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. 2023 Oct 24;23:305. doi: 10.1186/s12866-023-03057-8

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Microbial phenotype prediction by BugBase. (A,B,C) Dynamic characteristics of oral microbiota in SAP. The connection of fold lines represents a significant difference between different time points. (D) The different microbial compositions between pre-SAP and nonpneumonia. Pre-SAP before diagnosis of SAP samples. (E) The different microbial compositions between P1 and P2. P1 before diagnosis of SAP samples; P2 after diagnosis of SAP samples