论文标题
从梗塞心肌的第二次谐波生成图像评估原纤维胶原蛋白的一次性学习框架
A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
论文作者
论文摘要
心肌梗死（MI）是一个科学术语，指的是心脏病发作。在这项研究中，我们从胶原纤维中推断出高度相关的第二次谐波产生（SHG）线索，这些胶原纤维具有高度非中心对称组装，以及在梗塞小鼠心脏中的两光激发细胞自动荧光，以定量探针纤维化，尤其是在MI之后的早期。我们提出了一种强大的单发机学习算法，该算法可以通过高空间分辨率来测定胶原蛋白的2D组装，并具有光谱特异性和灵敏度的MI后心组织中的结构排列。早期纤维化程度的检测，评估和精确量化将指导人们开发治疗疗法，以防止进一步发展并确定患者生存的心脏移植需求。
Myocardial infarction (MI) is a scientific term that refers to heart attack. In this study, we infer highly relevant second harmonic generation (SHG) cues from collagen fibers exhibiting highly non-centrosymmetric assembly together with two-photon excited cellular autofluorescence in infarcted mouse heart to quantitatively probe fibrosis, especially targeted at an early stage after MI. We present a robust one-shot machine learning algorithm that enables determination of 2D assembly of collagen with high spatial resolution along with its structural arrangement in heart tissues post-MI with spectral specificity and sensitivity. Detection, evaluation, and precise quantification of fibrosis extent at early stage would guide one to develop treatment therapies that may prevent further progression and determine heart transplant needs for patient survival.
