No abstract

No abstract

No abstract

Yale University holds rights to patents that describe the potential utility of MIF inhibition and MIF genotype determination. RB has applied for a patent related to MIF modulators (9540322). RB and LL have applied for a patent related to the method of inhibiting binding or activity of MIF by administering a MIF antagonist (9221903). RB has applied for a patent related to MIF promoter polymorphism in inflammatory disease (9139877).

LGH00168 is a CHOP activator that inhibits A549 cell growth in vitro and lung tumor growth in vivo.

With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUIbased toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.

Olanzapine (OLZ) is efficacious whereas leads to adverse metabolic effects thus lead to higher risk of cardiovascular diseases (CVD) on schizophrenia. Cytokines have been found associated with metabolic disorders. Therefore, pretreatment prediction of OLZ-induced adverse metabolic effects is urgently needed. To investigate if baseline cytokine levels could become biomarkers for pathogenesis of schizophrenia or prediction for OLZ-induced adverse metabolic effects, we recruited 75 participants, including 23 schizophrenia inpatients, who were antipsychotic-free over the past 6 months or first episode and drug-naive and 52 matched health controls, in our prospective cohort study and cross-sectional study. We simultaneously examined 7 serum cytokine levels (IFN-γ, IL-1ra, IL-1β, IL-8, TNF-α, MCP-1, VEGF) before OLZ treatment by using liquid suspension array technique and obtained clinical correlates at 4-week intervals in total 8 weeks. The psychopathology was assessed with the Positive and Negative Symptom Scale (PANSS). The metabolic parameters were BMI, TG, total cholesterol, LDL, HDL, ApoA1, ApoB, lipoprotein a, fasting glucose, HbA1c, insulin, and leptin. At baseline, IL-1ra and MCP-1 levels in schizophrenia were significantly higher than health controls (t = 4.55, P = 0.0001, t = 3.08 P = 0.003). BMI, fasting insulin, cholesterol, triglyceride, LDL, ApoB and leptin were significantly increased in patients with schizophrenia after 8 weeks of olanzapine treatment. Correlation analysis showed that the baseline IL-1ra level were significantly correlated with the increased levels of cholesterol (P = 0.004), LDL (P = 0.005), ApoB (P = 0.018) and leptin (P = 0.010), but not with the increased BMI, insulin or triglycerides. Further stepwise multiple linear regression analysis indicated that IL-1ra levels prior to treatment remained significantly associated with increased levels of cholesterol, LDL, ApoB and leptin. Above all, higher IL-1ra and MCP-1 levels may be biomarkers indicating pathogenesis of schizophrenia. Higher serum levels of IL-1ra may predict subsequent higher possibility of hypercholesterolemia and hyperleptinemia following OLZ treatment in schizophrenia patients.

No abstract