The administration of systemic antimicrobials as adjuncts to mechanical treatments of periodontitis and sites with adverse clinical results leads to improved outcomes. This study aimed to assess the antimicrobial susceptibility of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Tannerella forsythia found in periodontitis patients to amoxicillin, metronidazole, azithromycin, and moxifloxacin. A total of 76 patients diagnosed with generalized periodontitis were included in the study. Subgingival samples were processed by culture. The epsilometric method was implemented to explore the susceptibility to amoxicillin, metronidazole, azithromycin, and moxifloxacin. A total of 141 isolates from 76 patients were evaluated: P. gingivalis (61), T. forsythia (43), and A. actinomycetemcomitans (37). The results showed a complete susceptibility of A. actinomycetemcomitans, P. gingivalis, and T. forsythia to moxifloxacin. However, the isolates presented a reduced susceptibility to the other antimicrobials investigated. A total of 70.3%, 40.5%, and 89.2% of the A. actinomycetemcomitans isolates were resistant to amoxicillin, azithromycin, and metronidazole, respectively. P. gingivalis samples showed relatively similar rates of resistance to amoxicillin (24.6%), azithromycin (21.3%), and metronidazole (24.6%). Similarly, 25.6%, 21%, and 25.6% of T. forsythia isolates were resistant to amoxicillin, azithromycin, and metronidazole, respectively. These findings show that moxifloxacin may be a promising antimicrobial against P. gingivalis, T. forsythia, and A. actinomycetemcomitans for the treatment of periodontitis. However, amoxicillin, azithromycin, and metronidazole were less effective, especially against A. actinomycetemcomitans in vitro.

Background: Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings. Methods: The search methodology encompassed the examination of several databases, such as PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO. An extensive electronic database search was conducted from the inception of these databases until November 2024. Results: After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. All included studies were cohort observational studies assessing 688,107 patients and 1,710,867 antimicrobial susceptibility tests. GBDT, Random Forest, and XGBoost were the top-performing ML models for predicting antibiotic resistance in CHPP infections. GBDT exhibited the highest AuROC values compared to Logistic Regression (LR), with a mean value of 0.80 (range 0.77–0.90) and 0.68 (range 0.50–0.83), respectively. Similarly, Random Forest generally showed better AuROC values compared to LR (mean value 0.75, range 0.58–0.98 versus mean value 0.71, range 0.61–0.83). However, some predictors selected by these algorithms align with those suggested by LR. Conclusions: ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice.

To test the antimicrobial sensitivity of two periodontal pathogens to a panel of five orally administrable antibiotics in periodontal disease. A total of 69 isolates of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis were processed using culture and biochemical tests. Selected colonies of A. actinomycetemcomitans and P. gingivalis were used to evaluate the antibacterial activity of clindamycin, metronidazole, amoxicillin, moxifloxacin and amoxicillin/clavulanic acid. Susceptibility testing revealed a sensitivity of 100% of A. actinomycetemcomitans and P. gingivalis to moxifloxacin and amoxicillin/clavulanic acid but moderate susceptibilities were found for the rest of antibiotics agents evaluated. The widespread use of antibiotics is reflected in the level of resistance of A. actinomycetemcomitans and P. gingivalis in patients with periodontal infections. This suggests that antibiotic susceptibility testing is necessary to determine efficacy of antimicrobial agents. Clinical studies with antibiotics should take these differences into account.

Background: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). Methods: The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). Results: The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.