Objective:To study the occlusion characteristics of the young subjects with bruxism before and after treatment by elastic occlusal splint. Methods:10 patients with bruxism and aged 22-27 years(n=10) were treated by elastic occlusal splint, before and 6 months after treatment they were examined by T-Scan occlusion detector. The occlusion time( OT) , disclusion time( DT) , oc-clusion force( OF) , the perentage of occlusion force( POF) , the changes of the position of center occlusal force( COF) and the asym-metry index of occlusion force(AOF) were observed and compared with those of the healthy controls(n=10). Results: ① In the case group there was significant difference in the OT and DT between left and right anterior teeth before treatment (P<0. 05), but not after treatment (P>0. 05). There was significant difference in the momevent of COF of left and right side, the POF of right side and the AOF between the 2 groups before treatment, but not after the treatment(P>0. 05). ② In the case group after treatment the OT and DT of left and right anterior teeth were shortened(P<0. 05). The POF of right side and the AOF were significantly improved. In the case group DT, OF center excursion, the left POF and the AOF showed no significant difference before and after treatment( P>0. 05). Conclusion:The occlusal factors such as early contact, lateral teeth synthetic interference, occlusal force center excursion have close relationship with bruxism. Elastic occlusal splint can effectively correct muscle dysfunction and make occlusal relationship more coordinated and stable, and therefore is effective in the treatment of bruxism.

OBJECTIVE:To establish the method for rapid determination of ginsenoside Rg1,Re,Rb1 in Panax quinquefolius crude slices.METHODS:HPLC method was adopted to determine the total contents of ginsenoside Rg1,Re,Rb1 (as reference value).NIRS combined PLS algorithm were adopted to establish total quantitative correction model of ginsenoside Rg1,Re,Rb1.According to the reference,62 samples were collected.The spectrum was pretreated with multivariate scattering correction method combined with first order derivative method.The optimal ranges of wave band for ginsenoside Rg1,Re,Rb1 were 7 664.23-5 236.05 cm-1.RESULTS:Methodology validation for total content determination of ginsenoside Rg1,Re,Rb1 was in line with the requirements.For total quantitative correction model of ginsenoside Rg1,Re,Rb1,related correction set coefficient was 0.991 03,corrected mean square deviation 0.010 26.CONCLUSIONS:The method is rapid,accurate,simple and free of contamination.It can be used for rapid determination of ginsenoside Rg1,Re,Rb1 in P quinquefolius crude slices.

Objective:To develop and validate an artificial intelligence (AI) diagnostic model for coronary artery disease based on facial photos.Methods:This study was a cross-sectional study. Patients who were scheduled to undergo coronary angiography (CAG) at Beijing Anzhen Hospital and Beijing Daxing Hospital from August 2022 to November 2023 were included consecutively. Before CAG, facial photos were collected (including four angles: frontal view, left and right 60° profile, and top of the head). Photo datasets were randomly divided into a training set, a validation set (70%), and a testing set (30%). The model was constructed using Masked Autoencoder (MAE) and Vision Transformer (ViT) architectures. Firstly, the model base was pre-training using 2 million facial photos obtained from the publicly available VGGFace dataset, and fine-tuned by the training and validation sets; the model was validated in the test set. In addition, the ResNet architecture was used to process the dataset, and its outputs were compared with those of the models based on MAE and ViT. In the test set, the area under the operating characteristic curve ( AUC) of the AI model was calculated using CAG results as the gold standard. Results:A total of 5 974 participants aged 61 (54, 67) years were included, including 4 179 males (70.0%), with a total of 84 964 facial photos. There were 79 140 facial photos in the training and validation sets, with 3 822 patients with coronary artery disease; there were 5 824 facial photos in the test set, with 239 patients with coronary artery disease. The AUC value of the MAE and ViT model initialized with pre-training model weights was 0.841 and 0.824, respectively. The AUC of the ResNet model initialized with random weights was 0.810, while the AUC of the ResNet model initialized with pre-training model weights was 0.816. Conclusion:The AI model based on facial photos showes good diagnostic performance for coronary artery disease and holds promise for further application in early diagnosis.