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US 12,609,206 B2Granted
Methods for modeling hepatic inflammation
AssigneeUniversity of Pittsburgh—Of the Commonwealth System of Higher Education
InventorsJoyeeta Dutta-Moscato, Yoram Vodovotz, Liang-I Kang, Kari Nejak-Bowen, Qi Mi
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Abstract
Provided herein are in silico methods of modeling hepatic inflammation, fibrosis/cirrhosis, and cancer. The models are computer-implemented agent-based models and are useful in determining patient prognoses in hepatic conditions, including viral infections, damage, inflammation, and cancer. The modeling system also is useful in modeling the effects of active agents on normal hepatic tissue or hepatic tissue perturbed by inflammation, infection, damage, fibrosis/cirrhosis, and cancer.
Claims20
A computer-implemented method of modeling progression of at least one hepatic condition in a patient, comprising:
- (i) receiving, with at least one processor, data relating to at least one first parameter of a plurality of parameters associated with a hepatic condition in a patient, wherein the at least one first parameter is a level of at least one cytokine;
- (ii) identifying, with at least one processor, at least one first rule from a rules database, the at least one first rule comprising:
- (a) determining a level of the cytokine at a first position associated with at least one hepatocyte;
- (b) comparing the level of the cytokine at the position with a threshold cytokine value;
- and
- (c) changing a status of a second position based on the cytokine level;
- (iii) generating, with at least one processor, a simulated progression of the hepatic condition by applying the at least one first rule to the data relating to the at least one first parameter, thereby generating a progression of the hepatic condition based on the at least one first rule;
- (iv) generating in a continuous space, using at least one processor and based on the simulated progression, image output providing predicted hepatic tissue structure, the image output comprising representations of one or more of hepatocytes, stellate cells, and Kupffer cells;
- (v) repeating steps (i)-(iv) to generate a stochastic range of the image output of the simulated progressions, the stochastic range of image output comprising a plurality of images generated based on interactions among one or more of hepatocytes, stellate cells, and Kupffer cells based at least in part on the cytokine levels and the at least one first rule;
- (vi) comparing, with at least one processor, the stochastic range of image output of the simulated progressions with one or more liver biopsy images, the comparison comprising at least a comparison of one or more hepatocytes, stellate cells, and Kupffer cells;
- and
- (vii) determining, using at least one processor and based at least on the comparing, a prognosis of the hepatic condition in the patient.
9 more elements
A system comprising:
- a display;
- a processor;
- and
- memory having stored thereon programming instructions that when executed by the processor cause the processor to:
- (i) receive data relating to at least one first parameter of a plurality of parameters associated with a hepatic condition in a patient, wherein the at least one first parameter is a level of at least one cytokine;
- (ii) identify at least one first rule from a rules database, the at least one first rule comprising:
- (a) determining a level of the cytokine at a first position associated with at least one hepatocyte;
- (b) comparing the level of the cytokine at the position with a threshold cytokine value;
- and
- (c) changing a status of a second position based on the cytokine level;
- (iii) generate a simulated progression of the hepatic condition by applying the at least one first rule to the data relating to the at least one first parameter, thereby generating a progression of the hepatic condition based on the at least one first rule;
- (iv) generate, in a continuous space and based on the simulated progression, image output providing predicted hepatic tissue structure, the image output comprising representations of one or more of hepatocytes, stellate cells, and Kupffer cells;
- (v) repeat steps (i)-(iv) to generate a stochastic range of the image output of the simulated progressions, the stochastic range of the image output comprising a plurality of images generated based on interactions among one or more of hepatocytes, stellate cells, and Kupffer cells based at least in part on the cytokine levels and the at least one first rule;
- (vi) compare the stochastic range of image output of the simulated progressions with one or more liver biopsy images, the comparison comprising at least a comparison of one or more hepatocytes, stellate cells, and Kupffer cells;
- and
- (vii) determine, based at least on the comparing, a prognosis of the hepatic condition in the patient.
13 more elements
Dependencies are inferred from the claim text.