Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy.

Characterizing nanoparticles (NPs) is crucial in nanoscience due to the direct influence of their physiochemical properties on their behavior. Various experimental techniques exist to analyze the size and shape of NPs, each with advantages, limitations, proneness to uncertainty, and resource requirements. One of them is electron microscopy (EM), often considered the gold standard, which offers visualization of the primary particles. However, despite its advantages, EM can be expensive, less accessible, and difficult to apply during dynamic processes. Therefore, using EM for specific experimental conditions, such as observing dynamic processes or visualizing low-contrast particles, is challenging. This study showcases the potential of machine learning in deriving EM parameters by utilizing cost-effective and dynamic techniques such as dynamic light scattering (DLS) and UV-vis spectroscopy. Our developed model successfully predicts the size and shape parameters of gold NPs based on DLS and UV-vis results. Furthermore, we demonstrate the practicality of our model in situations in which conducting EM measurements presents a challenge: Tracking in situ the synthesis of 100 nm gold NPs.

Epidermal growth factor alters silica nanoparticle uptake and improves gold-nanoparticle-mediated gene silencing in A549 cells.

Introduction: Delivery of therapeutic nanoparticles (NPs) to cancer cells represents a promising approach for biomedical applications. A key challenge for nanotechnology translation from the bench to the bedside is the low amount of administered NPs dose that effectively enters target cells. To improve NPs delivery, several studies proposed NPs conjugation with ligands, which specifically deliver NPs to target cells via receptor binding. One such example is epidermal growth factor (EGF), a peptide involved in cell signaling pathways that control cell division by binding to epidermal growth factor receptor (EGFR). However, very few studies assessed the influence of EGF present in the cell environment, on the cellular uptake of NPs. Methods: We tested if the stimulation of EGFR-expressing lung carcinomacells A549 with EGF affects the uptake of 59 nm and 422 nm silica (SiO2) NPs. Additionally, we investigated whether the uptake enhancement can be achieved with gold NPs, suitable to downregulate the expression of cancer oncogene c-MYC. Results: Our findings show that EGF binding to its receptor results in receptor autophosphorylation and initiate signaling pathways, leading to enhanced endocytosis of 59 nm SiO2 NPs, but not 422 nm SiO2 NPs. Additionally, we demonstrated an enhanced gold (Au) NPs endocytosis and subsequently a higher downregulation of c-MYC. Discussion: These findings contribute to a better understanding of NPs uptake in the presence of EGF and that is a promising approach for improved NPs delivery.

The Influence of Liquid Menisci on Nanoparticle Dosimetry in Submerged Cells.

Understanding the interaction between cells and nanoparticles (NPs) is vital to understand the hazard associated with nanoparticles. This requires quantifying and interpreting dose-response relationships. Experiments with cells cultured in vitro and exposed to particle dispersions mainly rely on mathematical models that estimate the received nanoparticle dose. However, models need to consider that aqueous cell culture media wets the inner surface of hydrophilic open wells, which results in a curved liquid-air interface called the meniscus. Here the impact of the meniscus on nanoparticle dosimetry is addressed in detail. Experiments and build an advanced mathematical model, to demonstrate that the presence of the meniscus may bring about systematic errors that must be considered to advance reproducibility and harmonization is presented. The script of the model is co-published and can be adapted to any experimental setup. Finally, simple and practical solutions to this problem, such as covering the air-liquid interface with a permeable lid or soft rocking of the cell culture well plate is proposed.

Designing the ultrasonic treatment of nanoparticle-dispersions via machine learning.

Ultrasonication is a widely used and standardized method to redisperse nanopowders in liquids and to homogenize nanoparticle dispersions. One goal of sonication is to disrupt agglomerates without changing the intrinsic physicochemical properties of the primary particles. The outcome of sonication, however, is most of the time uncertain, and quantitative models have been beyond reach. The magnitude of this problem is considerable owing to fact that the efficiency of sonication is not only dependent on the parameters of the actual device, but also on the physicochemical properties such as of the particle dispersion itself. As a consequence, sonication suffers from poor reproducibility. To tackle this problem, we propose to involve machine learning. By focusing on four nanoparticle types in aqueous dispersions, we combine supervised machine learning and dynamic light scattering to analyze the aggregate size after sonication, and demonstrate the potential to improve considerably the design and reproducibility of sonication experiments.