Digital measurement of deciduous tooth dimensions in China: A cross-sectional survey.

OBJECTIVE: Crown dimensions data of deciduous teeth hold anthropological, forensic, and archaeological value. However, such information remains scarce for the Chinese population. This multi-center study aimed to collect a large sample of deciduous crown data from Chinese children using three-dimensional measurement methods and to analyze their dimensions. DESIGN: A total of 1592 children's deciduous dentition samples were included, and the sample size was distributed according to Northeast, North, East, Northwest, Southwest and South China. Digital dental models were reconstructed from plaster dental models. Independent sample t test, paired t test, principal component analysis (PCA), and factor analysis (FA) were used to analyze the tooth crown dimensions. RESULT: 18,318 deciduous teeth from 1592 children were included. Males exhibited slightly larger values than females. The range of sexual dimorphism percentages for each measurement was as follows: mesiodistal diameter (0.40-2.08), buccolingual diameter (0.13-2.24), and maxillogingival diameter (0.48-3.37). The FA results showed that the main trend of crown dimensions changes was the simultaneous increase or decrease in mesiodistal diameter, buccolingual diameter and maxillogingival diameter in three directions. CONCLUSION: This is the first large-scale survey of deciduous tooth crown dimensions in China, which supplements the data of deciduous tooth measurement and provides a reference for clinical application.

Prioritization of candidate genes for periodontitis using multiple computational tools.

BACKGROUND: Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method. METHODS: Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising. RESULTS: Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-ß1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 ß), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important. CONCLUSIONS: A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.