrow. The correlation stability coefficients for both expected influence and bridge expected influence were 0.751. Patients with coronary heart disease and diabetes mellitus exhibited a moderate level of medication literacy. Network analysis identified M2 (comprehension) as a core node and MB1 (necessity of medication) as a key bridge node, suggesting that they may be considered potential priorities for assessment and intervention development.
Upper airway airflow biomechanics in children are inextricably linked to regional anatomy and physiological function. This study integrates advanced medical imaging with computational fluid dynamics (CFD) to characterize the aerodynamic properties of the pediatric upper airway via three-dimensional (3D) reconstruction. These biomechanical insights aim to clarify the pathogenesis of airway disorders and inform diagnostic and therapeutic strategies. We enrolled four healthy pediatric subjects (aged 6.8-11.9 years; two males, two females) with no history of upper respiratory pathology. DICOM datasets were imported into Mimics 21.0 to segment the air-fluid volumes (nasal cavity, nasopharynx, and pharynx) via Hounsfield unit thresholding. Subsequent 3D surface models were smoothed in Geomagic Studio 17.0 and volumetrically meshed using Ansys ICEM 21.0. Steady-state aerodynamic simulations were executed in Ansys Fluent 21.0 at a physiological flow rate of 500 mL/s. Under quiet breathing conditions, inlet flow velocities ranged from 2.122 to 9.8 m/s (specifically 3.959, 2.122, 3.070, and 9.8 m/s for Subjects A-D, respectively). The distances from the nasal vestibule to the choanae ranged from 5.37 to 6.38 cm. Nasal resistance was measured at 0.1148, 0.1002, 0.109, and 0.42 Pa/(cm3/s), respectively. Aerodynamic analyses revealed localized turbulence and pronounced pressure gradients primarily at the nasal valve, Little's area, bilateral choanae, and the epiglottis. While wall shear stress (WSS) was largely uniform throughout the airway, distinct peaks were identified at the nasal valve and Little's area. The elevated nasal resistance in Subject D correlated with localized anatomical narrowing and high inlet velocity, reflecting normal pediatric physiological variance. Pediatric upper airway aerodynamics exhibit substantial intra-regional and inter-individual heterogeneity, characterized by elevated turbulence and shear stress concentrated at the nasal valve and nasopharynx. The synergy of 3D anatomical modeling and CFD simulation establishes a robust paradigm for evaluating airway biomechanics. Clinically, these CFD-derived metrics hold significant promise for localizing functional stenoses, enhancing diagnostic precision, and facilitating personalized surgical interventions for pediatric upper airway obstruction.
Portrayals of spectacle-wearing characters in children-oriented media may influence attitudes towards glasses use and adherence. Negative stereotypes could contribute to reduced compliance, particularly in paediatric patients requiring consistent spectacle wear for optimal visual development. Eyeglasses may influence the self-image and social perception of children. As animated films are widely consumed by children and caregivers, portrayals of spectacle-wearing characters may shape attitudes towards glasses use. This study aimed to identify common character traits associated with glasses-wearing characters in animated films. A review of 167 animated films from seven major studios was conducted. Characters wearing corrective eyewear with at least one line of dialogue were included. Five independent reviewers evaluated character traits, and traits with ≥60% agreement were analysed. Statistical comparisons used Fisher's exact and two-sample proportion tests. Of 167 films, 39% featured at least one glasses-wearing character (121 characters total). Most were supporting characters (91%). Children with glasses were more likely than adults to be portrayed as socially awkward (52% vs 18%, p = 0.001) and weak (36% vs 11%, p = 0.006), and less likely to be mature (8% vs 44%, p < 0.001) or authoritative (8% vs 41%, p = 0.002). Common traits across all age groups included intelligent (41%), quirky (50%), and comedic (39%). Children with glasses are more frequently portrayed with negative or limiting traits compared to adults in animated films. These portrayals may reinforce stereotypes and influence social perceptions and self-image among children who wear glasses.
Dynamic scene reconstruction remains challenging due to the heterogeneous and spatially varying nature of real-world motion. Although recent 3D Gaussian Splatting methods have introduced diverse deformation formulations for dynamic novel view synthesis, each method typically relies on a single deformation model within its representation, which limits robustness across diverse dynamic scenarios. In this work, we study a fundamental problem-multi-deformation modeling for dynamic 3D Gaussian representations-under two distinct integration constraints that differ in when and how multiple deformation experts interact during training. From a Mixture-of-Experts (MoE) perspective, we view multi-deformation modeling as the problem of combining multiple specialized deformation models within a unified 3D representation. We first introduce Mixture of Deformation Experts (MoDE), which integrates multiple deformation experts directly into the deformable Gaussian Splatting pipeline through joint optimization. In MoDE, experts operate on a shared canonical Gaussian representation, enabling multi-deformation modeling without introducing additional training stages or modifying the original optimization schedule. In contrast, we further present Mixture of Experts for Dynamic Gaussian Splatting (MoE-GS) under a different integration constraint, where deformation experts are optimized independently and combined through a separate routing stage. As a result, expert interaction occurs over non-canonical Gaussian representations after individual optimization. Together, these two approaches provide alternative strategies for multi-deformation modeling, clarifying how integration constraints shape the design and behavior of deformation experts in dynamic 3D Gaussian representations. Our code is available at: https://github.com/cvsp-lab/MoE-GS-studio.
Video-based microlearning may support flexible learning in content-heavy nursing courses, but evidence in public health education remains limited. To examine students' perceptions and satisfaction, together with aggregate engagement indicators, regarding a video-based microlearning strategy in an undergraduate public health course. A mixed-methods cross-sectional study was conducted in an undergraduate public health course. Fifteen short videos were released as optional complementary resources. Engagement was described using aggregate YouTube Studio analytics, and perceptions were assessed among 45 students who completed an end-of-course educational evaluation using the Questionnaire on Satisfaction with Teaching Innovation, 7 additional items, and open-ended responses. The videos generated 883 views, 45.68 hours of watch time, and a mean percentage viewed of 57.25%. Respondents rated educational value, exam preparation support, and flexibility positively. Qualitative themes indicated that videos reinforced understanding and revision but should include more practice and clearer contextualization. Video-based microlearning was perceived by respondents as a useful complementary resource in public health nursing education, particularly when aligned with course activities and active-learning tasks.
This article examines queer tattooing as both a historical and a contemporary practice of resistance, belonging and community formation. Bringing together three archives - oral history interviews and portraits of queer elders, historical medical records documenting tattooed queer women engaged in sex work at the turn of the twentieth century and a dialogue with queer tattoo artist Sara Swanson - we consider what queer tattooing reveals across generations, bodies and spaces. Our interdisciplinary approach combines archival research, oral history, artistic practice and activist perspectives to trace the ways in which tattoos have functioned as bodily inscriptions of intimacy, queer identification and resistance. The first part of the article explores spatial practices of queer tattooing as sites of visibility, intimacy, belonging and community. The second part addresses bodily autonomy and resistance, demonstrating how queer tattooing enacts what Mona Lilja terms constructive resistance. Such resistance is not merely a refusal of systems of control (e.g., medical authorities), but a means of generating new narratives, subjectivities and communities. Across time and place - from hospital wards at the turn of the twentieth century to contemporary queer tattoo studios - tattooing emerges as a spatial, embodied and relational practice through which queerness is marked and reclaimed. By foregrounding diverse stories of queer tattooing across memory, archives and affect, this article contributes to the generation of more nuanced and inclusive queer histories and demonstrates how interwoven archives can collectively render queer tattooing visible across time.
To reduce environmental pollution caused by volatile organic compounds (VOCs) released during asphalt application, various porous materials have been used to adsorb asphalt VOCs due to their rich pore structures. However, asphalt VOCs are so complex that emission reduction mechanisms still require further study. In this study, Materials Studio was used to simulate the molecular dynamics of asphalt VOC adsorption by ZSM-5 zeolite. The adsorption heat, capacity, and energy of ZSM-5's adsorption of the main asphalt VOCs was obtained by means of molecular simulation to reveal the adsorption rules and selectivity. Zeolite model simulations with different structures were run to investigate possibilities for the optimization of ZSM-5. In addition, the actual VOC emission reduction effects of ZSM-5 in asphalt were compared with the MS simulation results. The VOC emission reduction mechanism was discussed based on both microscopic simulations and macroscopic verification. The results show that hydrocarbon derivative VOCs are more likely to be adsorbed due to their higher polarity. The smaller molecules of these VOCs are easier to adsorb because they occupy a smaller pore volume. When several molecules are mixed, competitive adsorption occurs. The selective adsorption probabilities of n-hexane, 1-methylcyclopentene, and toluene increase. In relation to the structure of zeolites, the Si/Al ratio and pore size of zeolites can both affect adsorption ability. A low Si/Al ratio can increase the number of surface acid active sites, while a micro-mesoporous structure increases the pore volume. The actual emission reduction data confirm that computational simulation has high accuracy in evaluating VOC emission reduction based on physical adsorption. Low-Si/Al-ratio and micro-mesoporous zeolites show better emission reduction ability for non-benzene VOCs than high-Si/Al-ratio and microporous zeolites. The emission reduction efficiency is up to 44%. However, the aromatization reaction was more easily catalyzed by zeolites, leading to the discrepancy between the simulated adsorption data and the actual situation. In future work, the boundary conditions and parameter settings of the simulations should be changed to achieve greater accuracy.
Although increased pain and balance problems in individuals with degenerative scoliosis (DS) are well documented, the associated changes in the central nervous system remain unclear. This study aimed to investigate the impact of postural imbalance and pain in individuals with DS on the central sensory pathways responsible for proprioception and pain modulation. In this context, tractography results of the proprioceptive and pain-modulating pathways were compared between individuals with DS and healthy controls. A total of 38 individuals with DS and 38 healthy individuals were included in the study. Bilateral tractography analyses of relevant pathways were conducted using brain magnetic resonance imaging data and DSI Studio software. Statistical analyses were performed using IBM SPSS 23.0, with P values <.05 considered statistically significant. No significant differences were found between groups in terms of age, weight, height, sex, or BMI (P > .05). Whole-brain average fiber length, mean diffusivity, axial diffusivity, and radial diffusivity values were significantly higher in the DS group compared to the control group (P < .05). Tractography metrics of the lemniscus medialis were significantly lower in individuals with DS (P < .05). In the left reticular formation, average fiber length, fiber volume, fiber area, and fiber proportion values were significantly higher in the DS group (P < .05). In addition, visual analog scale scores for pain were significantly higher in the DS group compared to controls (P < .05). This study demonstrates that increased pain perception and postural imbalance in individuals with DS may be associated with alterations in central sensory pathways, particularly the lemniscus medialis and reticular formation.
Stroke-related hemiplegia often results in significant lower limb dysfunction, severely affecting walking ability, balance, and daily activities. Although various non-pharmacological interventions have shown potential benefits, the optimal rehabilitation strategy remains unclear. To evaluate the efficacy and comparative ranking of non-pharmacological interventions in improving lower limb motor function, balance, walking ability, and activities of daily living in individuals with post-stroke hemiplegia. We conducted a search of PubMed, Embase, Cochrane Library, and Web of Science databases for randomized controlled trials (RCTs) published from January 2010 to August 2025. The Cochrane Risk of Bias Tool and Review Manager 5.4 were used to assess study quality, and evidence was graded with GRADEPro. Using R Studio software, a NMA was carried out to evaluate the clinical efficacy of various treatments in improving lower limb motor function in patients with post-stroke hemiplegia, ranked by the surface under the cumulative ranking curve (SUCRA). The study was officially registered in PROSPERO under the number CRD420251169037. A total of 82 RCTs involving 3514 participants and 16 non-pharmacological interventions were included. The results indicated that repetitive transcranial magnetic stimulation (rTMS) showed favorable effects on lower limb motor function measured by FMA-LE (MD = 3.7, 95% CI 2.5 to 4.9; SUCRA = 88.13%). rTMS also demonstrated positive effects on balance (MD = 8.5, 95% CrI: 5.1 to 11; SUCRA = 98.41%) and activities of daily living (MD = 14, 95% CrI: 11 to 16; SUCRA = 94.68%). For walking independence assessed by FAC, transcranial direct current stimulation (tDCS) showed considerable effects (MD = 1.5, 95% CrI: 0.41 to 2.5; SUCRA = 87.60%). Furthermore, virtual reality combined with robotic rehabilitation showed a relatively marked effect in reducing TUG time (MD = - 6.6, 95% CrI: - 8.9 to - 4.3; SUCRA = 95.27%). Different non-pharmacological interventions may provide distinct benefits for lower limb rehabilitation after stroke. rTMS appears favorable for improving motor function, balance, and daily living ability; tDCS may help enhance walking independence; and virtual reality combined with robotic rehabilitation may be beneficial for functional mobility. Further large-scale, multicenter, standardized RCTs with longer follow-up are needed to confirm these findings.
The rapid diffusion of text-to-image (T2I) generative AI has intensified pressures surrounding assessment and skill reconfiguration in art and design education. However, existing research on T2I adoption in studio-based pedagogy remains limited. This study examines technology acceptance from both educators' and students' perspectives, and illustrates the transformation of intentions and actions in creative learning situations. A modified exploratory sequential mixed-methods design with an explanatory qualitative follow-up phase (QUAL-QUAN-qual) was employed. Instructor focus groups were first conducted to identify key constructs and inform the development of a contextualized technology acceptance framework. This was followed by a questionnaire survey of 417 college students, and semi-structured interviews to explain unexpected quantitative results. The results indicate that performance expectancy, social influence, novelty value, and creative competence positively influence behavioral intention. In contrast, the negative effects of effort expectancy and facilitating conditions can be interpreted in light of students' shortcut-oriented use of T2I tools in coursework. Furthermore, students with different levels of competence perceive distinct risks across task stages, which helps explain the lack of significant translation from intention and creative competence into use behavior. The findings highlight a paradox: although creative competence positively supports behavioral intention, it may also lead to more selective or restrained engagement in actual use. Accordingly, the study extends technology acceptance models in creative education by showing that T2I adoption cannot be understood solely through conventional utilitarian predictors. Instead, it is also shaped by students' interpretations of risk and their developing creative identity, particularly in authorship, originality, and skill preservation. The results reconceptualize T2I adoption as a dynamic process of negotiation between diverse student profiles and technological evolution, ultimately providing an evidence-based foundation and practical recommendations for AI pedagogy in creative education.
Japanese Encephalitis (JE) remains a serious health threat with limited treatment options. This study aims to construct comprehensive libraries of high-frequency compounds from Chinese herbs and to screen active compounds against JE by integrating bioinformatics, network pharmacology, and experimental validation. A natural compound library was constructed through data mining and frequency analysis of Traditional Chinese medicine (TCM) prescriptions for the treatment of JE. ADMET prediction was performed to select compounds with Blood-Brain Barrier (BBB) permeability and to assess their potential toxicity. The compound-JE intersection targets were used to establish a PPI Network and to perform GO and KEGG enrichment analysis. Core targets were identified based on the PPI network by Cytoscape software. Molecular docking was performed with Discovery Studio software. Finally, in vitro experiments were carried out further to screen and validate the anti-inflammatory and antiviral effects of the active compounds in Neuro2a or BV2 cell models infected with Japanese Encephalitis Virus (JEV). Seven of the most commonly used herbs and 16 compounds were identified. Six compounds with BBB permeability and good druggability were screened. Network pharmacology revealed that these six compounds mainly targeted five core targets to exert anti-neuroinflammatory activity. In addition, molecular docking results suggested that JEV proteins were the major targets for these six compounds for antiviral activity. In JEV-induced cell models, Caryophyllene oxide, Qingdainone, and Isoliquiritigenin reduced inflammatory factors by regulating the PTGS2/NF-κB pathway in BV2 cells and exhibited the highest antiviral activity through JEV proteins in Neuro2a cells. This study establishes an integrative strategy bridging traditional medicine with modern pharmacology for anti-JEV drug discovery. The identification of Caryophyllene oxide, Qingdainone, and Isoliquiritigenin as dual-function agents, exerting both antiviral and anti-inflammatory effects, highlights the therapeutic potential of multi-target compounds against JE. Notably, this dual mechanism offers advantages over conventional singletarget therapies by simultaneously inhibiting viral replication and modulating host inflammation. Collectively, this work provides a framework for identifying multi-target anti-JEV agents from natural products, laying a foundation for the translational development of Caryophyllene oxide, Qingdainone, and Isoliquiritigenin. An integrative pipeline combining TCM-based compound library construction, bioinformatics, network pharmacology, and experimental validation has been established to identify novel anti-JE agents. Using this strategy, Caryophyllene oxide, Qingdainone, and Isoliquiritigenin were selected as candidates possessing a unique dual antiviral and anti-inflammatory mechanism for the treatment of JE.
Regular physical activity promotes health, quality of life, and functional independence in older adults. However, climate change is leading to increasing environmental stressors such as heat, air pollution, and pollen exposure, which increase health risks and hinder adherence to physical activity recommendations. Older adults, particularly those with cardiovascular or respiratory conditions and pollen allergies, are especially affected. The aim of this project was to develop evidence-based recommendations to safely enable physical activity under climatically challenging conditions. The recommendations were developed through a three-step process: (1) a systematic online search of relevant guidelines, professional and governmental publications, and information from patient organisations; (2) an interdisciplinary expert workshop to refine and adapt recommendations for the target group; (3) a modified Delphi process with experts to finalise recommendations through multiple feedback rounds. A total of 54 relevant articles published by 24 institutions were identified and compiled into a preliminary catalogue. The developed recommendations for heat, air pollution, and pollen exposure include: adaptation of activity according to timing and location (using climatically favourable time windows and indoor spaces), selection of appropriate intensity and types of exercise, breaks, hydration, sun protection, and self-monitoring of physiological warning signs. For individuals with pollen allergies, indoor activities, low-pollen locations, and the use of pollen forecasts are recommended. The recommendations provide practical guidance to maintain physical activity despite adverse environmental conditions. An evidence-based, systematic approach to complement physical activity recommendations with climate-change-related environmental stressors is feasible and necessary. Older adults can remain active, reduce health risks, and preserve independence. Tailored presentation and practical implementation of the recommendations represent the next crucial step. Regelmäßige körperliche Aktivität fördert Gesundheit, Lebensqualität und funktionale Selbstständigkeit im höheren Lebensalter. Der Klimawandel führt jedoch zu zunehmenden Umweltbelastungen wie Hitze, Luftverschmutzung und Pollenbelastung, die gesundheitliche Risiken erhöhen und die Umsetzung der Bewegungsempfehlungen erschweren. Ältere Menschen, insbesondere jene mit Herz-Kreislauf- oder Lungenerkrankungen sowie Pollenallergien, sind hiervon besonders betroffen. Ein Ziel dieses Projekts war die Entwicklung evidenzbasierter Empfehlungen, um körperliche Aktivität unter klimatisch herausfordernden Bedingungen sicher zu ermöglichen.Die Empfehlungen wurden in einem dreistufigen Prozess entwickelt: (1) Systematische Internetrecherche relevanter Leitlinien, Fachgesellschafts- und Behördenpublikationen sowie Informationen von Selbsthilfeorganisationen, (2) interdisziplinärer Expert*innenworkshop zur Präzisierung und Zielgruppenanpassung, (3) modifizierter Delphi-Prozess mit Expert*innen zur Finalisierung durch mehrere Rückkoppelungsschleifen.Insgesamt wurden 54 relevante Artikel, herausgegeben von 24 Institutionen, identifiziert und in einem vorläufigen Katalog zusammengefasst. Die erarbeiteten Empfehlungen bei Hitze, Luftverschmutzung und Pollenbelastung enthalten: zeitliche und räumliche Anpassung der Aktivität (Nutzung klimatisch günstiger Zeitfenster und Innenräume), Wahl geeigneter Intensität und Sportarten, Pausen, Flüssigkeitszufuhr, Sonnenschutz sowie Selbstbeobachtung körperlicher Warnsignale. Für Personen mit Pollenallergie werden Innenaktivitäten, pollenarme Orte und die Nutzung von Pollenfluginformationen empfohlen. Die Empfehlungen bieten praxisnahe Handlungshilfen, um körperliche Aktivität trotz klimatisch belastender Bedingungen aufrechtzuerhalten.Ein evidenzbasierter, systematischer Ansatz zur Ergänzung von Bewegungsempfehlungen an die Auswirkungen klimawandelbedingter Umweltbelastungen ist machbar und notwendig. Ältere Menschen können so aktiv bleiben, gesundheitliche Risiken reduzieren und ihre Selbstständigkeit wahren. Zielgruppenspezifische Aufbereitung und praxisgerechte Umsetzung der Empfehlungen sind als nächster wichtiger Schritt entscheidend.
Accurate estimation of prognosis and life expectancy is essential in patients with advanced cancer, as it guides clinical decision-making and helps avoid unnecessary interventions while facilitating timely integration of palliative and supportive care. Palliative radiotherapy plays a key role within multidisciplinary management, offering effective and well-tolerated symptom relief for complications such as pain, bleeding, and obstruction, with treatment strategies closely tailored to expected survival. Although recent advances in machine learning have improved prognostic accuracy by modeling complex variable interactions, their application in palliative care settings remains limited. To aid clinical decision-making, we developed a decision tree multi-classifier to predict the mortality at 3, 24, and 52 weeks following palliative radiotherapy for bone metastases. Data from 573 adults diagnosed with metastatic cancer were analyzed. The primary endpoint was the overall survival (OS) defined as the number of months from treatment to death event. Four clinically relevant classes were defined: Class 0 (OS: ≤ 3 weeks), Class 1 (OS: 3-24 weeks), Class 2 (OS: 24-52 weeks) and Class 3 (OS ≥ 52 weeks). Candidate covariate predictors consisted of 65 clinical, dosimetric and laboratory variables. Two supervised decision tree machine-learning models were trained and validated using the Python package. A SHapley Additive exPlanations (SHAP) explanaibility analysis was performed to infer the global and local feature importance. The SHAP analysis selected three laboratory variables, the interleukin8, haemoglobin and lymphocytes count as the first three ranked variables representing the major impact on OS in each of the four classes and accounting for more than 80% of contribution. In all classes, higher chance of OS was associated with low values of interleukin8 (IL8) and higher values of haemoglobin (HEM) and lymphocytes count (LYMPH). Pre-treatment values of IL8 > 36.7 relocated more than 50% of patients with survival < 3 weeks and only 1.5% of patient with survival > 52 weeks. On the other hand, pre-treatment values of IL8 < 19 relocated about 92% of patients with survival > 52 weeks. Patients are then additionally separated based on the lymphocytes count (LYMPH). LYMPH values higher than 7.5 will drive the probability of survival > 52 weeks still over 90% while it drops down to 2.1% for LYMPH < 7.5. An explainable machine learning approach based on decision trees is able to predict the survival at different timing after radiotherapy in patients with advanced cancer. This approach provides an intelligible explanation of individualized risk prediction, helping clinicians to identify the best strategy for patient stratification and treatment selection.
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