Baicalin, an important flavonoid in Scutellaria baicalensis Georgi extracts, exerts a variety of pharmacological effects. In this study, we explored the effects of baicalin on chronic hypoxia-induced pulmonary arterial hypertension (PAH) and investigated the mechanism underlying these effects. Moreover, we examined whether the inflammatory response was mediated by the A2A receptor (A2AR) and stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4)-induced phosphatidyl inositol-3-kinase (PI3K) signaling in vivo. We established a hypoxia-induced pulmonary hypertension (HPH) mouse model by subjecting wild-type (WT) and A2AR knockout (A2AR−/−) animals to chronic hypoxia, and we examined the effects of a 4-week treatment with baicalin or the A2AR agonist CGS21680 in these animals. Invasive hemodynamic parameters, the right ventricular hypertrophy index, pulmonary congestion, the pulmonary arterial remodeling index, blood gas parameters, A2AR expression, and the expression of SDF-1/CXCR4/PI3K/protein kinase B (PKB; AKT) signaling components were measured. Compared with WT mice, A2AR−/− mice exhibited increased right ventricular systolic pressure (RVSP), right ventricle-to-left ventricle plus septum [RV/(LV + S)] ratio, RV weight-to-body weight (RV/BW) ratio, and lung wet weight-to-body weight (Lung/BW) ratio in the absence of an altered mean carotid arterial pressure (mCAP). These changes were accompanied by increases in pulmonary artery wall area and thickness and reductions in arterial oxygen pressure (PaO2) and hydrogen ion concentration (pH). In the HPH model, A2AR−/− mice displayed increased CXCR4, SDF-1, phospho-PI3K, and phospho-AKT expression compared with WT mice. Treating WT and A2AR−/− HPH mice with baicalin or CGS21680 attenuated the hypoxia-induced increases in RVSP, RV/(LV + S) and Lung/BW, as well as pulmonary arterial remodeling. Additionally, baicalin or CGS21680 alone could reverse the hypoxia-induced increases in CXCR4, SDF-1, phospho-PI3K, and phospho-AKT expression. Moreover, baicalin improved the hypoxemia induced by 4 weeks of hypoxia. Finally, we found that A2AR levels in WT lung tissue were enhanced by hypoxia and that baicalin up-regulated A2AR expression in WT hypoxic mice. Baicalin exerts protective effects against clinical HPH, which are partly mediated through enhanced A2AR activity and down-regulated SDF-1/CXCR4-induced PI3K/AKT signaling. Therefore, the A2AR may be a promising target for baicalin in treating HPH.

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients’ necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN’s optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.