Deeper Insights on Alchornea cordifolia (Schumach. & Thonn.) Müll.Arg Extracts: Chemical Profiles, Biological Abilities, Network Analysis and Molecular Docking

Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma

In this study, we aimed to develop an invasion-related risk signature and prognostic model for personalized treatment and prognosis prediction in skin cutaneous melanoma (SKCM), as invasion plays a crucial role in this disease. We identified 124 differentially expressed invasion-associated genes (DE-IAGs) and selected 20 prognostic genes (TTYH3, NME1, ORC1, PLK1, MYO10, SPINT1, NUPR1, SERPINE2, HLA-DQB2, METTL7B, TIMP1, NOX4, DBI, ARL15, APOBEC3G, ARRB2, DRAM1, RNF213, C14orf28, and CPEB3) using Cox and LASSO regression to establish a risk score. Gene expression was validated through single-cell sequencing, protein expression, and transcriptome analysis. Negative correlations were discovered between risk score, immune score, and stromal score using ESTIMATE and CIBERSORT algorithms. High- and low-risk groups exhibited significant differences in immune cell infiltration and checkpoint molecule expression. The 20 prognostic genes effectively differentiated between SKCM and normal samples (AUCs >0.7). We identified 234 drugs targeting 6 genes from the DGIdb database. Our study provides potential biomarkers and a risk signature for personalized treatment and prognosis prediction in SKCM patients. We developed a nomogram and machine-learning prognostic model to predict 1-, 3-, and 5-year overall survival (OS) using risk signature and clinical factors. The best model, Extra Trees Classifier (AUC = 0.88), was derived from pycaret's comparison of 15 classifiers. The pipeline and app are accessible at https://github.com/EnyuY/IAGs-in-SKCM.

Promising remedies for cardiovascular disease: Natural polyphenol ellagic acid and its metabolite urolithins

BACKGROUND

Cardiovascular disease (CVD) is a significant worldwide factor contributing to human fatality and morbidity. With the increase of incidence rates, it is of concern that there is a lack of current therapeutic alternatives because of multiple side effects. Ellagic acid (EA), the natural polyphenol (C₁₄H₆O₈), is abundant in pomegranates, berries, and nuts. EA and its intestinal microflora metabolite, urolithins, have recently attracted much attention as a potential novel "medicine" because of their wide pharmacological properties.

PURPOSE

This study aimed to critically analyze available literature to summarize the beneficial effects of EA and urolithins, and highlights their druggability and therapeutic potential in various CVDs.

METHODS

We systematically studied research and review articles between 1984 and 2022 available on various databases to obtain the data on EA and urolithins with no language restriction. Their cardiovascular protective activities, underlying mechanism, and druggability were highlighted and discussed comprehensively.

RESULTS

We found that EA and urolithins may exert preventive and curative effects on CVD with negligible side effects and possibly regulate lipid metabolism imbalance, pro-inflammatory factor production, vascular smooth muscle cell proliferation, cardiomyocyte apoptosis, endothelial cell dysfunction, and Ca²⁺ intake and release. Potentially, this may lead to the prevention and amelioration of atherosclerosis, hypertension, myocardial infarction, cardiac fibrosis, cardiomyopathy, cardiac arrhythmias, and cardiotoxicities in vivo. Several molecules and signaling pathways are associated with their therapeutic actions, including phosphatidylinositol 3-kinase/protein kinase B, mitogen-activated protein kinase, NF-κB, nuclear factor erythroid-2 related factor 2, sirtuin1, miRNA, and extracellular signal-regulated kinase 1/2.

CONCLUSION

In vitro and in vivo studies shows that EA and urolithins could be used as valid candidates for early prevention and effective therapeutic strategies for various CVDs.

Skin-Whitening and Anti-Wrinkle Effects of Bioactive Compounds Isolated from Peanut Shell Using Ultrasound-Assisted Extraction

Response surface methodology was employed to optimize the ultrasound-assisted extraction (UAE) conditions for simultaneous optimization of dependent variables, including DPPH radical scavenging activity (RSA), tyrosinase activity inhibition (TAI), and collagenase activity inhibition (CAI) of peanut shell extracts. The effects of the main variables including extraction time (5.0~55.0 min, X₁), extraction temperature (26.0~94.0 °C, X₂), and ethanol concentration (0.0%~99.5%, X₃) were optimized. Based on experimental values from each condition, quadratic regression models were derived for the prediction of optimum conditions. The coefficient of determination (R²) of the independent variable was in the range of 0.89~0.96, which demonstrates that the regression model is suitable for the prediction. In predicting optimal UAE conditions based on the superimposing method, extraction time of 31.2 min, extraction temperature of 36.6 °C, and ethanol concentration of 93.2% were identified. Under these conditions, RSA of 74.9%, TAI of 50.6%, and CAI of 86.8% were predicted, showing good agreement with the experimental values. A reverse transcription polymerase chain reaction showed that peanut shell extract decreased mRNA levels of tyrosinase-related protein-1 and matrix metalloproteinase-3 genes in B16-F0 cell. Therefore, we identified the skin-whitening and anti-wrinkle effects of peanut shell extracts at protein as well as gene expression levels, and the results show that peanut shell is an effective cosmetic material for skin-whitening and anti-wrinkle effects. Based on this study, peanut shell, which was considered a byproduct, can be used for the development of healthy foods, medicines, and cosmetics.