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Background: The diagnosis and genetic characterization of Toxoplasma gondii (T. gondii) infection can make a significant influence to the prevention of the dangerous consequences of toxoplasmosis, particularly in immunocompromised people. Objective: The aim of this investigation was to assess the frequency and genotyping of T. gondiiin blood samples of patients with hemodialysis. Materials and methods: In the current investigation, a total of 379 blood samples were taken from subjects with hemodialysis who were referred to teaching hospital of Ahvaz in the southwest of Iran. The samples were evaluated using the Nested PCR by targeting the B1 gene, and then, sequencing and phylogenetic tree were constructed. Results: T. gondiiDNA was found in 112 (29.55%) of the blood samples by Nested PCR. Amplicons from T. gondiirevealed high identity with GenBank sequences. The phylogenetic analysis revealed that all sequences were closely related to Type I of T. gondii. Conclusion: Because of the high incidence of toxoplasmosis with type I prevalent in hemodialysis patients, we recommend a systematic screening for toxoplasmosis to carry out for monitoring the possible dissemination of toxoplasmosis during hemodialysis.

Actions that transform our energy system are the cornerstone of decarbonizing our economy but have been hindered by the ineffective interface between researchers and decision-makers in Canada. This paper begins by arguing for a more holistic perspective on energy system decarbonization modelling and exploring how insights can aid evidence-based decision making. We then respond with the development of a modelling platform that includes three core pillars: (1) a toolbox of models that together represent the integrated energy system, (2) a dataset containing the inputs required to populate those models, and (3) a visualization suite to analyze and communicate their outputs. The Spine Toolbox is leveraged to process these three components in an efficient workflow. Taken together, the platform promotes the usability of model results by fostering consistency, transparency, and timeliness. Furthermore, the epistemic limitations of energy systems modelling and implications for platform and model design, and engaging extended peer communities, are discussed. Our hope is that this platform can be a foundational resource that facilitates collaboration between energy system and decarbonization researchers, modelling teams and decision-makers, ultimately enabling the effective application of evidence-based policy.