Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

https://www.mdpi.com/1424-8247/15/5/572/pdf?version=1651635674

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide, yet as of currently, there is no disease-modifying therapy that could delay its progression. Paeonia lactiflora Pall. is the most frequently used herb in formulas for PD in Traditional Chinese Medicine and also a potential neuroprotective agent for neurodegenerative diseases, while its mechanisms remain poorly understood. In this study, we aim to explore the underlying mechanism of P. lactiflora in treating PD utilizing a network pharmacology approach. Methods: The protein targets of P. lactiflora ingredients and PD were first obtained from several databases. To clarify the key targets, a Protein-Protein-Interaction (PPI) network was constructed and analyzed on the String database, and then enrichment analysis was performed by the Metascape platform to determine the main Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathways. Finally, the Ingredient-Target-Pathway (I-T-P) network was constructed and analyzed by Cytoscape software. Results: Six active ingredients of P. lactiflora (kaempferol, s-sitosterol, betulinic acid, palbinone, paeoniflorin and (+)-catechin) as well as six core targets strongly related to PD treatment [AKT1, interleukin-6, CAT, Tumor necrosis factor (TNF), CASP3, and PTGS2] were identified. The main pathways were shown to involve neuroactive ligand-receptor interaction, Calcium signaling pathway, PI3-Akt signaling pathway, TNF signaling pathway, and apoptosis signaling pathway. The main biological process included the regulation of neurotransmitter levels. Conclusion: P. lactiflora may retard neurodegeneration by reducing neuroinflammation, inhibiting intrinsic and extrinsic apoptosis, and may improve motor and non-motor symptoms by regulating the levels of neurotransmitters. Our study has revealed the mechanism of P. lactiflora in the treatment of PD and may contribute to novel drug development for PD.

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The Underlying Mechanism of Paeonia lactiflora Pall. in Parkinson's Disease Based on a Network Pharmacology Approach.

Amyloid β (Aβ) plays a major role in the neurodegeneration of Alzheimer's disease (AD). The accumulation of misfolded Aβ causes oxidative stress and inflammatory damage leading to apoptotic cell death. Traditional Chinese herbal medicine (CHM) has been widely used in treating neurodegenerative diseases by reducing oxidative stress and neuroinflammation. We examined the neuroprotective effect of formulated CHM Shaoyao Gancao Tang (SG-Tang, made of Paeonia lactiflora and Glycyrrhiza uralensis at 1:1 ratio) in AD cell and mouse models. In Aβ-GFP SH-SY5Y cells, SG-Tang reduced Aβ aggregation and reactive oxygen species (ROS) production, as well as improved neurite outgrowth. When the Aβ-GFP-expressing cells were stimulated with conditioned medium from interferon (IFN)-γ-activated HMC3 microglia, SG-Tang suppressed expressions of inducible nitric oxide synthase (iNOS), NLR family pyrin domain containing 1 (NLRP1) and 3 (NLRP3), tumor necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6, attenuated caspase-1 activity and ROS production, and promoted neurite outgrowth. In streptozocin-induced hyperglycemic APP/PS1/Tau triple transgenic (3×Tg-AD) mice, SG-Tang also reduced expressions of NLRP1, NLRP3, Aβ and Tau in hippocampus and cortex, as well as improved working and spatial memories in Y maze and Morris water maze. Collectively, our results demonstrate the potential of SG-Tang in treating AD by moderating neuroinflammation.

Formulated Chinese medicine Shaoyao Gancao Tang reduces NLRP1 and NLRP3 in Alzheimer's disease cell and mouse models for neuroprotection and cognitive improvement.

Background The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics. Aims To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics. Methods A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model. Results Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively. Conclusion We construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.

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Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics

The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.,A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.,From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.,The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.,To the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC. The dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms. The three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on. The three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.

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Application of data mining methods to improve screening for the risk of early gastric cancer.

Parkinson's disease (PD) is another major neurodegenerative disorder following Alzheimer's disease, which not only seriously reduces the survival in patients, affecting patient's quality of life, but also imposes a tremendous burden on families and even the whole society. It is urgent to find out effective drugs without side effects. The present study applied a creative approach called network pharmacology to explore the active compounds and therapeutic targets of Shaoyao-Gancao Decoction (SYGCD) for treating PD. We identified a total of 48 active compounds mediating 30 PD-related targets to exert synergism, and the same target can be enriched in multiple signal pathways and biological processes, expounding that the decoction can exert synergistic effect on PD by multi-targets and multi-pathways. Furthermore, the molecular docking analysis showed that active compounds and targets can be well combined. These results highlighted the molecular mechanisms underlying the efficiency of SYGCD for PD treatment at a systematic level, investigating thoroughly the innovative therapeutic tactics for PD in traditional Chinese medicine.

A Network Pharmacology-Based Study of the Molecular Mechanisms of Shaoyao-Gancao Decoction in Treating Parkinson’s Disease

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2 = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

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A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

Epidemiological studies have shown that primary dysmenorrhea is the most common disease in gynecology, and its incidence rate is between 20% and 90%, which is a common cause of affecting women’s normal work and quality of life. Its high incidence rate, wide spread and economic losses and social harm have caused widespread concern worldwide. The present work adopted a network pharmacology-based approach to provide new insights into the active compounds and therapeutic targets of Danggui-Baishao herb pair for the treatment of primary dysmenorrhea. Fifteen active compounds of the herb pair possessing favorable pharmacokinetic profiles and biological activities were selected, interacting 17 dysmenorrhea-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as ABCB1, ESR1, PGR, AKR1C3, PTGS2, CYP2C8, PTGS1 were mainly involved in Steroid hormone biosynthesis, Arachidonic acid metabolism, serotonergic synapse and Ovarian steroidogenesis through steroid metabolic process, steroid hormone mediated signaling pathway, cyclooxygenase pathway, response to estradiol and response to oxidative stress.

Yong-Ming Cai

Papers

Application of data mining methods to improve screening for the risk of early gastric cancer.

A Network Pharmacology-Based Study of the Molecular Mechanisms of Shaoyao-Gancao Decoction in Treating Parkinson’s Disease

A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach