Toxoplasma gondii and schizophrenia: a review of published RCTs.

Over the last 60 years, accumulating evidence has suggested that acute, chronic, and maternal Toxoplasma gondii infections predispose to schizophrenia. More recent evidence suggests that chronically infected patients with schizophrenia present with more severe disease. After acute infection, parasites form walled cysts in the brain, leading to lifelong chronic infection and drug resistance to commonly used antiparasitics. Chronic infection is the most studied and closely linked with development and severity of schizophrenia. There are currently four published randomized controlled trials evaluating antiparasitic drugs, specifically azithromycin, trimethoprim, artemisinin, and artemether, in patients with schizophrenia. No trials have demonstrated a change in psychopathology with adjunctive treatment. Published trials have either selected drugs without evidence against chronic infection or used them at doses too low to reduce brain cyst burden. Furthermore, trials have failed to achieve sufficient power or account for confounders such as previous antipsychotic treatment, sex, age, or rhesus status on antiparasitic effect. There are currently no ongoing trials of anti-Toxoplasma therapy in schizophrenia despite ample evidence to justify further testing.

A program to identify prognostic and predictive gene signatures.

BACKGROUND: The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer. However, analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers. RESULTS: To overcome this limitation, we developed a freely accessible and user friendly Program to Identify Molecular Signatures (PIMS). Importantly, such signatures allow important insight into cancer biology, as well as provide clinical tools to identify potential biomarkers that might provide means to accurately stratify patients into different risk or treatment groups. We evaluated the performance of PIMS by identifying and testing predictive and prognostic gene signatures for breast cancer, using multiple breast tumor microarray cohorts representing hundreds of patients. Importantly, PIMS identified signatures classified patients into high and low risk groups with at least similar performance to other commonly used gene signature selection techniques. CONCLUSIONS: Our program is contained entirely within a Microsoft Excel file and therefore requires no installation of any additional programs or training. Hence, PIMS provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research.