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基于 DNA 序列的建筑物霉菌状况分类方法。

DNA Sequence-Based Approach for Classifying the Mold Status of Buildings.

机构信息

Department of Chemical and Environmental Engineering, Yale University, P.O. Box 208263 New Haven, Connecticut 06520-8286, United States.

Indoor Air Program, The University of Tulsa, 800 South Tucker Drive, Henneke 212, Tulsa, Oklahoma 74101-9700, United States.

出版信息

Environ Sci Technol. 2020 Dec 15;54(24):15968-15975. doi: 10.1021/acs.est.0c03904. Epub 2020 Dec 1.

Abstract

Dampness or water damage in buildings and human exposure to the resultant mold growth is an ever-present public health concern. This study provides quantitative evidence that the airborne fungal ecology of homes with known mold growth ("moldy") differs from the normal airborne fungal ecology of homes with no history of dampness, water damage, or visible mold ("no mold"). Settled dust from indoor air and outdoor air and direct samples from building materials with mold growth were examined in homes from 11 cities across dry, temperate, and continental climate regions within the United States. Community analysis based on the sequence of the internal transcribed spacer region of fungal ribosomal RNA encoding genes demonstrated consistent and quantifiable differences between the fungal ecology of settled dust in homes with inspector-verified water damage and visible mold versus the settled dust of homes with no history of dampness, water damage, or visible mold. These differences include lower community richness ( = 0.01) in the settled dust of moldy homes versus no mold homes, as well as distinct community taxonomic structures between moldy and no mold homes (ANOSIM, R = 0.15, = 0.001). We identified 11 taxa that were more highly enriched in moldy homes and 14 taxa from , and that were more highly enriched in no mold homes. The indoor air differences between moldy versus no mold homes were significant for all three climate regions considered. These distinct but complex differences between settled dust samples from moldy and no homes were used to train a machine learning-based model to classify the mold status of a home. The model was able to accurately classify 100% of moldy homes and 90% of no mold homes. The integration of DNA-based fungal ecology with advanced computational approaches can be used to accurately classify the presence of mold growth in homes, assist with inspection and remediation decisions, and potentially lead to reduced exposure to hazardous microbes indoors.

摘要

建筑物中的潮湿或水损坏以及人类对由此产生的霉菌生长的暴露是一个始终存在的公共卫生问题。本研究提供了定量证据,表明已知霉菌生长(“发霉”)的房屋的空气传播真菌生态与没有潮湿、水损坏或可见霉菌史的正常空气传播真菌生态(“无霉菌”)不同。从美国干燥、温带和大陆气候区的 11 个城市的房屋内空气和室外空气中沉降的灰尘以及有霉菌生长的建筑材料直接样本进行了检查。基于真菌核糖体 RNA 编码基因的内部转录间隔区序列的群落分析表明,经检查员验证有水分损坏和可见霉菌的房屋中沉降灰尘的真菌生态学与无潮湿、水分损坏或可见霉菌史的房屋中的沉降灰尘之间存在一致且可量化的差异。这些差异包括发霉房屋的沉降灰尘中的群落丰富度较低( = 0.01),以及发霉和无霉菌房屋之间明显的群落分类结构差异(ANOSIM,R = 0.15, = 0.001)。我们确定了 11 种在发霉房屋中高度富集的分类群,以及 14 种来自 、 和 的分类群在无霉菌房屋中高度富集。考虑到的所有三个气候区,发霉房屋与无霉菌房屋之间的室内空气差异均具有统计学意义。这些发霉和无霉菌房屋之间沉降灰尘样本之间存在明显但复杂的差异,用于训练基于机器学习的模型来分类房屋的霉菌状态。该模型能够准确地分类 100%的发霉房屋和 90%的无霉菌房屋。基于 DNA 的真菌生态学与先进的计算方法的结合可用于准确分类房屋中霉菌生长的存在,辅助检查和修复决策,并可能减少室内危险微生物的暴露。

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