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左心室节段应变可识别小鼠内在和外在应激后心肌的独特变形模式。

Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice.

机构信息

Division of Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, USA; Mitochondria, Metabolism & Bioenergetics Working Group, West Virginia University, Morgantown, West Virginia, USA.

Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA.

出版信息

Ultrasound Med Biol. 2022 Oct;48(10):2128-2138. doi: 10.1016/j.ultrasmedbio.2022.06.004. Epub 2022 Aug 4.

DOI:10.1016/j.ultrasmedbio.2022.06.004
PMID:35933241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9427680/
Abstract

We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.

摘要

我们使用节段应变分析来评估内在(饮食诱导肥胖[DIO])和外在(不可预测的慢性轻度应激[UCMS])应激源是否会改变左心室的变形模式。将 6 周龄雄性 C57BL/6J 小鼠随机分为瘦鼠组或肥胖鼠组(每组 n = 24)。小鼠接受高脂肪饮食(HFD)12 周诱导肥胖。在 18 周时,瘦鼠和肥胖鼠进一步随机分为 UCMS 组和非 UCMS 组(UCMS,每天 7 小时,每周 5 天,持续 8 周)。在基线(6 周)、高脂肪饮食后(18 周)和 UCMS 后(26 周)进行超声心动图检查。对 DIO 和 UCMS 组应用机器学习。肥胖鼠与瘦鼠相比具有很强的预测准确性(接受者操作特征曲线下面积[AUC] = 0.921),外侧壁(径向应变减少 64%,p ≤ 0.001)和前游离壁(径向应变减少 53%,p < 0.001)的应变变化最具信息性。还评估了无论饮食如何预测接受 UCMS 的小鼠的能力(AUC = 0.886),结果表明前中壁的纵向应变率和后间隔壁的径向应变是最重要的特征。这些壁节段表明变形模式向游离壁(DIO)和间隔壁(UCMS)改变的倾向,表明心肌存在特定疾病的改变。

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Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography.人工智能在超声心动图中自动测量左心室应变。
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Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors.基于超声心动图和危险因素的冠心病筛查的集成机器学习方法。
BMC Med Inform Decis Mak. 2021 Jun 11;21(1):187. doi: 10.1186/s12911-021-01535-5.
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Speckle-tracking echocardiography combined with imaging mass spectrometry assesses region-dependent alterations.斑点追踪超声心动图联合成像质谱分析评估区域依赖性改变。
Sci Rep. 2020 Feb 27;10(1):3629. doi: 10.1038/s41598-020-60594-2.
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Deep learning interpretation of echocardiograms.超声心动图的深度学习解读
NPJ Digit Med. 2020 Jan 24;3:10. doi: 10.1038/s41746-019-0216-8. eCollection 2020.
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Cardiovasc Ultrasound. 2019 Sep 2;17(1):18. doi: 10.1186/s12947-019-0168-9.
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