Ovalbumin interaction with polystyrene and polyethylene terephthalate microplastics alters its structural properties.

Contaminating microplastics can interact with food proteins in the food matrix and during digestion. This study investigated adsorption of chicken egg protein ovalbumin to polystyrene (PS, 110 and 260 µm) and polyethylene terephthalate (PET, 140 µm) MPs in acidic and neutral conditions and alterations in ovalbumin structure. Ovalbumin adsorption affinity depended on MPs size (smaller > larger), type (PS > PET) and pH (pH 3 > pH 7). In bulk solution, MPs does not change ovalbumin secondary structure significantly, but induces loosening (at pH 3) and tightening (at pH 7) of tertiary structure. Formed soft corona exclusively consists of full length non-native ovalbumin, while in hard corona also shorter ovalbumin fragments were found. At pH 7 soft corona ovalbumin has rearranged but still preserved level of ordered secondary structure, resulting in preserved thermostability and proteolytic stability, but decreased ability to form fibrils upon heating. Secondary structure changes in soft corona resemble changes in native ovalbumin induced by heat treatment (80 °C). Ovalbumin is abundantly present in corona around microplastics also in the presence of other egg white proteins. These results imply that microplastics contaminating food may bind and change structure and functional properties of the main egg white protein.

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams.

Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models were trained using at least 20,000 patches sampled from 99 fluorescence microscopy images of MP and their corresponding binary masks. MP-Net, which is derived from U-Net, was found to be the best performing model, exhibiting the highest mean F1-score (0.736) and mean IoU value (0.617). Test-time augmentation (using brightness, contrast, and HSV) was applied to MP-Net for robust learning. However, compared to the results obtained without augmentation, no clear improvement in predictive performance could be observed. Recovery assessment for both spiked and real images showed that, compared to already existing tools for MP quantification, the MP quantities predicted by MP-Net are those closest to the ground truth. This observation suggests that MP-Net allows creating masks that more accurately reflect the quantitative presence of fluorescent MP in microscopy images. Finally, MAP (Microplastics Annotation Package) is introduced, an integrated software environment for automated MP quantification, offering support for MP-Net, already existing MP analysis tools like MP-VAT, manual annotation, and model fine-tuning.

Comparative profiling and exposure assessment of microplastics in differently sized Manila clams from South Korea by µFTIR and Nile Red staining.

The accumulation of microplastics in marine organisms is an emerging concern. Due to trophic transfer, the safety of seafood is under investigation in view of the potential negative effects of microplastics on human health. In this study, market samples of Manila clams (Ruditapes philippinarum) from South Korea were segregated into two groups of considerably different size (p < 0.05), namely small clams with shell length of 40.69 ± 3.97 mm, and large clams of shell length 51.19 ± 2.86 mm. Comparative profiling of the number, size, shape, and polymer type of microplastics were performed using µFTIR imaging and Nile red staining. Overall, µFTIR detected only 1559 microplastics while 1996 microplastics were counted based on staining from 61 Manila clams (30 small and 31 large), leading to an overestimation of 18 to 75 %. Comparable microplastics concentration, based on µFTIR, were observed at 2.70 ± 1.66 MP/g or 15.64 ± 9.25 MP/individual for the small samples, and 3.65 ± 1.59 MP/g or 41.63 ± 16.90 MP/individual for the large ones (p > 0.05). Particle diameters of 20-100 µm was the most dominant, accounting for 44.6 % and 46.5 % of all microplastics from the small and large groups, respectively. Particles, with a circularity (resemblance to a circle) value between 0.6 and 1.0, were the most prevalent, followed by fragments and fibers. At least 50 % of microplastics from the small and large samples were polystyrene, making it the most abundant polymer type. Despite the substantial difference in the size of the animals, only a weak to moderate correlation was observed between microplastics content and the physical attributes of the clams such as shell length and weight, (soft) tissue weight, and total weight (Spearman's coefficient < 0.5). The estimated intake of microplastics by the Korean population was 1232 MP/person/year via small clams, 1663 MP/person/year via large clams, and 1489 MP/person/year via clams independent of size.