Objective To determine the level of interleukin (IL)-10 in sera and culture supernatants of CD4+CD25+T cells from children with atopic dermatitis (AD),and to evaluate its relationship with clinical course and severity of AD.Methods Totally,46 children with AD and 31 healthy controls were included in the study.All the patients were divided into 3 groups,i.e.,mild (n =10),moderate (n =16) and severe (n =20) group,according to severity scoring of atopic dermatitis (SCORAD) score.Venous blood samples were obtained from the patients and healthy controls.CD4+CD25+ regulatory T cells were separated from the blood samples by magnetic cell sorting (MACS) system in two steps and cultured in vitro.Enzyme linked immunosorbent assay (ELISA) was conducted to quantify the level of IL-10 in sera and culture supernatants of CD4+CD25 + T cells from these subjects.Analysis of variance was carried out to compare the level of supematant and serum IL-10 between the patients and controls,and Pearson correlation analysis to assess the relationship between the level of IL-10 and SCORAD score.Results The patients with mild,moderate and severe AD showed a similar serum IL-10 level compared with the healthy controls ((43.10 ± 25.07) pg/ml,(68.40 ± 36.65) pg/ml and (55.55 ± 41.97) pg/ml vs.(58.27 ± 36.84) pg/ml,all P ＞ 0.05).The level of supernatant IL-10 secreted by CD4+CD25+ T cells from the controls was significantly higher than that from the patients with severe AD ((55.15 ± 11.15) pg/ml vs.(27.25 ± 7.01) pg/ml,P ＜ 0.05),but similar to that from the patients with mild and moderate AD ((52.96 ± 11.69) pg/ml and (49.86 ± 9.18) pg/ml,respectively,both P ＞ 0.05).The level of secreted IL-10 was negatively correlated with SCORAD score (r =－0.757,P ＜ 0.01),whereas the serum level of IL-10 showed no statistical correlation with SCORAD score.Conclusion CD4+CD25+ T cells and IL-10 may be implicated in the development of AD.

OBJECTIVETo establish the differential templates of the skeletal profiles for Shanghainese with normal occlusion in early permanent dentition.

METHODS71 lateral cephalometric radiographs of subjects with normal occlusion in early permanent dentition, aged from 11 - 15 years (37 females and 34 males) were traced. 23 skeletal landmarks were identified on the tracings, which afterwards were scanned on the computer. An x-y coordinate system was established: the tracing superimposed on sella rotated 7 degrees down from the sella-nasion line as the x axis and the vertical line through sella perpendicular to the x axis as the y axis. A special length/depth ratio was designed. The length/depth ratio of each graph were calculated. Steiner analysis was applied to check the difference between genders.

RESULTSThere was no difference between genders. The tracings were divided into three categories: short face, normal face and long face according to the mean and standard deviation of the ratio. The three types of tracings were superimposed on the x axis with sella registered. Three templates were created.

CONCLUSIONSThe differences were apparent when the three templates were superimposed.

Adolescent ; Cephalometry ; methods ; Child ; Dental Occlusion ; Face ; anatomy & histology ; Facial Bones ; anatomy & histology ; Female ; Humans ; Male

Objective The aim of this study is to combine Raman spectroscopy and machine learning techniques to distinguish subgin-gival plaques among three groups of subjects,including patients with chronic periodontitis(CP)and type 2 diabetes mellitus(T2DM),patients with CP alone,and healthy controls.Methods The Raman spectra of the subgingival plaques from 20 patients with CP and T2DM(group A),23 patients with CP alone(group B),and 23 healthy controls(group C)were obtained using a portable Raman spec-trometer.Eight common machine learning algorithms were applied to build models to distinguish the Raman spectra of the three types of subgingival plaques.Results The model identified as optimal for distinguishing the three types of subgingival plaques was linear discri-minant analysis(LDA).The optimal model to distinguish groups A and B is LDA,groups A and C is extra trees(ET),and groups B and C group is LDA.Conclusion The proposed classification model based on Raman spectroscopy and machine learning algorithms can dis-tinguish subgingival plaques among patients with CP and T2DM,with CP alone,and healthy controls.This technique can be used in future clinical practice as a screening or diagnostic tool.