The emergence of novel binding proteins or antibody mimetics capable of binding to ligand analytes in a manner analogous to that of the antigen–antibody interaction has spurred increased interest in the biotechnology and bioanalytical communities. The goal is to produce antibody mimetics designed to outperform antibodies with regard to binding affinities, cellular and tumor penetration, large-scale production, and temperature and pH stability. The generation of antibody mimetics with tailored characteristics involves the identification of a naturally occurring protein scaffold as a template that binds to a desired ligand. This scaffold is then engineered to create a superior binder by first creating a library that is then subjected to a series of selection steps. Antibody mimetics have been successfully used in the development of binding assays for the detection of analytes in biological samples, as well as in separation methods, cancer therapy, targeted drug delivery, and in vivo imaging. This review describes recent advances in the field of antibody mimetics and their applications in bioanalytical chemistry, specifically in diagnostics and other analytical methods.

Summary EGF receptor (EGFR) is a critical signaling node throughout life. However, it has not been possible to directly visualize endogenous Egfr in mice. Using CRISPR/Cas9 genome editing, we appended a fluorescent reporter to the C-terminus of the Egfr. Homozygous reporter mice appear normal and EGFR signaling is intact in vitro and in vivo. We detect distinct patterns of Egfr expression in progenitor and differentiated compartments in embryonic and adult mice. Systemic delivery of EGF or amphiregulin results in markedly different patterns of Egfr internalization and trafficking in hepatocytes. In the normal intestine, Egfr localizes to the crypt rather than villus compartment, expression is higher in adjacent epithelium than in intestinal tumors, and following colonic injury expression appears in distinct cell populations in the stroma. This reporter, under control of its endogenous regulatory elements, enables in vivo monitoring of the dynamics of Egfr localization and trafficking in normal and disease states.

Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS). INDEX TERMS Northern maize leaf blight, disease detection, transmission module, retinex, single shot multiBox detector (SSD). I. INTRODUCTION Maize is one of the major food crops in the world. The planting area and output of maize in the world are only lower than that of wheat and rice [1]. In addition to be an excellent feed for animal husbandry, maize is also an important raw material for the development of light industrial products. The associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.

Circulating tumor cells (CTCs) have been recognized as a major contributor to distant metastasis. Their unique role as metastatic seeds renders them a potential marker in the circulation for early cancer diagnosis and prognosis as well as monitoring of therapeutic response. In the past decade, researchers mainly focused on the development of isolation techniques for improving the recovery rate and purity of CTCs. These developed techniques have significantly increased the detection sensitivity and enumeration accuracy of CTCs. Currently, significant efforts have been made toward comprehensive molecular characterization, ex vivo expansion of CTCs, and understanding the interactions between CTCs and their associated cells (e.g., immune cells and stromal cells) in the circulation. In this review, we briefly summarize existing CTC isolation technologies and specifically focus on advances in downstream analysis of CTCs and their potential applications in precision medicine. We also discuss the current challenges and future opportunities in their clinical utilization.

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