Suppr超能文献

测试细胞膜脂质的定量磁化转移模型。

Testing quantitative magnetization transfer models with membrane lipids.

机构信息

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

Department of Brain & Behavior, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

出版信息

Magn Reson Med. 2024 Nov;92(5):2149-2162. doi: 10.1002/mrm.30192. Epub 2024 Jun 14.

Abstract

PURPOSE

Quantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models.

METHODS

We formulated liposomes with various lipid types and water-to-lipids fractions and measured their signals with spoiled gradient-echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water-to-lipid fractions and with a simple MT (= MT/MT) calculation.

RESULTS

We found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water-to-lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MT analysis.

CONCLUSION

qMT parameters are sensitive to both the water-to-lipid fraction and to the lipid type. Estimating the water-to-lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MT analysis.

摘要

目的

定量磁化转移(qMT)模型旨在量化脂质和大分子对 MRI 信号的贡献。因此,一个与 qMT 参数及其分子来源相关的模型系统可以改善 qMT 参数的解释。在这里,我们使用膜脂质幻影作为一种有意义的工具来研究 qMT 模型。通过控制膜脂质的分数和类型,我们可以测试不同 qMT 模型的准确性、可靠性和可解释性。

方法

我们用不同的脂质类型和水-脂质分数制备了脂质体,并使用spoiled gradient-echo MT 测量了它们的信号。我们拟合了三个已知的 qMT 模型,并为每个模型估计了六个参数。我们测试了模型的准确性和可重复性,并比较了 qMT 参数之间的依赖性。我们将样品的 qMT 参数与其水-脂质分数以及简单的 MT(= MT/MT)计算进行了比较。

结果

我们发现三个 qMT 模型都能很好地拟合膜脂质信号。我们还发现,估计的 qMT 参数高度相关。有趣的是,估计的 qMT 参数是膜脂质类型的函数,也与水-脂质分数高度相关。最后,我们发现大多数脂质样本的信息可以用常见且易于估计的 MT 分析来捕捉。

结论

qMT 参数对水-脂质分数和脂质类型都很敏感。估计水-脂质分数可以改善对膜脂质对 qMT 参数贡献的特征描述。使用 MT 分析可以获得类似的特征描述。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验