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Development and Validation of Software for Quantitative Assessment of Microplastic Particles in Biological Specimens: A Rat Kidney Model
Summary
Scientists created a computer program that can automatically count and measure tiny plastic particles that build up in body tissue, using kidney samples from rats injected with different sizes of microplastic as a test case. The study found that even a single dose of these plastic particles accumulates in the kidneys, with smaller and larger particles behaving differently — some staying separate, others clumping together. This matters because as microplastics continue turning up in our food, water, and even our organs, having a reliable, objective way to measure them in tissue (instead of relying on expensive lab equipment or subjective visual checks) will help researchers better understand how these particles affect our health.
Background : Microplastic particles (MPs) are ubiquitous detected in the environment and have been shown to accumulate in animal and human tissues, inducing oxidative stress, inflammation, and other toxic effects. The accuracy of assessing their organ-specific distribution remains limited: highly sensitive spectroscopic techniques are expensive and labor-intensive, while visual histology is subjective. Objective: To develop and validate software for the automated quantitative assessment of microplastic particles in biological tissues using computer vision methods. Materials and methods : Experimental validation was conducted on biological samples obtained from laboratory animals exposed to polystyrene microplastic particles measuring of 100, 500 and 1000 nm. Microphotographs were processed using an EGFP filter set. The results of the development and validation of software for the quantitative assessment of microplastic particles in the tissue of experimental animals are presented, implemented using the OpenCV, NumPy and Tkinter libraries in Python 3.10. Results: The obtained quantitative data demonstrate that following a single systemic administration, polystyrene particles with diameters of 100, 500, and 1000 nm accumulate in the renal parenchyma, with deposition patterns dependent on their geometric parameters. The highest number of individual particles was observed in the 100 and 1000 nm groups, whereas the maximum density of conglomerates was found in the 500 nm fraction. Conclusions : The developed software provides flexibility through adjustable image processing parameters, making it applicable to various types of biological samples. The obtained data confirm the effectiveness of the proposed approach, which minimizes the influence of the human factor and enables standardization of the analysis process.