In this work, a novel electrochemical immunoassay protocol has been reported for simultaneous determination of multiple tumor biomarkers based on DNA hybridization chain reaction (HCR) for signal amplification. Alpha-fetoprotein (AFP) and prostate specific antigen (PSA) were selected as model biomarkers. The immunoassay protocol contained primary antibodies immobilized on gold nanoparticles (Au NPs), secondary antibodies conjugated with DNA concatemer from HCR of primer, auxiliary probe, and signal probe labeled with signal molecules (methyleneblue (MB) and ferrocene (Fc)). In the presence of target biomarkers, the sandwich immunocomplex was formed between the primary antibodies and secondary antibodies bioconjugates carrying numerous signal molecules. As a result, two well-resolved reduction peaks, one was at -0.35 V (corresponding to MB) and other was at 0.33 V (corresponding to Fc; both vs SCE), were obtained in differential pulse voltammetry, and peak currents changed were related to the level of biomarkers. Under optimal conditions, the electrochemical immunoassay exhibited a wide linear response range (0.5 pg mL(-1) to 50 ng mL(-1)) and low detection limits (PSA, 0.17 pg mL(-1); AFP, 0.25 pg mL(-1)) (at S/N = 3). In addition, the immunoassay was evaluated by analyzing simulate human serum sample, and the recoveries obtained were within 99.4-107.6% for PSA and 97.9-108.2% for AFP, indicating the immnuoassay could be applied to the simultaneous detection of AFP and PSA in human serum samples.

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Fluoroimmunoassay for simultaneous detection of CEA and AFP.

We describe an electrochemical immunosensor for the simultaneous determination of alpha-fetoprotein (AFP) and prostate specific antigen (PSA) via a modified glassy carbon electrode. Silica nanoparticles (200-300 nm i.d.) with good monodispersity and uniform shape were synthesized, and the following species were then consecutively immobilized on their surface: gold nanoparticles (AuNPs; 5-15 nm i.d.), secondary antibody (Ab 2 ) and the redox-probes Azure A or ferrocenecarboxy acid (Fc). In parallel, two types of primary antibodies (Ab 1 ) were co-immobilized on the surface of the dissolved reduced graphene oxide sheets (rGO) that were also decorated with AuNPs. In the presence of antigens (AFP or PSA), the Ab 2 /Si@AuNPs carrying Azure A and Fc are attached to the AuNP/rGO conjugate via a sandwich type immunoreaction. Differential pulse voltammetry (DPV) was employed to measure the resulting changes in the signal of Fc or Azure A. Two well-resolved oxidation peaks, one at −0.48 V (corresponding to Azure A) and other at+0.12 V (corresponding to Fc; both vs. SCE) can be observed in the DPV curves. Under optimal conditions, AFP and PSA can be simultaneously determined in the range from 0.01 to 25 ng mL‾ 1 for AFP, and from 0.012 to 25 ng mL‾ 1 for PSA. The detection limits are 3.3 pg mL‾ 1 for AFP and 4.0 pg mL‾ 1 for PSA (at a signal-to-noise ratio of 3). The method was applied to (spiked) real sample analysis, and the recoveries are within 96.0 and 107.2 % for PSA, and within 100.9 and 105.8 % for AFP, indicating that this dual immunosensor matches the requirements of clinical analysis.

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AimThe aim of the study was to observe the effect of acupuncture on regulating interleukin (IL)‐17, tumor necrosis factor (TNF)‐ɑ, and aquaporins (AQPs) in Sjögren's syndrome (SS) on patients and on non‐obese diabetic (NOD) models.MethodsLevels of anti‐AQP 1, 5, 8, and 9 antibodies, IL‐17, and TNF‐ɑ in the serum of SS patients were compared prior and following 20 acupuncture treatment visits during 8 weeks. While in murine model, five groups were divided to receive interventions for 4 weeks, including control, model, acupuncture, isoflurane, and hydroxychloroquine. The submaxillofacial gland index, histology, immunohistochemistry of AQP1, 5, salivary flow, together with IL‐17, and TNF‐ɑ expression in peripheral blood were compared among the groups.ResultsAcupuncture reduced IL‐17, TNF‐ɑ, and immunoglobin A levels, and numeric analog scale of dryness in 14 patients with SS (p < 0.05). The salivary flow was increased, and the water intake decreased in NOD mice receiving acupuncture treatments. IL‐17 and TNF‐ɑ levels in peripheral serum were down‐regulated (p < 0.05) and AQP1, 5 expression in the submandibular glands up‐regulated in mice.ConclusionThe effect on relieving xerostomia with acupuncture may be achieved by up‐regulating the expression of AQP1. AQP5, down‐regulating levels of IL‐17 and TNF‐ɑ, and a decrease in inflammation of glands.

(A) represents the preparation of the labels and (B) represents the immunosensor preparation and detection principle.

Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed focusing on extracting discriminative pixel feature representations. However, we observe that existing methods still suffer from two typical challenges, i.e. (i) large intra-class feature variation in different scenes, (ii) small inter-class feature distinction in the same scene. In this paper, we first rethink semantic segmentation from a perspective of similarity between pixels and class centers. Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset, which can be regarded as the embedding of the class center. Thus, the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers. Under this novel view, we propose a Class Center Similarity layer (CCS layer) to address the above-mentioned challenges by generating adaptive class centers conditioned on different scenes and supervising the similarities between class centers. It utilizes a Adaptive Class Center Module (ACCM) to generate class centers conditioned on each scene, which adapt the large intra-class variation between different scenes. Specially designed loss functions are introduced to control both inter-class and intra-class distances based on predicted center-to-center and pixel-to-center similarity, respectively. Finally, the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction. Extensive experiments demonstrate that our model performs favourably against the state-of-the-art CNN-based methods.