Hepatoprotective Effect of Antrodia camphorata Mycelium Powder on Alcohol-Induced Liver Damage

BACKGROUND/OBJECTIVES

Antrodia camphorata, also known as "Niuchangchih" in Taiwan, is a unique medicinal mushroom native to Taiwan. It is used in traditional medicine to treat various health conditions. In this study, we investigated the efficacy of A. camphorata mycelia on alcohol-induced liver damage, both in vitro and in vivo, in a Good Laboratory Practice (GLP) facility.

METHODS

The experimental groups consisted of a normal control group (G1), a negative control group (G2), an A. camphorata mycelium powder 50 mg/kg/day administration group (G3), a 100 mg/kg/day administration group (G4), a 200 mg/kg/day administration group (G5), and a positive control silymarin 200 mg/kg/day administration group (G6), with 10 Sprague Dawley rats assigned to each treatment group.

RESULTS

We found that treatment with A. camphorata mycelium powder significantly reduced alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, cholesterol, adiponectin, triglyceride, and malondialdehyde concentrations. Histopathological analysis also revealed that the inflammation score significantly decreased in the A. camphorata-treated groups.

CONCLUSION

Based on these results, we conclude that repeated oral administration of A. camphorata mycelium powder is effective in improving alcoholic liver disease.

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES

Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data. Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers. Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively. Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.