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伤口感染的诊断。

Diagnostics for Wound Infections.

机构信息

Department of Bioengineering, The University of Texas at Arlington, Arlington, Texas, USA.

Texas Health Physician's Group, Arlington, Texas, USA.

出版信息

Adv Wound Care (New Rochelle). 2021 Jun;10(6):317-327. doi: 10.1089/wound.2019.1103. Epub 2020 Jul 7.

DOI:10.1089/wound.2019.1103
PMID:32496977
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8082727/
Abstract

Infections can significantly delay the healing process in chronic wounds, placing an enormous economic burden on health care resources. Identification of infection biomarkers and imaging modalities to observe and quantify them has seen progress over the years. Traditionally, clinicians determine the presence of infection through visual observation of wounds and confirm their diagnosis through wound culture. Many laboratory markers, including C-reactive protein, procalcitonin, presepsin, and bacterial protease activity, have been quantified to assist diagnosis of infection. Moreover, imaging modalities like plain radiography, computed tomography, magnetic resonance imaging, ultrasound imaging, spatial frequency domain imaging, thermography, autofluorescence imaging, and biosensors have emerged for real-time wound infection diagnosis and showed their unique advantages in deeper wound infection diagnosis. While traditional diagnostic approaches provide valuable information, they are time-consuming and depend on clinicians' experiences. There is a need for noninvasive wound infection diagnostics that are highly specific, rapid, and accurate, and do not require extensive training. While innovative diagnostics utilizing various imaging instrumentation are being developed, new biomarkers have been investigated as potential indicators for wound infection. Products may be developed to either qualitatively or quantitatively measure these biomarkers. This review summarizes and compares all available diagnostics for wound infection, including those currently used in clinics and still under development. This review could serve as a valuable resource for clinicians treating wound infections as well as patients and wound care providers who would like to be informed of the recent developments.

摘要

感染会显著延迟慢性伤口的愈合过程,给医疗资源带来巨大的经济负担。多年来,人们一直在寻找用于观察和量化感染生物标志物和成像方式的方法,并取得了一定的进展。传统上,临床医生通过观察伤口来确定感染的存在,并通过伤口培养来确认诊断。许多实验室标志物,包括 C 反应蛋白、降钙素原、前降钙素和细菌蛋白酶活性等,已经被量化以协助感染的诊断。此外,成像方式如普通放射摄影、计算机断层扫描、磁共振成像、超声成像、空间频率域成像、热成像、自体荧光成像和生物传感器已经出现,用于实时诊断伤口感染,并在深部伤口感染诊断方面显示出其独特的优势。虽然传统的诊断方法提供了有价值的信息,但它们耗时且依赖于临床医生的经验。因此,需要开发非侵入性的伤口感染诊断方法,这些方法应具有高度的特异性、快速和准确性,并且不需要广泛的培训。虽然正在开发利用各种成像仪器的创新诊断方法,但新的生物标志物已被研究作为伤口感染的潜在指标。可能会开发出定性或定量测量这些生物标志物的产品。本综述总结和比较了所有可用于伤口感染的诊断方法,包括目前在临床中使用和仍在开发中的方法。本综述可为治疗伤口感染的临床医生以及希望了解最新进展的患者和伤口护理提供者提供有价值的信息。

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本文引用的文献

1
Imaging in Chronic Wound Diagnostics.慢性伤口诊断中的影像学。
Adv Wound Care (New Rochelle). 2020 May 1;9(5):245-263. doi: 10.1089/wound.2019.0967. Epub 2020 Mar 19.
2
Rapid detection of biofilm by wound blotting following sharp debridement of chronic pressure ulcers predicts wound healing: A preliminary study.锐性清创后伤口拭子快速检测慢性压力性溃疡生物膜与创面愈合的相关性:一项初步研究。
Int Wound J. 2020 Feb;17(1):191-196. doi: 10.1111/iwj.13256. Epub 2019 Nov 4.
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Design and evaluation of an imager for assessing wound inflammatory responses and bioburden in a pig model.评估猪模型中伤口炎症反应和生物负荷的成像仪的设计与评估。
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Real-time bacterial fluorescence imaging accurately identifies wounds with moderate-to-heavy bacterial burden.实时细菌荧光成像可准确识别细菌负荷为中度至重度的伤口。
J Wound Care. 2019 Jun 2;28(6):346-357. doi: 10.12968/jowc.2019.28.6.346.
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Burn wound classification model using spatial frequency-domain imaging and machine learning.基于空间频域成像和机器学习的烧伤创面分类模型。
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Understanding Real-Time Fluorescence Signals from Bacteria and Wound Tissues Observed with the MolecuLight i:X.理解通过MolecuLight i:X观察到的来自细菌和伤口组织的实时荧光信号。
Diagnostics (Basel). 2019 Feb 26;9(1):22. doi: 10.3390/diagnostics9010022.
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Biofilms in Chronic Wounds: Pathogenesis and Diagnosis.慢性伤口中的生物膜:发病机制与诊断。
Trends Biotechnol. 2019 May;37(5):505-517. doi: 10.1016/j.tibtech.2018.10.011. Epub 2018 Nov 26.
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