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Growsafe: A Chemical Method To Deactivate Cultivated Microorganisms Using Low-Cost Kitchen Supplies.

ABSTRACT: One limitation to engaging K-12 students and the public with microorganisms is the inability to cultivate and dispose of bacterial and fungal samples safely without expensive equipment or services. This barrier has been amplified with remote learning modalities and laboratory closures driven by safety precautions due to the COVID-19 pandemic. At-home lab kits are being used to bring hands-on experience in microorganism cultivation to students learning remotely, but these kits often fail to take into full consideration the safety aspects or the costs associated with microorganism disposal, limiting which experiments can be performed at home. Here, we outline a method that makes cultivating and deactivating microorganisms accessible to the public through low-cost and readily available equipment. This method reduces exposure to microorganisms by forgoing the need to open petri plates for chemical deactivation with sanitizing reagents. This technique may benefit remote K-12 and postsecondary students, students wishing to get hands-on microbiology research experience, and members of the public interested in cultivating microorganisms to contribute to citizen science efforts or for creative art applications.

SUBMITTER: Madden AA

PROVIDER: S-EPMC8060138 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a goup of genes that mainly invovled in cell cylce were significanly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. For in vitro experiment, primary cultured rat hepatocytes were treated one of 105 compounds with relative controls. At least three biological replicates were used for each unique condition. In total 531 arrays were used.

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Growsafe: A Chemical Method To Deactivate Cultivated Microorganisms Using Low-Cost Kitchen Supplies.

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