RESUMEN
Introduction: Attention deficit and hyperactivity disorder (ADHD) is a common inherited disease of the nervous system whose cause(s) and pathogenesis remain unclear. Currently, the diagnosis of ADHD is mainly based on clinical experience and guidelines that have laid out some diagnostic standards. Our study aimed to apply a learning-based classification method to assist the ADHD diagnosis based on high-dimensional resting-state fMRI. Methods: Our study selected the ADHD-200 Peking dataset of resting-state fMRI, which has an ADHD patient (n = 142) group and a typically developing control (TDC) healthy control (n = 102) group. We first used Pearson and partial correlation coefficients to perform functional connectivity (FC) analysis between ROIs. Then, the Pearson and partial correlation coefficient matrices were concatenated into a dual-channel feature to build a dual data channel as input to the transfer learning neural network (TLNN) architecture. Finally, we transferred the pretrained model from the auxiliary domain to our target domain and fine-tuned it. Results: Based on the Pearson correlation coefficient, FC between ROIs was detected in 22 brain regions, including the fusiform gyrus, superior frontal gyrus, posterior superior temporal sulcus, inferior parietal lobule, anterior cingulate cortex, and parahippocampal gyrus. Based on the partial correlation coefficient, we found FC in the salient network, default network, sensory-motor network, dorsal attention network, and cerebellum network. With the TLNN architecture, we solved the problem of insufficient training data and improved the sensitivity of the classification method. When the VGG model (fine-tuned transfer strategy, 1,024 fully connected layers) was applied, the accuracy of TLNN classification ultimately reached 82%. Conclusion: Our study suggests that completing the training of the target domain by transferring the prior knowledge of the auxiliary domain is effective in solving the classification problem of small sample datasets. Based on prior knowledge of FC analysis, TLNN classification may assist ADHD diagnosis in a new way.
RESUMEN
OBJECTIVE: This paper was to assess the risk for cross infection caused by blood-contaminated tampon after dental extraction and whether this risk was reduced after relevant education towards both dentists and patients. METHODS: From December 2014 to April 2015, a survey was conducted in dentists and patients randomly before and after relevant education. The questionnaire is being revised for this survey based on learning from Chatzoudi and Franklin' survey. This survey was approved by the institutional review board, and all participants were voluntary and all responses were anonymous. RESULTS: Only 2.82 % of dentists provided patients with the postoperative-advices regarding how to dispose of blood-contaminated tampon at the first time and 47.10 % at the second time (P < 0.01). Only 1.41 % of dentists given special postoperative-advices regarding disposal of tampon to patients with blood-transmitted diseases at the first time and 24.64 % at the second time (P < 0.01). Before education, most patients were lack of nosocomial infection knowledge. After education, 22.4 % of patients threw the blood-contaminated tampon away in a proper way (P < 0.01). 66.67 % of them washed hands immediately and thoroughly after they touched the tampon (P < 0.05), 92.71 % knew the blood-contaminated tampon can cause cross-infection (P < 0.01), and 80.21 % knew how to dispose of the blood-contaminated tampon correctly (P < 0.01). CONCLUSION: The high risk of cross infection caused by blood-contaminated tampon is evident, and a series of measures is proposed to control it. There is a need to improve both dentists' and patients' awareness, enhance the education of doctors and perfect the policies and guidelines.