Molecular Rotors for Universal Quantitation of Nanoscale Hydrophobic Interfaces in Microplate Format

Nanoplastics: Status and Knowledge Gaps in the Finalization of Environmental Risk Assessments

Nanoplastics (NPs) are particles ranging in size between 1 and 1000 nm, and they are a form of environmental contaminant of great ecotoxicological concern. Although NPs are widespread across ecosystems, they have only recently garnered growing attention from both the scientific community and regulatory bodies. The present study reviews scientific literature related to the exposure and effects of NPs and identifies research gaps that impede the finalization of related environmental risk assessments (ERAs). Approximately 80 articles published between 2012 and 2021 were considered. Very few studies (eight articles) focused on the presence of NPs in biotic matrices, whereas the majority of the studies (62 articles) assessed the lethal and sublethal effects of NPs on aquatic and terrestrial organisms. Whilst many studies focused on nude NPs, only a few considered their association with different aggregates. Amongst NPs, the effects of polystyrene are the most extensively reported to date. Moreover, the effects of NPs on aquatic organisms are better characterized than those on terrestrial organisms. NP concentrations detected in water were close to or even higher than the sublethal levels for organisms. An ERA framework specifically tailored to NPs is proposed.

Detection and Quantification of Nonlabeled Polystyrene Nanoparticles Using a Fluorescent Molecular Rotor

Plastic pollution has reached alarming levels in recent years. While macro- and microplastic pollution are attested and studied since the 1970s, much less is known about the associated nanoscopic fragments. Due to their ability to cross biological barriers and their extended surface area-to-volume ratio, nanoplastics (NPs) are currently considered as one of the major threats for aquatic and terrestrial environments. Therefore, analytical tools are urgently needed to detect and quantify NPs. In this study, a method exploiting the dependence of the fluorescence quantum yield of a probe, namely, 9-(2,2-dicyanovinyl)julolidine (DCVJ), toward its microenvironment was assessed to detect and quantify polystyrene nanoplastics (PSNs). In the presence of PSNs and after excitation at 450 nm, the single-emission band fluorescent molecular rotor (FMR) emission spectrum displays a second peak at 620 nm, which increases with the concentration of PSNs. In pure water, a limit of detection and quantification range of 475-563 μg·L^-1 and 1.582-1.875 mg·L^-1, respectively, were obtained for 49 nm diameter polystyrene beads (PSB49). The results associated with 100 nm diameter PSNs amount to 518 μg·L^-1 and 1.725 mg·L^-1. The robustness of the method toward different parameters, the complexity of the matrix, and the PSN characteristics was also assessed. Finally, the method was applied on biological samples. While PSB49 quantification was achieved using radish sprouts at concentrations up to 200 mg·L^-1, it was more challenging when handling mussel tissues. This work presents the feasibility to quantify PSNs using DCVJ fluorescence. It paves the way to new perspectives in the challenging field of NPs.

Artificial intelligence in nanomedicine

The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.