A paradigm for high-throughput screening of cell-selective surfaces coupling orthogonal gradients and machine learning-based cell recognition

ROS-responsive & scavenging NO nanomedicine for vascular diseases treatment by inhibiting endoplasmic reticulum stress and improving NO bioavailability

Vascular diseases seriously threaten human life and health. Exogenous delivery of nitric oxide (NO) represents an effective approach for maintaining vascular homeostasis during pathological events. However, the overproduction of reactive oxygen species (ROS) at vascular injury sites would react with NO to produce damaging peroxynitrite (ONOO-) species and limit the therapeutic effect of NO. Hence, we design a ROS-responsive NO nanomedicine (t-PBA&NO NP) with ROS scavenging ability to solve the dilemma of NO-based therapy. t-PBA&NO NP targets NO and anti-oxidant ethyl caffeate (ECA) to the injury sites via collagen IV homing peptide. The ROS-triggered ROS depletion and ECA release potently alleviate local oxidative stress via ROS scavenging, endoplasmic reticulum and mitochondrial regulation. It subsequently maximizes vascular modulation effects of NO, without production of harmful compounds, reactive nitrogen species (RNS). Therefore, it significantly increases competitiveness of human umbilical vein endothelial cells (HUVECs) over human aortic smooth muscle cells (HASMCs) both in vitro and in vivo. The strategy proved effective in inducing faster re-endothelialization, inhibiting neointimal formation and restoring vascular homeostasis. The synergy between ROS depletion and NO therapy served as a new inspiration for the treatment of cardiovascular diseases and other ROS-associated illnesses.

UV-Triggered Hydrogel Coating of the Double Network Polyelectrolytes for Enhanced Endothelialization

The left atrial appendage (LAA) occluder is an important medical device for closing the LAA and preventing stroke. The device-related thrombus (DRT) prevents the implantation of the occluder in exerting the desired therapeutic effect, which is primarily caused by the delayed endothelialization of the occluder. Functional coatings are an effective strategy for accelerating the endothelialization of occluders. However, the occluder surface area is particularly large and structurally complex, and the device is subjected to a large shear friction in the sheath during implantation, which poses a significant challenge to the coating. Herein, a hydrogel coating by the in situ UV-triggered polymerization of double-network polyelectrolytes is reported. The findings reveal that the double network and electrostatic interactions between the networks resulted in excellent mechanical properties of the hydrogel coating. The sulfonate and Arg-Gly-Asp (RGD) groups in the coating promoted hemocompatibility and endothelial growth of the occluder, respectively. The coating significantly accelerated the endothelialization of the LAA occluder in a canine model is further demonstrated. This study has potential clinical benefits in reducing both the incidence of DRT and the postoperative anticoagulant course for LAA closure.

Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow

The discovery of new antibacterials within the vast chemical space is crucial in combating drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). However, the traditional approach of screening the entire chemical library in an ergodic manner can be laborious and time-consuming. Machine learning-assisted screening of antibacterials alleviates the exploration effort but suffers from the lack of reliable and related datasets. To address these challenges, we devised a combinatorial library comprising over 110 000 candidates based on the Ugi reaction. A focused library was subsequently generated through uniform sampling of the entire library to narrow down the preliminary screening scale. A novel feature-fusion architecture called the latent space constraint neural network was developed which incorporated both fingerprint and physicochemical molecular descriptors to predict the antibacterial properties. This integration allowed the model to leverage the complementary information provided by these descriptors and improve the accuracy of predictions. Three lead compounds that demonstrated excellent efficacy against MRSA while alleviating drug resistance were identified. This workflow highlights the integration of machine learning with the combinatorial chemical library to expedite high-quality data collection and extensive data mining for antibacterial screening.

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.