Potential Therapeutic Mechanism of Scutellaria baicalensis Georgi against Ankylosing Spondylitis Based on a Comprehensive Pharmacological Model

Preliminary study on molecular mechanism of COVID-19 intervention by Polygonum cuspidatum through computer bioinformatics

To explore the mechanism of action of Polygonum cuspidatum in intervening in coronavirus disease 2019 using a network pharmacology approach and to preliminarily elucidate its mechanism. The active ingredients and action targets of P cuspidatum were classified and summarized using computer virtual technology and molecular informatics methods. The active ingredients and relevant target information of P cuspidatum were identified using the TCM Systematic Pharmacology Database and Analysis Platform, the TCM Integrated Pharmacology Research Platform v2.0, and the SwissTarget database. The GENECARDS database was used to search for COVID-19 targets. The STRING database was analyzed and combined with Cytoscape 3.7.1 software to construct a protein interaction network map to screen the core targets. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was then performed. The core compound, polydatin, was selected and the core targets were analyzed by computer virtual docking using software such as discovery studio autodock tool. In vitro cell models were constructed to experimentally validate the activity of the core compound, polydatin. By computer screening, we identified 9 active ingredients and their corresponding 286 targets from P cuspidatum. A search of the GENECARDS database for COVID-19 yielded 303 core targets. By mapping the active ingredient targets to the disease targets, 27 overlapping targets could be extracted as potential targets for the treatment of COVID-19 with P cuspidatum. In addition, the enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathway on core targets showed that the coronavirus disease, MAPK signaling pathway, NF kappa B signaling pathway, and other signaling pathways were highly enriched. Combined with the degree-high target analysis in the protein interaction network, it was found to be mainly concentrated in the NF-kappaB (NF-κB) signaling pathway, indicating that the NF-κB signaling pathway may be an important pathway for P cuspidatum intervention. In vitro assays showed no effect of 0.1 to 10 μM polydatin on cell viability, but an inhibitory effect on the transcriptional activity of NF-κB-RE. Molecular docking showed stable covalent bonding of polydatin molecules with Il-1β protein at residue leu-26, TNF protein ser-60, residue gly-121, and residue ile-258 of ICAM-1 protein, indicating a stable docking result. The treatment of COVID-19 with P cuspidatum is characterized by multi-component, multi-target, and multi-pathway, which can exert a complex network of regulatory effects through the interaction between different targets, providing a new idea and basis for further exploration of the mechanism of action of P cuspidatum in the treatment of COVID-19.

Improved pre-test likelihood estimation of coronary artery disease using phonocardiography

Aims

Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models.

Methods and results

Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (P < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL.

Conclusion

The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.