About this Research Topic
Solid organ transplantation (SOT) has become the best curative treatment for patients with end-stage organ disease. Despite improved medical technology that reduces the likelihood of graft rejection, metabolic factors are still present that lead to life-threatening complications. An unavoidable complication leading to primary nonfunction (PNF) and early allograft dysfunction (EAD) is mediated primarily by ischemia/reperfusion injury (IRI). This peritransplant clinical condition is characterized by interrelated phases involving donor-tissue ischemic necrosis and recipient reperfusion, that introduces the innate-adaptive immune interface that promotes pro- and anti-inflammatory responses. Distinct experimental conditions and a wide array of organs are subjected to cell-specific IR-triggered tissue damage. As the consequences of I/R injury remain vast, understanding the mechanistic factors leading to I/R injury remain a top priority.
Despite the many scientific reports published each year highlighting various pathways in IRI, no comprehensive strategy(s) has emerged from Phase III clinical trials to prevent this condition in transplant recipients. The challenge is to design experiments that elucidate how these biological pathways intersect. Three-dimensional knowledge is needed using bioinformatics tools that combine novel machine learning and deep learning techniques to create classifiers to prospectively predict which molecular peri-transplant signals are essential and which are irrelevant for long-term clinical outcomes. Determining what treatments worked previously, identifying which pathways should be targeted, what dose and side effects should be considered, the efficacy of combination therapies and the determination of appropriate endpoints of sample size and statistical tests await the successful development of any treatment against IRI.
The scope of the research topic should include the strategies targeted for clinical therapy in treating IRI so far. These include using agents to reduce OS and inflammation, modulate the immune response, and stimulate the production of protective enzymes that promote tissue repair. Other interesting areas of IRI research include nanotechnology and machine perfusion to regenerate marginalized organs. The specific theme of this research topic relates to the clinical treatment of IRI. The types of manuscripts that would be most interesting involve mechanistic animal studies supported by human clinical data that identify predictive models of the probability of developing graft rejection failure by IRI.
Despite the many scientific reports published each year highlighting various pathways in IRI, no comprehensive strategy(s) has emerged from Phase III clinical trials to prevent this condition in transplant recipients. The challenge is to design experiments that elucidate how these biological pathways intersect. Three-dimensional knowledge is needed using bioinformatics tools that combine novel machine learning and deep learning techniques to create classifiers to prospectively predict which molecular peri-transplant signals are essential and which are irrelevant for long-term clinical outcomes. Determining what treatments worked previously, identifying which pathways should be targeted, what dose and side effects should be considered, the efficacy of combination therapies and the determination of appropriate endpoints of sample size and statistical tests await the successful development of any treatment against IRI.
The scope of the research topic should include the strategies targeted for clinical therapy in treating IRI so far. These include using agents to reduce OS and inflammation, modulate the immune response, and stimulate the production of protective enzymes that promote tissue repair. Other interesting areas of IRI research include nanotechnology and machine perfusion to regenerate marginalized organs. The specific theme of this research topic relates to the clinical treatment of IRI. The types of manuscripts that would be most interesting involve mechanistic animal studies supported by human clinical data that identify predictive models of the probability of developing graft rejection failure by IRI.
Keywords: IRI, Organ Transplantation, Liver, Immunity, DAMPs, HMGB1, Machine Perfusion, ROS
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
