OBJECTIVETo detect the expression of transient receptor potential canonical 1 (TRPC1) in a mouse model of ozone-induced lung inflammation and explore its role in lung inflammation.

METHODSIn a mouse model of lung inflammation established by ozone exposure, the expression of TRPC1 in the inflammatory lung tissues was detected by RT-PCR, Wstern blotting and immunohistochemistry.

RESULTSCompared to the control mice, the mice exposed to ozone showed significantly increased expression level of TRPC1 mRNA and protein in the inflammatory lung tissues (P<0.05). Immunohistochemistry showed increased TRPC1 protein expressions in the alveolar epithelial cells, bronchial epithelial cells, and inflammatory cells in the inflammatory lung tissues (P<0.05). The mRNA and protein expression levels of TRPC1 were positively correlated with the counts of white blood cells, macrophages, neutrophils and lymphocytes in the bronchoalveolar lavage fluid of the exposed mice (P<0.01).

CONCLUSIONTRPC1 may play a role in ozone-induced lung inflammation in mice.

Animals ; Bronchoalveolar Lavage Fluid ; Disease Models, Animal ; Gene Expression ; Inflammation ; pathology ; Lung ; metabolism ; pathology ; Mice ; Ozone ; adverse effects ; Pneumonia ; metabolism ; pathology ; RNA, Messenger ; TRPC Cation Channels ; metabolism

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
Artificial Intelligence ; Diabetes Mellitus ; Diabetic Foot ; Humans