Search Results (10,134)

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article

Background/Objectives: The clinical potential of antimicrobial peptides (AMPs) against dual threats like antimicrobial resistance (AMR) and cancer is often limited by their high host cell toxicity. Here, we focused on brevinin-2OS (B2OS), a novel peptide from the skin of Odorrana schmackeri with potent haemolytic activity. The objective was to study the structure–activity relationship and optimise the safety via targeted modifications. Methods: A dual-modification strategy involving C-terminal truncation and subsequent N-terminal D-amino acid substitution was employed. The bioactivities and safety profiles of the resulting analogues were evaluated using antimicrobial, haemolysis, and cytotoxicity assays. Result: Removal of the rana box in B2OS(1-22)-NH₂ substantially reduced haemolysis while maintaining bioactivities. Remarkably, the D-leucine substitution in [D-Leu²]B2OS(1-22)-NH₂ displayed a superior HC₅₀ value of 118.1 µM, representing a more than ten-fold improvement compared to its parent peptide (HC₅₀ of 10.44 µM). This optimised analogue also demonstrated faster bactericidal kinetics and enhanced membrane permeabilisation, leading to a greater than 22-fold improvement in its therapeutic index against Gram-positive bacteria. Conclusions: The C-terminal rana box is a primary determinant of toxicity rather than a requirement for activity in the B2OS scaffold. The engineered peptide [D-Leu²]B2OS(1-22)-NH₂ emerges as a promising lead compound, and this dual-modification strategy provides a powerful design principle for developing safer, more effective peptide-based therapeutics. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

We planned a systemic review and meta-analysis to evaluate the diagnostic accuracy of Monocyte Distribution Width (MDW) in aiding the diagnosis of sepsis in the Emergency Department (ED) and Intensive Care Unit (ICU). A systematic literature search was performed in PubMed, Scopus, and OVID to retrieve studies published up to 29 January 2024. We examined results using mean difference and conducted a diagnostic test accuracy (DTA) meta-analysis using a bivariate random effects model. Pooled results showed that MDW was significantly higher in sepsis patients admitted to the ED (MD = 5.59, 95%CI: 4.14–7.05) or to the ICU (MD = 8.30, 95%CI: 2.98–13.62). Nine studies conducted in the ED were included in the DTA review. The overall sensitivity was 0.80 (95%CI: 0.75–0.85), the specificity was 0.76 (95%CI: 0.66–0.83), and the false-positive rate (FPR) was 0.24 (95%CI: 0.17–0.34). Three studies were conducted in the ICU, but only two were included in the DTA meta-analysis. Of the 662 patients admitted to the ICU, 175 developed sepsis, showing higher MDW values than non-septic patients. However, significant heterogeneity was noted among the studies. MDW is a helpful biomarker for sepsis in adult patients admitted to the ED and ICU. In the ED, MDW could aid clinicians in ruling out sepsis. Full article

Background: Septic arthritis is an orthopedic emergency. However, optimal biomarkers and diagnostic criteria remain unclear. The study aimed to evaluate the diagnostic performance of routinely used and novel biomarkers, including serum C-reactive protein (CRP), synovial white blood cells (WBC), pentraxin-3 (PTX3), interleukin-6 (IL-6), and presepsin, in distinguishing septic from non-septic arthritis. Methods: Thirty-one patients undergoing arthrocentesis were included. Patients were categorized into septic and non-septic arthritis groups. Synovial fluid and serum samples were analyzed for five biomarkers. Diagnostic performance was assessed by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Synovial WBC demonstrated the highest diagnostic performance among single biomarkers (AUC = 0.837, p = 0.012). Among novel biomarkers, PTX3 showed the highest accuracy and sensitivity. The serum CRP and synovial WBC combination yielded an AUC of 0.853, with 100% sensitivity, 68.0% specificity, 42.9% PPV, and 100% NPV. Adding all three novel biomarkers to this combination increased the AUC to 0.887 (p = 0.004), maintaining 100% sensitivity and NPV. When individually added, PTX3 achieved 100% sensitivity and NPV, while presepsin showed the highest specificity (96.0%), PPV (75.0%), and accuracy (87.1%). Conclusions: Serum CRP and synovial WBC remain essential biomarkers for diagnosing septic arthritis; however, combining them with PTX3, IL-6, and presepsin improved diagnostic accuracy. PTX3 is best suited for ruling out septic arthritis due to its high sensitivity and NPV, whereas presepsin is more useful for confirmation, given its specificity and PPV. These results support a tailored biomarker approach aligned with diagnostic intent. Full article

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R²X = 0.3; R²Y = 0.7; Q² = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers. Full article

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

Protein–polyelectrolyte nanostructures assembled via electrostatic interactions offer versatile applications in biomedicine, tissue engineering, and food science. However, several open questions remain regarding their intermolecular interactions and the influence of external conditions—such as temperature and pH—on their assembly, stability, and responsiveness. This study explores the formation and stability of networks between poly(acrylic acid) (PAA) and lysozyme (LYZ) at the nanoscale upon thermal treatment, using a combination of experimental and simulation measures. Experimental techniques of static and dynamic light scattering (SLS and DLS), Fourier transform infrared spectroscopy (FTIR), and circular dichroism (CD) are combined with all-atom molecular dynamics simulations. Model systems consisting of multiple PAA and LYZ molecules explore collective assembly and complexation in aqueous solution. Experimental results indicate that electrostatic complexation occurs between PAA and LYZ at pH values below LYZ’s isoelectric point. This leads to the formation of nanoparticles (NPs) with radii ranging from 100 to 200 nm, most pronounced at a PAA/LYZ mass ratio of 0.1. These complexes disassemble at pH 12, where both LYZ and PAA are negatively charged. However, when complexes are thermally treated (TT), they remain stable, which is consistent with earlier findings. Atomistic simulations demonstrate that thermal treatment induces partially reversible structural changes, revealing key microscopic features involved in the stabilization of the formed network. Although electrostatic interactions dominate under all pH and temperature conditions, thermally induced conformational changes reorganize the binding pattern, resulting in an increased number of contacts between LYZ and PAA upon thermal treatment. The altered hydration associated with conformational rearrangements emerges as a key contributor to the stability of the thermally treated complexes, particularly under conditions of strong electrostatic repulsion at pH 12. Moreover, enhanced polymer chain associations within the network are observed, which play a crucial role in complex stabilization. These insights contribute to the rational design of protein–polyelectrolyte materials, revealing the origins of association under thermally induced structural rearrangements. Full article

To study the effect of cold plasma (CP) on the refrigerator shelf life of coconut water, microorganism growth and diversity and physicochemical properties were investigated. Results indicated that CP treatment did not cause significant color changes in coconut water, with turbidity remaining lower than the control even after 6 days of storage. Enzymatic activity analysis revealed reduced polyphenol oxidase (PPO) and peroxidase (POD) levels in treated samples. Specifically, the 12 s CP treatment resulted in the lowest antioxidant capacity values: 15.77 Fe²⁺/g for ferric reducing antioxidant power (FRAP), 37.15% for DPPH radical scavenging, and 39.51% for ABTS⁺ radical scavenging. Microbial enumeration showed that extended CP treatment effectively inhibited the growth of total viable counts, psychrophilic bacteria, lactic acid bacteria, and yeast. High-throughput sequencing identified Leuconostoc, Carnobacterium, and Lactobacillus as the dominant bacterial genera. During storage, Carnobacterium was the primary genus in the early stage, while Leuconostoc emerged as the dominant genus by the end of the storage period. In summary, CP as an effective non-thermal technology was able to maintain quality and antioxidant capacity, inhibit microbial growth, and delay the spoilage in coconut water to help extend the refrigerated shelf life of the product. Full article

Background/Objectives: Viral respiratory infections have a significant impact on global health and the economy. While vaccines are effective in preventing infection, they might not be available or sufficient when used alone and must be complemented by specific therapeutic strategies. The development of new antiviral agents is increasingly important due to the continual emergence of novel respiratory pathogens. Previously we identified bovine lactoferrin (bLf)-derived tetrapeptides and peptidomimetics that showed potent in vitro activity against the influenza A virus in the picomolar range. Methods: Inspired by these results, in this study, we evaluated the antiviral potential of these compounds against HCoV-229E, a human coronavirus that can cause severe disease in immunocompromised individuals, using a compound repositioning approach. Results: Functional studies revealed that SK(N-Me)HS (3) interferes with viral entry and replication, while compound SNKHS (5) primarily blocks infection in the early stages. Biophysical analyses confirmed the occurrence of high-affinity binding to the viral spike protein, and computational studies suggested that the compounds target a region involved in conformational changes necessary for membrane fusion. Conclusions: These findings highlight these compounds as promising candidates for coronavirus entry inhibition and underscore the value of compound repurposing in antiviral development. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

Background/Objectives: Acrylamide is a probably carcinogenic to humans that naturally forms during the thermal processing of foods. An individual’s lifestyle—especially dietary habits and physical activity—may influence the severity of acrylamide’s adverse health effects. This study aimed to examine the relationship between adolescents’ dietary and exercise behaviors and their dietary acrylamide exposure and associated health risks. Methods: This descriptive and cross-sectional study was conducted with 370 high school students in Türkiye. Data were collected using the Nutrition Exercise Behavior Scale (NEBS) and a retrospective 24-h dietary recall questionnaire. Acrylamide exposure was calculated based on food intake to estimate carcinogenic (CR) and non-corcinogenic (target hazard quotient: THQ) health risks and analyzed in relation to NEBS scores. Results: Findings indicated that while adolescents are beginning to adopt healthy eating and exercise habits, these behaviors are not yet consistent. Emotional eating and unhealthy food choices still occur. Higher acrylamide exposure and risk values were observed in boys and underweight individuals. This can be explained mainly by the fact that boys consume more of certain foods—especially bread, which contains relatively higher levels of acrylamide—than girls do, and that underweight individuals have lower body weights despite consuming similar amounts of food as other groups. Bread products emerged as the primary source of daily acrylamide intake. Positive correlations were found between NEBS total and subscale scores and acrylamide exposure and health risk values. Conclusions: The study demonstrates a significant association between adolescents’ health behaviors and acrylamide exposure. These results underscore potential public health concerns regarding acrylamide intake during adolescence and emphasize the need for targeted nutritional interventions to reduce risk and promote sustainable healthy behaviors. Full article