Effect of woohwangchungsimwon and donepezil co-treatment on cognitive function and serum metabolic profiles in a scopolamine-induced model of Alzheimer's disease.

ETHNOPHARMACOLOGICAL RELEVANCE: Woohwangchungsimwon (WCW) is a traditional medicine used in East Asian countries to treat central nervous system disorders. Reported pharmacological properties include antioxidant effects, enhanced learning and memory, and protection against ischemic neuronal cell death, supporting its use in treating neurodegenerative diseases like Alzheimer's disease (AD). AIM OF THE STUDY: The study aims to assess the effects of co-treatment with WCW and donepezil on cognitive functions and serum metabolic profiles in a scopolamine-induced AD model. MATERIALS AND METHODS: Cell viability and reactive oxygen species (ROS) levels were measured in amyloid ß-peptide25-35 (Aß25-35)-induced SH-SY5Y cells. An AD model was established in ICR mice by intraperitoneal scopolamine administration. Animals underwent the step-through passive avoidance test (PAT) and Morris water maze (MWM) test. Hippocampal tissues were collected to examine specific protein expression. Serum metabolic profiles were analyzed using nuclear magnetic resonance (NMR) spectroscopy. RESULTS: Co-treatment with WCW and donepezil increased cell viability and reduced ROS production in Aß25-35-induced SH-SY5Y cells compared to that with donepezil treatment alone. Co-treatment improved cognitive functions and was comparable to donepezil treatment alone in the PAT and MWM tests. Pathways related to tyrosine, phenylalanine, and tryptophan biosynthesis, phenylalanine metabolism, and cysteine and methionine metabolism were altered by co-treatment. Levels of tyrosine and methionine, major serum metabolites in these pathways, were significantly reduced after co-treatment. CONCLUSIONS: Co-treatment with WCW and donepezil shows promise as a therapeutic strategy for AD and is comparable to donepezil alone in improving cognitive function. Reduced tyrosine and methionine levels after co-treatment may enhance cognitive function by mitigating hypertyrosinemia and hyperhomocysteinemia, known risk factors for AD. The serum metabolic profiles obtained in this study can serve as a foundation for developing other bioactive compounds using a scopolamine-induced mouse model.

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms.

We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fourier transform-infrared (FT-IR) spectroscopy coupled with machine learning (ML) algorithms. This prospective, observational, multicenter study enrolled 62 UC patients and 30 healthy controls. The patients were treated with adalimumab for 56 weeks, and clinical remission was evaluated using the Mayo score. Baseline fecal samples were collected and analyzed using FT-IR spectroscopy. Various data preprocessing methods were applied, and prediction models were established by 10-fold cross-validation using various ML methods. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation of healthy controls and UC patients, applying area normalization and Pareto scaling. OPLS-DA models predicting short- and long-term remission (8 and 56 weeks) yielded area-under-the-curve values of 0.76 and 0.75, respectively. Logistic regression and a nonlinear support vector machine were selected as the best prediction models for short- and long-term remission, respectively (accuracy of 0.99). In external validation, prediction models for short-term (logistic regression) and long-term (decision tree) remission performed well, with accuracy values of 0.73 and 0.82, respectively. This was the first study to develop prediction models for clinical remission associated with adalimumab treatment in UC patients by fecal analysis using FT-IR spectroscopy coupled with ML algorithms. Logistic regression, nonlinear support vector machines, and decision tree were suggested as the optimal prediction models for remission, and these were noninvasive, simple, inexpensive, and fast analyses that could be applied to personalized treatments.