Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble “mystery boxes”. Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.     

Astragalin identification in graviola pericarp indicates a possible participation in the anticancer activity of pericarp crude extracts: In vitro and in silico approaches

Graviola, soursop, or guanabana (Annona muricata L.), is an ethnomedical fruit consumed to alleviate headache, diarrhea, diabetes, and cancer. Pericarp is the inedible part of graviola least studied in comparison to seeds and leaves, even thought, it contains the highest concentration of graviola total polyphenols. Anticancer effect of graviola pericarp has been demonstrated in crude extracts attributing the effect to acetogenins, however, crude extracts contain several active molecules. Thus, the present work aimed to fractionate and purify an ethanolic crude extract from graviola pericarp. Purified graviola pericarp fraction (PGPF) was evaluated on cancerous and non-cancerous cell lines, and then was identified by NMR, TOF-MS, and HPLC. Finally, an in silico analysis was performed to predict targets cancer-related of the molecule detected. Our results revealed IC₅₀ values for cervix adenocarcinoma (HeLa), hepatocellular carcinoma (HepG2), triple-negative breast cancer (MDA-MB-231), and non-cancerous cell line (HaCaT) of 92.85 ± 1.23, 81.70 ± 1.09, 84.28 ± 1.08, and 170.2 ± 1.12 µg PGPF/mL, respectively. In vitro therapeutic indexes estimated as quantitative relationship between safety and efficacy of PGPF were 1.83, 2.08, and 2.02 for HeLa, HepG2, and MDA-MB-231, respectively. The NMR analysis revealed astragalin (kaempferol-3-O-glucoside) in PGPF, a flavonoid not reported in graviola pericarp until now. Astragalin identity was confirmed by TOF-MS and HPLC. In silico results support previous reports about astragalin modulating proteins such as Bcl-2, CDK2, CDK4, MAPK and RAF1. Also, results suggest that astragalin may interact with other cancer-related proteins not associated previously with astragalin. In conclusion, astragalin may be contributing to the anticancer effect observed in graviola pericarp extracts.     

基于GA-WPA优化的BP神经网络目标威胁估计

在防空作战中,目标威胁估计是指挥控制过程的重要一环,是决策和指挥的重要依据。BP神经网络能够解决目标威胁估计问题,但存在收敛速度慢、易陷入局部最优等缺点。提出将遗传算法(Genetic Algorithm,GA)的选择、交叉和变异操作融入到狼群算法(Wolf Pack Algorithm,WPA)中,提出了GA-WPA算法,以提高狼群算法的收敛速度。在此基础上,利用所提出的GA-WPA算法对BP神经网络进行优化,确定最优初始权值和阈值。最后,将优化后的BP神经网络解决地面防空系统目标威胁估计问题。仿真实验表明,所提算法能够有效克服BP神经网络收敛速度慢、易陷入局部最优等缺点,能够提高目标威胁估计的准确性和适应性。     

PASS: identification of probable targets and mechanisms of toxicity†

Toxicity of chemical compound is a complex phenomenon that may be caused by its interaction with different targets in the organism. Two distinct types of toxicity can be broadly specified: the first one is caused by the strong compound's interaction with a single target (e.g. AChE inhibition); while the second one is caused by the moderate compound's interaction with many various targets. Computer program PASS predicts about 2500 kinds of biological activities based on the structural formula of chemical compounds. Prediction is based on the robust analysis of structure-activity relationships for about 60,000 biologically active compounds. Mean accuracy exceeds 90% in leave-one-out cross-validation. In addition to some kinds of adverse effects and specific toxicity (e.g. carcinogenicity, mutagenicity, etc.), PASS predicts ～2000 kinds of biological activities at the molecular level, that providing an estimated profile of compound's action in biological space. Such profiles can be used to recognize the most probable targets, interaction with which might be a reason of compound's toxicity. Applications of PASS predictions for analysis of probable targets and mechanisms of toxicity are discussed.     

基于灰度形态学和邻域熵值的弱小目标检测

 提出了一种弱小目标检测的新方法。从实际应用出发，考虑到复杂的背景和大量的干扰噪声，对传统熵值检测算法进行了改进，采用邻域熵值变化为检测标准。为了提高此方法的有效性，结合了灰度形态学滤波来对图像进行预处理。该检测算法的全过程为:首先对图像进行形态运算;然后对形态波后的图像进行邻域熵的计算;接着以计算所得的邻域熵的最大值和最小值为依据对图像进行分割,得到目标或目标边缘所处位置;最后用实地拍摄的空中弱小目标真实图像进行了实验验证。结果发现：该新方法可对弱小目标、大目标、多目标进行检测，且检测速度快，抗噪声干扰能力强。     

The Defect Relation for Slowly Moving Target Hypersurfaces

Under certain assumptions, we extend the Carlson-Griffiths-King theory to prove a second main theorem for moving target hypersurfaces. This allows us to obtain the desired defect relation $$ \sum\limits_{j = 1}^q {\delta (f , g_j )} \le {{n + 1} \over p} $$