ABSTRACT
Citrus is widely grown all over the world, and citrus fruits have long been recognized for their nutritional and medical value for human health. However, some local citrus varieties with potentially important value are still elusive. In the current study, we elucidated the biological characteristics, phylogenetic and phytochemical profiling, antioxidants and antioxidant activities of the two local citrus varieties, namely Zangju and Tuju. The physiological and phylogenetic analysis showed that Zangju fruit has the characteristics of wrinkled skin, higher acidity, and phylogenetically closest to sour mandarin Citrus sunki, whereas, Tuju is a kind of red orange with vermilion peel, small fruit and high sugar content, and closely clustered with Citrus erythrosa. The phytochemical analysis showed that many nutrition and antioxidant related differentially accumulated metabolites (DAMs) were detected in the peel and pulp of Zangju and Tuju fruits. Furthermore, it was found that the relative abundance of some key flavonoids and phenolic acids, such as tangeritin, sinensetin, diosmetin, nobiletin, and sinapic acid in the peel and pulp of Zangju and Tuju were higher than that in sour range Daidai and satsuma mandarin. Additionally, Zangju pulp and Tuju peel showed the strongest ferric reducing/antioxidant power (FRAP) activity, whereas, Tuju peel and pulp showed the strongest DPPH and ABTS free radical scavenging activities, respectively. Moreover, both the antioxidant activities of peel and pulp were significantly correlated with the contents of total phenols, total flavonoids or ascorbic acid. These results indicate that the two local citrus varieties have certain nutritional and medicinal value and potential beneficial effects on human health. Our findings will also provide an important theoretical basis for further conservation, development and medicinal utilization of Zangju and Tuju.
ABSTRACT
OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.
