[Component identification and analysis in vivo of Sanhan Huashi formula].

Sanhan Huashi formula(SHF) is the intermediate of a newly approved traditional Chinese medicine(TCM) Sanhan Huashi Granules for the treatment of COVID-19 infection. The chemical composition of SHF is complex since it contains 20 single herbal medicines. In this study, UHPLC-Orbitrap Exploris 240 was used to identify the chemical components in SHF and in rat plasma, lung and feces after oral administration of SHF, and heat map was plotted for characterizing the distribution of the chemical components. Chromatographic separation was conducted on a Waters ACQUITY UPLC BEH C_(18)(2.1 mm×100 mm, 1.7 µm) using 0.1% formic acid(A)-acetonitrile(B) as mobile phases in a gradient elution. Electrospray ionization(ESI) source was used to acquire data in positive and negative mode. By reference to quasi-molecular ions and MS/MS fragment ions and in combination with MS spectra of reference substances and compound information in literature reports, 80 components were identified in SHF, including 14 flavonoids, 13 coumarins, 5 lignans, 12 amino-compounds, 6 terpenes and 30 other compounds; 40 chemical components were identified in rat plasma, 27 in lung and 56 in feces. Component identification and characterization of SHF in vitro and in vivo lay foundations for disclosure of its pharmacodynamic substances and elucidation of the scientific connotation.

Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19.

BACKGROUND: Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. METHODS: This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. RESULTS: There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. INTERPRETATION: Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. FUNDING: None.

Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression.

Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.