Smart materials: rational design in biosystems via artificial intelligence

Water-Stable Magnetic Lipiodol Micro-Droplets as a Miniaturized Robotic Tool for Drug Delivery

Magnetic microrobots, designed to navigate the complex environments of the human body, show promise for minimally invasive diagnosis and treatment. However, their clinical adoption faces hurdles such as biocompatibility, precise control, and intelligent tracking. Here a novel formulation (referred to water-stable magnetic lipiodol micro-droplets, MLMD), integrating clinically approved lipiodol, gelatin, and superparamagnetic iron oxide nanoparticles (SPION) with a fundamental understanding of the structure-property relationships is presented. This formulation demonstrates multiple improved properties including flowability, shape adaptability, efficient drug loading, and compatibility with digital subtraction angiography (DSA) imaging in both in vitro and in vivo experiments. This enables the MLMD as a versatile tool for image-guided therapy, supported by a close-looped magnetic navigation system featuring artificial intelligence (AI)-driven visual feedback for autonomous control. The system effectively performs navigational tasks, including pinpointing specific locations of MLMD, recognizing and avoiding obstacles, mapping and following predetermined paths, and utilizing magnetic fields for precise motion planning to achieve visual drug delivery. The MLMD combines magnetic actuation with an AI-directed close-looped navigation, offering a transformative platform for targeted therapeutic delivery.

Engineering Stimuli-Responsive Materials for Precision Medicine

Over the past decade, precision medicine has garnered increasing attention, making significant strides in discovering new therapeutic drugs and mechanisms, resulting in notable achievements in symptom alleviation, pain reduction, and extended survival rates. However, the limited target specificity of primary drugs and inter-individual differences have often necessitated high-dosage strategies, leading to challenges such as restricted deep tissue penetration rates and systemic side effects. Material science advancements present a promising avenue for these issues. By leveraging the distinct internal features of diseased regions and the application of specific external stimuli, responsive materials can be tailored to achieve targeted delivery, controllable release, and specific biochemical reactions. This review aims to highlight the latest advancements in stimuli-responsive materials and their potential in precision medicine. Initially, we introduce disease-related internal stimuli and capable external stimuli, elucidating the reaction principles of responsive functional groups. Subsequently, we provide a detailed analysis of representative pre-clinical achievements of stimuli responsive materials across various clinical applications, including enhancements in the treatment of cancers, injury diseases, inflammatory diseases, infection diseases, and high-throughput microfluidic biosensors. Finally, we discuss some clinical challenges, such as off-target effects, long-term impacts of nano-materials, potential ethical concerns, and offer insights into future perspectives of stimuli-responsive materials.

NIR-II Surface-Enhanced Raman Scattering Nanoprobes in Biomedicine: Current Impact and Future Directions

The field of second near-infrared (NIR-II) surface-enhanced Raman scattering (SERS) nanoprobes has made commendable progress in biomedicine. This article reviews recent advances and future development of NIR-II SERS nanoprobes. It introduces the fundamental principles of SERS nanoprobes and highlights key advances in the NIR-II window, including reduced tissue attenuation, deep penetration, maximized allowable exposure, and improved photostability. The discussion of future directions includes the refinement of nanoprobe substrates, emphasizing the tailoring of optical properties of metallic SERS-active nanoprobes, and exploring non-metallic alternatives. The intricacies of designing Raman reporters for the NIR-II resonance and the potential of these reporters to advance the field are also discussed. The integration of artificial intelligence (AI) into nanoprobe design represents a cutting-edge approach to overcome current challenges. This article also examines the emergence of deep Raman techniques for through-tissue SERS detection, toward NIR-II SERS tomography. It acknowledges instrumental advancements like improved charge-coupled device sensitivity and accelerated imaging speeds. The article concludes by addressing the critical aspects of biosafety, ease of functionalization, compatibility, and the path to clinical translation. With a comprehensive overview of current achievements and future prospects, this review aims to illuminate the path for NIR-II SERS nanoprobes to innovate diagnostic and therapeutic approaches in biomedicine.

Special Issue: "Rational Design and Synthesis of Bioactive Molecules"

The rational design of novel bioactive molecules is a critical but challenging task in drug discovery [...].

A review on intelligence of cellulose based materials

Cellulose based materials are widely used in various fields such as papermaking, packaging, composite materials, textiles and clothing due to their diverse types, environmental friendliness, natural degradation, high specific strength, and low cost. The intelligence of cellulose based materials will further expand their application fields. This article first gives an in-depth analyzation on the intelligent structural design of these materials according to the two major categories of isotropic and anisotropic, then lists the main preparation methods of cellulose based intelligent materials. Subsequently, this article systematically summarizes the recent intelligent response methods and characteristics of cellulose based materials, and extensively elaborates on the intelligent application of these materials. Finally, the prospects for the intelligence of cellulose based materials are discussed.

Recent advances in the metamaterial and metasurface-based biosensor in the gigahertz, terahertz, and optical frequency domains

Recently, metamaterials and metasurface have gained rapidly increasing attention from researchers due to their extraordinary optical and electrical properties. Metamaterials are described as artificially defined periodic structures exhibiting negative permittivity and permeability simultaneously. Whereas metasurfaces are the 2D analogue of metamaterials in the sense that they have a small but not insignificant depth. Because of their high optical confinement and adjustable optical resonances, these artificially engineered materials appear as a viable photonic platform for biosensing applications. This review paper discusses the recent development of metamaterial and metasurface in biosensing applications based on the gigahertz, terahertz, and optical frequency domains encompassing the whole electromagnetic spectrum. Overlapping features such as material selection, structure, and physical mechanisms were considered during the classification of our biosensing applications. Metamaterials and metasurfaces working in the GHz range provide prospects for better sensing of biological samples, THz frequencies, falling between GHz and optical frequencies, provide unique characteristics for biosensing permitting the exact characterization of molecular vibrations, with an emphasis on molecular identification, label-free analysis, and imaging of biological materials. Optical frequencies on the other hand cover the visible and near-infrared regions, allowing fine regulation of light-matter interactions enabling metamaterials and metasurfaces to offer excellent sensitivity and specificity in biosensing. The outcome of the sensor's sensitivity to an electric or magnetic field and the resonance frequency are, in theory, determined by the frequency domain and features. Finally, the challenges and possible future perspectives in biosensing application areas have been presented that use metamaterials and metasurfaces across diverse frequency domains to improve sensitivity, specificity, and selectivity in biosensing applications.

AI-Based Metamaterial Design

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Engineering aspects of lipid-based delivery systems: In vivo gene delivery, safety criteria, and translation strategies

Defects in the genome cause genetic diseases and can be treated with gene therapy. Due to the limitations encountered in gene delivery, lipid-based supramolecular colloidal materials have emerged as promising gene carrier systems. In their non-functionalized form, lipid nanoparticles often demonstrate lower transgene expression efficiency, leading to suboptimal therapeutic outcomes, specifically through reduced percentages of cells expressing the transgene. Due to chemically active substituents, the engineering of delivery systems for genetic drugs with specific chemical ligands steps forward as an innovative strategy to tackle the drawbacks and enhance their therapeutic efficacy. Despite intense investigations into functionalization strategies, the clinical outcome of such therapies still needs to be improved. Here, we highlight and comprehensively review engineering aspects for functionalizing lipid-based delivery systems and their therapeutic efficacy for developing novel genetic cargoes to provide a full snapshot of the translation from the bench to the clinics. We outline existing challenges in the delivery and internalization processes and narrate recent advances in the functionalization of lipid-based delivery systems for nucleic acids to enhance their therapeutic efficacy and safety. Moreover, we address clinical trials using these vectors to expand their clinical use and principal safety concerns.

Advancing 3D printed microfluidics with computational methods for sweat analysis

The intricate tapestry of biomarkers, including proteins, lipids, carbohydrates, vesicles, and nucleic acids within sweat, exhibits a profound correlation with the ones in the bloodstream. The facile extraction of samples from sweat glands has recently positioned sweat sampling at the forefront of non-invasive health monitoring and diagnostics. While extant platforms for sweat analysis exist, the imperative for portability, cost-effectiveness, ease of manufacture, and expeditious turnaround underscores the necessity for parameters that transcend conventional considerations. In this regard, 3D printed microfluidic devices emerge as promising systems, offering a harmonious fusion of attributes such as multifunctional integration, flexibility, biocompatibility, a controlled closed environment, and a minimal requisite analyte volume-features that leverage their prominence in the realm of sweat analysis. However, formidable challenges, including high throughput demands, chemical interactions intrinsic to the printing materials, size constraints, and durability concerns, beset the landscape of 3D printed microfluidic devices. Within this paradigm, we expound upon the foundational aspects of 3D printed microfluidic devices and proffer a distinctive perspective by delving into the computational study of printing materials utilizing density functional theory (DFT) and molecular dynamics (MD) methodologies. This multifaceted approach serves manifold purposes: (i) understanding the complexity of microfluidic systems, (ii) facilitating comprehensive analyses, (iii) saving both cost and time, (iv) improving design optimization, and (v) augmenting resolution. In a nutshell, the allure of 3D printing lies in its capacity for affordable and expeditious production, offering seamless integration of diverse components into microfluidic devices-a testament to their inherent utility in the domain of sweat analysis. The synergistic fusion of computational assessment methodologies with materials science not only optimizes analysis and production processes, but also expedites their widespread accessibility, ensuring continuous biomarker monitoring from sweat for end-users.

The state-of-art polyurethane nanoparticles for drug delivery applications

Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field.

Toxicity of carbon nanofibers in earthworms (Lumbricus terrestris) naturally infected with Monocystis sp

Although the ecotoxicity of carbon-based nanomaterials (CBNs) is known, the potential effect of carbon nanofibers (CNFs) on edaphic organisms has been insufficiently explored. Thus, we aimed at the ecotoxicity of CNFs (at 10 and 100 mg/kg) in Lumbricus terrestris earthworms naturally infected with Monocystis sp. After 28 days of exposure, treatments did not affect the survival rate. However, we observed a significant loss of body biomass, and Monocystis sp. infection in seminal vesicles was potentiated by exposure to CNFs. Earthworms exposed to CNFs showed a redox imbalance in the seminal vesicle, muscle, and intestine and an alteration in nitric oxide production in these organs. In muscles, we also noticed a significant reduction in AChE activity in earthworms exposed to CNFs. The histopathological analyses revealed the treatments' significant effect on the structures of the different evaluated tissues. Although we did not notice a concentration-response for several of the biomarkers, when taken together and after the application of Integrated Biomarker Response (IBR) and principal component analysis (PCA), we noticed that the response of earthworms to CNFs at 100 mg/kg showed a more significant deviation from the unexposed group. This was mainly determined by inhibiting antioxidant activity in the seminal vesicle, biochemical biomarkers assessed in muscle and intestine, and histomorphometric muscle biomarkers from earthworms exposed to CNFs at 100 mg/kg. Thus, we demonstrate that CNFs increase the parasite load of Monocystis sp. of adult L. terrestris earthworms and induce biochemical and histopathological changes, especially at 100 mg/kg. Our results point to the additional impact these nanomaterials can have on the health of earthworms, signaling the need for greater attention to their disposal and ecotoxicological effects on soil organisms.

Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.

Perspective on the development of synthetic microbial community (SynCom) biosensors

Synthetic microbial community (SynCom) biosensors are a promising technology for detecting and responding to environmental cues and target molecules. SynCom biosensors use engineered microorganisms to create a more complex and diverse sensing system, enabling them to respond to stimuli with enhanced sensitivity and accuracy. Here, we give a definition of SynCom biosensors, outline their construction workflow, and discuss current biosensing technology. We also highlight the challenges and future for developing and optimizing SynCom biosensors and the potential applications in agriculture and food management, biotherapeutic development, home sensing, urban and environmental monitoring, and the One Health foundation. We believe SynCom biosensors could be used in a real-time and remote-controlled manner to sense the chaos of constantly dynamic environments.

Regenerative bioelectronics: A strategic roadmap for precision medicine

In the past few decades, stem cell-based regenerative engineering has demonstrated its significant potential to repair damaged tissues and to restore their functionalities. Despite such advancement in regenerative engineering, the clinical translation remains a major challenge. In the stance of personalized treatment, the recent progress in bioelectronic medicine likewise evolved as another important research domain of larger significance for human healthcare. Over the last several years, our research group has adopted biomaterials-based regenerative engineering strategies using innovative bioelectronic stimulation protocols based on either electric or magnetic stimuli to direct cellular differentiation on engineered biomaterials with a range of elastic stiffness or functional properties (electroactivity/magnetoactivity). In this article, the role of bioelectronics in stem cell-based regenerative engineering has been critically analyzed to stimulate futuristic research in the treatment of degenerative diseases as well as to address some fundamental questions in stem cell biology. Built on the concepts from two independent biomedical research domains (regenerative engineering and bioelectronic medicine), we propose a converging research theme, 'Regenerative Bioelectronics'. Further, a series of recommendations have been put forward to address the current challenges in bridging the gap in stem cell therapy and bioelectronic medicine. Enacting the strategic blueprint of bioelectronic-based regenerative engineering can potentially deliver the unmet clinical needs for treating incurable degenerative diseases.

Biomimetic strategies and technologies for artificial tactile sensory systems

The sense of touch events, achieved by artificial tactile sensory systems (ATSSs), is a milestone in the progress of human-machine interactions. However, it has been a challenge for ATSSs to serve functions comparable with the human tactile perception system (HTPS). The biomimetic strategies and technologies inspired by HTPS are considered an optimal solution to this challenge. Recent studies have reported bioinspired strategies for improving specific aspects of ATSS performance, such as feature collection, signal conversion, and information computation. Here, we present a systematic interpretation of biomechanisms for HTPSs, and correspondingly, address biomimetic strategies and technologies contributing to ATSSs as an integral system. This review will benefit the development and application of ATSSs in the future.

Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications

The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.

Perspectives on development of biomedical polymer materials in artificial intelligence age

Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

Current status and future prospects in cannabinoid production through in vitro culture and synthetic biology

For centuries, cannabis has been a rich source of fibrous, pharmaceutical, and recreational ingredients. Phytocannabinoids are the most important and well-known class of cannabis-derived secondary metabolites and display a broad range of health-promoting and psychoactive effects. The unique characteristics of phytocannabinoids (e.g., metabolite likeness, multi-target spectrum, and safety profile) have resulted in the development and approval of several cannabis-derived drugs. While most work has focused on the two main cannabinoids produced in the plant, over 150 unique cannabinoids have been identified. To meet the rapidly growing phytocannabinoid demand, particularly many of the minor cannabinoids found in low amounts in planta, biotechnology offers promising alternatives for biosynthesis through in vitro culture and heterologous systems. In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems. However, there are still several bottlenecks (e.g., the complexity of the cannabinoid biosynthetic pathway and optimizing the bioprocess), hampering biosynthesis and scaling up the biotechnological process. The current study reviews recent advances related to in vitro culture-mediated cannabinoid production. Additionally, an integrated overview of promising conventional approaches to cannabinoid production is presented. Progress toward cannabinoid production in heterologous systems and possible avenues for avoiding autotoxicity are also reviewed and highlighted. Machine learning is then introduced as a powerful tool to model, and optimize bioprocesses related to cannabinoid production. Finally, regulation and manipulation of the cannabinoid biosynthetic pathway using CRISPR- mediated metabolic engineering is discussed.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.