The blood–brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, etc.). In addition, we also address the opportunities and challenges across the nanorobots’ development and design. Finally, we address how computationally powered machine learning (ML) tools and artificial intelligence (AI) can be partnered with robotics to predict and design the next generation nanorobots to interact and deliver across the BBB without causing damage, toxicity, or malfunctions. The content of this review could be references to multidisciplinary science to clinicians, roboticists, chemists, and bioengineers involved in cutting-edge pharmaceutical design and BBB research.

In an in vitro nanotoxicity system, cell–nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or “corona” have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell–NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell–NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Development and market introduction of new nanomaterials trigger the need for an adequate risk assessment of such products alongside suitable risk communication measures. Current application of classical and new nanomaterials is analyzed in context of regulatory requirements and standardization for chemicals, food and consumer products. The challenges of nanomaterial characterization as the main bottleneck of risk assessment and regulation are presented. In some areas, e.g., quantification of nanomaterials within complex matrices, the establishment and adaptation of analytical techniques such as laser ablation inductively coupled plasma mass spectrometry and others are potentially suited to meet the requirements. As an example, we here provide an approach for the reliable characterization of human exposure to nanomaterials resulting from food packaging. Furthermore, results of nanomaterial toxicity and ecotoxicity testing are discussed, with concluding key criteria such as solubility and fiber rigidity as important parameters to be considered in material development and regulation. Although an analysis of the public opinion has revealed a distinguished rating depending on the particular field of application, a rather positive perception of nanotechnology could be ascertained for the German public in general. An improvement of material characterization in both toxicological testing as well as end-product control was concluded as being the main obstacle to ensure not only safe use of materials, but also wide acceptance of this and any novel technology in the general public.

The global community decided in 2015 to improve people’s lives by 2030 by setting 17 global goals for sustainable development. The second goal of this community was to end hunger. Plant seeds are an essential input in agriculture; however, during their developmental stages, seeds can be negatively affected by environmental stresses, which can adversely affect seed vigor, seedling establishment, and crop production. Seeds resistant to high salinity, droughts and climate change can result in higher crop yield. The major findings suggested in this review refer nanopriming as an emerging seed technology towards sustainable food amid growing demand with the increasing world population. This novel growing technology could influence the crop yield and ensure the quality and safety of seeds, in a sustainable way. When nanoprimed seeds are germinated, they undergo a series of synergistic events as a result of enhanced metabolism: modulating biochemical signaling pathways, trigger hormone secretion, reduce reactive oxygen species leading to improved disease resistance. In addition to providing an overview of the challenges and limitations of seed nanopriming technology, this review also describes some of the emerging nano-seed priming methods for sustainable agriculture, and other technological developments using cold plasma technology and machine learning.

The perspective of machine learning for modeling plasma treatment parameters in agriculture for the development of synergistic protocols for different types of seed priming.

Inhalation is the most relevant entry point for nanoparticles (NPs) into the human body. To date, toxicity testing of nanomaterials in respect to oral, dermal and inhalative application is mainly based on animal experiments. The development of alternative test methods is the subject of current research. In vitro models can help to investigate mechanistic aspects, as e.g. cellular uptake or genotoxicity and might help to reduce in vivo testing. Lung cell lines are proper in vitro tools to assess NP toxicity. In respect to this, various cell models have been developed during the recent years, but often lack in a proper intact barrier function. However, besides other important in vivo criteria which are still missing like e.g. circulation, this is one basic prerequisite to come closer to the in vivo situation in certain mechanistic aspects such as particle translocation which is an important task for risk assessment of nanomaterials. Novel developed in vitro models may help to investigate the translocation of nanomaterials from the lung. We investigated the barrier function of the recently developed human lung cell lines CI-hAELVi and CI-huAEC. The cells were further exposed to CeO2 NPs and ZnO NPs, and their suitability as in vitro models for toxicological investigations was proven. The obtained data were compared with data generated with the A549 cell line. Measurement of transepithelial resistance and immunohistochemical examination of tight junctions confirmed the formation of a functional barrier for both cell lines for submerged and air–liquid cultivation. For particle exposure, hAELVi and huAEC cells showed comparable results to A549 cells without losing the barrier function. CeO2 NP exposure revealed no toxicity for all cell lines. In contrast, ZnO NPs was toxic for all cell lines at a concentration between 10–50 μg ml−1. Due to the comparable results to A549 cells CI-hAELVi and CI-huAEC offer new opportunities to investigate nanoparticle cell interactions more realistic than recent 2D cell models.