Development of An Automated Data Analysis Program in Python on the Influence of Extracts on Changes in Lipofuscin Content in the C. Elegans Organism

This study focuses on developing a Python program and algorithm for regression analysis to screen plantderived extracts with geroprotective properties. The Python 3.9 program generates dependencies of characteristics based on input data. Regression analysis utilizes data measuring lipofuscin autofluorescence at $340 / 420 \mathrm{~nm}$ in C. elegans after 5 and 15 days of treatment with extracts (Ginkgo biloba, St. John’s wort, Baikal skullcap, forgotten pennyroyal, and true ginseng) at 10, 100, and 1000 -fold dilutions. Polynomial regression reveals data spread, with determination coefficients near 1 indicating a small spread and close to 0 indicating a large one. A significant scatter in measurements for the same extract and dilution may signal measurement errors. Increasing polynomial degree enhances accuracy until the second degree; further increases lead to decreasing determination coefficients. The regression analysis program predicts changes in lipofuscin autofluorescence post-treatment, aiding in evaluating medicinal plant extracts under varied experimental conditions. The developed automatic regression analysis program enables predicting changes in lipofuscin autofluorescence after treatment with medicinal plant extracts across diverse experimental settings.