Atherosclerotic plaque rupture results in thrombus formation and vessel occlusion, and is the leading cause of death worldwide. There is a pressing need to identify plaque vulnerability for the treatment of carotid and coronary artery diseases. Nanomaterials with enzyme-like properties have attracted significant interest by providing biological, diagnostic and prognostic information about the diseases. Here we showed that bioengineered magnetoferritin nanoparticles (M-HFn NPs) functionally mimic peroxidase enzyme and can intrinsically recognize plaque-infiltrated active macrophages, which drive atherosclerotic plaque progression and rupture and are significantly associated with the plaque vulnerability. The M-HFn nanozymes catalyze the oxidation of colorimetric substrates to give a color reaction that visualizes the recognized active macrophages for one-step pathological identification of plaque vulnerability. We examined 50 carotid endarterectomy specimens from patients with symptomatic carotid disease and demonstrated that the M-HFn nanozymes could distinguish active macrophage infiltration in ruptured and high-risk plaque tissues, and M-HFn staining displayed a significant correlation with plaque vulnerability (r = 0.89, P < 0.0001).
Emerging privacy-preserving technologies help protect sensitive data during application executions. Recently, the secure two-party computing (TPC) scheme has demonstrated its potential, especially for the secure model inference of a deep learning application by protecting both the user input data and the model parameters. Nevertheless, existing TPC protocols incur excessive communications during the program execution, which lengthens the execution time. In this work, we propose the precomputing scheme, POPS, to address the problem, which is done by shifting the required communications from during the execution to the time prior to the execution. Particular, the multiplication triple generation is computed beforehand with POPS to remove the overhead at runtime. We have analyzed the TPC protocols to ensure that the precomputing scheme conforms the existing secure protocols. Our results show that POPS takes a step forward in the secure inference by delivering up to \(20\times \) and \(5\times \) speedups against the prior work for the microbenchmark and the convolutional neural network experiments, respectively.