The use of oral agents that can modify the gut microbiota (GM) could be a novel preventative or therapeutic option for Parkinson's disease (PD). Maslinic acid (MA), a pentacyclic triterpene acid with GM-dependent biological activities when it is taken orally, has not yet been reported to be effective against PD. The present study found both low and high dose MA treatment significantly prevented dopaminergic neuronal loss in a classical chronic PD mouse model by ameliorating motor functions and improving tyrosine hydroxylase expressions in the substantia nigra pars compacta (SNpc) and increasing dopamine and its metabolite homovanillic acid levels in the striatum. However, the effects of MA in PD mice were not dose-responsive, since similar beneficial effects for low and high doses of MA were observed. Further mechanism studies indicated that low dose MA administration favored probiotic bacterial growth in PD mice, which helped to increase striatal serotonin, 5-hydroxyindole acetic acid, and γ-aminobutyric acid levels. High dose MA treatment did not influence GM composition in PD mice but significantly inhibited neuroinflammation as indicated by reduced levels of tumor necrosis factor alpha and interleukin 1β in the SNpc; moreover, these effects were mainly mediated by microbially-derived acetic acid in the colon. In conclusion, oral MA at different doses protected against PD via distinct mechanisms related to GM. Nevertheless, our study lacked in-depth investigations of the underlying mechanisms involved; future studies will be designed to further delineate the signaling pathways involved in the interactive actions between different doses of MA and GM.

Background: Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB. Objective: We aim to predict the status of liver injury in patients with TB at the clinical treatment stage. Methods: We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019. Results: In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients'most recent alanine transaminase levels, average rate of change of patients' last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days. Conclusions: Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.