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Advanced training program to development of point-of-care testing

Grant number: 23/14983-8
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): March 01, 2024
Effective date (End): February 28, 2025
Field of knowledge:Agronomical Sciences - Animal Husbandry - Animal Production
Principal Investigator:Heidge Fukumasu
Grantee:Alanne Tenório Nunes
Supervisor: David S Wishart
Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil
Research place: University of Alberta, Canada  
Associated to the scholarship:22/14260-3 - Feed efficiency in beef cattle: a phenomic approach to point-of-care test prototyping, BP.DR


Metabolomics is a promising alternative aiming to define markers for residual feed intake (RFI) for selection of feed efficiency in beef cattle in a more accurate, faster, and cost-effective way. Studies evaluating metabolites associated with feed efficiency in beef cattle are still scarce and, for the most part, carried out on a small scale, generating discoveries that lack statistical robustness and validation. Therefore, several authors have suggested that efforts be directed towards carrying out large multicenter cohort studies and validation of biomarkers associated with RFI, which increases the potential of its use in the selection process. The present PhD project, in general, aims to define and validate a profile of metabolites associated with RFI with the initial objective of prototyping a point-of-care testing for feed efficiency prediction. In pursuit of this goal, the main objective of this internship is to engage in an advanced training program focused on the development of point-of-care tests, aiming the prediction of feed efficiency in beef cattle. Besides that, the internship has secondary objectives, including to realize a meta-analysis of systematic literature review data, to perform the mass spectrometry analysis of plasma samples at the Wishart lab and to develop skills involving machine learning analysis. The internship will be fundamental for the execution of the project stages, in addition to a significantly professional development in the area of metabolomics, point-of-care testing development and data analysis, which will contribute to further research in the area.

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