RESUMO
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.
RESUMO
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.
