伤口形状对伤口愈合动力学的影响:是时候重新审视伤口愈合措施了吗?

Gennadi Saiko
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引用次数: 4

摘要

创面愈合是一个多方面的过程,可能受到许多内源性(如免疫系统受损、血液供应有限)或外源性(如敷料、感染)因素的影响。伤口管理的一个重要步骤是跟踪伤口愈合的进展。它通常包括跟踪伤口的大小(长度、宽度和面积)。伤口面积是伤口管理中最常用的指标。特别是,伤口闭合是FDA用来衡量伤口治疗效率的单一参数。在这里,我们提出了一些关于为什么伤口面积本身不足以预测伤口愈合进展的争论。方法:我们开发了一种基于伤口面积和周长的分析方法来表征上皮化过程。结果:我们获得了几种伤口形状的伤口愈合轨迹的明确结果:圆形(2D),细长切口(1D)和矩形。结果可以扩展到复杂的形状。结论:从几何角度考虑,创面愈合时间由创面的最短维度(宽度)决定。然而,在这段时间内,伤口愈合的轨迹可能会有所不同。我们的计算表明,伤口的形状可能对伤口愈合轨迹有重要的影响。例如,在伤口愈合过程中(t/ t =0.5), 1D伤口模型预测闭合50%,而2D模型预测闭合75%(剩余25%)。在分析临床数据或设计临床或临床前实验时,这些考虑可能会有所帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Impact of the Wound Shape on Wound Healing Dynamics: Is it Time to Revisit Wound Healing Measures?
Introduction: Wound healing is a multifaceted process, which can be impacted by many endogenous (e.g., compromised immune system, limited blood supply) or exogenous (e.g., dressing, presence of infection) factors. An essential step in wound management is to track wound healing progress. It typically includes tracking the wound size (length, width, and area). The wound area is the most often used indicator in wound management. In particular, wound closure is the single parameter used by the FDA to measure wound therapeutics' efficiency. Here, we present some arguments on why the wound area alone is insufficient to predict wound healing progress. Methods: We have developed an analytical approach to characterize an epithelization process based on the wound's area and perimeter. Results: We have obtained the explicit results for wound healing trajectory for several wound shapes: round (2D), elongated cut (1D), and rectangular. The results can be extended to complex shapes. Conclusions: From geometrical considerations, the wound healing time is determined by the shortest dimension (the width) of the wound. However, within that time, the wound healing trajectory can be different. Our calculations show that the shape of the wound may have significant implications on a wound healing trajectory. For example, in the middle of the wound healing process (t/T=0.5), the 1D wound model predicts 50% closure, while the 2D model predicts 75% closure (25% remaining). These considerations can be helpful while analyzing clinical data or designing clinical or pre-clinical experiments.
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