METHODS: Phase III, randomized, double-blind, placebo-controlled study (EP0083; NCT03083665) evaluating BRV 50 mg/day and 200 mg/day in patients (≥16-80 years) with FOS with/without secondary generalization (focal to bilateral tonic-clonic seizures) despite current treatment with 1 or 2 concomitant antiseizure medications. Following an 8-week baseline, patients were randomized 1:1:1 to placebo, BRV 50 mg/day, or BRV 200 mg/day, and entered a 12-week treatment period. Efficacy outcomes: percent reduction over placebo in 28-day FOS frequency (primary); 50% responder rate in FOS frequency; median percent reduction in FOS frequency from baseline; seizure freedom during treatment period (secondary). Primary safety endpoints: incidences of treatment-emergent adverse events (TEAEs); TEAEs leading to discontinuation; serious TEAEs.
RESULTS: In this study, 448/449 randomized patients (mean age, 34.5 years; 53.8% female) received ≥1 dose of study medication (placebo/BRV 50 mg/BRV 200 mg/day: n = 149/151/148). Percent reduction over placebo in 28-day adjusted FOS frequency was 24.5% (p = 0.0005) and 33.4% (p
METHODS: Analyses were conducted post hoc of this 24-month, phase III, double-blind study, in which RRMS patients were randomized (1:1:1) to once daily oral fingolimod 0.5 mg, 1.25 mg or placebo. The key outcomes were the association between baseline RNFLT and baseline clinical characteristics and clinical/imaging outcomes up to 24 months. Change of RNFLT with fingolimod versus placebo within 24 months and time to retinal nerve fiber layer (RNFL) thinning were evaluated.
RESULTS: Altogether 885 patients were included. At baseline, lower RNFLT was correlated with higher Expanded Disability Status Scale score (r = -1.085, p = 0.018), lower brain volume (r = 0.025, p = 0.006) and deep gray matter volume (r = 0.731, p
METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.
RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.
DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.
METHODS: G. lucidum samples from various sources and in varying stages were identified by using δ 13C, δD, δ 18O, δ 15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum.
RESULTS: In the stage model, the δ 13C, δD, δ 18O, δ 15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ 13C, δD, δ 18O, and δ 15N). In the origin model, only four variables, namely δ 13C, δD, δ 18O, and δ 15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%.
CONCLUSION: C, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.
METHODS: An online survey of convenience sampling technique was adopted for data collection. A total of 373 valid questionnaires were subjected to descriptive analysis, and confirmatory factor analysis and structural equation modeling were performed for the testing of the hypotheses.
RESULTS: The results suggested that biospheric and collectivistic value positively influence explicit environmental attitude while altruistic value positively influences intrinsic environmental attitude, but negatively influences extrinsic environmental attitude. Social norm was shown to have a positive impact on personal norm and green purchase intention. Furthermore, implicit environmental attitude was shown to influence personal norm and intention, while personal norm positively influences green purchase intention to visit green hotels.
DISCUSSION: This study provided an alternative perspective on the selection of green hotels among consumers based on value-belief-norm theory in the tourism literature. These empirical findings would greatly benefit green hotel managers and other key stakeholders in the hospitality industry.