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Journal of Theoretical Medicine
Volume 2, Issue 1, Pages 9-18

A Simple Mathematical Method to Predict the Course of a First Episode of Liver Viral Cytolysis

1Direzione Scientifica, Istituto Clinico Humanitas, Via Manzoni 56, 20089 Rozzano, Milan, Italy
2Centro di Medicina Teoretica, University of the Study, 20100 Milan, Italy
3Ospedale S. Giuseppe, 20100 Milan, Italy
4Istituto Clinico Humanitas, 20089 Rozzano, Milan, Italy
5Centro Servizi “M. Branca”, Milano, Italy

Received 9 July 1998; Revised 28 October 1998; Accepted 8 December 1998

Copyright © 1999 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Early detection of the self-limiting or chronic tendency towards disease at the beginning of the hepatitis process is a canonical element of the analysis and synthesis to formulate the diagnosis and to decide the most useful and economic therapeutic strategy. Early treatment with antiviral drugs before liver functions undergo deterioration may reduce death and disability especially in young patients.

A formula suggested by a Max Planck equation provides mathematical curves that can be obtained very early during the course of hepatitis using just a few initial serum measurements of the alanine-aminotransferase (ALT) enzyme. The clinical ALT data forming the unpredictable early natural curve of ALT can be transformed into mathematical values capable of providing a deterministic curve that intersects the normal ALT value at what is known as the G point, and allows some predictions to be made concerning the acute or chronic evolution of the disease.

The G point seems to be a good predictor of whether or not a patient with clinically evident first episode of viral hepatitis will develop chronic liver disease as it revealed the initial phase of the first cytolytic episode in 56 untreated patients of our 59 cases; the three wrong predictions involved patients being treated with interferon.