[Influence of drying method on content of epigoitrin and uridine in isatidis radix].

OBJECTIVE: To study the influence of different drying methods on the content of epigoitrin and uridine in Isatidis Radix. METHODS: Fresh Isatidis Radix was processed by four drying methods including airing drying and drying in far infrared oven at different temperature,drying in the sun and drying in the shade. The contents of epigoitrin and uridine were determined by HPLC. RESULTS: The contents of epigoitrin as well as uridine in samples valued from 3.847 - 5.204 mg/g and 0.701 - 1.028 mg/g, respectively. CONCLUSION: The optimal drying method is airing drying at 55 degrees C, which will be serviced in the large-scale processing of Isatidis Radix.

A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3-D Non-Contrast-Enhanced Computed Tomography.

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen-Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0.831, a sensitivity of 0.938, and an F1-score of 0.847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice. The source codes and supplementary materials for our framework are available at https://github.com/yXiangXiong/CMTGF.