OBJECTIVE: Evaluate the relationship between the chemical composition of C. nutans and its anti-inflammatory properties using nuclear magnetic resonance (NMR) metabolomics approach.
METHODOLOGY: The anti-inflammatory effect of C. nutans air-dried leaves extracted using five different binary extraction solvent ratio and two extraction methods was determined based on their nitric oxide (NO) inhibition effect in lipopolysaccharide-interferon-gamma (LPS-IFN-γ) activated RAW 264.7 macrophages. The relationship between extract bioactivity and metabolite profiles and quantifications were established using 1 H-NMR metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The possible metabolite biosynthesis pathway was constructed to further strengthen the findings.
RESULTS: Water and sonication prepared air-dried leaves possessed the highest NO inhibition activity (IC50 = 190.43 ± 12.26 μg/mL, P least square (PLS) biplot suggested that sulphur containing glucoside, sulphur containing compounds, phytosterols, triterpenoids, flavones and some organic and amino acids were among the potential NO inhibitors. LC-MS/MS targeted quantification further supported sonicated water extract was among the extract that possessed the most abundant C-glycosyl flavones.
CONCLUSION: The present study may serve as a preliminary reference for the selection of optimum extract in further C. nutans in vivo anti-inflammatory study.
METHODS: In this randomized, double-blind, phase 3 trial, we assigned, in a 2:1 ratio, adults with transfusion-dependent β-thalassemia to receive best supportive care plus luspatercept (at a dose of 1.00 to 1.25 mg per kilogram of body weight) or placebo for at least 48 weeks. The primary end point was the percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval. Other efficacy end points included reductions in the transfusion burden during any 12-week interval and results of iron studies.
RESULTS: A total of 224 patients were assigned to the luspatercept group and 112 to the placebo group. Luspatercept or placebo was administered for a median of approximately 64 weeks in both groups. The percentage of patients who had a reduction in the transfusion burden of at least 33% from baseline during weeks 13 through 24 plus a reduction of at least 2 red-cell units over this 12-week interval was significantly greater in the luspatercept group than in the placebo group (21.4% vs. 4.5%, P<0.001). During any 12-week interval, the percentage of patients who had a reduction in transfusion burden of at least 33% was greater in the luspatercept group than in the placebo group (70.5% vs. 29.5%), as was the percentage of those who had a reduction of at least 50% (40.2% vs. 6.3%). The least-squares mean difference between the groups in serum ferritin levels at week 48 was -348 μg per liter (95% confidence interval, -517 to -179) in favor of luspatercept. Adverse events of transient bone pain, arthralgia, dizziness, hypertension, and hyperuricemia were more common with luspatercept than placebo.
CONCLUSIONS: The percentage of patients with transfusion-dependent β-thalassemia who had a reduction in transfusion burden was significantly greater in the luspatercept group than in the placebo group, and few adverse events led to the discontinuation of treatment. (Funded by Celgene and Acceleron Pharma; BELIEVE ClinicalTrials.gov number, NCT02604433; EudraCT number, 2015-003224-31.).
METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.