To study the Realgar induced T lymphocytic leukemia cell line CEM apoptosis in vitro.

To explore the effects of inactivated rabbit serum containing compound realgar and natural indigo tablet (CRNIT) on cell line NB(4).

Lung cancer is a highly malignant tumor with poor prognosis.For advanced lung cancer patients,a wide range of invasion,multiple distant metastases and the limited treatment options have led to extremely poor prognosis.Better treatment outcome with a high 5-year survival rate can be achieved by early detection and treatment of lung cancer.The early diagnosis is the key to the treatment of lung cancer,and the early diagnosis of lung cancer depends on the identification of benign and malignant of pulmonary nodules.With the increased safety and diagnostic accuracy of biopsy of pulmonary nodules guided by different imaging techniques,the advantage of biopsy of pulmonary nodules in diagnosis of benign and malignant lesions is prominent,which is worthy of clinical application.

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.