Dijous 11 de juny, el Dipòsit Digital no estarà operatiu de 15:00 a 17:00 h per tasques de manteniment. Disculpeu les molèsties.
El jueves 11 de Junio, el Dipòsit Digital no estará operativo de 15:00 a 17:00 h debido a tareas de mantenimiento. Disculpen las molestias.
Thursday, Jun 11th, the Digital Repository will be unavailable due to a system update.

Document type

Article

Version

Published version

Publication date

Publication license

cc-by (c) Martínez de la Maza, LiliaMartínez de la Maza, L. et al., 2019
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/162606

A novel and simple formula to predict liver mass in porcine experimental models.

Journal Title

Director/Tutor

Journal ISSN

Volume Title

Abstract

A primary limitation in hepatic surgery is leaving a remnant liver of adequate size and function. Experimental models have been designed to study processes of liver injury and regeneration in this context, yet a formula to accurately calculate liver mass in an animal model is lacking. This study aims to create a novel and simple formula to estimate the mass of the native liver in a species of pigs commonly used in experimental liver surgery protocols. Using data from 200 male weanling Landrace-Large White hybrid pigs, multiple linear regression analysis is used to generate the formula. Clinical features used as variables for the predictive model are body mass and length. The final formula for pig liver mass is as follows: Liver mass (g) = 26.34232 * Body mass (kg) - 1.270629 * Length (cm) + 163.0076; R2 = 0.7307. This formula for porcine liver mass is simple to use and may be helpful in studies using animals of similar characteristics to evaluate restoration of liver mass following major hepatectomy.

Citation

Citation

MARTÍNEZ DE LA MAZA, Lilia, et al. A novel and simple formula to predict liver mass in porcine experimental models. Scientific Reports. 2019. Vol. 9, num. 12459-1245. ISSN 2045-2322. [consulted: 12 of June of 2026]. Available at: https://hdl.handle.net/2445/162606

Export metadata

JSON - METS

Share record