Atherosclerosis is a primary cause of cardiovascular and cerebrovascular diseases, with the unpredictable rupture of vulnerable atherosclerotic plaques enriched with lipid-laden macrophages being able to lead to heart attacks and strokes. Activating macrophage autophagy presents itself as a promising strategy for preventing vulnerable plaque formation and reducing the risk of rupture. In this study, we have developed a novel metal-free nanozyme (HCN@DS) that integrates the functions of multimodal imaging-guided therapy for atherosclerosis. HCN@DS has demonstrated high macrophage-targeting abilities due to its affinity toward scavenger receptor A (SR-A), along with excellent photoacoustic and photothermal imaging capabilities for guiding the precise treatment. It combines mild photothermal effects with moderate reactive oxygen species (ROS) generation to treat atherosclerosis. This controlled approach activates autophagy in atherosclerotic macrophages, inhibiting foam cell formation by reducing the uptake of oxidized low-density lipoproteins (oxLDL) and promoting efferocytosis and cholesterol efflux in macrophages. Additionally, it prevents plaque rupture by inhibiting apoptosis and inflammation within the plaque. Therefore, this metal-free nanozyme holds great potential for reducing the risk of atherosclerosis due to its high biosafety, excellent targeting ability, dual-modality imaging capability, and appropriate modulation of autophagy.

This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events. The data of 180 patients with carotid atherosclerosis plaques were analysed from January 2017 to December 2021; 110 patients (64.03±9.58 years old, 20 women, 90 men) were allocated to the symptomatic group, and 70 patients (64.70±9.89 years old, 50 women, 20 men) were allocated to the asymptomatic group. Overall, five machine learning models using the XGBoost algorithm, based on different CT and clinical features, were developed in the training cohort. The performances of all five models were assessed in the testing cohort using receiver operating characteristic curves, accuracy, recall rate, and F1 score. The shapley additive explanation (SHAP) value ranking showed fat fraction (FF) as the highest among all CT and clinical features and normalised iodine density (NID) as the 10th. The model based on the top 10 features from the SHAP measurement showed optimal performance (area under the curve [AUC] .885, accuracy .833, recall rate .933, F1 score .861), compared with the other four models based on conventional CT features (AUC .588, accuracy .593, recall rate .767, F1 score .676), DECT features (AUC .685, accuracy .648, recall rate .667, F1 score .678), conventional CT and DECT features (AUC .819, accuracy .740, recall rate .867, F1 score .788), and all CT and clinical features (AUC .878, accuracy .833, recall rate .867, F1 score .852). FF and NID can serve as useful imaging markers of symptomatic carotid plaques. This tree-based machine learning model incorporating both DECT and clinical features could potentially comprise a non-invasive method for identification of symptomatic carotid plaques to guide clinical treatment strategies.

Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. The SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. Deep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

Despite advances in our understanding of spinal cord injury (SCI) mechanisms, there are still no effective treatment approaches to restore functionality. Although many studies have demonstrated that transplanting NT3 gene‐transfected bone marrow‐derived mesenchymal stem cells (BMSCs) is an effective approach to treat SCI, the approach is often low efficient in the delivery of engrafted BMSCs to the site of injury. In this study, we investigated the therapeutic effects of magnetic targeting of NT3 gene‐transfected BMSCs via lumbar puncture in a rat model of SCI. With the aid of a magnetic targeting cells delivery system, we can not only deliver the engrafted BMSCs to the site of injury more efficiently, but also perform cells imaging in vivo using MR. In addition, we also found that this composite strategy could significantly improve functional recovery and nerve regeneration compared to transplanting NT3 gene‐transfected BMSCs without magnetic targeting system. Our results suggest that this composite strategy could be promising for clinical applications.

Stem cell imaging in vivo is critical to elucidate the homing, distribution, survival, and repair mechanisms and to evaluate the therapeutic effects of engrafted stem cells. Unfortunately, unimodal imaging of stem cells does not simultaneously satisfy all current requirements owing to their intrinsic limitations. Obviously, bimodal or multimodal imaging of stem cells is a promising strategy for circumventing this issue. This study aimed to design and synthesize a novel dual-modal polyethylene glycol-modified magnetic nanoparticle (Fe³⁺-PEG-MNP) based on natural biomaterials including melanin and Fe ions for photoacoustic (PA) and magnetic resonance (MR) imaging of stem cells in vivo. The Fe³⁺-PEG-MNPs were characterized and their PA/MR imaging capability and cytotoxicity were evaluated. Bone marrow mesenchymal stem cells (BM-MSCs) labeled with Fe³⁺-PEG-MNPs were subjected to PA and MR imaging in vitro and in vivo. Consequently, Fe³⁺-PEG-MNPs displayed many superior properties, including ultra-small particle size, higher stability, water solubility, easy labeling of cells, lower cytotoxicity, high biosafety, excellent capability of PA/MR imaging, high sensitivity and long-term monitoring in vitro and in vivo. In particular, PA and MR signals of labeled BM-MSCs were maintained for at least 35 and 28 d, respectively, in vivo. Therefore, Fe³⁺-PEG-MNPs are ideal dual-modal PA/MR nanoparticles for non-invasive and effective monitoring of engrafted stem cells in vivo.