Xuefu Zhuyu decoction alleviates deep vein thrombosis through inhibiting the activation of platelets and neutrophils via sirtuin 1/nuclear factor kappa-B pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Xuefu Zhuyu Decoction (XZD), a renowned traditional Chinese medicine prescription, is widely employed for the management of conditions characterized by qi-stagnation and blood stasis. Although its anti-thrombotic effect on deep vein thrombosis (DVT) patients has been clinically observed, the underlying mechanism remains largely unexplored. AIM OF THE STUDY: Our aim was to investigate the mechanisms by which XZD exerted its effect on DVT. MATERIALS AND METHODS: The ultra performance liquid chromatography (UPLC) technique was employed to evaluate quality of XZD. To examine the effect of XZD on DVT, a DVT rat model with inferior vena cava (IVC) stenosis was established. The 4D-label-free proteomics approach was then utilized to uncover the possible mechanisms of XZD against DVT. Based on proteomics, citrullinated histone H3 (CitH3), along with serum levels of tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1ß) were observed the inhibitory activity of XZD on neutrophil activation. Subsequently, the marker of platelet activation, specifically glycoprotein IIb (CD41) and glycoprotein IIIa (CD61), were assessed along with the secretion of von Willebrand factor (vWF) to investigate the inhibitory activity of XZD on platelet activation. Finally, we explored the impact of XZD on the sirtuin 1 (SIRT1)/nuclear factor kappa-B (NF-κB) pathway, which was associated with the activation of platelets and neutrophils. RESULTS: Eight distinct components were identified for the quality control of XZD. XZD effectively reduced thrombus weight and length in DVT rats, without affecting the coagulation function or hematological parameters in the systemic circulation. Proteomics analysis revealed that XZD alleviated DVT by inhibiting the activation of platelets and neutrophils. The protein expression of CitH3, along with serum levels of TNF-α and IL-1ß, were reduced in XZD-treated DVT rats. Similarly, protein expressions of CD41 and CD61, along with the release of vWF, were markedly down-regulated in XZD-treated DVT rats. Finally, treatment with XZD resulted in an up-regulation of SIRT1 protein expression and a down-regulation of both acetylated NF-κB/p65 and phosphorylated NF-κB/p65 protein expressions in endothelium. CONCLUSIONS: XZD alleviates DVT by inhibiting the activation of platelets and neutrophils at the injured endothelium via the regulation of SIRT1/NF-κB pathway.

Predicting drug-Protein interaction with deep learning framework for molecular graphs and sequences: Potential candidates against SAR-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regarding viral infection, many therapeutics have been investigated to alleviate the epidemiology such as vaccines and receptor decoys. However, the continuous mutating coronavirus, especially the variants of Delta and Omicron, are tended to invalidate the therapeutic biological product. Thus, it is necessary to develop molecular entities as broad-spectrum antiviral drugs. Coronavirus replication is controlled by the viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme, which is required for the virus's life cycle. In the cases of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV), 3CLpro has been shown to be a promising therapeutic development target. Here we proposed an attention-based deep learning framework for molecular graphs and sequences, training from the BindingDB 3CLpro dataset (114,555 compounds). After construction of such model, we conducted large-scale screening the in vivo/vitro dataset (276,003 compounds) from Zinc Database and visualize the candidate compounds with attention score. geometric-based affinity prediction was employed for validation. Finally, we established a 3CLpro-specific deep learning framework, namely GraphDPI-3CL (AUROC: 0.958) achieved superior performance beyond the existing state of the art model and discovered 10 molecules with a high binding affinity of 3CLpro and superior binding mode.

Enterprise Risk Assessment Based on Machine Learning.

Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise's risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.