Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Investigation roles of Adamts1 and Adamts5 in scleral fibroblasts under hypoxia and mice with form-deprived myopia.

Scleral hypoxia is considered a trigger in scleral remodeling-induced myopia. Identifying differentially expressed molecules within the sclera is essential for understanding the mechanism of myopia. We developed a scleral fibroblast hypoxia model and conducted RNA sequencing and bioinformatic analysis. RNA interference technology was then applied to knock down targeted genes with upregulated expression, followed by an analysis of COLLAGEN I protein level. Microarray data analysis showed that the expression of Adamts1 and Adamts5 were upregulated in fibroblasts under hypoxia (t-test, p < 0.05). Western blot analysis confirmed increased protein levels of ADAMTS1 and ADAMTS5, and a concurrent decrease in COLLAGEN I in hypoxic fibroblasts. The knockdown of either Adamts1 or Adamts5 in scleral fibroblasts under hypoxia resulted in an upregulation of COLLAGEN I. Moreover, a form-deprivation myopia (FDM) mouse model was established for validation. The sclera tissue from FDM mice exhibited increased levels of ADAMTS1 and ADAMTS5 protein and a decrease in COLLAGEN I, compared to controls. The study suggests that Adamts1 and Adamts5 may be involved in scleral remodeling induced by hypoxia and the development of myopia.

Prediction models for retinopathy of prematurity occurrence based on artificial neural network.

INTRODUCTION: Early prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine. OBJECTIVE: To establish predictive models for ROP occurrence based on the risk factors using artificial neural network. METHODS: A cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models. RESULTS: ROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively. CONCLUSIONS: We developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.