College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China.
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Comput Biol Med. 2023 Sep;164:107293. doi: 10.1016/j.compbiomed.2023.107293. Epub 2023 Jul 31.
Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.
人体健康受到肺动脉高压（PH）的威胁，其特征是肺血管阻力降低和肺血管收缩，导致右心衰竭和功能障碍。因此，预防 PH 并在治疗前监测其进展至关重要。从黄芩的叶子中提取的汉黄芩素表现出显著的药理活性。在这项研究中，我们使用右心导管术和苏木精-伊红（HE）染色检查汉黄芩素在减轻小鼠 PH 进展中的作用。作为一种替代方法，可以最大限度地减少伤害小动物的可能性，我们提出了一种科学有效的特征选择方法（BSCDWOA-KELM），该方法将使我们能够开发一种治疗 PH 的汉黄芩素新型简单的非侵入性预测方法。在该方法中，我们使用所提出的增强型鲸鱼优化器（SCDWOA）结合核极限学习机（KELM）。首先，我们让 SCDWOA 在 IEEE CEC2014 基准函数集中进行全局优化实验，以验证其核心优势。最后，使用 BSCDWOA-KELM 对 12 个公共和 PH 数据集进行特征选择实验。从全局优化的实验结果来看，所提出的 SCDWOA 具有更好的收敛性能。同时，所提出的二进制 SCDWOA（BSCDWOA）显著提高了 KELM 对数据分类的能力。通过利用 BSCDWOA-KELM，可以在 Pulmonary hypertension 数据集高效筛选红细胞（RBC）、血红蛋白（HGB）、淋巴细胞百分比（LYM%）、血细胞比容（HCT）和红细胞分布宽度-大小分布（RDW-SD）等关键指标，并以其准确率大于 0.98 为其最关键的一点。因此，本文介绍的 BSCDWOA-KELM 可用于简单、非侵入性地预测汉黄芩素治疗肺动脉高压。
