Suppr超能文献

从基本原理理解髓鞘比率、其推导、用途及伪影。

Understanding the Myelin Ratio from First Principles, Its Derivation, Uses and Artifacts.

作者信息

Gow Alexander

机构信息

Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA.

Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA.

出版信息

ASN Neuro. 2025;17(1):2445624. doi: 10.1080/17590914.2024.2445624. Epub 2025 Jan 24.

Abstract

In light of the increasing importance for measuring myelin ratios - the ratio of axon-to-fiber (axon + myelin) diameters in myelin internodes - to understand normal physiology, disease states, repair mechanisms and myelin plasticity, there is urgent need to minimize processing and statistical artifacts in current methodologies. Many contemporary studies fall prey to a variety of artifacts, reducing study outcome robustness and slowing development of novel therapeutics. Underlying causes stem from a lack of understanding of the myelin ratio, which has persisted more than a century. An extended exploratory data analysis from first principles (the axon-fiber diameter relation) is presented herein and has major consequences for interpreting published ratio studies. Indeed, a model of the myelin internode naturally emerges because of (1) the strong positive correlation between axon and fiber diameters and (2) the demonstration that the relation between these variables is one of direct proportionality. From this model, a robust framework for data analysis, interpretation and understanding allows specific predictions about myelin internode structure under normal physiological conditions. Further, the model establishes that a regression fit to ratio plots has zero slope, and it identifies the underlying causes of several data processing artifacts that can be mitigated by plotting ratios against fiber diameter (not axon diameter). Hypothesis testing can then be used for extending the model and evaluating myelin internodal properties under pathophysiological conditions (forthcoming). For without a statistical model as anchor, hypothesis testing is aimless like a rudderless ship on the ocean.

摘要

鉴于测量髓鞘比率(即髓鞘节段中轴突直径与纤维直径之比,纤维直径为轴突直径与髓鞘厚度之和)对于理解正常生理机能、疾病状态、修复机制以及髓鞘可塑性的重要性日益增加,迫切需要尽量减少当前方法中的处理和统计假象。许多当代研究都受到各种假象的影响,降低了研究结果的稳健性,并减缓了新型疗法的开发。根本原因在于对髓鞘比率缺乏理解,这种情况已经持续了一个多世纪。本文从第一原理(轴突 - 纤维直径关系)进行了扩展的探索性数据分析,这对解释已发表的比率研究具有重大影响。实际上,由于(1)轴突直径与纤维直径之间存在强正相关,以及(2)证明这些变量之间的关系是直接比例关系之一,髓鞘节段的模型自然就出现了。基于这个模型,一个用于数据分析、解释和理解的稳健框架能够对正常生理条件下的髓鞘节段结构做出具体预测。此外,该模型确定了比率图的回归拟合斜率为零,并指出了几种数据处理假象的根本原因,通过绘制比率与纤维直径(而非轴突直径)的关系图可以减轻这些假象。然后可以使用假设检验来扩展模型并评估病理生理条件下的髓鞘节段特性(即将推出)。因为没有统计模型作为支撑,假设检验就像在海洋中没有舵的船一样漫无目的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ce7/11877616/f1e90c67fbdd/TASN_A_2445624_F0001_C.jpg

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