A new phenylheptanoid from the leaves ofGnetum gnemonL

Plant ingredients in Thai food: a well-rounded diet for natural bioactive associated with medicinal properties

Background

Seeking cures for chronic inflammation-associated diseases and infectious diseases caused by critical human pathogens is challenging and time-consuming. Even as the research community searches for novel bioactive agents, consuming a healthy diet with functional ability might be an effective way to delay and prevent the progression of severe health conditions. Many plant ingredients in Thai food are considered medicinal, and these vegetables, herbs, and spices collectively possess multiple biological and pharmacological activities, such as anti-inflammatory, antimicrobial, antidiabetic, antipyretic, anticancer, hepatoprotective, and cardioprotective effects.

Methodology

In this review, the selected edible plants are unspecific to Thai food, but our unique blend of recipes and preparation techniques make traditional Thai food healthy and functional. We searched three electronic databases: PUBMED, Science Direct, and Google Scholar, using the specific keywords "Plant name" followed by "Anti-inflammatory" or "Antibacterial" or "Antiviral" and focusing on articles published between 2017 and 2021.

Results

Our selection of 69 edible and medicinal plant species (33 families) is the most comprehensive compilation of Thai food sources demonstrating biological activities to date. Focusing on articles published between 2017 and 2021, we identified a total of 245 scientific articles that have reported main compounds, traditional uses, and pharmacological and biological activities from plant parts of the selected species.

Conclusions

Evidence indicates that the selected plants contain bioactive compounds responsible for anti-inflammatory, antibacterial, and antiviral properties, suggesting these plants as potential sources for bioactive agents and suitable for consumption for health benefits.

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by d S T D - P R O method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors' structural features. In addition, 10 quantitative structure-activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (r ²) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.

A new lignan from the stems of Buchanania lucida Blume (Anacardiaceae)

From the EtOAc-soluble extract of the stems of Buchanania lucida, one new lignan, (+)-(8S,8'S)-5'-methoxy-4,4'-di-O-methylsecoisolariciresinol (1), together with five known compounds (2-6) were isolated. Their structures were elucidated on the basis of NMR spectroscopic interpretation. The absolute configuration of 1 was determined based on the Cotton effects in the ECD spectrum. In the tyrosinase inhibitory activity test, p-hydroxybenzoic acid (6) showed the strong effect, with an IC₅₀ value of 9.35 µM.