The identification of potential diagnostic markers and target molecules among the plethora of tumour oncoproteins for cancer diagnosis requires facile technology that is capable of quantitatively analysing multiple biomarkers in tumour cells and tissues. Diagnostic and prognostic classifications of human tumours are currently based on the western blotting and single-colour immunohistochemical methods that are not suitable for multiplexed detection. Herein, we report a general and novel method to prepare single-band upconversion nanoparticles with different colours. The expression levels of three biomarkers in breast cancer cells were determined using single-band upconversion nanoparticles, western blotting and immunohistochemical technologies with excellent correlation. Significantly, the application of antibody-conjugated single-band upconversion nanoparticle molecular profiling technology can achieve the multiplexed simultaneous in situ biodetection of biomarkers in breast cancer cells and tissue specimens and produce more accurate results for the simultaneous quantification of proteins present at low levels compared with classical immunohistochemical technology.

We have developed a novel multiplexed bead-based mesofluidic system (MBMS) based on the specific recognition events on the surface of a series of microbeads (diameter 250 μm) arranged in polydimethylsiloxane (PDMS) microchannels (diameter 300 μm) with the predetermined order and assembled an apparatus implementing automatically the high-throughput bead-based assay and further demonstrated its feasibility and flexibility of gene diagnosis and genotyping, such as β-thalassemia mutation detection and HLA-DQA genotyping. The apparatus, consisting of bead-based mesofluidic PDMS chip, liquid-processing module, and fluorescence detection module, can integrate the procedure of automated-sampling, hybridization reactions, washing, and in situ fluorescence detection. The results revealed that MBMS is fast, has high sensitivity, and can be automated to carry out parallel and multiplexed genotyping and has the potential to open up new routes to flexible, high-throughput approaches for bioanalysis.

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Iris segmentation is an important step in the process of iris recognition. Iris images collected under non-cooperative conditions always contain various noise, which is a challenge for iris segmentation. Most U-Net-based methods have made great achievements in iris segmentation. However, this architecture lacks of focusing on target structures of varying shapes, and robustness in segmenting objects with significant shape variations. In this paper, we propose RAG-Net: an efficient iris segmentation method based on deep learning. In contrast to many previous convolutional neural network (CNN)-based iris segmentation methods, we adopted the attention gate (AG) mechanism and ResNet-50 in the U-Net architecture to improve iris segmentation accuracy, the AG module was included in the skip connection part of the RAG-Net architecture to further identify salient feature regions and prune feature responses, which preserve only the activations relevant to the required information, and the ResNet-50 module was used to improve the robustness of the segmentation performance. Using this model, efficient iris segmentation in a non-cooperative environment can be realized. The proposed method was trained and evaluated using the CASIA.v4-distance, CASIA.v4-thousand, UBIRIS.v2, and MICHE-I databases. From the view of the segmentation results, the proposed RAG-Net is one of effective architecture in iris segmentation methods.