Magnetic resonance angiography using gadolinium-based molecular contrast agents suffers from short diagnostic window, relatively low resolution and risk of toxicity. Taking into account the chemical exchange between metal centers and surrounding protons, magnetic nanoparticles with suitable surface and interfacial features may serve as alternative T1 contrast agents. Herein, we report the engineering on surface structure of iron oxide nanoplates to boost T1 contrast ability through synergistic effects between exposed metal-rich Fe3O4(100) facets and embedded Gd2O3 clusters. The nanoplates show prominent T1 contrast in a wide range of magnetic fields with an ultrahigh r1 value up to 61.5 mM(-1) s(-1). Moreover, engineering on nanobio interface through zwitterionic molecules adjusts the in vivo behaviors of nanoplates for highly efficient magnetic resonance angiography with steady-state acquisition window, superhigh resolution in vascular details, and low toxicity. This study provides a powerful tool for sophisticated design of MRI contrast agents for diverse use in bioimaging applications.

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We report the synthesis of a novel 2D hybrid nanosheet constructed by few layered MoSe2 grown on reduced graphene oxide (rGO), which exhibits excellent electrochemical performance as anodes for lithium ion batteries.

Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.

Recent advances in wearable sweat sensors with noninvasive health monitoring capabilities have provided enormous potential for point-of-care testing (POCT) diagnostics and personalized medicine. In this paper, we present a flexible,...

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To realize the nature of water coordination on the solid surfaces may provoke essential understanding of T1 relaxation enhancement, especially in nanoparticulate systems. We report herein that T1 relaxivity of Gd2O3 nanoplates is highly dependent on water coordinating behaviors on different surfaces. Gd2O3 nanoplates with metal-rich {100} facets showed an approximately 4-fold higher r1 value comparing to that with oxygen-terminated {111} facets. Density functional theory (DFT) calculations show that the enhanced T1 relaxivity of Gd2O3 {100} nanoplates may be ascribed to the high density of accessible Gd3+, fast exchange of water, and more importantly, multicenter (one-to-two) coordination for water molecules with magnetic centers on the metal-rich surface.

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.