Liver regeneration after partial hepatectomy

肝再生的相互作用网络是稳健和精确的，肝切除导致受控增生（细胞增殖），当肝脏恢复其失去的质量时终止。负责这种增生的开始和终止的细胞因子和生长因子的相互作用还不清楚。 我们开发了一个模型，这个网络的相互作用的基础上已知的数据肝切除。 该模型再现了有关肝再生的已发表数据，并为实验观察提供了几何见解。 由于尸体器官的短缺，活体供体肝移植（LDLT）越来越多地用于治疗终末期肝病，例如肝细胞癌、非酒精性脂肪肝病、原发性硬化性胆管炎等。LDLT具有在受者病情恶化前进行积极治疗的优点，但供者出现罕见的并发症。 成人间活体肝移植队列研究（A2 ALL）旨在研究LDLT供体和受体的风险和获益。招募A2 ALL研究中的供体亚组进行详细的6个月肝功能和再生研究，称为DQLFT（供体定量肝功能检查）研究（1）。在4个时间点（手术后0天、4天、3个月、6个月）对这些供体进行肝脏体积测量和血液测量。 DQLFT研究的独特之处在于，它在最初的2周期间量化了肝脏再生，此时人类肝脏再生最快（2-6）。这些详细的数据使我们对Furchtgott等人开发的大鼠肝再生数学模型进行了改进。（7）并采用Periwal等人的有限数据进行了调整。（8） 利用我们与Testa博士合作获得的新表达数据，我们正在努力建立一个用于肝再生的虚拟患者模型。我们的目标是在双向映射中将可访问的基因表达血液测量与我们模型中的变量相关联。目的是找到特定的基因，其在肝脏供体血液中的表达可以用来定义我们的数学模型变量的特定值。然后可以用一些患者特征来模拟数学模型，并且模型变量的所得未来值可以用于预测特定基因的预期值。然后可以研究与这些预测值的任何测量偏差。困难在于，只有少数受试者和大约40，000个基因被测量，因此我们正在开发技术来找到这个双向图谱的最佳基因集。

The network of interactions underlying liver regeneration is robust and precise with liver resections resulting in controlled hyperplasia (cell proliferation) that terminates when the liver regains its lost mass. The interplay of cytokines and growth factors responsible for the inception and termination of this hyperplasia is not well understood. We developed a model for this network of interactions based on the known data of liver resections. This model reproduces the relevant published data on liver regeneration and provides geometric insights into the experimental observations. Live donor liver transplants (LDLT) are increasingly used to treat end-stage liver diseases such as hepatocellular carcinomas, non-alcoholic fatty liver disease, primary sclerosing cholangitis, and others, due to shortages of cadaveric organs. LDLT have the advantage of proactive treatment before the recipients condition deteriorates, but rare complications have occurred in donors. The Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL) was undertaken to investigate the risks and benefits to LDLT donors and recipients. A subset of donors in the A2ALL study was recruited for a detailed 6-month study of hepatic function and regeneration known as the DQLFT (Donor Quantitative Liver Function Tests) study (1). Liver volume measurements and blood measurements were taken at 4 time points (0 days, 4 days, 3 months, 6 months post-surgery) from these donors. The DQLFT study was distinct in that it quantified liver regeneration during the initial 2-week period when human livers regenerate most quickly (2-6). This detailed data led us to make improvements in a mathematical model of rat liver regeneration developed by Furchtgott et al. (7) and adapted with limited data for human liver regeneration by Periwal et al. (8) With new expression data from our collaboration with Dr. Testa, we are working towards a virtual patient model for liver regeneration. The aim is to correlate accessible blood measurements of gene expression with variables in our model in a two-way map. The aim is to find specific genes whose expression in the blood of liver donors can be used to define specific values of our mathematical model's variables. The mathematical model may then be simulated with some patient characteristics and the resulting future values of the model variables can be used to predict expected values of specific genes. Any measured deviation from these predicted values could then be investigated. The difficulty is that there are only a few subjects and about 40,000 genes that were measured, so we are developing techniques to find the optimal set of genes for this two-way map to be predictive.