University of Pittsburgh

Qi Mi

Yoram Vodovotz

Gregory M Constantine

David L Steed

David Okonkwo

Maxim Mikheev

Ivan Petrov Yotov

Gary An

Jorg Gerlach

Alexey Solovyev

Gilles Clermont

Patricia A Hebda

Yee Key Li

Joshua Thomas Sullivan

Katherine Verdolini Abbott

Timothy R Billiar

Kari Nejak-Bowen

Liang-I Kang

Lukas Schimunek

Rami Namas

Joyeeta Dutta-Moscato

Provided herein are methods for segregating trauma, e.g., blunt trauma, patients into different cohorts based on risk of multiple organ dysfunction syndrome using patient data obtained within a short time window following injury. The methods are useful in providing treatment to trauma patients, and for separating trauma patients into cohorts.

Time window-based platform for the rapid stratification of blunt trauma patients into distinct outcome cohorts

A bioreactor is provided which contains cells capable of producing cytokine inhibitors in response to cytokines, in a manner regulated by the local or systemic milieu of an individual patient and predicted by mechanistic computational simulations. The bioreactor transfers the cytokine inhibitors to a patient in need of control of the inflammation process as part of a disease or condition in the patient, such as sepsis, trauma, traumatic brain injury, or wound healing. Related methods also are provided.

Self-regulating device for modulating inflammation

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.

Methods for modeling hepatic inflammation

Provided are methods of simulating tissue healing. The methods comprise using a mechanistic computer model of the interrelated effects of inflammation, tissue damage or dysfunction and tissue healing to predict an outcome of healing of damaged tissue in vivo, thereby predicting the outcome of healing of damaged tissue in vivo. Implementations of these methods on a computing device also are provided. Non-limiting examples of diseases and/or conditions that are amenable to simulation according to the methods described herein include: a diabetes, diabetic foot ulcers, necrotizing enterocolitis, ulcerative colitis, Crohn's disease, inflammatory bowel disease, restenosis (post-angioplasty or stent implantation), incisional wounding, excisional wounding, surgery, accidental trauma, pressure ulcer, stasis ulcer, tendon rupture, vocal fold phonotrauma, otitis media and pancreatitis.

Modeling wound healing

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Pittsburgh, PA, United States of America

Qi Mi