Foveal structure changes in infants treated with anti-VEGF therapy or laser therapy guided by optical coherence tomography angiography for retinopathy of prematurity.

AIM: To evaluate foveal vessel density (VD) and foveal thickness using optical coherence tomography angiography (OCTA) in retinopathy of prematurity (ROP) children treated with laser photocoagulation or anti-vascular endothelial growth factor (VEGF) injection. Additionally, we assessed the relationship between foveal microvascular anomalies and different therapies in ROP children. METHODS: This was a single-center, retrospective study of patients with a diagnosis of type 1 ROP. Twenty-three eyes (14 patients) treated with anti-VEGF injection and twenty-nine eyes (17 patients) treated with laser coagulation were included in this study. The foveal VD, inner thickness and full thickness were measured at the central 0°, 2° to 8°, and 8° of the retina (centered on the fovea) using OCTA and cross-sectional OCT, respectively. RESULTS: Foveal VD, inner thickness and full thickness were significantly smaller within the central 8° of the retina in ROP children treated with anti-VEGF injection than in those treated with laser photocoagulation (P=0.013, 0.009, 0.036, respectively). The full thickness was also smaller in the anti-VEGF group than in the laser group at the central 0° of the retina (P=0.010). The grade of foveal hypoplasia is lower in the anti-VEGF group than in the laser group (P=0.045). Multivariable analysis did not find any risk factors associated with visual acuity in our study. CONCLUSION: In children with type 1 ROP, the better structural development of fovea in those who were treated with anti-VEGF injection compared with laser photocoagulation are identified. However, visual acuity outcomes are similar 70mo after the treatments.

Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC-MS urinary metabolomics.

Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis.