A Novel Bioswitchable miRNA Mimic Delivery System: Therapeutic Strategies Upgraded from Tetrahedral Framework Nucleic Acid System for Fibrotic Disease Treatment and Pyroptosis Pathway Inhibition

Abstract There has been considerable interest in gene vectors and their role in regulating cellular activities and treating diseases since the advent of nucleic acid drugs. MicroRNA (miR) therapeutic strategies are research hotspots as they regulate gene expression post‐transcriptionally and treat a range of diseases. An original tetrahedral framework nucleic acid (tFNA) analog, a bioswitchable miR inhibitor delivery system (BiRDS) carrying miR inhibitors, is previously established; however, it remains unknown whether BiRDS can be equipped with miR mimics. Taking advantage of the transport capacity of tetrahedral framework nucleic acid (tFNA) and upgrading it further, the treatment outcomes of a traditional tFNA and BiRDS at different concentrations on TGF‐β‐ and bleomycin‐induced fibrosis simultaneously in vitro and in vivo are compared. An upgraded traditional tFNA is designed by successfully synthesizing a novel BiRDS, carrying a miR mimic, miR‐27a, for treating skin fibrosis and inhibiting the pyroptosis pathway, which exhibits stability and biocompatibility. BiRDS has three times higher efficiency in delivering miRNAs than the conventional tFNA with sticky ends. Moreover, BiRDS is more potent against fibrosis and pyroptosis‐related diseases than tFNAs. These findings indicate that the BiRDS can be applied as a drug delivery system for disease treatment

Recent Progress on Hydrogel-Based Piezoelectric Devices for Biomedical Applications

Flexible electronics have great potential in the application of wearable and implantable devices. Through suitable chemical alteration, hydrogels, which are three-dimensional polymeric networks, demonstrate amazing stretchability and flexibility. Hydrogel-based electronics have been widely used in wearable sensing devices because of their biomimetic structure, biocompatibility, and stimuli-responsive electrical properties. Recently, hydrogel-based piezoelectric devices have attracted intensive attention because of the combination of their unique piezoelectric performance and conductive hydrogel configuration. This mini review is to give a summary of this exciting topic with a new insight into the design and strategy of hydrogel-based piezoelectric devices. We first briefly review the representative synthesis methods and strategies of hydrogels. Subsequently, this review provides several promising biomedical applications, such as bio-signal sensing, energy harvesting, wound healing, and ultrasonic stimulation. In the end, we also provide a personal perspective on the future strategies and address the remaining challenges on hydrogel-based piezoelectric electronics

A Comprehensive Coal Reservoir Classification Method Base on Permeability Dynamic Change and Its Application

Due to the unique adsorption and desorption characteristics of coal, coal reservoir permeability changes dynamically during coalbed methane (CBM) development. Coal reservoirs can be classified using a permeability dynamic characterization in different production stages. In the single-phase water flow stage, four demarcating pressures are defined based on the damage from the effective stress on reservoir permeability. Coal reservoirs are classified into vulnerable, alleviative, and invulnerable reservoirs. In the gas desorption stage, two demarcating pressures are used to quantitatively characterize the recovery properties of permeability based on the recovery effect of the matrix shrinkage on permeability, namely the rebound pressure (the pressure corresponding to the lowest permeability) and recovery pressure (the pressure when permeability returns to initial permeability). Coal reservoirs are further classified into recoverable and unrecoverable reservoirs. The physical properties and influencing factors of these demarcating pressures are analyzed. Twenty-six wells from the Shizhuangnan Block in the southern Qinshui Basin of China were examined as a case study, showing that there is a significant correspondence between coal reservoir types and CBM well gas production. This study is helpful for identifying geological conditions of coal reservoirs as well as the productivity potential of CBM wells

DNA framework signal amplification platform-based high-throughput systemic immune monitoring

Abstract Systemic immune monitoring is a crucial clinical tool for disease early diagnosis, prognosis and treatment planning by quantitative analysis of immune cells. However, conventional immune monitoring using flow cytometry faces huge challenges in large-scale sample testing, especially in mass health screenings, because of time-consuming, technical-sensitive and high-cost features. However, the lack of high-performance detection platforms hinders the development of high-throughput immune monitoring technology. To address this bottleneck, we constructed a generally applicable DNA framework signal amplification platform (DSAP) based on post-systematic evolution of ligands by exponential enrichment and DNA tetrahedral framework-structured probe design to achieve high-sensitive detection for diverse immune cells, including CD4+, CD8+ T-lymphocytes, and monocytes (down to 1/100 μl). Based on this advanced detection platform, we present a novel high-throughput immune-cell phenotyping system, DSAP, achieving 30-min one-step immune-cell phenotyping without cell washing and subset analysis and showing comparable accuracy with flow cytometry while significantly reducing detection time and cost. As a proof-of-concept, DSAP demonstrates excellent diagnostic accuracy in immunodeficiency staging for 107 HIV patients (AUC > 0.97) within 30 min, which can be applied in HIV infection monitoring and screening. Therefore, we initially introduced promising DSAP to achieve high-throughput immune monitoring and open robust routes for point-of-care device development

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness

Abstract The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability