Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

Introduction: Alveolar cleft (AC) is a common congenital defect in people with cleft lip and palate (CLP). Alveolar bone grafting (ABG) is typically performed during adolescence, resulting in the fissure remaining in the mouth for a longer length of time. Patients with AC have a greater rate of oral diseases such as dental caries than the normal population, and the precise characteristics of the bacterial alterations caused by AC are unknown.Methods: We recruited a total of 87 subjects and collected dental plaque samples from AC adolescents (AAP), post-operative ABG adolescents (PAP), healthy control adolescents (CAP), AC young adults (AYP), post-operative ABG young adults (PYP), and healthy control young adults (CYP). The sequencing of 16S rRNA genes was performed.Results: The microbial composition of plaque from alveolar cleft patients differed significantly from age-matched healthy controls. Linear discriminant analysis effect size (LEfSe) analysis revealed that AAP was enriched for Neisseria, Haemophilus, Fusobacterium, Rhodococcus, Aggregatibacter, Gemella, and Porphyromonas, whereas AYP was enriched for Capnocytophaga, Rhodococcus, and Actinomyces-f0332. There were phenotypic differences in facultatively anaerobic, Gram-negative, Gram-positive, and oxidative stress tolerance between the AYP group with longer alveolar cleft and the healthy control group according to Bugbase phenotypic predictions. Alveolar bone grafting did not alter the functional phenotype of alveolar cleft patients but reduced the number of differential genera between alveolar cleft patients and healthy controls at both ages.Conclusions: Our study systematically characterized the supragingival plaque microbiota of alveolar cleft patients, post-alveolar bone grafting patients, and matched healthy controls in two ages to gain a better understanding of plaque ecology and microbiology associated with alveolar clefts.

To investigate the characteristics of oral salivary microbiota in children with cleft lip and palate (CLP) under different states of caries. Based on the presence or absence of CLP and the total number of decayed, missing and filled surfaces, 105 participants were divided into four groups: children with CLP and high caries (CLP-HC), children with CLP and low caries (CLP-LC), children without CLP but with high caries (NCLP-HC) and children without CLP but with low caries (NCLP-LC). Saliva samples from them were analyzed using 16S rRNA gene sequencing. Alpha diversity differed significantly among the four groups with Ace and Chao indices, but not with Shannon and Simpson. Principal coordinate analysis based on the Weighted-UniFrac distance matrix showed no significant differences among the four groups, while results from the Unweighted-UniFrac distance matrix revealed significant differences. CLP-HC and CLP-LC had significantly higher Microbial Dysbiosis Index than NCLP-LC. LEfSe analysis was performed for pairwise comparisons, identifying differentially enriched bacterial genera among groups. Network analysis based on Spearman correlation revealed the interaction characteristics of microbial communities in different groups. Differences in microbial features were observed among the four groups, which could provide new insights for personalized oral health management for CLP patients.