Interleukin-4 (IL-4) has been considered as one of the tolerogenic cytokines in many autoimmune animal models and clinical settings. Despite its role in antagonizing pathogenic Th1 responses, little is known about whether IL-4 possesses functions that affect regulatory T cells (Tregs). Tregs are specialized cells responsible for the maintenance of peripheral tolerance through their immune modulatory capabilities. Interestingly, it has been suggested that IL-4 supplement at a high concentration protects responder T cells (Tresps) from Treg-mediated immune suppression. In addition, such supplement also impedes TGF-β-induced Treg differentiation in vitro. However, these phenomena may contradict the tolerogenic role of IL-4, and the effects of IL-4 on Tregs are therefore needed to be further elucidated. In this study, we utilized IL-4 knockout (KO) mice to validate the role of IL-4 on Treg-mediated immune suppression. Although IL-4 KO and control animals harbor similar frequencies of Tregs, Tregs from IL-4 KO mice weakly suppressed autologous Tresp activation. In addition, IL-4 deprivation impaired the ability of Tregs to modulate immune response, whereas IL-4 supplementation reinforced IL-4 KO Tregs in their function in suppressing Tresps. Finally, the presence of IL-4 was associated with increased cell survival and granzyme expression of Tregs. These results suggest the essential role of IL-4 in supporting Treg-mediated immune suppression, which may benefit the development of therapeutic strategies for autoimmune diseases.

Background/AimTo automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).MethodsThis retrospective cross-sectional study was conducted in a referral medical centre in Taiwan. Only premature infants with no ROP, stage 1 ROP or stage 2 ROP were enrolled. Overall, 11 372 retinal fundus images were compiled and split into 10 235 images (90%) for training, 1137 (10%) for validation and 244 for testing. A deep CNN was implemented to classify images according to the ROP stage. Data were collected from December 17, 2013 to May 24, 2019 and analysed from December 2018 to January 2020. The metrics of sensitivity, specificity and area under the receiver operating characteristic curve were adopted to evaluate the performance of the algorithm relative to the reference standard diagnosis.ResultsThe model was trained using fivefold cross-validation, yielding an average accuracy of 99.93%±0.03 during training and 92.23%±1.39 during testing. The sensitivity and specificity scores of the model were 96.14%±0.87 and 95.95%±0.48, 91.82%±2.03 and 94.50%±0.71, and 89.81%±1.82 and 98.99%±0.40 when predicting no ROP versus ROP, stage 1 ROP versus no ROP and stage 2 ROP, and stage 2 ROP versus no ROP and stage 1 ROP, respectively.ConclusionsThe proposed system can accurately differentiate among ROP early stages and has the potential to help ophthalmologists classify ROP at an early stage.

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