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Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome associated with a highly penetrant cancer spectrum characterized by germline TP53 mutations. We characterized the first LFS zebrafish hotspot mutants, tp53 R217H and R242H (human R248H and R273H), and found these mutants exhibit partial-to-no activation of p53 target genes, have defective cell-cycle checkpoints, and display partial-to-full resistance to apoptosis, although the R217H mutation has hypomorphic characteristics. Spontaneous tumor development histologically resembling human sarcomas was observed as early as 6 months. tp53 R242H mutants had a higher lifetime tumor incidence compared to tp53 null and R217H mutants, suggesting it is a more aggressive mutation. We observed mutation-specific tumor phenotypes across tp53 mutants with associated diverse transcriptomic and DNA methylome profiles in tp53 mutant larvae, impacting metabolism, cell signalling, and biomacromolecule synthesis and degradation. These tp53 zebrafish mutants demonstrate fidelity to their human counterparts and provide new insights into underlying tumorigenesis mechanisms and kinetics that suggest metabolic rewiring and cellular signalling changes occur prior to tumor initiation, which will guide targeted therapeutics for LFS., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jason Berman reports financial support was provided by Canadian Institutes of Health Research. Jason Berman reports a relationship with Oxford Immune Algorithmics that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. J.N.B. is a member of the Scientific Advisory Board of Oxford Immune Algorithmics., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

In summary, a mathematical model has been developed, tested, and applied which measures effectively the number as well as the type of radiographic rooms needed to meet the current and projected radiographic demand for a typical health care facility. The model first required a projection of radiographic procedures, based on historical data. Secondly, the projected total by patient classification was subdivided into procedure categories. Finally, the projected categorised radiographic procedures are introduced into the formula (Equation 1) and is shown below: (formula: see text). The formula is applied to each procedure category. The grand total for each of the four separately calculated categories will measure the total number of rooms and type of facility needed in the future. (formula: see text).