EXTRACTION, CHARACTERIZATION, AND ANTIMICROBIAL ACTIVITY OF F7 BIOSURFACTANT FROM BACILLUS CLAUSII AGAINST ORAL PATHOGEN KEY PLAYERS Introduction: Biosurfactants are surface-active molecules produced by various microorganisms. It holds exceptional properties, including its capacity to lower surface tension, antiadhesive activity, non-toxicity, biodegradability, and antimicrobial activity. Objectives: This study investigated the production, characteristics, and antimicrobial potential of biosurfactants extracted from Bacillus clausii, isolated from a crude oil sample obtained from a natural oil reservoir. Methods: Biosurfactant was extracted using the chloroform-methanol extraction method. Characterizations were assessed through Fourier-Transform Infrared Spectroscopy (FTIR), determination of surface tension, Critical Micelle Concentration (CMC), and emulsification index. Antimicrobial activity was determined by the Minimum Inhibitory Concentration (MIC) evaluation against Streptococcus mutans ATCC 25175, Enterococcus faecalis ATCC 24212, and Candida albicans ATCC 14503 using the microdilution method. Results: The biosurfactant extraction yielded at 1.6 mg mL-1, and was labelled as F7 biosurfactant. FTIR analysis revealed that the F7 biosurfactant belonged to the lipopeptide group, as evidenced by the presence of an aliphatic chain (CH3 and CH2). It exhibited a surface tension of 12.0 mN m-1, a CMC of 157.5 mg L-1, and an emulsification index of 56.5%. The MIC for each tested organism was 0.2 mg/mL-1, 0.4 mg/mL-1, and 0.8 mg/mL-1 with inhibition percentages of 3.20%, 4.03%, and 7.87% against S. mutans, E. faecalis, and C. albicans, respectively. Conclusions: The antimicrobial activities of the F7 biosurfactant demonstrated dose-dependent. These findings suggest that increasing the F7 biosurfactant concentration could lead to a more effective antimicrobial effect, making it a potential antimicrobial agent for oral applications.

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.