RESUMEN
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
RESUMEN
Diabetic retinopathy (DR) remains a leading cause of vision loss worldwide, with early detection critical for preventing irreversible damage. This review explores the current landscape and future directions of artificial intelligence (AI)-enhanced detection of DR from fundus images. Recent advances in deep learning and computer vision have enabled AI systems to analyze retinal images with expert-level accuracy, potentially transforming DR screening. Key developments include convolutional neural networks achieving high sensitivity and specificity in detecting referable DR, multi-task learning approaches that can simultaneously detect and grade DR severity, and lightweight models enabling deployment on mobile devices. While these AI systems show promise in improving the efficiency and accessibility of DR screening, several challenges remain. These include ensuring generalizability across diverse populations, standardizing image acquisition and quality, addressing the "black box" nature of complex models, and integrating AI seamlessly into clinical workflows. Future directions in the field encompass explainable AI to enhance transparency, federated learning to leverage decentralized datasets, and the integration of AI with electronic health records and other diagnostic modalities. There is also growing potential for AI to contribute to personalized treatment planning and predictive analytics for disease progression. As the technology continues to evolve, maintaining a focus on rigorous clinical validation, ethical considerations, and real-world implementation will be crucial for realizing the full potential of AI-enhanced DR detection in improving global eye health outcomes.
RESUMEN
Atrial fibrillation (AF) is a common cardiac arrhythmia with a significant impact on patient outcomes and healthcare systems. Given the rising incidence of AF with age and its association with conditions, such as diabetes, there is growing interest in exploring pharmacological interventions that might mitigate AF risk. Metformin, a widely prescribed antihyperglycemic agent for type 2 diabetes mellitus (T2DM), has demonstrated various cardiovascular benefits, including anti-inflammatory and antioxidative properties, leading to speculations about its potential role in AF prevention. This systematic review synthesizes findings from five studies examining the association between metformin use and AF risk in patients with T2DM. The review included a dynamic cohort study, three retrospective cohort studies, and a case report, all sourced from databases, such as PubMed, Embase, and the Cochrane Library. The results are mixed; while some studies suggest that metformin use is linked to a reduced incidence of AF, others report no significant association, particularly in postoperative settings. The largest cohort study highlighted a dose-response relationship, suggesting prolonged metformin use correlates with lower AF risk. Conversely, a case report raised concerns about metformin-induced lactic acidosis potentially triggering AF episodes. The review underscores the heterogeneity in study designs and outcomes, pointing to the need for more robust research to establish causality and clarify underlying mechanisms. Future studies should prioritize prospective designs and explore the pleiotropic effects of metformin on atrial remodeling and electrophysiology to better understand its potential role in AF prevention.
RESUMEN
Alzheimer's disease (AD), a neurodegenerative disorder characterized by cognitive decline, poses a significant healthcare challenge worldwide. The accumulation of amyloid-beta (Aß) plaques and hyperphosphorylated tau protein drives neuronal degeneration and neuroinflammation, perpetuating disease progression. Despite advancements in understanding the cellular and molecular mechanisms, treatment hurdles persist, emphasizing the need for innovative intervention strategies. Quantum dots (QDs) emerge as promising nanotechnological tools with unique photo-physical properties, offering advantages over conventional imaging modalities. This systematic review endeavors to elucidate the theranostic potential of QDs in AD by synthesizing preclinical and clinical evidence. A comprehensive search across electronic databases yielded 20 eligible studies investigating the diagnostic, therapeutic, or combined theranostic applications of various QDs in AD. The findings unveil the diverse roles of QDs, including inhibiting Aß and tau aggregation, modulating amyloidogenesis pathways, restoring membrane fluidity, and enabling simultaneous detection of AD biomarkers. The review highlights the potential of QDs in targeting multiple pathological hallmarks, delivering therapeutic payloads across the blood-brain barrier, and facilitating real-time imaging and high-throughput screening. While promising, challenges such as biocompatibility, surface modifications, and clinical translation warrant further investigation. This systematic review provides a comprehensive synthesis of the theranostic potential of QDs in AD, paving the way for translational research and clinical implementation.