RESUMO
Background: Toxoplasma gondii is an opportunistic protozoan parasite that causes a life-threatening disease - toxoplasmosis - in immunocompromised individuals, including patients with cancer. This prospective cross-sectional study set out to determine the prevalence of toxoplasmosis in patients with cancer compared with that of healthy individuals. Methods: A prospective cross-sectional study was conducted in Sulaimani City of Iraq from November 2019 to May 2020. Anti-T. gondii IgG and IgM antibodies were measured in the blood samples of 113 patients with cancer (80 with solid organ tumors and 33 with haematological malignancies) entered to Hiwa Cancer Hospital and 82 healthy controls, who were referred to the Directorate of Blood Transfusion for blood donation, using chemiluminescence microparticle immunoassay (CMIA). Results: The prevalence of anti-T. gondii IgG was 39.8% in the patient group and 24.4% in the control group, which amounted to a significant difference (P = 0.024). Only one case of anti-T. gondii IgM positivity was observed in the patient group, and no IgM seropositivity was reported in the control group. Moreover, the seroprevalence of anti-T. gondii IgG was non-significantly higher (P = 0.102) in the patients with haematological malignancies (51.5%) than in those with solid organ tumors (35%). Occupation was the only risk factor which had a significant association with T gondii infection (odds ratio [OR]: 1.3, 95% confidence interval [CI]: 0.6746163 - 2.4282788, P = 0.029). Conclusion: The prevalence of T. gondii infection is higher in patients with cancer than in healthy individuals. Therefore, T. gondii screening in patients with cancer is recommended.
RESUMO
Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.