Analyzing the molecular mechanism of xuefuzhuyu decoction in the treatment of pulmonary hypertension with network pharmacology and bioinformatics and verifying molecular docking.

BACKGROUND: XueFuZhuYu (XFZY), a typical Chinese herbal formula, has remarkable clinical effects for treating Pulmonary Hypertension (PH) with unclear mechanisms. Our research involved the utilization of network pharmacology to explore the traditional Chinese herbal monomers and their related targets within XFZY for PH treatment. Furthermore, molecular docking verification was performed. METHODS: The XFZY's primary active compounds, along with their corresponding targets, were both obtained from the TCMSP, ChEMBL, and UniProt databases. The target proteins relevant to PH were sifted through OMIM, GeneCards and TTD databases. The common "XFZY-PH" targets were evaluated with Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses with the assistance of R software. The Protein-Protein Interaction (PPI) network and compound-target-pathway network were constructed and a systematic analysis of network parameters was performed by the powerful software Cytoscape. Molecular docking was employed for assessing and verifying the interactions between the core targets and the top Chinese herbal monomer. RESULTS: The screening included 297 targets of active compounds in XFZY and 8400 PH-related targets. DO analysis of the above common 268 targets indicated that the treatment of the diseases by XFZY is mediated by genes related to Chronic Obstructive Pulmonary Disease (COPD), Obstructive Lung Disease (OLD), ischemia, and myocardial infarction. The findings from molecular docking indicated that the binding energies of 57 ligand-receptor pairs in PH and 20 ligand-receptor pairs in COPD-PH were lower than -7kJ•mol-1. CONCLUSIONS: This study indicates that XFZY is a promising option within traditional Chinese medicine compound preparation for combating PH, particularly in cases associated with COPD. Our demonstration of the specific molecular mechanism of XFZY anti-PH and its effective active ingredients provides a theoretical basis for better clinical application of the compound.

Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension.

Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH.