High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Hydrophilic/Omniphobic Droplet Arrays for High-throughput and Quantitative Enzymology

Engineered enzymes with enhanced or novel functions are specific catalysts with wide-ranging applications in industry and medicine. Here, we introduce droplet array microfluidic enzyme kinetics (DA-MEK), a high-throughput enzyme screening platform that combines water-in-air droplet microarrays formed on patterned superhydrophilic/omniphobic surfaces with cell-free protein synthesis to enable cost-effective expression and quantitative kinetic characterization of enzyme variants. By printing DNA templates encoding enzyme variants onto hydrophilic spots, stamping slides to add cell-free expression mix, and imaging the resulting arrays, we demonstrate reproducible expression of enzyme variants across hundreds of microwells per slide, with line of sight toward replicating this across larger libraries. By specifically patterning slides with antibodies, we further demonstrate parallel immobilization, purification, and iterative characterization of the expressed variants. Subsequent stamping of fluorogenic substrates and time-lapse imaging allows determination of Michaelis-Menten parameters for each variant, with measured catalytic efficiencies spanning 5 orders of magnitude and agreeing well with values obtained via traditional microtiter plate assays. DA-MEK consumes orders of magnitude less reagents than plate-based assays, while providing accurate and detailed kinetic information for both beneficial and deleterious mutations. In future work, we anticipate that DA-MEK will provide a powerful and versatile platform to accelerate enzyme engineering and enable screening of large variant libraries under diverse conditions.

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells

The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential but has not yet been realized. Current models rely on large data sets that generally do not exist for specialized research fields. We demonstrate a closed-loop workflow that combines high-throughput synthesis of organic semiconductors to create large datasets and Bayesian optimization to discover new hole-transporting materials with tailored properties for solar cell applications. The predictive models were based on molecular descriptors that allowed us to link the structure of these materials to their performance. A series of high-performance molecules were identified from minimal suggestions and achieved up to 26.2% (certified 25.9%) power conversion efficiency in perovskite solar cells.

A review of biopolymer-based hydrogels and IoT integration for enhanced diabetes diagnosis, management, and treatment

The prevalence of diabetes has been increasing globally, necessitating innovative approaches beyond conventional blood sugar monitoring and insulin control. Diabetes is associated with complex health complications, including cardiovascular diseases. Continuous Glucose Monitoring (CGM) devices, though automated, have limitations such as irreversibility and interference with bodily fluids. Hydrogel technologies provide non-invasive alternatives to traditional methods, addressing the limitations of current approaches. This review explores hydrogels as macromolecular biopolymeric materials capable of absorbing and retaining a substantial amount of water within their structure. Due to their high-water absorption properties, these macromolecules are utilized as coating materials for wound care and diabetes management. The study emphasizes the need for early diagnosis and monitoring, especially during the COVID-19 pandemic, where heightened attention to diabetic patients is crucial. Additionally, the article examines the role of the Internet of Things (IoT) and machine learning-based systems in enhancing diabetes management effectiveness. By leveraging these technologies, there is potential to revolutionize diabetes care, providing more personalized and proactive solutions. This review explores cutting-edge hydrogel-based systems as a promising avenue for diabetes diagnosis, management, and treatment, highlighting key biopolymers and technological integrations.

Multiscale engineering of brain organoids for disease modeling

Brain organoids hold great potential for modeling human brain development and pathogenesis. They recapitulate certain aspects of the transcriptional trajectory, cellular diversity, tissue architecture and functions of the developing brain. In this review, we explore the engineering strategies to control the molecular-, cellular- and tissue-level inputs to achieve high-fidelity brain organoids. We review the application of brain organoids in neural disorder modeling and emerging bioengineering methods to improve data collection and feature extraction at multiscale. The integration of multiscale engineering strategies and analytical methods has significant potential to advance insight into neurological disorders and accelerate drug development.

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.