We developed a novel Ru(bpy)-based electrochemiluminescence (ECL) immunosensor utilizing palladium nanoparticle (Pd NP)-functionalized graphene-aerogel-supported FeO (FGA-Pd) for real-sample analysis of prostate specific antigen (PSA). 3D nanostructured FGA-Pd, as a novel ECL carrier, was prepared by in situ reduction. Large amounts of Ru(bpy) could combine with FGA-Pd via electrostatic interaction to establish a brand-new ECL emitter (Ru@FGA-Pd) for improving ECL efficiency. The obtained Ru@FGA-Pd composite was utilized to label the secondary antibody, which generated strong ECL signals with tripropylamine (TPrA) as a coreactant. Furthermore, we demonstrated that the participation of Pd NPs endowed FGA with favorable electrocatalytic ability in the luminescence process to produce more excited state [Ru(bpy)]* for realizing desirable signal amplification. In addition, the primary antibody was captured by gold nanoparticle (Au NP)-functionalized FeO nanodendrites (Au-FONDs), which possessed good electrical conductivity and favorable biocompatibility. Under optimum conditions, the fabricated sandwich-type ECL immunosensor showed a sensitive response to PSA with a low detection limit of 0.056 pg/mL (S/N = 3) and a calibration range of 0.0001-50 ng/mL. Featuring favorable selectivity, stability, and repeatability, the proposed immunosensor is expected to blaze a novel trail for the real sample detection of PSA and other biomarkers.

The detection of insulin by electrochemical (EC) immunoassay is desirable but highly challenged due to the obstacle of improving its accuracy, especially in a single-response system. In this work, based on Cu 7 S 4 −Au as a dual signal indicator, we fabricated a dual-mode electrochemical immunoassay for insulin. Especially, Cu 7 S 4 presents a strong differential pulse voltammetry (DPV) signal for the electron transfer between Cu 2+ and Cu + , without the addition of K 3 [Fe(CN) 6 ] or other electron transfer mediators. Furthermore, Cu 7 S 4 displays high sensitivity and high electrocatalytic activity toward the reduction of H 2 O 2 through chronoamperometry (CA). The introduction of Au nanoparticles can not only link on the surface of Cu 7 S 4 by the chemical bond of Au−SH, but also connect the second antibody (Ab 2 ) by the chemical bond of Au−N. Due to the superior electroconductivity of Au nanoparticles and the synergistic effect between the Au nanoparticles and Cu 7 S 4 , a high sensitivity is achieved by means of DPV and CA. To improve the loading capacity of antibodies, nanofiber polyaniline covalently grafted graphene (GS−PANI) linked with Au nanoparticles (GS− PANI−Au) as the matrix material was prepared. Based on Cu 7 S 4 −Au as a double signal indicator, the developed EC immunoassay for insulin exhibits a wide linear response for insulin detection in the range from 0.1 pg/mL to 50 ng/mL, with a low detection limit of 35.8 and 12.4 fg/mL through DPV and CA modes, respectively. Furthermore, the immunosensor displays an excellent analytical capability for insulin and promises application in quantitative detection of other disease markers in clinical diagnosis.

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Directing of the task-specific attention while tracking instrument in surgery holds great potential in robot-assisted intervention. For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction. Our model is designed with a weight-shared encoder and two task-oriented decoders and optimized for the joint tasks. We introduce batch-Wasserstein (bW) loss and construct a soft attention module to refine the distinctive visual region for efficient saliency learning. For multitask optimization, it is always challenging to obtain convergence of both tasks in the same epoch. We deal with this problem by adopting 'poly' loss weight and two phases of training. We further propose a novel way to generate task-aware saliency map and scanpath of the instruments on MICCAI robotic instrument segmentation dataset. Compared to the state of the art segmentation and saliency models, our model outperforms most of the evaluation metrics.

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