In order to investigate the type of infiltrating T cells in allergic contact dermatitis (ACD) reaction, by the methods of RT-PCR and immunohistochemistry, we tried to detect IL-2mRNA, IL-2, IFN-?, IL-4 in the lesions of patients with ACD. The results showed that Th1 cytokines were expressed aberrantly in ACD patients and Th1-like clone which secrets IL-2, IFN-? appeared to be predominant in ACD reaction.

Objective:To explore the intervention effect of motivational interviews based on timing theory on self-efficacy, negative affect and coping styles of parents with infantile spasms children.Methods:Cluster sampling was used to select 82 parents of infantile spasms hospitalized in the Department of Neurology of a children’s hospital, a three-A hospital from January 2019 to October 2019. They were divided into control group and observation group with 41 cases each according to random number table. The control group received routine health education, and the observation group received five motivational interviews based on timing theory interventions on the basis of routine care. The effect of the intervention was evaluated by General Self-Efficacy Scale (GSES), Hospital Anxiety and Depression Scale (HADS), and the Chinese version of Coping Health Inventory for Parents (CHIP) before intervention, on the day of discharge, and 3 months after discharge.Results:Before the intervention, there was no significant difference in the scores of GSES, HADS and CHIP scales between the parents of the two groups ( P>0.05). After intervention, The GSES scores of the observation group on the day of discharge and 3 months after discharge were (19.63±0.87) and (22.58±1.28) points, which were significantly higher than (18.92±0.74) and (19.46±1.25) points of the control group. The difference between both groups was statistically significant ( t values were -3.865, -10.926, P<0.01). HADS-A/HADS-D scores of the observation group on the day of discharge and 3 months after discharge were (12.50±0.82), (10.50±0.87) and (9.78±0.80), (8.63±0.87) points, respectively. The HADS-A/HADS-D scores of the control group on the day of discharge and 3 months after discharge were (12.92±0.74), (11.72±0.99) and (10.23±0.78), (9.38±1.04) points, respectively. The difference was statistically significant ( t values were 2.412-5.764, P<0.05 or 0.01). The observation group scores on CHIP subscales on the day of discharge and 3 months after discharge are higher than the control group, the difference was statistically significant (t values were -7.93--2.490, P<0.05 or 0.01). Conclusions:Motivational interviews based on timing theory can enhance parents’ self-efficacy, improve their negative emotions and family coping styles, and thereby promote the recovery of children.

Objective: To research the effectiveness of deep learning techniques in intelligently diagnosing dental caries and periapical periodontitis and to explore the preliminary application value of deep learning in the diagnosis of oral diseases Methods: A dataset containing 2 298 periapical films, including healthy teeth, dental caries, and periapical periodontitis, was used for the study. The dataset was randomly divided into 1 573 training images, 233 validation images, and 492 test images. By comparing various neural network models, the MobileNetV3 network model with better performance was selected for dental disease diagnosis, and the model was optimized by tuning the network hyperparameters. The accuracy, precision, recall, and F1 score were used to evaluate the model's ability to recognize dental caries and periapical periodontitis. Class activation map was used to visualization analyze the performance of the network model Results: The algorithm achieved a relatively ideal intelligent diagnostic effect with precision, recall, and accuracy of 99.42%, 99.73%, and 99.60%, respectively, and the F1 score was 99.57% for classifying healthy teeth, dental caries, and periapical periodontitis. The visualization of the class activation maps also showed that the network model can accurately extract features of dental diseases. Conclusion The tooth lesion detection algorithm based on the MobileNetV3 network model can eliminate interference from image quality and human factors and has high diagnostic accuracy, which can meet the needs of dental medicine teaching and clinical applications.