Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis

Background & Aims Microscopic inflammation has significant prognostic value in ulcerative colitis (UC); however, its assessment is complex with high interobserver variability. We aimed to develop and validate an artificial intelligence (AI) computer-aided diagnosis system to evaluate UC biopsies and predict prognosis. Methods A total of 535 digitalized biopsies (273 patients) were graded according to the PICaSSO Histologic Remission Index (PHRI), Robarts, and Nancy Histological Index. A convolutional neural network classifier was trained to distinguish remission from activity on a subset of 118 biopsies, calibrated on 42 and tested on 375. The model was additionally tested to predict the corresponding endoscopic assessment and occurrence of flares at 12 months. The system output was compared with human assessment. Diagnostic performance was reported as sensitivity, specificity, prognostic prediction through Kaplan-Meier, and hazard ratios of flares between active and remission groups. We externally validated the model in 154 biopsies (58 patients) with similar characteristics but more histologically active patients. Results The system distinguished histological activity/remission with sensitivity and specificity of 89% and 85% (PHRI), 94% and 76% (Robarts Histological Index), and 89% and 79% (Nancy Histological Index). The model predicted the corresponding endoscopic remission/activity with 79% and 82% accuracy for UC endoscopic index of severity and Paddington International virtual ChromoendoScopy ScOre, respectively. The hazard ratio for disease flare-up between histological activity/remission groups according to pathologist-assessed PHRI was 3.56, and 4.64 for AI-assessed PHRI. Both histology and outcome prediction were confirmed in the external validation cohort. Conclusion We developed and validated an AI model that distinguishes histologic remission/activity in biopsies of UC and predicts flare-ups. This can expedite, standardize, and enhance histologic assessment in practice and trials

Effects of Dietary Phosphatidylcholine Supplementation on Growth Performance, Antioxidant Capacity, Fatty Acid Composition, and Lipid Metabolism of Juvenile Eriocheir sinensis-Fed Different Oil Sources

The present study investigates the effects of dietary phosphatidylcholine (PC) deficiency and its addition on growth and physiological and biochemical indicators of juvenile Eriocheir sinensis under different oil sources. There were no significant differences in the growth and feed utilization between the vegetable oils and fish oil (FO) groups under PC-devoid conditions. In contrast, the FO and perilla oil (PO) groups showed better growth-promoting effects and higher feed utilization than the safflower oil (SO) and olive oil (OO) groups under 3% PC-added condition. Both dietary PC and oil sources (FO or PO) could inhibit lipid accumulation of the whole crab, and dietary PC also observably facilitated whole-body protein deposition. In addition, dietary FO and PO increased the burden of the antioxidant system and the risk of lipid peroxidation in juvenile E. sinensis. Meanwhile, diets supplemented with PC effectively alleviated oxidative stress and lipid peroxidation caused by dietary FO and PO. The composition of fatty acids in muscle and hepatopancreas was positively associated with that in diets. Compared with SO and OO, FO and PO significantly reduced the lipid deposition in the hepatopancreas at 3% PC supplementation, possibly because FO and PO formed new physiological-active PC contained n-3 polyunsaturated fatty acids (PUFAs) with dietary PC through activating PC remodeling reaction, and promoting fatty acid utilization, and finally inhibiting the lipid accumulation in the hepatopancreas. This study indicates that FO and PO are better lipid sources (LSs) for E. sinensis, providing alternative oil sources in the crab diet in combination with PC supplementation