Advances in ultrasound-assisted starch modification: Parameter optimization, structure-property relationships, and synergistic strategies.

Flavor perception in the oral processing of mixed grain foods: Flavor release and AI.

Artificial Intelligence in Diagnostic Oral and Maxillofacial Imaging, Surgical Applications, and Teledentistry.

Generalized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures

Adopting Artificial Intelligence (Al) Technology in Advertising from the Customer’s Perspective in Egypt

A comprehensive review of emerging protein modification methods: modified properties and potential applications.

The value of artificial intelligence combined with multimodal data analysis in tumor immunotherapy and targeted therapy.

Chitosan nanocomposite films with metal nanoparticles: Synthesis, antimicrobial mechanisms and applications in sustainable packaging.

Unlocking component-level chemical structural information for AI-driven targeted nanoparticle design.

Background: Amblyopia (lazy eye) is the leading cause of preventable monocular vision loss in children worldwide. In low-resource regions, early detection and ongoing treatment are hindered by a shortage of trained specialists and inadequate healthcare infrastructure. Methods: We developed BRIGHTEYE, a mobile health platform designed to provide accessible screening, monitoring, and gamified therapy for pediatric amblyopia. The platform integrates a lightweight artificial intelligence (AI) engine, a secure blockchain-inspired data management framework, and telemedicine features, all optimized for low-end smartphones and limited-bandwidth environments. The AI component employs a compact convolutional neural network (CNN) architecture trained on 1,200 annotated ocular images and evaluated on a held-out test set. Model quantization and pruning were implemented to ensure efficient inference on mobile devices. Results: The BRIGHTEYE AI engine achieved an accuracy of 91.2%, sensitivity of 87.5%, and specificity of 96.3%, with an Area Under the Curve (AUC) of 0.94, indicating strong discriminative capability. Inference times averaged under one second on entry-level Android devices, demonstrating the model’s suitability for deployment in low-resource environments. Conclusion: The BRIGHTEYE platform offers a technically feasible and robust approach to pediatric amblyopia management. Its capacity to deliver accurate screening and continuous therapy monitoring through affordable devices provides a promising foundation for clinical validation and large-scale implementation, potentially transforming eye care accessibility in underserved communities.

PreLora: A fine-tuning approach with low-rank matrix decomposition and prefix tuning for pre-hospital emergency text classification.

LBMS-SAM: Segment anything model guided SEM image segmentation for lithium battery materials.

Two sides to every coin: The mixed impacts of intelligent technology disruption and employees’ implicit belief on taking charge

Generative artificial intelligence technologies are increasingly being used in professional preparation by pre-service teachers, although there are limited empirical data on how these technologies facilitate authoring emergent pedagogical identities. This qualitative research study used the Framework Method to explore the experiences of twenty-three female pre-service teachers pursuing Initial Teacher Education programmes in Punjab, Pakistan, using generative AI in reflection, planning lessons, and writing about their professional experiences. The semi-structured interviews, which were held over Zoom, covered the repetitive and iterative nature of creating, critiquing, and revising AI-mediated pedagogical texts by the participants. Systematic cross-case analysis based on framework matrices demonstrated that there are four broad themes identified through analysis: Strategic Appropriation; Recursive Editing in Identity Formation; Conflicts Between Efficiency and Authenticity as Imperatives; and Different Patterns of Critical Engagement Influenced by previous Technological Experiences. The results show that revision patterns are reproduced, instead of offering first-time AI performances, indicating pedagogical sophistication, making generative tools a kind of mediational resource as novice teachers rehearse, challenge, and otherwise perform the construction of professional selves.

The rapid rise of artificial intelligence (AI) and digital health technologies presents new opportunities for personalized care in multiple sclerosis (MS). However, implementation in routine practice is limited by regulatory hurdles, fragmented infrastructure and a lack of agile real-world evaluation methods. Living Labs (LLs) emerge as dynamic environments for advancing MS care and research, supporting early testing and iterative development of digital tools, while fostering structured collaboration among patients, clinicians, researchers and regulators. In this review, we conceptually frame LLs in MS and provide a concrete, clinic-ready implementation framework for AI-enabled application in real-world practice. Using a digital-based voice task as an exemplar with automated feature extraction, we detail integration patterns and define key performance indicators for feasibility, data quality, usability and clinical utility. We show how this co-designed model can generate decision-relevant evidence, may help shorten time-to-action and embed innovation seamlessly into clinical workflows. Finally, we align LL operations with ethical and regulatory standards and outline strategies to responsibly scale across centres.

This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artificial Intelligence and Machine Learning (AI/ML) technologies for automated diagnosis of Oral Cancer (OC) and Oral Potentially Malignant Disorders (OPMD). The dataset consists of 2,469 images from 331 subjects from four distinct classes: healthy/normal, variations from normal, OPMD, and OC. The images are captured using Android and iOS smartphone cameras under real-world clinical conditions in visible light. Each image is annotated by expert dental surgeons using the open-source VGG image annotator. Elaborate patient metadata, including clinical diagnosis, age, sex, and lifestyle-based risk indicators such as smoking, smokeless tobacco usage, alcohol consumption, and areca nut chewing, are recorded via a customized Jotform. The data collection and handling procedures are adhered to the ethical guidelines outlined in the Declaration of Helsinki and its amendments for research involving human subjects, with informed consent obtained from each subject. The SMART-OM dataset is intended to advance research and development of AI/ML algorithms for automated oral lesion detection.

ABSTRACT The rapid scaling of generative artificial intelligence (GenAI) technology presents opportunities for personalised learning experiences and facilitates collaborative learning, including collaborative argumentation (CA). However, empirical research examining students' perceptions of GenAI‐assisted CA within classroom contexts remains limited. This study explored university students' experiences with GenAI‐assisted CA through in‐depth interviews with 36 students following a CA activity using a ChatGPT4‐embedded argumentation platform developed by the research team. Findings indicate that students viewed GenAI as serving multiple roles, including tool, facilitator, teaching assistant and machine buddy. Students perceived that GenAI‐assisted CA could empower task performance and create a collaborative learning environment. Meanwhile, they found three challenges, including students'‐, AI‐ and learning environment‐related challenges during GenAI‐assisted CA. These findings offer evidence‐based strategies for educators seeking to integrate GenAI into the design of collaborative learning activities and guidelines for developers on the design of pedagogical AI for creating student‐centered GenAI‐powered collaborative learning.

Cancer is one of the leading causes of death worldwide, and early tumor detection can significantly reduce mortality rates. Liquid biopsy is a minimally invasive, repeatable method with a high economic benefit ratio, and it shows excellent prospects for tumor diagnosis. However, the detection methods relying on classical biomarkers have limited sensitivity and accuracy. The application of auxiliary reagents, such as iRGD, promotes the release of alpha-fetoprotein (AFP) to improve the detection efficiency of liver cancer. Artificial intelligence (AI) technology is increasingly being applied as an assistant in tumor diagnosis. It can automatically identify tumor lesions in imaging, analyze tumor-related gene mutations, classify circulating tumor cells (CTCs), and integrate multi-omics data. These auxiliary means have enhanced the efficiency of tumor screening or detection. In this review, we summarize the combined applications of iRGD and AFP. We also discuss emerging new detection techniques, including CTCs, circulating tumor DNA (ctDNA), exosomes, and tumor-educated platelets (TEPs), specifically with the help of AI. The aim is to better understand the auxiliary role of the iRGD reagent and AI technology in early tumor detection.

ABSTRACT The concept of the rule of law is similar to the idea of ‘good’ in that it enjoys universal support. However, a major impediment to the rule of law is emerging through the proliferation of artificial intelligence (AI) technologies. As AI progresses in the academic realm, it is being increasingly integrated into digital decision-making systems in many countries, supplanting human decision-makers. An illustrative case is the use of AI to help judges resolve cases. Nevertheless, this technology often operates as a ‘black box’ because of its intricate nature and legal protections, rendering it opaque. The resulting lack of transparency and the complexity of these systems pose a threat to traditional rule of law principles, such as accountability, transparency, explainability, and fairness. This study delves into the impact of AI on the rule of law, shedding light on the adverse consequences for humanity. It explores the diminishing role of the rule of law as AI becomes deeply embedded in society and it raises questions about its viability in a technocratic society.

This study analyzes how the use of artificial intelligence (AI) technologies influences the emotional well-being of adolescents, taking gender into account. A non-experimental correlational methodology was employed with a sample of 400 secondary school students, selected using probabilistic sampling. Significant links were found between intensive AI use and higher levels of anxiety and depression, as well as lower self-esteem.