Abstract Objectives: To summarize and differentiate abdominal ultrasound findings of necrotizing enterocolitis and food protein-induced enterocolitis syndrome. Methods: From January 2017 to December 2018, the abdominal ultrasound results of 304 cases diagnosed necrotizing enterocolitis or food protein-induced enterocolitis syndrome were retrospectively analyzed. The presence of pneumatosis intestinalis, portal venous gas, bowel wall thickening, intestinal motility, focal fluid collections and hypoechoic change of gallbladder wall were calculated, and the results were compared and analyzed. Results: Pneumatosis intestinalis, portal venous gas, bowel wall thickening, intestinal motility weakened/absent, focal fluid collections and hypoechoic change of gallbladder wall can be found in both necrotizing enterocolitis and food protein-induced enterocolitis syndrome infants. However, in infants with necrotizing enterocolitis, intestinal motility was weakened/absent in whole abdomen, and in food protein-induced enterocolitis syndrome, it only involved isolated segment of bowel. The positive rates of above signs in necrotizing enterocolitis infants were significantly higher than those in food protein-induced enterocolitis syndrome (p < 0.01). Moreover, it was observed that the rate of weakened intestinal motility besides the lesion segment of bowel in necrotizing enterocolitis infants was 100%, and in food protein-induced enterocolitis syndrome infants, it was 0%, which is supposed to be a main sign for identification. Conclusion: In the early stage, abdominal ultrasound can be used to differentiate necrotizing enterocolitis and food protein-induced enterocolitis syndrome.

ABSTRACT Background: primary biliary cholangitis (PBC) patients with a suboptimal response to ursodeoxycholic acid (UDCA) have a significantly worse survival rate. Fenofibrate has been shown to improve the short-term biochemical response in this group of patients. However, there is limited data available on the safety and efficacy of its long-term use, especially in patients with cirrhosis. Methods: in this retrospective cohort study, fenofibrate was given to PBC patients with a suboptimal response to at least 12 months of UDCA (13-15 mg/kg/d) therapy. Biochemistry data, GLOBE score and UK-PBC risk score at baseline and at different time points of treatment were compared. The safety profiles were also compared between cirrhotic and non-cirrhotic patient groups. Results: fenofibrate (200 mg/day) was given to 39 PBC patients with a suboptimal response to UDCA (15 cirrhotic and 24 non-cirrhotic patients). In the 26 patients who completed more than one year of combination therapy, the alkaline phosphatase (ALP) levels were 215 (185, 326) U/l, 122 (110, 202) U/l, 128 (106, 194) U/l, 124 (100, 181) U/l and 120 (82, 168) U/l, at baseline, three months, six months, 12 months and 24 months, respectively. All p values were < 0.01 when compared to baseline values. After two years of combination therapy, the UK-PBC risk score and GLOBE score did not significantly improve. The overall rates of adverse events were not significantly different between the cirrhotic and non-cirrhotic group. The elevation of liver enzymes was the most frequent side effect (n = 7), leading to a discontinuation in four patients. Furthermore, after two years of combination therapy, the serum creatinine levels and estimated glomerular filtration rates (eGFR) were significantly worse in both groups. Conclusion: fenofibrate add-on therapy could improve ALP and -GT levels in both non-cirrhotic and cirrhotic PBC patients with a suboptimal response to UDCA. However, patients need to be monitored carefully for a potential liver injury and nephrotoxicity.

ABSTRACT: Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.