Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".

Objective: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs).

Method: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI.

Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System.

Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data. A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study.

[Simultaneous determination of thirteen components in Tong'an injection by LC-MS/MS].

The aim of this study was to develop a simple, sensitive ultra performance liquid chromatography mass spectrometry (UPLC-MS/MS) method for the determination of syringaresinol, N-trans-feruloyltyramine, chelerythrine chloride, sinomenine, coptisine chloride, sanguinarine, chelidonine, magnolflorine, allocryptopine, protopine, farrerol, stylopine and dihydrosanguin-arine in Tong'an injection (TAI), which could be used for the quality control of TAI. The UPLC analysis was performed on Agilent Zorbax SB-Aq column (2.1 mm×150 mm,3.