Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy

A practical guide to implementing artificial intelligence in traditional East Asian medicine research

In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.

Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches

Background

Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms.

Purpose

We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood.

Methods

Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model.

Results

We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms.

Conclusion

our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

Identifying Candidate Polyphenols Beneficial for Oxidative Liver Injury through Multiscale Network Analysis

Oxidative stress, a driver of liver pathology, remains a challenge in clinical management, necessitating innovative approaches. In this research, we delved into the therapeutic potential of polyphenols for oxidative liver injury using a multiscale network analysis framework. From the Phenol-Explorer database, we curated a list of polyphenols along with their corresponding PubChem IDs. Verified target information was then collated from multiple databases. We subsequently measured the propagative effects of these compounds and prioritized a ranking based on their correlation scores for oxidative liver injury. This result underwent evaluation to discern its effectiveness in differentiating between known and unknown polyphenols, demonstrating superior performance over chance level in distinguishing these compounds. We found that lariciresinol and isopimpinellin yielded high correlation scores in relation to oxidative liver injury without reported evidence. By analyzing the impact on a multiscale network, we found that lariciresinol and isopimpinellin were predicted to offer beneficial effects on the disease by directly acting on targets such as CASP3, NR1I2, and CYP3A4 or by modulating biological functions related to the apoptotic process and oxidative stress. This study not only corroborates the efficacy of identified polyphenols in liver health but also opens avenues for future investigations into their mechanistic actions.

Protective Effects of Red Ginseng Against Tacrine-Induced Hepatotoxicity: An Integrated Approach with Network Pharmacology and Experimental Validation

Introduction

Tacrine, an FDA-approved acetylcholinesterase inhibitor, has shown efficacy in treating Alzheimer's disease, but its clinical use is limited by hepatotoxicity. This study investigates the protective effects of red ginseng against tacrine-induced hepatotoxicity, focusing on oxidative stress.

Methods

A network depicting the interaction between compounds and targets was constructed for RG. Effect of RG was determined by MTT and FACS analysis with cells stained by rhodamine 123. Proteins were extracted and subjected to immunoblotting for apoptosis-related proteins.

Results

The outcomes of the network analysis revealed a significant association, with 20 out of 82 identified primary RG targets aligning with those involved in oxidative liver damage including notable interactions within the AMPK pathway. in vitro experiments showed that RG, particularly at 1000μg/mL, mitigated tacrine-induced apoptosis and mitochondrial damage, while activating the LKB1-mediated AMPK pathway and Hippo-Yap signaling. In mice, RG also protected the liver injury induced by tacrine, as similar protective effects to silymarin, a well-known drug for liver toxicity protection.

Discussion

Our study reveals the potential of RG in mitigating tacrine-induced hepatotoxicity, suggesting the administration of natural products like RG to reduce toxicity in Alzheimer's disease treatment.

Interaction between dietary flavonoid intake and trouble sleeping on non-alcoholic fatty liver disease risk: a cross-sectional study

OBJECTIVE

The possible interaction of dietary flavonoid intake and sleep on non-alcoholic fatty liver disease (NAFLD) has not been well studied. This study investigated the interaction between dietary flavonoid intake and trouble sleeping on the risk of NAFLD.

METHODS

Three discrete National Health and Nutrition Examination Survey data cycles from 2007 to 2010 and 2017 to 2018 were used. NAFLD was diagnosed by a US Fatty Liver Index ≥30. A sleep questionnaire diagnosed trouble sleeping. Univariate and multivariate logistic regression, restricted cubic spline (RCS) and subgroup analyses were used to evaluate the association between dietary flavonoids, trouble sleeping and NAFLD. We employed the relative excess risk due to interaction, attributable proportion of interaction and synergy index to evaluate additive interactions.

RESULTS

Ultimately, 5056 participants were enrolled, and higher anthocyanidins and flavanones intake was negatively correlated with NAFLD. Conversely, trouble sleeping was positively associated with NAFLD. These correlations remained stable after adjusting for confounders, and there was a sex difference in this relationship. In the RCS model, anthocyanins were negatively non-linearly related to NAFLD, while flavanones showed a negative linear relationship. Moreover, there was a synergistic interplay between low dietary anthocyanin intake and trouble sleeping on the risk of NAFLD. A similar relationship existed for flavanone intake.

CONCLUSION

Anthocyanin and flavanone intake were negatively associated, whereas trouble sleeping was positively associated with NAFLD risk. There was a synergistic effect of low anthocyanin intake and trouble sleeping. The same relationship existed for low flavanone intake.