Search Results (1,636)

This article presents a case of strategic and participatory institutional innovation in higher education, focused on developing teacher digital competence (TDC) as a key enabler of sustainable digital transformation. In response to the post-pandemic challenges faced by the National University of Cuyo (UNCuyo), a large and multi-campus public university in Argentina, the European CUTE methodology was adapted and implemented to align professional development with institutional planning. Grounded in the DigCompEdu framework, this action-oriented process moved beyond individual initiatives to create a coordinated, multi-level strategy involving educators, department leaders, and university authorities. Through a research-based design that included context analysis, participatory diagnosis, and co-designed interventions, the project built a shared understanding of digital teaching needs and institutional readiness. The implementation highlights how locally adapted frameworks, collaborative structures, and iterative decision-making can drive meaningful change across a complex university system. This case contributes to the international conversation on how higher education institutions can operationalize innovation at scale by investing in teacher competence, inclusive processes, and strategic alignment. Lessons learned from this experience are relevant for universities seeking to build institutional capacity for digital transformation in diverse educational contexts with potential downstream benefits for student learning and inclusion. Full article

Drawing inspiration from biological brains’ energy-efficient information-processing mechanisms, photonic integrated circuits (PICs) have facilitated the development of ultrafast artificial neural networks. This in turn is envisaged to offer potential solutions to the growing demand for artificial intelligence employing machine learning in various domains, from nonlinear optimization and telecommunication to medical diagnosis. In the meantime, silicon photonics has emerged as a mainstream technology for integrated chip-based applications. However, challenges still need to be addressed in scaling it further for broader applications due to the requirement of co-integration of electronic circuitry for control and calibration. Leveraging physics in algorithms and nanoscale materials holds promise for achieving low-power miniaturized chips capable of real-time inference and learning. Against this backdrop, we present the State of the Art in neuromorphic photonic computing, focusing primarily on architecture, weighting mechanisms, photonic neurons, and training, while giving an overall view of recent advancements, challenges, and prospects. We also emphasize and highlight the need for revolutionary hardware innovations to scale up neuromorphic systems while enhancing energy efficiency and performance. Full article

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive type of pancreatic cancer. PDAC is difficult to diagnose due to a lack of symptoms in early stages, resulting in a survival rate of less than 10%. Moreover, often cancerous tissues cannot be surgically resected due to their deep abdomen location. Therefore, early detection is the essential strategy enabling effective PDAC treatment. Over the past few years, the development of nanomaterials for Magnetic Resonance Imaging (MRI) has expanded and improved imaging quality and diagnostic accuracy. Nanomaterials can be currently designed, manufactured and synthesized with other structures to provide improved diagnosis and advanced therapy. Although MRI equipped with the innovative nanomaterials became a powerful tool for the diagnosis and treatment of patients with various cancers, the detection of PDAC remains challenging. Nevertheless, recent advancements in PDAC theranostics provided progress in the detection and treatment of this challenging type of cancer. Present research in this area is focused on suitable carriers, eliminating delivery barriers, and the development of efficient anti-cancer drugs. Herein we discuss the current applications of iron oxide nanoparticles to the MRI diagnosis and treatment of pancreatic cancer. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Nanotechnology offers innovative methodologies for enhancing the diagnosis and treatment of ovarian cancer by utilizing specialized nanoparticles. The utilization of nanoparticles offers distinct advantages, specifically that these entities enhance the bioavailability of therapeutic agents and facilitate the targeted delivery of pharmacological agents to neoplastic cells. A diverse array of nanoparticles, including but not limited to liposomes, dendrimers, and gold nanoparticles, function as proficient carriers for drug delivery. Nevertheless, notwithstanding the auspicious potential of these applications, challenges pertaining to toxicity, biocompatibility, and the necessity for comprehensive clinical evaluations pose considerable barriers to the widespread implementation of these technologies. The incorporation of nanotechnology into clinical practice holds the promise of significantly transforming the management of ovarian cancer, offering novel diagnostic tools and therapeutic strategies that enhance patient outcomes and prognoses. In summary, the deployment of nanotechnology in the context of ovarian cancer epitomizes a revolutionary paradigm in medical science, amalgamating sophisticated materials and methodologies to enhance both diagnostic and therapeutic outcomes. Continued research and development endeavors are essential to fully realize the extensive potential of these innovative solutions and address the existing challenges associated with their application in clinical settings. Full article

Acute lymphoblastic leukemia (ALL) is a heterogeneous hematologic malignancy defined by the uncontrolled proliferation of lymphoid precursors. Accurate diagnosis and effective therapeutic strategies hinge on a comprehensive understanding of the genetic and molecular landscape of ALL. This review synthesizes the latest updates in cytogenetic and molecular classifications, emphasizing the 2022 World Health Organization (WHO) and International Consensus Classification (ICC) revisions. Key chromosomal alterations such as BCR::ABL1 and ETV6::RUNX1 and emerging subtypes including Ph-like ALL, DUX4, and MEF2D rearrangements are examined for their prognostic significance. Furthermore, we assess novel diagnostic tools, notably next-generation sequencing (NGS) and optical genome mapping (OGM). While NGS excels at identifying point mutations and small indels, OGM offers high-resolution structural variant detection with 100% sensitivity in multiple validation studies. These advancements enhance our grasp of leukemogenesis and pave the way for precision medicine in both B- and T-cell ALL. Ultimately, integrating these innovations into routine diagnostics is crucial for personalized patient management and improving clinical outcomes. Full article

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article

Neurodevelopmental disorders (NDDs) significantly impact children’s health and development. They pose a substantial burden to families and the healthcare system. Challenges in early identification, accurate and timely diagnosis, and effective treatment persist due to overlapping symptoms, lack of appropriate diagnostic biomarkers, significant stigma and discrimination, and systemic barriers. Generative Artificial Intelligence (GenAI) offers promising solutions to these challenges by enhancing screening, diagnosis, personalized treatment, and research. Although GenAI is already in use in some aspects of NDD management, effective and strategic leveraging of evolving AI tools and resources will enhance early identification and screening, reduce diagnostic processing by up to 90%, and improve clinical decision support. Proper use of GenAI will ensure individualized therapy regimens with demonstrated 36% improvement in at least one objective attention measure compared to baseline and 81–84% accuracy relative to clinician-generated plans, customize learning materials, and deliver better treatment monitoring. GenAI will also accelerate NDD-specific research and innovation with significant time savings, as well as provide tailored family support systems. Finally, it will significantly reduce the mortality and morbidity associated with NDDs. This article explores the potential of GenAI in transforming NDD management and calls for policy initiatives to integrate GenAI into NDD management systems. Full article

Molar pregnancy (MP) is a gestational disorder resulting from abnormal fertilization, leading to atypical trophoblastic proliferation and the formation of a complete or partial hydatidiform mole. This condition represents the most common form of gestational trophoblastic disease (GTD) and carries a significant risk of progression to gestational trophoblastic neoplasia (GTN). Although rare in high-income countries, MP remains up to ten times more prevalent in low-income and developing countries, contributing to preventable maternal morbidity and mortality. This narrative review provides an updated, practical overview of the clinical presentation, diagnosis, treatment, and follow-up of MP. A key focus is the challenge of early diagnosis, particularly given the increasing frequency of first-trimester detection, where classical histopathological criteria may be subtle, leading to diagnostic errors. The review innovates by integrating advanced diagnostic methods—combining histopathology, immunohistochemistry using p57Kip2, Ki-67, and p53 markers, along with cytogenetic analysis—to improve diagnostic accuracy in early gestation. The central role of referral centers is also emphasized, not only in facilitating timely treatment and access to chemotherapy, but also in implementing standardized post-molar follow-up protocols that reduce progression to GTN and maternal mortality. By focusing on both advanced diagnostic strategies and the organization of care through referral centers, this review offers a comprehensive, practice-oriented perspective to optimize patient outcomes in GTD and address persistent care gaps in high-burden regions. Full article

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Predicting its progression is crucial for preventing late-stage AMD, as it is an irreversible retinal disease. Both genetic factors and retinal images are instrumental in diagnosing and predicting AMD progression. Previous studies have explored automated diagnosis using single fundus images and genetic variants, but they often fail to utilize the valuable longitudinal data from multiple visits. Longitudinal retinal images offer a dynamic view of disease progression, yet standard Long Short-Term Memory (LSTM) models assume consistent time intervals between training and testing, limiting their effectiveness in real-world settings. To address this limitation, we propose time-varied Long Short-Term Memory (TV-LSTM), which accommodates irregular time intervals in longitudinal data. Our innovative approach enables the integration of both longitudinal fundus images and AMD-associated genetic variants for more precise progression prediction. Our TV-LSTM model achieved an AUC-ROC of 0.9479 and an AUC-PR of 0.8591 for predicting late AMD within two years, using data from four visits with varying time intervals. Full article

Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical knowledge, capturing global multimodal features, and accurately delineating tumor boundaries. To address these challenges, the Hybrid Network for Multimodal Brain Tumor Segmentation (HN-MBTS) is proposed, which incorporates prior medical knowledge to refine feature extraction and boundary precision. Key innovations include the Two-Branch, Two-Model Attention (TB-TMA) module for efficient multimodal feature fusion, the Linear Attention Mamba (LAM) module for robust multi-scale feature modeling, and the Residual Attention (RA) module for enhanced boundary refinement. Experimental results demonstrate that this method significantly outperforms existing approaches. On the BraT2020 and BraT2023 datasets, the method achieved average Dice scores of 87.66% and 88.07%, respectively. These results confirm the superior segmentation accuracy and efficiency of the approach, highlighting its potential to provide valuable assistance in clinical settings. Full article

Background/Objectives: Neurodegenerative proteinopathies, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB), are increasingly prevalent worldwide mainly due to population aging. These conditions are marked by complex etiologies, overlapping pathologies, and progressive clinical decline, with significant consequences for patients, caregivers, and healthcare systems. This review aims to synthesize evidence on the healthcare complexities of major neurodegenerative proteinopathies to highlight current knowledge gaps, and to inform future care models, policies, and research directions. Methods: We conducted a comprehensive literature search in PubMed/MEDLINE using combinations of MeSH terms and keywords related to neurodegenerative diseases, proteinopathies, diagnosis, sex, management, treatment, caregiver burden, and healthcare delivery. Studies were included if they addressed the clinical, pathophysiological, economic, or care-related complexities of aging-related neurodegenerative proteinopathies. Results: Key themes identified include the following: (1) multifactorial and unclear etiologies with frequent co-pathologies; (2) long prodromal phases with emerging biomarkers; (3) lack of effective disease-modifying therapies; (4) progressive nature requiring ongoing and individualized care; (5) high caregiver burden; (6) escalating healthcare and societal costs; and (7) the critical role of multidisciplinary and multi-domain care models involving specialists, primary care, and allied health professionals. Conclusions: The complexity and cost of neurodegenerative proteinopathies highlight the urgent need for prevention-focused strategies, innovative care models, early interventions, and integrated policies that support patients and caregivers. Prevention through the early identification of risk factors and prodromal signs is critical. Investing in research to develop effective disease-modifying therapies and improve early detection will be essential to reducing the long-term burden of these disorders. Full article

Theranostic materials, which combine therapeutic and diagnostic capabilities, represent a promising advancement in cancer treatment by improving both the precision and personalization of therapies. Recently, metal peroxides (MePOs) have attracted significant interest from researchers for their potential use in both cancer diagnosis and therapy. This review provides an overview of recent developments in the application of MePOs for innovative cancer treatment strategies. The unique properties of MePOs, such as oxygen generation, are highlighted for their potential to improve therapeutic outcomes, especially in hypoxic tumor microenvironments. Initially, methods for MePO synthesis are briefly discussed, including hydrolyzation–precipitation, reversed-phase microemulsion, and sonochemical techniques, emphasizing the role of surfactants in regulating the particle size and enhancing bioactivity. Next, we discuss the main therapeutic approaches where MePOs have shown promise. These applications include chemotherapy, photodynamic therapy (PDT), immunotherapy, and radiation therapy. Overall, we focus on integrating MePOs into theranostic platforms to enhance cancer treatment and enable diagnostic imaging for improved clinical outcomes. Finally, we discuss potential future research directions that could lead to clinical translation and the development of advanced medicines. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine. Full article