Search Results (798)

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor with limited therapeutic options despite multimodal treatment. Small interfering RNA (siRNA)-based therapeutics can silence tumor-promoting genes, but achieving efficient and tumor-specific delivery remains challenging. Lipid nanoparticles (LNPs) are promising siRNA carriers; however, conventional antibody conjugation can impair antigen recognition and complicate manufacturing. This study aimed to establish a modular Fc-binding peptide (FcBP)-mediated post-insertion strategy to enable PD-L1-targeted delivery of VEGF-siRNA via LNPs for GBM therapy. Methods: Preformed VEGF-siRNA-loaded LNPs were functionalized with FcBP–lipid conjugates, enabling non-covalent anchoring of anti-PD-L1 antibodies through Fc interactions. Particle characteristics were analyzed using dynamic light scattering and encapsulation efficiency assays. Targeted cellular uptake and VEGF gene silencing were evaluated in PD-L1-positive GL261 glioma cells. Anti-tumor efficacy was assessed in a subcutaneous GL261 tumor model following repeated intratumoral administration using tumor volume and bioluminescence imaging as endpoints. Results: FcBP post-insertion preserved LNP particle size (125.2 ± 1.3 nm), polydispersity, zeta potential, and siRNA encapsulation efficiency. Anti-PD-L1–FcBP-LNPs significantly enhanced cellular uptake (by ~50-fold) and VEGF silencing in PD-L1-expressing GL261 cells compared to controls. In vivo, targeted LNPs reduced tumor volume by 65% and markedly suppressed bioluminescence signals without inducing weight loss. Final tumor weight was reduced by 63% in the anti-PD-L1–FcBP–LNP group (656.9 ± 125.4 mg) compared to the VEGF-siRNA LNP group (1794.1 ± 103.7 mg). The FcBP-modified LNPs maintained antibody orientation and binding activity, enabling rapid functionalization with targeting antibodies. Conclusions: The FcBP-mediated post-insertion strategy enables site-specific, modular antibody functionalization of LNPs without compromising physicochemical integrity or antibody recognition. PD-L1-targeted VEGF-siRNA delivery demonstrated potent, selective anti-tumor effects in GBM murine models. This platform offers a versatile approach for targeted nucleic acid therapeutics and holds translational potential for treating GBM. Full article

Aldehyde dehydrogenase 1A1 (ALDH1A1) has emerged as a significant biomarker associated with tumor progression, chemoresistance, and poor prognosis in various cancers, including breast, lung, prostate, and lymphoma. Current diagnostic methods for ALDH1A1, such as flow cytometry and ELISA, are limited by long detection times, the need for labeling, and a reduced sensitivity in complex biological matrices. This study presents a novel optical fiber biosensor based on magnesium silicate nanoparticle-doped fibers for the label-free detection of ALDH1A1. The biosensor design incorporated protein G for enhanced antibody orientation and binding efficiency and anti-ALDH1A1 antibodies for specific recognition. Several sensor configurations were fabricated using a semi-distributed interferometer (SDI) format, and their performances were evaluated across a wide concentration range (10 fM–100 nM) in both phosphate-buffered saline (PBS) and fetal bovine serum (FBS). Our findings demonstrated that the inclusion of protein G significantly improved sensor sensitivity and reproducibility, achieving a limit of detection (LoD) of 172 fM in PBS. The sensor also maintained a positive response trend in FBS, indicating its potential applicability in clinically relevant samples. This work introduces the first reported optical fiber biosensor for soluble ALDH1A1 detection, offering a rapid, label-free, and highly sensitive approach suitable for future use in cancer diagnostics. Full article

As heritage cognition evolves, aligning conceptual understanding with conservation strategies becomes essential for effective practice. This study develops the Heritage Cognition–Conservation Paradigm Correspondence Framework, a methodological tool designed to evaluate the alignment between heritage cognition and conservation paradigms. Methodologically, the framework is constructed through document analysis, conceptual classification, and framing co-construction. Building on a critical review of the development trajectory of heritage conservation, it integrates four cognitive phases and three conservation paradigms into a dual-axis matrix, operationalized through six analytical dimensions for heritage cognition and four for conservation paradigms. The framework is subsequently applied through a case study of Traditional Chinese Villages, demonstrating its diagnostic capacity and analytical utility. The case study reveals a significant misalignment: while official discourse reflects pluralistic heritage thinking (within the most advanced, fourth cognitive phase), conservation practice remains rooted in value-based logics and material-based approaches (within the initial paradigms). This misalignment stems from fragmented object recognition, form-focused objectives, and top–down governance structures that marginalize local agency and overlook cultural processes as the heritage nature of those villages. By establishing and operationalizing the correspondence framework, this study provides a transferable tool for diagnosing cognition–practice disjunctions across heritage contexts. Beyond its empirical findings, the study advances a methodological contribution for heritage conservation and advocates a strategic shift toward process-oriented, community-embedded approaches that emphasize cultural continuity, reframed objectives, and participatory governance. Full article

The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K-means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs. Full article

The growing sophistication of cyberattacks underscores the need for the automated extraction of machine-readable intelligence from unstructured Cyber Threat Intelligence (CTI), commonly achieved through Named Entity Recognition (NER). However, existing CTI-oriented NER research faces two major limitations: the scarcity of standardized datasets and the lack of advanced models tailored to domain-specific entities. To address the dataset challenge, we present CTINER, the first STIX 2.1-aligned dataset, comprising 42,549 annotated entities across 13 cybersecurity-specific types. CTINER surpasses existing resources in both scale (+51.82% more annotated entities) and vocabulary coverage (+40.39%), while ensuring label consistency and rationality. To tackle the modeling challenge, we propose PROMPT-BART, a novel NER model built upon the BART generative framework and enhanced through three types of prompt designs. Experimental results show that PROMPT-BART improves F1 scores by 4.26–8.3% over conventional deep learning baselines and outperforms prompt-based baselines by 1.31%. Full article

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

This study introduces a mobile application designed to assist visually impaired persons (VIPs) in navigating complex environments, such as supermarkets. Recent assistive tools often identify objects in isolation without providing contextual awareness. In contrast, our proposed system uses seamless scene and object recognition to help users efficiently locate target items and understand their surroundings. Employing a “human-in-the-loop approach”, users control their smartphone camera direction to explore the space. Experiments conducted in a simulated shopping environment show that the system enhances object-finding efficiency and improves user orientation. This approach not only increases independence, but also promotes inclusivity by enabling VIPs to perform everyday tasks with greater confidence and autonomy. Full article

With the acceleration of global population aging, rural areas face particularly severe challenges due to youth outmigration and uneven resource distribution. Taking Jiashan Village in Wuhan as a case study, this research combines the planning–activity model of new time–geography with Maslow’s hierarchy of needs to investigate the behavioral and emotional characteristics of the elderly and their spatial adaptation requirements. Using GPS tracking of 30 participants, questionnaires (152 valid responses; 73.4% response rate), facial expression recognition, and the stated preference (SP) method, the study classified elderly lifestyles into four types: leisure-oriented, agricultural-labor-oriented, caregiving-oriented, and self-employment-oriented. The results show significant heterogeneity in spatial needs, social intensity, and emotional responses. A quantitative analysis using the multinomial logit model indicates that farmland optimization had the greatest positive utility (+1.5873), followed by the addition of new plazas and leisure facilities, both significantly enhancing satisfaction. A correlation analysis further revealed that prolonged use of farmland, parks, and walking paths was negatively correlated with satisfaction, underscoring the urgency of targeted renovations. On this basis, the study proposes a three-tiered demand framework of “local service–social interaction–personal value”, offering both theoretical support and practical strategies for multi-level and collaborative retrofitting of suburban rural public spaces, aiming to mitigate “aging depression” and promote urban–rural integration. Full article

Background: Indigenous populations worldwide face a disproportionate burden of HIV due to structural inequities, cultural marginalization, and limited access to health services. Despite growing recognition of the need for culturally adapted responses, the effectiveness of combination HIV prevention strategies in these communities remains underexplored. Objectives: This study aimed to evaluate and compare the effectiveness of multiple HIV prevention strategies among Indigenous populations using a systematic review and network meta-analysis (NMA), to inform equity-oriented public health interventions. Methods: Following PRISMA-NMA 2020 guidelines, a comprehensive literature search was conducted across four databases (PubMed, SciELO, LILACS, Science Direct) for quantitative studies published between January 2000 and June 2025. Eligible studies evaluated HIV prevention interventions among Indigenous populations and reported risk or odds ratios. A frequentist NMA model was used to calculate effect estimates (OR, 95% CI) and SUCRA rankings for seven types of interventions, combining biomedical, behavioral, and structural approaches. Results: Four high-to-moderate quality studies enclosing 4523 participants were included. The most effective intervention was home-based counseling and testing for HIV, followed by medical consultation combined with HIV testing. Standalone testing, while effective, was significantly less impactful than when combined with culturally sensitive educational strategies. Information-only strategies showed the least efficacy. The SUCRA analysis ranked home-based testing highest (45.17%), highlighting the importance of decentralization, community participation, and intercultural mediation. Conclusions: Culturally adapted combination prevention strategies—especially those integrating home-based testing and counseling—are more effective than isolated biomedical interventions in Indigenous populations. These findings reinforce the urgent need for participatory, context-driven public health responses that center Indigenous knowledge, reduce stigma, and expand equitable access to HIV care and prevention. Full article

Despite the growing recognition of emotional intelligence (EI) and its significant associations with academic outcomes, less is known about the underlying mechanisms through which EI mindsets affect academic engagement and burnout. Drawing on regulatory focus theory and social comparison theory, this study aims to reveal how different types of EI mindsets influence college students’ academic engagement and burnout through regulatory focus (i.e., promotion and prevention focus) and further examines the moderating role of performance-prove goal orientation—defined as the motivation to demonstrate competence and outperform others—in these pathways. To test these associations, we conducted two studies. A scenario experiment (Study 1) indicates that a growth mindset of EI (GMOE) has the potential to enhance academic engagement while reducing academic burnout, whereas a fixed mindset of EI (FMOE) exhibits the opposite pattern. Study 2, based on three-wave data, demonstrates that GMOE is positively associated with academic engagement and negatively associated with academic burnout via promotion focus, whereas FMOE is positively associated with academic burnout and negatively associated with academic engagement through prevention focus. Of note, performance-prove goal orientation moderates these pathways: Individuals with higher levels of performance-prove goal orientation exhibit a weakened indirect effect of GMOE on academic engagement via promotion focus, whereas those with lower levels of performance-prove goal orientation display a strengthened version of this pathway. Conversely, the indirect effect of FMOE on academic burnout through prevention focus is stronger when performance-prove goal orientation is high and weaker when it is low. Theoretical and practical implications are also discussed. Full article

This study presents a deep learning-based approach that integrates cone-beam computed tomography (CBCT) with facial scan data, aiming to enhance diagnostic accuracy and treatment planning in medical imaging, particularly in cosmetic surgery and orthodontics. The method combines facial mesh detection with the iterative closest point (ICP) algorithm to address common challenges such as differences in data acquisition times and extraneous details in facial scans. By leveraging a deep learning model, the system achieves more precise facial mesh detection, thereby enabling highly accurate initial alignment. Experimental results demonstrate average registration errors of approximately 0.3 mm (inlier RMSE), even when CBCT and facial scans are acquired independently. These results should be regarded as preliminary, representing a feasibility study rather than conclusive evidence of clinical accuracy. Nevertheless, the approach demonstrates consistent performance across different scan orientations, suggesting potential for future clinical application. Furthermore, the deep learning framework effectively handles diverse and complex facial geometries, thereby improving the reliability of the alignment process. This integration not only enhances the precision of 3D facial recognition but also improves the efficiency of clinical workflows. Future developments will aim to reduce processing time and enable simultaneous data capture to further improve accuracy and operational efficiency. Overall, this approach provides a powerful tool for practitioners, contributing to improved diagnostic outcomes and optimized treatment strategies in medical imaging. Full article

We developed an artificial intelligence (AI)-based size detection system by combining image recognition and weight analysis to address inconsistent sizing in manually processed sashimi products. Using a custom imaging setup and YOLOv11_obb, the system detects sashimi pieces and accounts for orientation variations. It measures their size based on their area and weight, ensuring compliance with quality standards. This system reduces human error, identifies out-of-spec products at an early stage, and prevents defective shipments. The developed system demonstrated high detection accuracy, although its classification precision needs to be enhanced. The system is a promising tool for enhancing efficiency and quality control in seafood processing environments. Full article

Background/Objectives: Previous research has demonstrated that the initial letters of a word likely play a privileged role in visual word recognition, such that reading and visual recognition errors reflecting changes in this position are much less likely. For example, prior case studies of attentional dyslexia reported that participants were most accurate at rejecting nonwords formed by transposing a word’s first two letters (e.g., WONER from OWNER) compared to transpositions in later positions. The current study aimed to replicate and extend this finding in patients with posterior cortical atrophy (PCA), a neurodegenerative condition associated with visuospatial and attentional impairments. Methods: Two PCA patients completed lexical decision tasks involving five-letter real words and nonwords created either by transposing adjacent letters (in positions 1 + 2, 2 + 3, 3 + 4, or 4 + 5) or using matched nonword controls. To assess robustness, tasks were repeated across test–retest sessions. Stimuli were presented in both canonical horizontal and non-canonical vertical (marquee) formats. Accuracy, response bias, and sensitivity (d′) were estimated, with 95% confidence intervals derived from a nonparametric bootstrap procedure. Within-case logistic regressions were also conducted to illustrate the findings. Results: Both patients showed significantly higher accuracy and lower response bias for 1 + 2 transposition nonwords relative to other positions. This early-letter advantage persisted across test–retest observations and was maintained when words were presented in the vertical format, suggesting orientation-invariant effects. The bootstrap and regression analyses provided convergent support for these results. Conclusions: The findings provide novel evidence in PCA that the encoding of early letter positions operates independently of visual orientation and persists despite attentional deficits. This supports models in which the initial letters serve as a key anchor point in orthographic processing, highlighting the privileged and resilient status of early letter encoding in visual word recognition. Full article

Self-medication is increasingly prevalent among healthcare students, raising concerns about the adequacy of current medical education in promoting safe medication practices. This study aimed to assess the frequency, motivations, and perceptions of self-medication among medical and pharmacy students and to identify educational gaps. A cross-sectional survey was conducted using a structured, anonymous questionnaire distributed to medical and pharmacy students at a single academic institution. The questionnaire assessed self-medication frequency, substances used, motivations, perceived risks, confidence in knowledge, sources of information, and attitudes toward curriculum improvements. Over 50% of participants reported practicing self-medication at least once a month. The most commonly used substances were analgesics and dietary supplements. Main motivations included recognition of symptoms, confidence in personal knowledge, and avoidance of waiting times. Despite receiving university instruction on self-medication risks, students continued to self-medicate, with many relying on the internet as a primary source of information. Only 8% felt very confident in counseling patients on self-medication. A majority (over 70%) expressed a strong interest in integrating dedicated educational modules into the curriculum. There is a clear need for improved, practice-oriented education on self-medication. Future interventions should focus on interdisciplinary teaching, digital literacy, and simulation-based training to foster safer medication practices. Full article

The hybrid work environment significantly undermines the social well-being of employees in the workplace. Existing research predominantly addresses academics’ well-being challenges without offering practical strategies to counter these issues. This study identifies strategies that higher education institutions must adopt to enhance the social well-being of their academics in hybrid work settings. It employs Demerouti’s Job Demands-Resources (JD-R) model and Baumeister and Leary’s theory of the need to belong as its theoretical framework. Using a cross-sectional qualitative approach, semi-structured interviews were guided by an interview schedule to gather data. The sample comprised 23 academics from three campuses of North-West University, and thematic analysis was utilized to analyse the data. The study revealed that growth strategies, such as training, development, and mentoring, are crucial for fostering a sense of belonging, strengthening work relationships, and helping academics connect in a hybrid work environment. Support strategies like providing peer support, management support, physical resources, effective communication, and improvements in job quality enhance academics’ social well-being in this setting. Relationship strategies, which entail organizing social events and promoting a positive organizational culture, are key to encouraging social well-being in the hybrid work environment. Additionally, reward strategies, such as recognition and direct compensation, are essential for reinforcing a sense of belonging, improving work relationships, and enhancing social connections in a hybrid work environment. Intentional, coach-oriented, sensible, and inclusive leadership is vital. The findings offer valuable insights for higher education institutions to adopt a more comprehensive approach to managing the well-being of academic employees. This highlights the need to focus not only on mental and psychological health but also on social well-being. Full article