Proposal for an initial screening method for identifying microplastics in marine sediments

A low-cost TICT-based staining agent for identification of microplastics: Theoretical studies and simple, cost-effective smartphone-based fluorescence microscope application

A novel staining agent, (5-(4-(diethylamino)benzylidene)- 1,3-dimethylpyrimidine-2,4,6(1 H,3 H,5 H)-trione) (DDB) was developed for the effective detection of environmentally harmful microplastics. DDB has competitive cost advantages, namely its facile synthesis and high yield, over Nile Red (NR), which is commonly used for microplastic staining. The unique photophysical properties of DDB, including emissive twisted intramolecular charge transfer (TICT) and aggregation-induced emission (AIE), were corroborated via spectroscopic investigations and density functional theory (DFT) calculations. Notably, DDB demonstrated superior selectivity for staining microplastics (polyethylene (PE), polyurethane (PU), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS), and polyethylene terephthalate (PET)) over non-plastic materials in water. Furthermore, modulation of the solvent environment during the staining process yielded distinct fluorescence in both the green and red channels for specific types of plastic with the interplay between locally excited (LE) and TICT states. Treatment with 5% ethanol results in the selective staining of PE and PET with the emission of red fluorescence, whereas treatment with 30% ethanol facilitates the selective staining of PU, PVC, and PET with the emission of green fluorescence. Additionally, DDB could selectively stain microplastics in spiked soil and river water samples. Furthermore, a smartphone-based fluorescence microscope was developed at a cost below $100, validating the effective detection of microplastics stained with the newly synthesized DDB. The outcomes of this research demonstrate the potential of DDB as an economical and efficient agent for selective microplastic detection.

Microplastics induced inflammation and apoptosis via ferroptosis and the NF-κB pathway in carp

Microplastics (MPs), a new class of pollutant that threatens aquatic biodiversity, are becoming increasingly prevalent around the world. Fish growth may be severely inhibited by microplastics, resulting in severe mortality. Exposure to microplastics increases the likelihood of intestinal injuries, but the underlying mechanisms remain equivocal. The objective of this study was to investigate the potential toxic mechanisms underlying microplastic-induced intestinal injury in fish and to assist researchers in identifying novel therapeutic targets. In this study, a model of carp exposed to microplastics was established successfully. Histological observation showed that exposure to polyethylene microplastics caused damage to the intestinal mucosal surface and a significant increase in goblet cells, which aggregated on the surface of the mucosa. The mucosal layer was observed to fall off. Lymphocytes in the intestinal wall proliferated and aggregated. TUNEL staining showed that apoptosis occurred in the group exposed to microplastics. The qPCR results showed that the expression of Ferroptosis apoptotic factors COX-2 and ACSL4 was upregulated, while the expression of TFRC, FIH1, SLC7A11, and GPX4 was downregulated. The NF-κB pathway (p-p65, IκBα), inflammatory cytokines (TNF-α, IL-8, IL-6) and apoptosis genes (Bax, Caspase3) were upregulated. Semi-quantitative detection of related proteins by Western blotting was consistent with the gene expression results. In addition, the ELISA assay showed that lipid peroxidation and inflammatory cytokines (TNF-α, IL-1β, IL-6) were increased in the microplastic exposed group. To conclude, lipid peroxidation induced by microplastics activates the NF-κB pathway and causes ferroptosis, ultimately resulting in intestinal damage and cellular apoptosis.

Deep learning based approach for automated characterization of large marine microplastic particles

The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic particles are normally subjected to visual characterization prior to more sophisticated chemical analyses. However, the misidentification rate of current visual inspection approaches remains high. This study proposed a state-of-the-art deep learning-based approach, Mask R-CNN, to locate, classify, and segment large marine microplastic particles with various shapes (fiber, fragment, pellet, and rod). A microplastic dataset including 3000 images was established to train and validate this Mask R-CNN algorithm, which was backboned by a Resnet 101 architecture and could be tuned in less than 8 h. The fully trained Mask R-CNN algorithm was compared with U-Net in characterizing microplastics against various backgrounds. The results showed that the algorithm could achieve Precision = 93.30%, Recall = 95.40%, F1 score = 94.34%, AP^bb (Average precision of bounding box) = 92.7%, and AP^m (Average precision of mask) = 82.6% in a 250 images test dataset. The algorithm could also achieve a processing speed of 12.5 FPS. The results obtained in this study implied that the Mask R-CNN algorithm is a promising microplastic characterization method that can be potentially used in the future for large-scale surveys.