The primary obstacle to enabling wide spread adoption of composite tissue transplantation, as well as to improving long term solid organ transplant outcomes, is establishing a personalized medication regimen optimizing the balance between immunosuppression and immune function the individual minimum effective level of immunosuppression. Presently, the clinical gold standard for monitoring immune function is histologic inspection of biopsy for tissue damage, or monitoring blood chemistry for signs of organ failure. These trailing indicators reflect damage that has already accumulated, and are of little use in proactively determining the immunologic state of a patient. Samples collected from small animal surgical models were used to quantify the amount of immune signaling protein present (cytokines and chemokines) under various experimental conditions. Patterns in protein expression that reliably discriminate amongst the groups were then investigated with statistical inference methods such as the logistic classifier, decision tree, and random forest, operating in both the original feature space and in transformed feature spaces. This work demonstrates computational methods are effective in elucidating and classifying cytokine profiles, allowing the detection of rejection in composite tissue allografts well in advance of the current clinical gold standard, and shows that the methods can be effective in solid organ contexts as well. This work further determines that cytokine patterns of inflammation associated with rejection are specific to the structure and composition of the tissue in which they occur, and can be distinguished from immune signaling patterns associated with unspecific inflammation, wound healing, or immunosuppressed tissue. Clinical translation of these findings may provide novel computational tools that enable physicians to design personalized immunosuppression strategies for patients. The methods described in this work also provide information that can be used to investigate the biological basis for the observed immune signaling patterns. Further development may provide a computational framework for identifying novel therapeutic strategies in other pathologies.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2018-07-26T11:55:59Z No. of bitstreams: 1 ruyfreitasreis.pdf: 3379949 bytes, checksum: a3dba87d62cc183caa47e77b29eea593 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-09-03T16:19:42Z (GMT) No. of bitstreams: 1 ruyfreitasreis.pdf: 3379949 bytes, checksum: a3dba87d62cc183caa47e77b29eea593 (MD5) / Made available in DSpace on 2018-09-03T16:19:42Z (GMT). No. of bitstreams: 1 ruyfreitasreis.pdf: 3379949 bytes, checksum: a3dba87d62cc183caa47e77b29eea593 (MD5) Previous issue date: 2018-07-20 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Edema é um dos sintomas mais comuns em uma doença infecciosa, juntamente com calor, vermelhidão e dor. Muitas vezes, o edema é consequência da interação entre o sistema imunológico e a dinâmica do fluido intersticial. Deste modo, quando um patógeno entra no corpo de um animal, a consequência natural é uma reação imunológica ativada por citocinas produzidas pelos macrófagos. Esta resposta imune recruta outras células do sistema imunológico, e.g. neutrófilos, que são responsáveis por localizar e destruir estes invasores. Este processo fisiológico pode ser matematicamente descrito por um sistema não linear de equações diferenciais parciais (EDP) com uma aproximação em meios porosos. A fim de simplificar a modelagem, foi considerada somente a interação entre o neutrófilo e o patógeno (uma bactéria não especifica). A dinâmica do fluido intersticial pode ser influenciada pelo sistema linfático, capilares sanguíneos, além da reação inflamatória. Inicialmente, neste trabalho é apresentada uma aproximação com a porosidade constante, seguida por uma segunda abordagem utilizando a teoria da poroelasticidade proposta por Biot. A influência do sistema imune é feita por meio de um acoplamento com a equação de Starling, que modela o fluxo nas membranas capilares. As simulações foram apresentadas, em sua maioria, em um domínio unidimensional, a fim de facilitar a compreensão dos resultados. Além disso, um estudo de caso bidimensional no eixo curto do coração também é apresentado, para simular o edema devido a uma miocardite bacteriana. O método numérico utilizado para as simulações unidimensionais é o método dos volumes finitos (MVF) e para as simulações bidimensionais é o método dos elementos finitos (MEF). Finalmente, este estudo também realizou validações qualitativa dos resultados in silico com dados in vivo, a fim de avaliar a modelagem proposta. / Edema is one of the most common symptoms found in infectious diseases along with heat, redness, and pain. Often, edema may be a consequence of interstitial fluid dynamics and their interactions with the immune system. So, when a pathogen enters into the body, the natural consequence is an immunological reaction triggered by the production of cytokines by macrophages. This immunological response recruits other immune cells, e.g. the neutrophils, responsible for seeking and destroying these foreign invaders. This physiological process can be mathematically modeled by a nonlinear system of partial differential equations (PDE) based on porous media approach. In order to simplify the model, just the interaction between neutrophils and pathogens (an unspecified bacteria) is considered. The interstitial fluid dynamics can be influenced by the lymphatic system, blood capillaries, along with the inflammatory reaction. Initially, this work presents an approximation using constant porosity followed by a second approach using the poroelasticity theory proposed by Biot. The influence of the immune system is coupled by the Starling equation which models the flow in the capillary membranes. The simulations were presented, mostly, in a unidimensional domain, in order to make easier the comprehension of the results. Moreover, a two-dimensional study in the heart short axis is also presented to simulate a bacterial myocarditis. The numerical method used in unidimensional simulations is the finite volume method (FVM) and for the two-dimensional simulation is the finite element method (FEM). Finally, this study makes a qualitative validation of the in silico results with in vivo experimental data, in order to evaluate the proposed model.