Stem cell transplantation is a promising therapeutic strategy for myocardial infarction. However, transplanted cells face low survival rates due to oxidative stress and the inflammatory microenvironment in ischemic heart tissue. Melatonin has been used as a powerful endogenous antioxidant to protect cells from oxidative injury. However, melatonin cannot play a long-lasting effect against the hostile microenvironment. Nano drug delivery carriers have the ability to protect the loaded drug from degradation in physiological environments in a controlled manner, which results in longer effects and decreased side effects. Therefore, we constructed poly(lactide-co-glycolide)-monomethoxy-poly-(polyethylene glycol) (PLGA-mPEG) nanoparticles to encapsulate melatonin. We tested whether the protective effect of melatonin encapsulated by PLGA-mPEG nanoparticles (melatonin nanoparticles [Mel-NPs]) on adipose-derived mesenchymal stem cells (ADSCs) was enhanced compared to that of free melatonin both in vitro and in vivo. In the in vitro study, we found that Mel-NPs reduced formation of the p53- cyclophilin D complex, prevented mitochondrial permeability transition pores from opening, and rescued ADSCs from hypoxia/reoxygenation injury. Moreover, Mel-NPs can achieve higher ADSC survival rates than free melatonin in rat myocardial infarction areas, and the therapeutic effects of ADSCs pretreated with Mel-NPs were more apparent. Hence, the combination of Mel-NPs and stem cell transplantation may be a promising strategy for myocardial infarction therapy. Stem Cells 2018;36:540-550.
Current intravascular imaging modalities face hurdles in the molecular evaluation of progressed plaques. This study aims to construct a novel hybrid imaging system (intravascular ultrasound/intravascular photoacoustic, IVPA/IVUS)
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RGDfk peptide-targeted nanoparticles for monitoring angiogenesis in progressed atherosclerotic plaques in a rabbit model. An atherosclerotic rabbit model was induced by abdominal aorta balloon de-endothelialization followed by a high-fat diet. A human serum albumin (HSA)-based nanoprobe modified with RGDfk peptide was constructed by encapsulating indocyanine green (ICG)
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electrostatic force (ICG-HSA-RGDfk NPs, IHR-NPs). A hybrid intravascular imaging system that combined IVUS and IVPA was self-assembled for RGDfk visualization within atherosclerotic plaques in the rabbit abdominal aorta. Through IHR-NPs and the hybrid IVUS/IVPA imaging platform, multiple comprehensive pieces of information on progressed plaques, including anatomical information, composition information and molecular information, can be obtained simultaneously, which may improve the precise diagnosis of plaque characteristics and the evaluation of early interventions for atherosclerosis.
Aims
More patients with suspected coronary artery disease underwent coronary computed tomography angiography (CCTA) as gatekeeper. However, the prospective relation of plaque features to acute coronary syndrome (ACS) events has not been previously explored.
Methods and results
One hundred and one out of 452 patients with documented ACS event and received more than once CCTA during the past 12 years were recruited. Other 101 patients without ACS event were matched as case control. Baseline, follow-up, and changes of anatomical, compositional, and haemodynamic parameters [e.g. luminal stenosis, plaque volume, necrotic core, calcification, and CCTA-derived fractional flow reserve (CT-FFR)] were analysed by independent CCTA measurement core laboratories. Baseline anatomical, compositional, and haemodynamic parameters of lesions showed no significant difference between the two cohorts (P > 0.05). While the culprit lesions exhibited significant increase of luminal stenosis (10.18 ± 2.26% vs. 3.62 ± 1.41%, P = 0.018), remodelling index (0.15 ± 0.14 vs. 0.09 ± 0.01, P < 0.01), and necrotic core (4.79 ± 1.84% vs. 0.43 ± 1.09%, P = 0.019) while decrease of CT-FFR (−0.05 ± 0.005 vs. −0.01 ± 0.003, P < 0.01) and calcium ratio (−4.28 ± 2.48% vs. 4.48 ± 1.46%, P = 0.004) between follow-up CCTA and baseline scans in comparison to that of non-culprit lesion. The XGBoost model comprising the top five important plaque features revealed higher predictive ability (area under the curve 0.918, 95% confidence interval 0.861–0.968).
Conclusions
Dynamic changes of plaque features are highly relative with subsequent ACS events. The machine learning model of integrating these lesion characteristics (e.g. CT-FFR, necrotic core, remodelling index, plaque volume, and calcium) can improve the ability for predicting risks of ACS events.
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