Objective:To observe the clinical efficacy of Xiaosheng Powder ultrasonic nebulization combined with meibomian gland massage in treating liver depression and Yin deficiency pattern of meibomian gland dysfunction dry eye.Methods:Randomized controlled trial. From March 2021 to September 2021, a total of 50 patients (100 eyes) diagnosed with meibomian gland dysfunction dry eye of the liver depression and Yin deficiency pattern at the Ophthalmology Hospital of China Academy of Chinese Medical Sciences were selected. They were randomly divided into two groups using a random number table, with 25 patients (50 eyes) in each group. The control group received 0.1% sodium hyaluronate eye drops combined with meibomian gland massage, while the treatment group received Xiaosheng Powder nebulization combined with meibomian gland massage. Both groups were treated for 4 weeks. Before and after treatment, the tear film break-up time (BUT), Schirmer I test (SIT), corneal fluorescein staining (FL) scores, TCM symptom scores, and meibomian gland function scores were observed and evaluated. Adverse reactions were recorded.Results:After treatment, the treatment group exhibited statistically significant improvements compared to the control group in the following aspects: BUT was significantly longer in the treatment group (t=8.76, P<0.01); SIT values were significantly higher in the treatment group ( t=6.18, P<0.01); FL scores, TCM symptom scores and meibomian gland function scores were significantly lower than in the treatment group ( t values were 2.19, 5.36 and 12.09, P<0.01). The total effective rate in the treatment group was 96.0% (24/25), while in the control group, it was 68.0% (17/25), and the difference between the two groups was statistically significant ( χ2=4.88, P=0.027). Conclusion:The combination of Xiaosheng Powder ultrasonic nebulization and meibomian gland massage is effective in improving the clinical symptoms of patients with meibomian gland dysfunction dry eye of the liver depression and Yin deficiency pattern. Furthermore, this approach is considered safe and efficient.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.