Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples.

Nanomechanical Atomic Force Microscopy to Probe Cellular Microplastics Uptake and Distribution.

The concerns regarding microplastics and nanoplastics pollution stimulate studies on the uptake and biodistribution of these emerging pollutants in vitro. Atomic force microscopy in nanomechanical PeakForce Tapping mode was used here to visualise the uptake and distribution of polystyrene spherical microplastics in human skin fibroblast. Particles down to 500 nm were imaged in whole fixed cells, the nanomechanical characterization allowed for differentiation between internalized and surface attached plastics.

DNA/Magnetic Nanoparticles Composite to Attenuate Glass Surface Nanotopography for Enhanced Mesenchymal Stem Cell Differentiation.

Mesenchymal stem cells (MSCs) have extensive pluripotent potential to differentiate into various cell types, and thus they are an important tool for regenerative medicine and biomedical research. In this work, the differentiation of hTERT-transduced adipose-derived MSCs (hMSCs) into chondrocytes, adipocytes and osteoblasts on substrates with nanotopography generated by magnetic iron oxide nanoparticles (MNPs) and DNA was investigated. Citrate-stabilized MNPs were synthesized by the chemical co-precipitation method and sized around 10 nm according to microscopy studies.

Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study.

The development of an automatic method of identifying microplastic particles within live cells and organisms is crucial for high-throughput analysis of their biodistribution in toxicity studies. State-of-the-art technique in the data analysis tasks is the application of deep learning algorithms. Here, we propose the approach of polystyrene microparticle classification differing only in pigmentation using enhanced dark-field microscopy and a residual neural network (ResNet).

Comparative Toxicity of Fly Ash: An In Vitro Study.

Fly ash produced during coal combustion is one of the major sources of air and water pollution, but the data on the impact of micrometer-size fly ash particles on human cells is still incomplete. Fly ash samples were collected from several electric power stations in the United States (Rockdale, TX; Dolet Hill, Mansfield, LA; Rockport, IN; Muskogee, OK) and from a metallurgic plant located in the Russian Federation (Chelyabinsk Electro-Metallurgical Works OJSC). The particles were characterized using dynamic light scattering, atomic force, and hyperspectral microscopy.