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Background To date, the difference in microRNA expression profiles in tears of dry eye patients and healthy people has not been reported. In current study, we evaluated the significance of microRNAs and transforming growth factor beta2 (TGF-β2) in distinguishing dry eye. Methods A total of 138 patients with dry eye from October 2017 to October 2018 were selected. During the same period, 138 healthy persons were collected. All patients were followed up for 12 months through outpatient, telephone or medical records and the time of corneal injury was recorded. Results Compared with healthy people, TGF-β2 concentrations in dry eye patients were significantly decreased ( P < 0.05). Array analysis, predictive software and dual-luciferase reporter assays showed that miR-450b-5p, miR-1283 and miR-3671 can target TGF-β2 expression. Tear miR-450b-5p, miR-1283 and miR-3671 concentrations were significantly higher in dry eye patients than healthy people. A logistic regression model combining miR-450b-5p, miR-1283, miR-3671 and TGF-β2 was performed. This model presented a high discriminating value (AUC: 0.907, 0.876–0.939, P < 0.001) than any single indicator, and the sensitivity and specificity were 77.7% and 92.7%, respectively. Compared with the low miR-450b-5p, low miR-1283, low miR-3671 and high TGF-β2 groups, the high miR-450b-5p, high miR-1283, high miR-3671 and low TGF-β2 groups had a significantly higher probability of corneal injury (TGF-β2: χ2 = 5.762, P = 0.016; miR-450b-5p: χ2 = 13.267, P < 0.001; miR-1283: χ2 = 19.431, P < 0.001; miR-3671: χ2 = 8.131, P = 0.004). Conclusion Current model combining tear miR-450b-5p, miR-1283, miR-3671 and TGF-β2 had important values in the identification of dry eye and was of great value in evaluating the risk of corneal injury.

Background Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. Methods In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19–87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. Results The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists’ grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists’ diagnostic results (κ = 0.752). Conclusion Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

To date, the difference in microRNA expression profiles in tears of dry eye patients and healthy people has not been reported. In current study, we evaluated the significance of microRNAs and transforming growth factor beta2 (TGF-β2) in distinguishing dry eye.A total of 138 patients with dry eye from October 2017 to October 2018 were selected. During the same period, 138 healthy persons were collected. All patients were followed up for 12 months through outpatient, telephone or medical records and the time of corneal injury was recorded.Compared with healthy people, TGF-β2 concentrations in dry eye patients were significantly decreased (Current model combining tear miR-450b-5p, miR-1283, miR-3671 and TGF-β2 had important values in the identification of dry eye and was of great value in evaluating the risk of corneal injury.