AimTo describe the microbiological composition of subgingival dental plaque and molecular profile of gingival crevicular fluid (GCF) of periodontal furcation‐involved defects.Materials and MethodsFifty‐seven participants with periodontitis contributed with a degree II–III furcation involvement (FI), a non‐furcation (NF) periodontal defect and a periodontally healthy site (HS). Subgingival plaque was analysed by sequencing the V3–V4 region of the 16S rRNA gene, and a multiplex bead immunoassay was carried out to estimate the GCF levels of 18 GCF biomarkers. Aiming to explore inherent patterns and the intrinsic structure of data, an AI‐clustering method was also applied.ResultsIn total, 171 subgingival plaque and 84 GCF samples were analysed. Four microbiome clusters were identified and associated with FI, NF and HS. A reduced aerobic microbiota (p = .01) was detected in FI compared with NF; IL‐6, MMP‐3, MMP‐8, BMP‐2, SOST, EGF and TIMP‐1 levels were increased in the GCF of FI compared with NF.ConclusionsThis is the first study to profile periodontal furcation defects from a microbiological and inflammatory standpoint using conventional and AI‐based analyses. A reduced aerobic microbial biofilm and an increase of several inflammatory, connective tissue degradation and repair markers were detected compared with other periodontal defects.

中文翻译：

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未经治疗的牙周炎人群分叉缺损的微生物学和分子学特征


目的描述牙龈下牙菌斑的微生物组成和牙周分叉受累缺损的牙龈沟液 （GCF） 的分子特征。材料和方法57 名牙周炎参与者表现为 II-III 度分叉受累 （FI）、非分叉 （NF） 牙周缺损和牙周健康部位 （HS）。通过对 16S rRNA 基因的 V3-V4 区进行测序来分析龈下斑块，并进行多重珠免疫测定以估计 18 种 GCF 生物标志物的 GCF 水平。为了探索数据的内在模式和内在结构，还应用了人工智能聚类方法。结果共分析了 171 个龈下斑块和 84 个 GCF 样本。鉴定出 4 个微生物组簇，并与 FI 、 NF 和 HS 相关。与 NF 相比，在 FI 中检测到好氧微生物群减少 （p = .01）;与 NF 相比，FI 的 GCF 中 IL-6 、 MMP-3 、 MMP-8 、 BMP-2 、 SOST 、 EGF 和 TIMP-1 水平升高。结论这是第一项使用常规和基于 AI 的分析从微生物学和炎症角度分析牙周分叉缺损的研究。与其他牙周缺损相比，检测到需氧微生物生物膜减少和几种炎症、结缔组织降解和修复标志物的增加。