Endocrine pulse identification using penalized methods and a minimum set of assumptions
Endocrine pulse identification using penalized methods and a minimum set of assumptions
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DOI：
10.1152/ajpendo.00048.2009
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发表时间：
2010-02-01
影响因子：
5.1
通讯作者：
Smilde, Age K.
中科院分区：
文献类型：
--
作者：
Vis, Daniel J.;Westerhuis, Johan A.;Smilde, Age K.
维斯DJ，Westerhuis JA，Hoefsloot HC，Pijl H，Roelfsema F，货车der Greef J，Smilde AK.使用惩罚方法和最小假设集进行内分泌脉搏识别。Am J Physiol Endocrinol Metab 298：E146-E155，2010.首次发表于2009年10月27日; doi：10.1152/ajpendo.00048.2009.-激素分泌事件的检测对于了解正常和异常的内分泌功能是重要的，但是从用短间隔采样测量的激素中识别脉搏是具有挑战性的。此外，为了获得有用的结果，激素分泌和清除的基础模型必须根据生物学上可接受的假设进行限制。在这里，假设只有几个时间点分泌激素，我们使用现代惩罚非线性最小二乘设置来选择分泌事件的数量。我们没有假设分泌脉冲的特定形状或频率分布。我们的脉搏识别方法，VisPulse，与黄体生成素（LH），皮质醇，生长激素或睾酮一起工作。特别是，将我们的建模策略应用于以前的LH数据显示了建模和测量的LH激素浓度之间的良好相关性，估计的分泌模式是稀疏的，并且小且无结构的残差表明具有良好拟合的适当模型。我们将我们的方法与AutoDecon（一种常用的激素分泌模型）进行了基准测试，并进行了释放激素输注实验。这些实验的结果证实了我们的方法是准确的，并且优于AutoDecon，特别是对于检测沉默期和小分泌事件，这表明高分泌事件分辨率。使用（释放激素）输注数据进行的方法验证显示，VisPulse和AutoDecon的灵敏度和选择性分别为0.88和0.95以及0.69和0.91。
Vis DJ, Westerhuis JA, Hoefsloot HC, Pijl H, Roelfsema F, van der Greef J, Smilde AK. Endocrine pulse identification using penalized methods and a minimum set of assumptions. Am J Physiol Endocrinol Metab 298: E146-E155, 2010. First published October 27, 2009; doi:10.1152/ajpendo.00048.2009.-The detection of hormone secretion episodes is important for understanding normal and abnormal endocrine functioning, but pulse identification from hormones measured with short interval sampling is challenging. Furthermore, to obtain useable results, the model underlying hormone secretion and clearance must be augmented with restrictions based on biologically acceptable assumptions. Here, using the assumption that there are only a few time points at which a hormone is secreted, we used a modern penalized nonlinear least-squares setup to select the number of secretion events. We did not assume a particular shape or frequency distribution for the secretion pulses. Our pulse identfication method, VisPulse, worked well with luteinizing hormone (LH), cortisol, growth hormone, or testosterone. In particular, applying our modeling strategy to previous LH data revealed a good correlation between the modeled and measured LH hormone concentrations, the estimated secretion pattern was sparse, and the small and structureless residuals indicated a proper model with a good fit. We benchmarked our method to AutoDecon, a commonly used hormone secretion model, and performed releasing hormone infusion experiments. The results of these experiments confirmed that our method is accurate and outperforms AutoDecon, especially for detecting silent periods and small secretion events, suggesting a high-secretion event resolution. Method validation using (releasing hormone) infusion data revealed sensitivities and selectivities of 0.88 and 0.95 and of 0.69 and 0.91 for VisPulse and AutoDecon, respectively.