Immune Cell-Based Microrobots for Remote Magnetic Actuation, Antitumor Activity, and Medical Imaging

Translating medical microrobots into clinics requires tracking, localization, and performing assigned medical tasks at target locations, which can only happen when appropriate design, actuation mechanisms, and medical imaging systems are integrated into a single microrobot. Despite this, these parameters are not fully considered when designing macrophage-based microrobots. This study presents living macrophage-based microrobots that combine macrophages with magnetic Janus particles coated with FePt nanofilm for magnetic steering and medical imaging and bacterial lipopolysaccharides for stimulating macrophages in a tumor-killing state. The macrophage-based microrobots combine wireless magnetic actuation, tracking with medical imaging techniques, and antitumor abilities. These microrobots are imaged under magnetic resonance imaging and optoacoustic imaging in soft-tissue-mimicking phantoms and ex vivo conditions. Magnetic actuation and real-time imaging of microrobots are demonstrated under static and physiologically relevant flow conditions using optoacoustic imaging. Further, macrophage-based microrobots are magnetically steered toward urinary bladder tumor spheroids and imaged with a handheld optoacoustic device, where the microrobots significantly reduce the viability of tumor spheroids. The proposed approach demonstrates the proof-of-concept feasibility of integrating macrophage-based microrobots into clinic imaging modalities for cancer targeting and intervention, and can also be implemented for various other medical applications.

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.

Shrimp Waste Upcycling: Unveiling the Potential of Polysaccharides, Proteins, Carotenoids, and Fatty Acids with Emphasis on Extraction Techniques and Bioactive Properties

Shrimp processing generates substantial waste, which is rich in valuable components such as polysaccharides, proteins, carotenoids, and fatty acids. This review provides a comprehensive overview of the valorization of shrimp waste, mainly shrimp shells, focusing on extraction methods, bioactivities, and potential applications of these bioactive compounds. Various extraction techniques, including chemical extraction, microbial fermentation, enzyme-assisted extraction, microwave-assisted extraction, ultrasound-assisted extraction, and pressurized techniques are discussed, highlighting their efficacy in isolating polysaccharides, proteins, carotenoids, and fatty acids from shrimp waste. Additionally, the bioactivities associated with these compounds, such as antioxidant, antimicrobial, anti-inflammatory, and antitumor properties, among others, are elucidated, underscoring their potential in pharmaceutical, nutraceutical, and cosmeceutical applications. Furthermore, the review explores current and potential utilization avenues for these bioactive compounds, emphasizing the importance of sustainable resource management and circular economy principles in maximizing the value of shrimp waste. Overall, this review paper aims to provide insights into the multifaceted aspects of shrimp waste valorization, offering valuable information for researchers, industries, and policymakers interested in sustainable resource utilization and waste-management strategies.

Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation

Bioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual-free, repeatable, self-cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance. Previous fibril designs for shear optimization are limited by predefined standard shapes in a narrow range primarily based on human intuition, which restricts their maximum performance. This study combines the machine learning-based optimization and finite-element-method-based shear mechanics simulations to find shear-optimized fibril designs automatically. In addition, fabrication limitations are integrated into the simulations to have more experimentally relevant results. The computationally discovered shear-optimized structures are fabricated, experimentally validated, and compared with the simulations. The results show that the computed shear-optimized fibrils perform better than the predefined standard fibril designs. This design optimization method can be used in future real-world shear-based gripping or nonslip surface applications, such as robotic pick-and-place grippers, climbing robots, gloves, electronic devices, and medical and wearable devices.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

Optimization of Gelatin Methacryloyl Hydrogel Properties through an Artificial Neural Network Model

Gelatin methacryloyl (GelMA) hydrogels are promising materials for tissue engineering applications due to their biocompatibility and tunable properties. However, the time-consuming process of preparing GelMA hydrogels with desirable properties for specific biomedical applications limits their clinical use. Visible-light-induced cross-linking is a well-known method for the preparation of GelMA hydrogels; however, a comprehensive investigation on the influence of critical parameters such as Eosin Y (EY), triethanolamine (TEA), and N-vinyl-2-pyrrolidone (NVP) concentrations on the stiffness and gelation time has yet to be performed. In this study, we systematically investigated the effect of these critical parameters on the stiffness and gelation time of GelMA hydrogels. We developed an artificial neural network (ANN) model with three input variables, EY, TEA, and NVP concentrations, and two output variables, Young's modulus and gelation time, derived from our experimental design. Through the alteration of individual chemical concentrations, [EY] between 0.005 and 0.5 mM and [TEA] and [NVP] between 10 and 1000 mM, we studied the impact of these alterations on the real-time values of stiffness and gelation time. Furthermore, we demonstrated the validity of the ANN model in predicting the properties of GelMA hydrogels. We also studied cell survival to establish nontoxic concentration ranges for each component, enabling safer use of GelMA hydrogels in relevant biomedical applications. Our results showed that the ANN model can accurately predict the properties of GelMA hydrogels, allowing for the synthesis of hydrogels with desirable stiffness for various biomedical applications. In conclusion, our study provides a comprehensive library that characterizes the stiffness and gelation time and demonstrates the potential of the ANN model to predict these properties of GelMA hydrogels depending on the critical parameters. The ANN models developed here can facilitate the optimization of GelMA hydrogels with the most efficient mechanical properties that resemble a native extracellular matrix and better address the need in the in vivo microenvironment. The approach of this study is to bring research about the synthesis of GelMA hydrogels to a new level where the synthesis of these hydrogels can be standardized with minimum cost and effort.

Chitosan-Based Nanoparticles for Nucleic Acid Delivery: Technological Aspects, Applications, and Future Perspectives

Chitosan is a naturally occurring polymer derived from the deacetylation of chitin, which is an abundant carbohydrate found mainly in the shells of various marine and terrestrial (micro)organisms. Chitosan has been extensively used to construct nanoparticles (NPs), which are biocompatible, biodegradable, non-toxic, easy to prepare, and can function as effective drug delivery systems. Moreover, chitosan NPs have been employed in gene and vaccine delivery, as well as advanced cancer therapy, and they can also serve as new therapeutic tools against viral infections. In this review, we summarize the most recent developments in the field of chitosan-based NPs intended as nucleic acid delivery vehicles and gene therapy vectors. Special attention is given to the technological aspects of chitosan complexes for nucleic acid delivery.

Pangolin-inspired untethered magnetic robot for on-demand biomedical heating applications

Untethered magnetic miniature soft robots capable of accessing hard-to-reach regions can enable safe, disruptive, and minimally invasive medical procedures. However, the soft body limits the integration of non-magnetic external stimuli sources on the robot, thereby restricting the functionalities of such robots. One such functionality is localised heat generation, which requires solid metallic materials for increased efficiency. Yet, using these materials compromises the compliance and safety of using soft robots. To overcome these competing requirements, we propose a pangolin-inspired bi-layered soft robot design. We show that the reported design achieves heating > 70 °C at large distances > 5 cm within a short period of time <30 s, allowing users to realise on-demand localised heating in tandem with shape-morphing capabilities. We demonstrate advanced robotic functionalities, such as selective cargo release, in situ demagnetisation, hyperthermia and mitigation of bleeding, on tissue phantoms and ex vivo tissues.

Preparation, properties, and applications of gelatin-based hydrogels (GHs) in the environmental, technological, and biomedical sectors

Gelatin's versatile functionalization offers prospects of facile and effective crosslinking as well as combining with other materials (e.g., metal nanoparticles, carbonaceous, minerals, and polymeric materials exhibiting desired functional properties) to form hybrid materials of improved thermo-mechanical, physio-chemical and biological characteristics. Gelatin-based hydrogels (GHs) and (nano)composite hydrogels possess unique functional features that make them appropriate for a wide range of environmental, technical, and biomedical applications. The properties of GHs could be balanced by optimizing the hydrogel design. The current review explores the various crosslinking techniques of GHs, their properties, composite types, and ultimately their end-use applications. GH's ability to absorb a large volume of water within the gel network via hydrogen bonding is frequently used for water retention (e.g., agricultural additives), and absorbency towards targeted chemicals from the environment (e.g., as wound dressings for absorbing exudates and in water treatment for absorbing pollutants). GH's controllable porosity makes its way to be used to restrict access to chemicals entrapped within the gel phase (e.g., cell encapsulation), regulate the release of encapsulated cargoes within the GH (e.g., drug delivery, agrochemicals release). GH's soft mechanics closely resembling biological tissues, make its use in tissue engineering to deliver suitable mechanical signals to neighboring cells. This review discussed the GHs as potential materials for the creation of biosensors, drug delivery systems, antimicrobials, modified electrodes, water adsorbents, fertilizers and packaging systems, among many others. The future research outlooks are also highlighted.