Study on the mechanism of treating COVID-19 with Shenqi Wan based on network pharmacology.

PURPOSE: Through the method of network pharmacology, the active components and targets of Shenqi Wan (SQW) were excavated, the relationship with novel Coronavirus pneumonia (COVID-19) was discussed, and the possible mechanism of SQW in the treatment of COVID-19 was revealed from the aspects of multicomponents, multitargets, and multipathways. METHODS: Firstly, the active components of SQW were screened from traditional Chinese medicine systems pharmacology database and analysis platform and the 2020 edition of Chinese Pharmacopeia, and the related targets of the components were obtained. Then the disease targets related to COVID-19 were screened from GeneCards and Online Mendelian Inheritance in Man. Venny was used to map the relationship between component-target and disease-target, and String was used to analyze the interaction of common targets. The network was constructed and analyzed by Cytoscape, the function of Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) genes was enriched by Metascape, and the molecular docking was verified by CB-Dock. RESULTS: Finally, 45 active components of SQW were obtained, and 72 potential targets were related to COVID-19, angiotensin-converting enzyme 2 (ACE2), interleukin (IL)-6, nitric oxide synthase (NOS3) and, C-reactive protein (CRP),may be the key targets. GO enrichment of 1715 projects, such as lipopolysaccharide stress response, active oxygen metabolism, positive regulation of cell migration, and other GO enrichment. About 136 KEGG pathways, tumor necrosis factor signaling pathway, IL-17 signaling pathway, hypoxia-inducible factor 1-α signaling pathway were obtained. Molecular docking showed that kaempferol, quercetin, luteolin, astragaloside, calyx isoflavone glucoside, matrine, and other COVID-19-related targets such as ACE2, chymotrypsin-like protease (3CLpro), papain-like protease (PLpro), prostaglandin-endoperoxide synthase 2 (PTGS2) have good binding ability. CONCLUSION: According to the above results, it is suggested that SQW may play a role in the treatment of COVID-19 by directly or indirectly combining kaempferol, quercetin, and luteolin with ACE2, 3CLpro, PLpro, and PTGS2 to regulate multiple biological functions and signaling pathways.

Understanding the need of ventricular pressure for the estimation of diastolic biomarkers.

The diastolic function (i.e., blood filling) of the left ventricle (LV) is determined by its capacity for relaxation, or the decay in residual active tension (AT) generated during systole, and its constitutive material properties, or myocardial stiffness. The clinical determination of these two factors (diastolic residual AT and stiffness) is thus essential for assessing LV diastolic function. To quantify these two factors, in our previous work, a novel model-based parameter estimation approach was proposed and successfully applied to multiple cases using clinically acquired motion and invasively measured ventricular pressure data. However, the need to invasively acquire LV pressure limits the wide application of this approach. In this study, we address this issue by analyzing the feasibility of using two kinds of non-invasively available pressure measurements for the purpose of inverse mechanical parameter estimation. The prescription of pressure based on a generic pressure-volume (P-V) relationship reported in literature is first evaluated in a set of 18 clinical cases (10 healthy and 8 diseased), finding reasonable results for stiffness but not for residual active tension. We then investigate the use of non-invasive pressure measures, now available through imaging techniques and limited by unknown or biased offset values. Specifically, three sets of physiologically realistic synthetic data with three levels of diastolic residual active tension (i.e., impaired relaxation capability) are designed to quantify the percentage error in the parameter estimation against the possible pressure offsets within the physiological limits. Maximum errors are quantified as 11 % for the magnitude of stiffness and 22 % for AT, with averaged 0.17 kPa error in pressure measurement offset using the state-of-the-art non-invasive pressure estimation method. The main cause for these errors is the limited temporal resolution of clinical imaging data currently available. These results demonstrate the potential feasibility of the estimation diastolic biomarkers with non-invasive assessment of pressure through medical imaging data.

The estimation of patient-specific cardiac diastolic functions from clinical measurements.

An unresolved issue in patients with diastolic dysfunction is that the estimation of myocardial stiffness cannot be decoupled from diastolic residual active tension (AT) because of the impaired ventricular relaxation during diastole. To address this problem, this paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastolic residual AT. Dynamic C1-continuous meshes are automatically built from the anatomy and deformation captured from dynamic MRI sequences. Diastolic deformation is simulated using a mechanical model that combines passive and active material properties. The problem of non-uniqueness of constitutive parameter estimation using the well known Guccione law is characterized by reformulation of this law. Using this reformulated form, and by constraining the constitutive parameters to be constant across time points during diastole, we separate the effects of passive constitutive properties and the residual AT during diastolic relaxation. Finally, the method is applied to two clinical cases and one control, demonstrating that increased residual AT during diastole provides a potential novel index for delineating healthy and pathological cases.

Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter.

Parameter estimation from non-invasive measurements is a crucial step in patient-specific cardiac modeling. It also has the potential to provide significant assistance in the clinical diagnosis of cardiac diseases through the quantification of myocardial material heterogeneity. In this paper, we formulate a novel Reduced-order Unscented Kalman Filter (rUKF) applied to the left ventricular (LV) nonlinear mechanical model based on cubic-Hermite finite elements. Material parameters in the widely-employed transversely isotropic Guccione's constitutive law are successfully identified for both homogeneous and heterogeneous cases. We conclude that the four parameters in Guccione's law can be uniquely and correctly determined in-silico from noisy displacement measurements of material points located on the myocardial surfaces. The future application of this novel and effective approach to real clinical measurements is thus promising.