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Ultrasensitive detection of sequence-specific DNA and uracil-DNA glycosylase (UDG) activity shows great practical significance in clinical diagnostic and biomedical studies. Here, a methodology based on a CRISPR/ Cas12a system coupled with enhanced strand displacement amplification (E-SDA) was innovatively established for sequence-specific DNA or UDG activity detection. Sequence-specific DNA or DNA primers processed by UDG and Endonuclease IV can initiate E-SDA, generating auxiliary DNA chains, which act as activators to unlock the indiscriminate collateral cleavage activities (trans-cleavage) of the CRISPR/Cas12a. Then, the activated CRISPR/Cas12a, which intrinsically possesses the ability of significant signal amplification, can indiscriminately cleave the added cleavage reporters in the system. Thus, the multistep amplification of the method was obtained. Under the selected experimental conditions, the established method can achieve an actual sensitivity of sequence-specific DNA up to 100 aM within 2.5 h or ultralow UDG activity (3.1×10 −5 U/mL) detection within 3.5 h. We believe that the proposed method will have great potential for practical application in ultrasensitive detection of sequence-specific DNA or UDG activity.

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

Because of limitations in the current understanding of the exact pathogenesis of tendinopathy, and the lack of an optimal experimental model, effective therapy for the disease is currently unavailable. This study aims to prove that repression of oxidative stress modulates the differentiation of tendon-derived cells (TDCs) sustaining excessive tensile strains, and proposes a novel bioreactor capable of applying differential tensile strains to cultured cells simultaneously. TDCs, including tendon-derived stem cells, tenoblasts, tenocytes, and fibroblasts, were isolated from the patellar tendons of Sprague‒Dawley rats. Cyclic uniaxial stretching with 4% or 8% strain at 0.5 Hz for 8 h was applied to TDCs. TDCs subjected to 8% strain were treated with epigallocatechin gallate (EGCG), piracetam, or no medication. Genes representing non-tenocyte lineage (Pparg, Sox9, and Runx2) and type I and type III collagen were analyzed by quantitative polymerase chain reaction. The 8% strain group showed increased expression of non-tenocyte lineage genes and type III/type I collagen ratios compared with the control and 4% strain groups, and the increased expression was ameliorated with addition of EGCG and piracetam. The model developed in this work could be applied to future research on the pathophysiology of tendinopathy and development of treatment options for the disease. Repression of oxidative stress diminishes the expression of genes indicating aberrant differentiation in a rat cell model, which indicates potential therapeutic intervention of tendinopathy, the often relentlessly degenerate condition.

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