Research on multi-class orthodontic image recognition system based on deep learning network model / 中华口腔医学杂志

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.

Automated diagnostic classification with lateral cephalograms based on deep learning network model / 中华口腔医学杂志

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.

Testicular infarction due to polyarteritis nodosa: a case report and literature review / 现代泌尿外科杂志

【Objective】 To report a case of testicular infarction due to polyarteritis nodosa (PAN), and to discuss its clinical diagnosis and treatment based on relevant literatures at home and abroad, so as to have a better understanding of this rare disease. 【Methods】 Clinical data of a case complaining of scrotal pain who was initially diagnosed as testicular torsion and later confirmed to be testicular infarction due to PAN were retrospectively analyzed, and relevant literatures were reviewed. 【Results】 With glucocorticoid, vasodilator and antioxidant treatment, the patient’s testicular blood flow was improved. 【Conclusion】 Testicular infarction due to PAN is a rare disease which is difficult to diagnose timely. The diagnosis depends on biopsy and the standards formulated by American College of Rheumatology (ACR). Good prognosis can be achieved with timely diagnosis and correct treatment.

Advances in clinical and safety studies of phosphodiesterase 4 inhibitors / 药学学报

Phosphodiesterase 4 (PDE4) is an important member of the phosphodiesterase enzyme family that specifically catalyzes the hydrolysis of cyclic adenosine monophosphate (cAMP), activates the downstream phosphorylation cascade pathway by altering cAMP concentration, and is strongly associated with multiple diseases. Inhibition of PDE4 is clinically investigated as a therapeutic strategy in a broad range of disease areas, including respiratory system diseases, autoimmune disorders, central nervous system diseases, and dermatological conditions. However, the incidence of adverse reactions such as nausea and vomiting is relatively high in the marketed PDE4 inhibitors, which has stalled their clinical development. In this review, we provide an overview of the clinical progression and safety issues of the marketed PDE4 inhibitors. We also review the main causes underlying PDE4-mediated adverse effects by combining the structural analysis of the PDE4 protein, the mechanism of action of PDE4 inhibitors, and the related side effect mechanism research, aiming to provide a reference for the development of safe and effective PDE4 inhibitors.

Role of particulate matter 2.5 in the development and progression of liver cancer / 临床肝胆病杂志

Particulate matter 2.5 (PM2.5), as one of the main components of atmospheric particulate matter, has become an important factor affecting people's health; however, there are relatively few studies on the association of PM2.5 with the development and progression of liver cancer. This article reviews the epidemiological study of PM2.5 and liver cancer, summarizes the mechanisms of PM2.5 causing liver cancer (including changing enzyme activity, affecting gene expression, inducing endoplasmic mesh stress, causing liver fat degeneration, and leading to liver fibrosis), and discusses the influence of PM2.5 exposure on the prognosis of liver cancer.

Determination of correction factors of pantoprazole sodium impurity / 中国药学杂志

OBJECTIVE: To select appropriate liquid chromatography conditions to determine the correction factors for the related compounds A, B, C, D + F and E of pantoprazole thus to establish the determination method of the related substances. METHODS: The separation was achieved on a C18 column(4. 6 mm × 250 mm, 5 μm) utilizing a mobile phase of acetonitrile and 0.01 mol · mL-1 dipo-tassium hydrogen phosphate(0 min, 80:20; 40 min, 40:60; 45 min, 20:80)at a flow rate of 1.0 mL · min-1. The column temperature was set at 35°C. The compounds were monitored at 288 nm. RESULTS: Pantoprazole sodium and its impurities were well separated by the method. The correction factors of pantoprazole related compounds A, B, C, D + F and E were 0.94, 0.93, 0.67, 0.96 and 0.99. CONCLUSION: The method can be used for the quality control of pantoprazole sodium and its related substances in the preparations.