Amino acids analysis reveals serum methionine contributes to diagnosis of the Kawasaki disease in mice and children.

BACKGROUND: Kawasaki disease (KD) patients often lack early and definitive diagnosis due to insufficient clinical criteria, whereas biomarkers might accelerate the diagnostic process and treatment. METHODS: The KD mouse models were established and thirteen amino acids were determined. A total of 551 serum samples were collected including KD patients (n = 134), HCs (n = 223) and KD patients after intravascular immunoglobulin therapy (IVIG, n = 194). A paired analysis of pre- and post-IVIG was employed in 10 KD patients. RESULTS: The pathological alterations of the aorta, myocardial interstitium and coronary artery vessel were observed in KD mice; the serum levels of methionine in KD mice (n = 40) were markedly altered and negatively correlated with the C-reactive protein levels. Consistent with the mouse model, serum methionine were significantly decreased in KD children, with the relative variation ratio of KD with HCs above 30% and AUROC value of 0.845. Serum methionine were correlated with Z-Score and significantly restored to the normal ranges after KD patient IVIG treatment. Another case-control study with 10 KD patients with IVIG sensitivity and 20 healthy controls validated serum methionine as a biomarker for KD patients with AUROC of 0.86. Elevation of serum DNMT1 activities, but no differences of DNMT3a and DNMT3b, were observed in KD patients when comparing with those in the HCs. CONCLUSIONS: Our study validated that serum methionine was a potential biomarker for KD, the alteration of which is associated with the activation of DNMT1 in KD patients.

Irregular Scene Text Detection Based on a Graph Convolutional Network.

Detecting irregular or arbitrary shape text in natural scene images is a challenging task that has recently attracted considerable attention from research communities. However, limited by the CNN receptive field, these methods cannot directly capture relations between distant component regions by local convolutional operators. In this paper, we propose a novel method that can effectively and robustly detect irregular text in natural scene images. First, we employ a fully convolutional network architecture based on VGG16_BN to generate text components via the estimated character center points, which can ensure a high text component detection recall rate and fewer noncharacter text components. Second, text line grouping is treated as a problem of inferring the adjacency relations of text components with a graph convolution network (GCN). Finally, to evaluate our algorithm, we compare it with other existing algorithms by performing experiments on three public datasets: ICDAR2013, CTW-1500 and MSRA-TD500. The results show that the proposed method handles irregular scene text well and that it achieves promising results on these three public datasets.