Microenvironment-regulated dual-hydrophilic coatings for glaucoma valve surface engineering.

Glaucoma valves (GVs) play an essential role in treating glaucoma. However, fibrosis after implantation has limited their long-term success in clinical applications. In this study, we aimed to develop a comprehensive surface-engineering strategy to improve the biocompatibility of GVs by constructing a microenvironment-regulated and dual-hydrophilic antifouling coating on a GV material (silicone rubber, SR). The coating was based on a superhydrophilic polydopamine (SPD) coating with good short-range superhydrophilicity and antifouling abilities. In addition, SPD coatings contain many phenolic hydroxyl groups that can effectively resist oxidative stress and the inflammatory microenvironment. Furthermore, based on its in situ photocatalytic free-radical polymerization properties, the SPD coating polymerized poly 2-methylacryloxyethylphosphocholine, providing an additional long-range hydrophilic and antifouling effect. The in vitro test results showed that the microenvironment-regulated and dual-hydrophilic coatings had anti-protein contamination, anti-oxidation, anti-inflammation, and anti-fiber proliferation capabilities. The in vivo test results indicated that this coating substantially reduced the fiber encapsulation formation of the SR material by inhibiting inflammation and fibrosis. This design strategy for dual hydrophilic coatings with microenvironmental regulation can provide a valuable reference for the surface engineering design of novel medical implantable devices. STATEMENT OF SIGNIFICANCE: Superhydrophilic polydopamine (SPD) coatings were prepared on silicone rubber (SR) by a two-electron oxidation method. Introduction of pMPC to SPD surface using photocatalytic radical polymerization to obtain a dual-hydrophilic coating. The dual-hydrophilic coating effectively modulates the oxidative and inflammatory microenvironment. This coating significantly reduced protein contamination and adhesion of inflammatory cells and fibroblasts in vitro. The coating-modified SR inhibits inflammatory and fibrosis responses in vivo, promising to serve the glaucoma valves.

Two-electron oxidized polyphenol chemistry-inspired superhydrophilic drug-carrying coatings for the construction of multifunctional nasolacrimal duct stents.

Nasolacrimal duct obstruction due to infection, inflammation, or excessive fibroblast proliferation may result in persistent tearing, intraocular inflammation, or even blindness. In this study, surface engineering techniques are applied to nasolacrimal duct stents for the first time. Based on the functioning of marine mussels, "one-pot" and "stepwise" methods were employed to construct a novel multifunctional superhydrophilic PDA/RAP coating using dopamine and rapamycin. Micron-sized rapamycin crystals combined with nano-sized polydopamine particles form a micro-nano topographical structure. Therefore, acting synergistically with in situ-generated hydrophilic groups (amino, carboxyl, and phenolic hydroxyl), they impart excellent and long-lasting superhydrophilicity to the nasolacrimal duct stent. The PDA/RAP coating effectively maintained the stability of the initial microenvironment during stent implantation by inhibiting the onset of acute inflammation and infection during the early stages of implantation. Meanwhile, the rapamycin crystals, supported by the superhydrophilic platform, exhibited a sustained-release capability that helped them to better exert their anti-inflammatory, antibacterial, and anti-fibroblast proliferative properties, ensuring conducive conditions for the rapid repair of nasolacrimal duct epithelial cells, verified by a series of experiments. In conclusion, the PDA/RAP hydrophilic coating has anti-inflammatory, antifibrotic, antibacterial, and antithrombotic properties, offering a new strategy to address restenosis following clinical nasolacrimal duct stent implantation.

The role of N6-methyladenosine (m6A) in kidney diseases.

Chemical modifications are a specific and efficient way to regulate the function of biological macromolecules. Among them, RNA molecules exhibit a variety of modifications that play important regulatory roles in various biological processes. More than 170 modifications have been identified in RNA molecules, among which the most common internal modifications include N6-methyladenine (m6A), n1-methyladenosine (m1A), 5-methylcytosine (m5C), and 7-methylguanine nucleotide (m7G). The most widely affected RNA modification is m6A, whose writers, readers, and erasers all have regulatory effects on RNA localization, splicing, translation, and degradation. These functions, in turn, affect RNA functionality and disease development. RNA modifications, especially m6A, play a unique role in renal cell carcinoma disease. In this manuscript, we will focus on the biological roles of m6A in renal diseases such as acute kidney injury, chronic kidney disease, lupus nephritis, diabetic kidney disease, and renal cancer.

Intelligent Diagnosis of Multiple Peripheral Retinal Lesions in Ultra-widefield Fundus Images Based on Deep Learning.

INTRODUCTION: Compared with traditional fundus examination techniques, ultra-widefield fundus (UWF) images provide 200° panoramic images of the retina, which allows better detection of peripheral retinal lesions. The advent of UWF provides effective solutions only for detection but still lacks efficient diagnostic capabilities. This study proposed a retinal lesion detection model to automatically locate and identify six relatively typical and high-incidence peripheral retinal lesions from UWF images which will enable early screening and rapid diagnosis. METHODS: A total of 24,602 augmented ultra-widefield fundus images with labels corresponding to 6 peripheral retinal lesions and normal manifestation labelled by 5 ophthalmologists were included in this study. An object detection model named You Only Look Once X (YOLOX) was modified and trained to locate and classify the six peripheral retinal lesions including rhegmatogenous retinal detachment (RRD), retinal breaks (RB), white without pressure (WWOP), cystic retinal tuft (CRT), lattice degeneration (LD), and paving-stone degeneration (PSD). We applied coordinate attention block and generalized intersection over union (GIOU) loss to YOLOX and evaluated it for accuracy, sensitivity, specificity, precision, F1 score, and average precision (AP). This model was able to show the exact location and saliency map of the retinal lesions detected by the model thus contributing to efficient screening and diagnosis. RESULTS: The model reached an average accuracy of 96.64%, sensitivity of 87.97%, specificity of 98.04%, precision of 87.01%, F1 score of 87.39%, and mAP of 86.03% on test dataset 1 including 248 UWF images and reached an average accuracy of 95.04%, sensitivity of 83.90%, specificity of 96.70%, precision of 78.73%, F1 score of 81.96%, and mAP of 80.59% on external test dataset 2 including 586 UWF images, showing this system performs well in distinguishing the six peripheral retinal lesions. CONCLUSION: Focusing on peripheral retinal lesions, this work proposed a deep learning model, which automatically recognized multiple peripheral retinal lesions from UWF images and localized exact positions of lesions. Therefore, it has certain potential for early screening and intelligent diagnosis of peripheral retinal lesions.