Comparing In Silico Fungi Toxicity Prediction with In Vitro Cytotoxicity Assay for Indoor Airborne Fungi

Author:

Ahn Sung-Yoon1ORCID,Kim Mira2ORCID,Jeong Hye-Won2ORCID,Yoon Wonsuck3ORCID,Bang Iel-Soo2ORCID,Lee Sang-Woong1ORCID

Affiliation:

1. Pattern Recognition and Machine Learning Laboratory, School of Computing, Gachon University, Seungnam 13306, Republic of Korea

2. Department of Microbiology and Immunology, School of Dentistry, Chosun University, Gwangju 61452, Republic of Korea

3. Allergy Immunology Center, Korea University, Seoul 02708, Republic of Korea

Abstract

Technological advancements have shifted human living and working environments from outdoor to indoor. Although indoor spaces offer protection from unfavorable weather conditions, they also present new health challenges. Stale, humid, and warm indoor air creates an ideal breeding ground for bacteria and fungi, leading to health issues such as asthma and bacterial infections. Although proper ventilation is crucial, a comprehensive inspection of local indoor air quality is necessary to prevent widespread diseases. In vitro experiments involving bacteria and fungi collected from indoor air yield accurate results but are time- and cost-intensive. In silico methods offer faster results and provide valuable insights for guiding further in vitro experiments. In this study, we conduct an in vitro cytotoxicity assay on 32 fungi species and compare its results with a memory-efficient in silico modeling method using parameter-efficient fine-tuning (PEFT) and ProtBERT. This study suggests a potential methodology for predicting the toxicity of indoor airborne fungi when their identities are known.

Funder

Korea Environment Industry Technology Institute

Korea Ministry of Environment

Chosun University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference33 articles.

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