Bioinformatic assay reveal the potential mechanism of Guizhi-Shaoyao-Zhimu decoction against rheumatoid arthritis and mild-to-moderate COVID-19.

BACKGROUND AND OBJECTIVE: Patients with rheumatoid arthritis (RA) are more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than healthy population, but there is still no therapeutic strategy available for RA patients with corona virus disease 2019 (COVID-19). Guizhi-Shaoyao-Zhimu decoction (GSZD), Chinese ancient experience decoction, has a significant effect on the treatment of Rheumatism and gout. To prevent RA patients with mild-to-moderate COVID-19 from developing into severe COVID-19, this study explored the potential possibility and mechanism of GSZD in the treatment of this population. METHODS: In this study, we used bioinformatic approaches to explore common pharmacological targets and signaling pathways between RA and mild-to-moderate COVID-19, and to assess the potential mechanisms of in the treatment of patients with both diseases. Beside, molecular docking was used to explore the molecular interactions between GSZD and SARS-CoV-2 related proteins. RESULTS: Results showed that 1183 common targets were found in mild-to-moderate COVID-19 and RA, of which TNF was the most critical target. The crosstalk signaling pathways of the two diseases focused on innate immunity and T cells pathways. In addition, GSZD intervened in RA and mild-to-moderate COVID-19 mainly by regulating inflammation-related signaling pathways and oxidative stress. Twenty hub compounds in GSZD exhibited good binding potential to SARS-CoV-2 spike (S) protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), papain-like protease (PLpro) and human angiotensin-converting enzyme 2 (ACE2), thereby intervening in viral infection, replication and transcription. CONCLUSIONS: This finding provides a therapeutic option for RA patients against mild-to-moderate COVID-19, but further clinical validation is still needed.

Effects of COVID-19 home quarantine on pregnancy outcomes of patients with gestational diabetes mellitus: a retrospective cohort study.

OBJECTIVE: This study aimed to evaluate the effects of the home quarantine on pregnancy outcomes of gestational diabetes mellitus (GDM) patients during the COVID-19 outbreak. METHODS: The complete electronic medical records of patients with GDM with home quarantine history were collected and classified into the home quarantine group from 24 February 2020 to 24 November 2020. The same period of patients with GDM without home quarantine history were included in the control group from 2018 to 2019. The pregnant outcomes of the home quarantine and control groups were systematically compared, such as neonatal weight, head circumference, body length, one-minute Apgar score, fetal macrosomia, and pre-term delivery. RESULTS: A total of 1358 patients with GDM were included in the analysis, including 484 in 2018, 468 in 2019, and 406 in 2020. Patients with GDM with home quarantine in 2020 had higher glycemic levels and adverse pregnancy outcomes than in 2018 and 2019, including higher cesarean section rates, lower Apgar scores, and higher incidence of macrosomia and umbilical cord around the neck. More importantly, the second trimester of home quarantine had brought a broader impact on pregnant women and fetuses. CONCLUSION: Home quarantine has aggravated the condition of GDM pregnant women and brought more adverse pregnancy outcomes during the COVID-19 outbreak. Therefore, we suggested governments and hospitals strengthen lifestyle guidance, glucose management, and antenatal care for patients with GDM with home quarantine during public health emergencies.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.