ABSTRACT
BACKGROUND/OBJECTIVES: Green tea is being recognized as a beverage with potential benefits for human health and cognitive functions. In vivo studies provide preliminary evidence that green tea intake may have a positive role in improving effects on cognitive functions. We aimed to examine the neural effects of green tea extract on brain activation in humans. SUBJECTS/METHODS: Functional magnetic resonance imaging was recorded while 12 healthy volunteers performed a working memory task following administration of 250 or 500 ml of a milk whey based green tea containing soft drink or milk whey based soft drink without green tea as control in a double-blind, controlled repeated measures within-subject design with counterbalanced order of substance administration. A whole-brain analysis with a cluster-level threshold of P<0.001 (unadjusted) was followed by an a priori-defined region of interest (ROI) analysis of the dorsolateral prefrontal cortex (DLPFC) including a cluster-level threshold of P<0.05 and family-wise error (FWE) adjustment for multiple comparisons. RESULTS: Whole-brain analyses revealed no significant effects after correction for multiple comparisons (FWE P<0.05). Using a ROI approach, green tea extract increased activation in the DLPFC relative to a control condition (FWE P<0.001). This neural effect was related to green tea dosage. Green tea extract was not associated with any significant attenuation in regional activation relative to control condition. CONCLUSIONS: These data suggest that green tea extract may modulate brain activity in the DLPFC, a key area that mediates working memory processing in the human brain. Moreover, this is the first neuroimaging study implicating that functional neuroimaging methods provide a means of examining how green tea extract acts on the brain.
Subject(s)
Camellia sinensis , Memory, Short-Term/drug effects , Plant Extracts/pharmacology , Prefrontal Cortex/drug effects , Adult , Brain Mapping , Double-Blind Method , Humans , Male , Young AdultABSTRACT
Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.
Subject(s)
Decision Support Techniques , Decision Trees , Drug-Related Side Effects and Adverse Reactions , Algorithms , Artificial Intelligence , Central Nervous System Diseases/chemically induced , Chemical and Drug Induced Liver Injury/etiology , Computer Simulation , Databases, Factual , Drug Evaluation, Preclinical , Drug Hypersensitivity/etiology , Forecasting , Humans , Kidney Diseases/chemically induced , Pharmaceutical Preparations/chemistry , Reproducibility of Results , Small Molecule Libraries , SoftwareABSTRACT
A 55-year-old male patient was hospitalized with severe nausea, vomiting and icterus. Laboratory testing showed hepatocellular damage. After exhaustive testing, the exclusion diagnosis of a toxic hepatitis was reached. There was a strong temporal correlation with the ingestion of Hong Hua 29, a preparation from Traditional Chinese Medicine (TCM). This medication had been started twelve days prior to the first appearance of symptoms. The existing drug regimen included gabapentin (Neurontin), esomeprazole (Nexium) and prednisone (Prednison Streuli) for the therapy of an acute sensory and motor neuropathy of unknown aetiology. After cessation of Hong Hua 29, gabapentin and esomeprazole, transaminase levels started to declined and normalized within three months. According to the Swissmedic criteria of imputability, a causal correlation between the observed symptoms and the administration of Hong Hua 29 is possible.
