RESUMO
OBJECTIVES: The purpose of this study was to identify microorganisms isolated from various periapical tissue diseases using Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) and classify them via an unsupervised machine learning approach. METHODS: A total of 150 patients with various apical conditions and teeth in need of endodontic retreatment were divided into five groups, including Retreatment, Acute Apical Abscess, Chronic Apical Abscess, Acute Apical Periodontitis, and Chronic Apical Periodontitis. Samples were collected from root canals using paper points after agitating with a #10 K file then microorganisms were identified using MALDI-TOF-MS. Data were analyzed using a hierarchical clustering method. Quadruple clusters and dendrograms were formed according to similarities and dissimilarities. RESULTS: A total of 80 species were identified representative of six different phyla. The most similar microorganism species identified were: ''Enterococcus faecalis'' between 21 and 23-year-old female cases in Retreatment group; ''Lactobacillus rhamnosus'' between 20 and 18-year-old male cases in Symptomatic Apical Abscess cases; ''Lactobacillus paracasei'' between 26 and 40-year-old male cases in Asymptomatic Apical Abscess cases; ''Enterococcus faecalis'' between 48 and 50-year-old female cases in Symptomatic Apical Periodontitis cases; ''Lactobacillus rhamnosus'' between 48 and 60-year-old male cases in Asymptomatic Apical Periodontitis cases. CONCLUSIONS: MALDI-TOF MS can be considered a fast and high-throughput screening technique for microbial species identification in endodontics. Thus, it will provide valuable data for future research designs regarding periapical tissue diseases. As the MALDI-TOF MS database expands and comprehensive data becomes available, the relationship between microbial profiles and disease progression will become increasingly apparent.
