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Innovating dialysis through computational modelling of hollow-fibre haemodialysers

Haemodialyser technology has not advanced much in decades, despite its unresolved shortcomings. Sophisticated new computational tools such as high-fidelity surrogate in silico dialyser models could reduce the time and expense of exploring alternative designs, dialysis dose and operating conditions compared with the current gold standard in vitro studies.

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A.E.S. thanks Danesh Tafti (Virginia Tech) and Steven Brunton (University of Washington) for insightful feedback on parts of the article before submission. R.S. is supported by the Regenerative Medicine Interdisciplinary Graduate Education Program at Virginia Tech. A.E.S. is supported by the US National Science Foundation under award number 2014181.

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Correspondence to Ruhit Sinha.

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Sinha, R., Rocco, M.V., Daeihagh, P. et al. Innovating dialysis through computational modelling of hollow-fibre haemodialysers. Nat Rev Nephrol 20, 269–270 (2024).

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