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As nanotechnologies advance into clinical medicine, novel methods for applying nanomedicine to cardiovascular diseases are emerging. Extensive research has been undertaken to unlock the complex pathogenesis of atherosclerosis. However, this complexity presents challenges to develop effective imaging and therapeutic modalities for early diagnosis and acute intervention. The choice of ligand-receptor system vastly influences the effectiveness of nanomedicine. This review collates current ligand-receptor systems used in targeting functionalized nanoparticles for diagnosis and treatment of atherosclerosis. Our focus is on the binding affinity and selectivity of ligand-receptor systems, as well as the relative abundance of targets throughout the development and progression of atherosclerosis. Antibody-based targeting systems are currently the most commonly researched due to their high binding affinities when compared with other ligands, such as antibody fragments, peptides, and other small molecules. However, antibodies tend to be immunogenic due to their size. Engineering antibody fragments can address this issue but will compromise their binding affinity. Peptides are promising ligands due to their synthetic flexibility and low production costs. Alongside the aforementioned binding affinity of ligands, the choice of target and its abundance throughout distinct stages of atherosclerosis and thrombosis is relevant to the intended purpose of the nanomedicine. Further studies to investigate the components of atherosclerotic plaques are required as their cellular and molecular profile shifts over time.

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Objectives : Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai)- for detection of traumatic injuries on supine chest radiographs. Methods: Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen’s κ and sensitivity/specificity for both AI and radiologists were calculated. Results: There were 1404 cases identified with a median age of 52 (IQR 33–69) years, 949 male. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum. Conclusion: The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes. Advances in knowledge: Clinically useful AI programs represent promising decision support tools.

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