Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism-nano systems are crucial to facilitate the biological applications of NPs. Here, we propose an interpretable causal system optimization (ICSO) framework and construct the upstream and downstream tasks of accurate prediction and intelligent NP optimization. ICSO framework screens the key drivers (recovery duration, specific surface area, and nanomaterial size) and potential causal information for immune responses and organ burden, revealing the hidden priming/constraint effects in bionano interactions. ICSO can be used to quantify the thresholds of biological responses to multiple properties (e.g., the specific surface area, diameter, and zeta potential). ICSO provides quantitative information and constraint conditions for the design of highly biocompatible and targeted organ delivery nanomaterials. For example, negative inflammation is reduced by 36.19%, and positive lung accumulation is promoted by 40.14% by optimizing the specific surface areas and shape and increasing the diameter-to-length ratio. ICSO overcomes the limitations of experience-dependent approaches and provides powerful and automated solutions for decision-makers during nanomaterial design.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Nanomaterials in tumor immunotherapy: new strategies and challenges

Tumor immunotherapy exerts its anti-tumor effects by stimulating and enhancing immune responses of the body. It has become another important modality of anti-tumor therapy with significant clinical efficacy and advantages compared to chemotherapy, radiotherapy and targeted therapy. Although various kinds of tumor immunotherapeutic drugs have emerged, the challenges faced in the delivery of these drugs, such as poor tumor permeability and low tumor cell uptake rate, had prevented their widespread application. Recently, nanomaterials had emerged as a means for treatment of different diseases due to their targeting properties, biocompatibility and functionalities. Moreover, nanomaterials possess various characteristics that overcome the defects of traditional tumor immunotherapy, such as large drug loading capacity, precise tumor targeting and easy modification, thus leading to their wide application in tumor immunotherapy. There are two main classes of novel nanoparticles mentioned in this review: organic (polymeric nanomaterials, liposomes and lipid nanoparticles) and inorganic (non-metallic nanomaterials and metallic nanomaterials). Besides, the fabrication method for nanoparticles, Nanoemulsions, was also introduced. In summary, this review article mainly discussed the research progress of tumor immunotherapy based on nanomaterials in the past few years and offers a theoretical basis for exploring novel tumor immunotherapy strategies in the future.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Utilization of Functionalized Metal–Organic Framework Nanoparticle as Targeted Drug Delivery System for Cancer Therapy

Cancer is a multifaceted disease that results from the complex interaction between genetic and environmental factors. Cancer is a mortal disease with the biggest clinical, societal, and economic burden. Research on better methods of the detection, diagnosis, and treatment of cancer is crucial. Recent advancements in material science have led to the development of metal–organic frameworks, also known as MOFs. MOFs have recently been established as promising and adaptable delivery platforms and target vehicles for cancer therapy. These MOFs have been constructed in a fashion that offers them the capability of drug release that is stimuli-responsive. This feature has the potential to be exploited for cancer therapy that is externally led. This review presents an in-depth summary of the research that has been conducted to date in the field of MOF-based nanoplatforms for cancer therapeutics.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

A bibliometric analysis of the application of stem cells in glaucoma research from 1999 to 2022

Background: Glaucoma, a neurodegenerative disease of the retina, is the leading cause of irreversible blindness. Stem cells have therapeutic potential for glaucoma. However, few bibliometric studies have been published in this field. Concerning a visual map, this article aims to characterize the research context, cooperation relationship, hotspots, and trends concerning the application of stem cells in glaucoma research. Methods: Publications focusing on stem cell research and glaucoma were retrieved from the Web of Science Core Collection. VOSviewer, CiteSpace, Microsoft Excel, and Scimago Graphica were used to map the contributions of countries or regions, authors, organizations, and journals. Journal Impact Factor data were obtained from the Web of Science Core Collection. We analyzed the tendencies, hotspots, and knowledge networks using VOSviewer, and CiteSpace. Results: We analyzed 518 articles published from 1999 through 2022. In the first decade, the number of articles in this field increased slowly, and there was a marked acceleration in publication frequency after 2010. The United States, China, and England were the main contributors. Yiqin Du was the most prolific author, and among the top 10 prolific writers, Keith R. Martin's work was cited most frequently. Investigative Ophthalmology and Visual Science, Experimental Eye Research, and Cornea published the most articles in this domain. The three most commonly co-cited journals were Investigative Ophthalmology and Visual Science, Experimental Eye Research, and Proceedings of the National Academy of Sciences of the United States of America. The Central South University, the University of Pittsburgh, and the National Institutes of Health National Eye Institute were highly prolific institutions in this research area. Our keywords analysis with VOSviewer suggested directions of future research and yielded the following recent key themes, extracellular vesicles, exosomes, mitochondria, growth factors, oxidative stress, and ocular diseases. Four co-cited references had a citation burst duration until 2022. Conclusion: With improvements in overall quality of life and demographic transitions toward population aging, research and clinical focus on eye care has increased, with glaucoma as a key area of emphasis. This study added to our understanding of the global landscape and Frontier hotspots in this field.

Biomaterial-assisted biotherapy: A brief review of biomaterials used in drug delivery, vaccine development, gene therapy, and stem cell therapy

Biotherapy has recently become a hotspot research topic with encouraging prospects in various fields due to a wide range of treatments applications, as demonstrated in preclinical and clinical studies. However, the broad applications of biotherapy have been limited by critical challenges, including the lack of safe and efficient delivery systems and serious side effects. Due to the unique potentials of biomaterials, such as good biocompatibility and bioactive properties, biomaterial-assisted biotherapy has been demonstrated to be an attractive strategy. The biomaterial-based delivery systems possess sufficient packaging capacity and versatile functions, enabling a sustained and localized release of drugs at the target sites. Furthermore, the biomaterials can provide a niche with specific extracellular conditions for the proliferation, differentiation, attachment, and migration of stem cells, leading to tissue regeneration. In this review, the state-of-the-art studies on the applications of biomaterials in biotherapy, including drug delivery, vaccine development, gene therapy, and stem cell therapy, have been summarized. The challenges and an outlook of biomaterial-assisted biotherapies have also been discussed.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Proteins Adsorbing onto Surface-Modified Nanoparticles: Effect of Surface Curvature, pH, and the Interplay of Polymers and Proteins Acid-Base Equilibrium

Protein adsorption onto nanomaterials is a process of vital significance and it is commonly controlled by functionalizing their surface with polymers. The efficiency of this strategy depends on the design parameters of the nanoconstruct. Although significant amount of work has been carried out on planar surfaces modified with different types of polymers, studies investigating the role of surface curvature are not as abundant. Here, we present a comprehensive and systematic study of the protein adsorption process, analyzing the effect of curvature and morphology, the grafting of polymer mixtures, the type of monomer (neutral, acidic, basic), the proteins in solution, and the conditions of the solution. The theoretical approach we employed is based on a molecular theory that allows to explicitly consider the acid-base reactions of the amino acids in the proteins and the monomers on the surface. The calculations showed that surface curvature modulates the molecular organization in space, but key variables are the bulk pH and salt concentration (in the millimolar range). When grafting the NP with acidic or basic polymers, the surface coating could disfavor or promote adsorption, depending on the solution's conditions. When NPs are in contact with protein mixtures in solution, a nontrivial competitive adsorption process is observed. The calculations reflect the balance between molecular organization and chemical state of polymers and proteins, and how it is modulated by the curvature of the underlying surface.

The Landscape of Nanovectors for Modulation in Cancer Immunotherapy

Immunotherapy represents a promising strategy for the treatment of cancer, which functions via the reprogramming and activation of antitumor immunity. However, adverse events resulting from immunotherapy that are related to the low specificity of tumor cell-targeting represent a limitation of immunotherapy's efficacy. The potential of nanotechnologies is represented by the possibilities of immunotherapeutical agents being carried by nanoparticles with various material types, shapes, sizes, coated ligands, associated loading methods, hydrophilicities, elasticities, and biocompatibilities. In this review, the principal types of nanovectors (nanopharmaceutics and bioinspired nanoparticles) are summarized along with the shortcomings in nanoparticle delivery and the main factors that modulate efficacy (the EPR effect, protein coronas, and microbiota). The mechanisms by which nanovectors can target cancer cells, the tumor immune microenvironment (TIME), and the peripheral immune system are also presented. A possible mathematical model for the cellular communication mechanisms related to exosomes as nanocarriers is proposed.

A practical guide to promote informatics-driven efficient biotopographic material development

Micro/nano topographic structures have shown great utility in many biomedical areas including cell therapies, tissue engineering, and implantable devices. Computer-assisted informatics methods hold great promise for the design of topographic structures with targeted properties for a specific medical application. To benefit from these methods, researchers and engineers require a highly reusable "one structural parameter - one set of cell responses" database. However, existing confounding factors in topographic cell culture devices seriously impede the acquisition of this kind of data. Through carefully dissecting the confounding factors and their possible reasons for emergence, we developed corresponding guideline requirements for topographic cell culture device development to remove or control the influence of such factors. Based on these requirements, we then suggested potential strategies to meet them. In this work, we also experimentally demonstrated a topographic cell culture device with controlled confounding factors based on these guideline requirements and corresponding strategies. A "guideline for the development of topographic cell culture devices" was summarized to instruct researchers to develop topographic cell culture devices with the confounding factors removed or well controlled. This guideline aims to promote the establishment of a highly reusable "one structural parameter - one set of cell responses" database that could facilitate the application of informatics methods, such as artificial intelligence, in the rational design of future biotopographic structures with high efficacy.

Novel insights into nanomaterials for immunomodulatory bone regeneration

Bone defect repair caused by trauma, congenital malformation, tumors, infection or systemic diseases remains the focus of attention in regeneration medicine. Recent advances in osteoimmunology indicate that immune cells and correlative cytokines modulate the delicate balance between osteoblasts and osteoclasts and induce a favorable microenvironment for bone regeneration. With superior attributes that imitate the three-dimensional architecture of natural bone, excellent fabricability, mechanical and biological properties, nanomaterials (NMs) are becoming attractive in the field of bone tissue engineering. Particularly, it could be an effective strategy for immunomodulatory bone regeneration by engineering NMs involved in composition nature, nanoarchitectural morphology, surface chemistry, topography and biological molecules, whose mechanisms potentially refer to regulating the phenotype of high-plastic immune cells and inducing cytokine secretion to accelerate osteogenesis. Despite these prominent achievements, the employment of NMs is poorly translated into clinical trials due to the lack of knowledge about the interaction between NMs and the immune system. For this reason, we sketch out the hierarchical structure of bone and its natural healing process, followed by discussion about the effects of immune cells on bone regeneration. Novel horizons focusing on recent progressions in the architectural and physicochemical performances of NMs and their impacts on the body defence mechanism are also emphasized, hoping to provide novel insights for the fabrication of bone graft materials in tissue engineering.

Interaction of Graphene Oxide Modified with Linear and Branched PEG with Monocytes Isolated from Human Blood

Multiple graphene-based therapeutics have recently been developed, however potential risks related to the interaction between nanomaterials and immune cells are still poorly understood. Therefore, studying the impact of graphene oxide on various populations of immune cells is of importance. In this work, we aimed to investigate the effects of PEGylated graphene oxide on monocytes isolated from human peripheral blood. Graphene oxide nanoparticles with lateral sizes of 100–200 nm and 1–5 μm were modified with linear and branched PEG (GO-PEG). Size, elemental composition, and structure of the resulting nanoparticles were characterized. We confirmed that PEG was successfully attached to the graphene oxide surface. The influence of GO-PEG on the production of reactive oxygen species (ROS), cytokines, phagocytosis, and viability of monocytes was studied. Uptake of GO-PEG by monocytes depends on PEG structure (linear or branched). Branched PEG decreased the number of GO-PEG nanoparticles per monocyte. The viability of monocytes was not altered by co-cultivation with GO-PEG. GO-PEG decreased the phagocytosis of Escherichia coli in a concentration-dependent manner. ROS formation by monocytes was determined by measuring luminol-, lucigenin-, and dichlorodihydrofluorescein-dependent luminescence. GO-PEG decreased luminescent signal probably due to inactivation of ROS, such as hydroxyl and superoxide radicals. Some types of GO-PEG stimulated secretion of IL-10 by monocytes, but this effect did not correlate with their size or PEG structure.

Antibacterial Zeolite Imidazole Frameworks with Manganese Doping for Immunomodulation to Accelerate Infected Wound Healing

Numerous nanomedicines currently emerge to reduce the dramatic threat in antibiotics resistance for antibacterial application against severe bacterial infections, while it is restricted by over-reacted immune response to pathogenic bacteria. Herein, enzymatic activity is introduced into the zeolitic imidazolate framework-8 (ZIF-8) to achieve sterilization by releasing Zn ions, as well as inflammation regulation through the variable valence of Mn ions that are uniformly doped into its framework. Within this simple metal organic framework (MOF) structure design, Mn-ZIF-8 possesses the co-existence of Mn²⁺ /Mn⁴⁺ to endow the nanocomposite with the anti-inflammatory capabilities, which can be adjusted through the redox environment. The enzymatic activity of Mn ions and superiority of pore structure of ZIF-8 are effectively combined to realize the substrate selection via reactant molecular size and high-efficiency internal catalytic performance. By such design, this nanocomposite would not only exhibit an excellent antibacterial performance against pathogenic bacteria, but also reshape the inflammatory immunity by regulating macrophage polarization to suppress over-reacted inflammation, leading to a favorably therapeutic efficiency on bacteria-infected wound healing in animal models. Taken together, this nanoplatform provides effective approach for accelerating infected wound healing via bacteria killing and inflammation modulation, and may be extended for the therapy of other severe bacteria-induced infections.

Colon-Targeted Adhesive Hydrogel Microsphere for Regulation of Gut Immunity and Flora

Intestinal immune homeostasis and microbiome structure play a critical role in the pathogenesis and progress of inflammatory bowel disease (IBD), whereas IBD treatment remains a challenge as the first-line drugs show limited therapeutic efficiency and great side effect. In this study, a colon-targeted adhesive core-shell hydrogel microsphere is designed and fabricated by the ingenious combination of advanced gas-shearing technology and ionic diffusion method, which can congregate on colon tissue regulating the gut immune-microbiota microenvironment in IBD treatment. The degradation experiment indicates the anti-acid and colon-targeted property of the alginate hydrogel shell, and the in vivo imaging shows the mucoadhesive ability of the thiolated-hyaluronic acid hydrogel core of the microsphere, which reduces the systematic exposure and prolongs the local drug dwell time. In addition, both in vitro and in vivo study demonstrate that the microsphere significantly reduces the secretion of pro-inflammatory cytokines, induces specific type 2 macrophage differentiation, and remarkably alleviates colitis in the mice model. Moreover, 16S ribosomal RNA sequencing reveals an optimized gut flora composition, probiotics including Bifidobacterium and Lactobacillus significantly augment, while the detrimental communities are inhibited, which benefits the intestinal homeostasis. This finding provides an ideal clinical candidate for IBD treatment.

Artificial intelligence and guidance of medicine in the bubble

Microbubbles are nanosized gas-filled bubbles. They are used in clinical diagnostics, in medical imaging, as contrast agents in ultrasound imaging, and as transporters for targeted drug delivery. They can also be used to treat thrombosis, neoplastic diseases, open arteries and vascular plaques and for localized transport of chemotherapies in cancer patients. Microbubbles can be filled with any type of therapeutics, cure agents, growth factors, extracellular vesicles, exosomes, miRNAs, and drugs. Microbubbles protect their cargo from immune attack because of their specialized encapsulated shell composed of lipid and protein. Filled with curative medicine, they could effectively circulate through the whole body safely and efficiently to reach the target area. The advanced bubble-based drug-delivery system, integrated with artificial intelligence for guidance, holds great promise for the targeted delivery of drugs and medicines.

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

V2C Nanosheets as Dual-Functional Antibacterial Agents

Antibiotic-resistant bacterial strains have been continuously increasing and becoming a supreme threat to public health globally. The nanoparticle-based photothermal treatment has emerged as a powerful tool to combat toxic bacteria. Photothermal agents (PTAs) with cost-effective and high photothermal conversion efficiency are highly desirable. Herein, we unite the green process for delamination of V2AlC to produce a high yield mass of two-dimensional (2D) V2C nanosheets (NSs) by using algae extracts and demonstrate their high antibacterial efficiency. The resultant V2C NSs present decent structural reliability and intrinsic antibacterial ability. Powerful near-infrared (NIR) absorption and extraordinary photothermal conversion proficiency make it a good PTA for the photothermal treatment of bacteria. The antibacterial efficiency evaluation indicated that V2C NSs could effectively kill both Gram-positive S. aureus and Gram-negative E. coli. About 99.5% of both types of bacteria could be killed with low-dose of V2C NSs suspension (40 μg/mL) with 5 min NIR irradiation due to the intrinsic antibacterial ability and photothermal effect of V2C NSs, which is much higher than previous reports on Ta4C3, Ti3C2, MoSe2, and Nb2C. This work expands the application of MXene V2C NSs for rapid bacteria-killing and would gain promising attention for applications in the sterilization industry.

Biomaterial-based immunoengineering to fight COVID-19 and infectious diseases

Infection by SARS-CoV-2 virus often induces the dysregulation of immune responses, tissue damage, and blood clotting. Engineered biomaterials from the nano- to the macroscale can provide targeted drug delivery, controlled drug release, local immunomodulation, enhanced immunity, and other desirable functions to coordinate appropriate immune responses and to repair tissues. Based on the understanding of COVID-19 disease progression and immune responses to SARS-CoV-2, we discuss possible immunotherapeutic strategies and highlight biomaterial approaches from the perspectives of preventive immunization, therapeutic immunomodulation, and tissue healing and regeneration. Successful development of biomaterial platforms for immunization and immunomodulation will not only benefit COVID-19 patients, but also have broad applications for a variety of infectious diseases.

Big data and machine learning for materials science

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.