• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

应用白细胞红外光谱技术快速诊断发热性儿科肿瘤患者的感染病因。

Rapid diagnosis of infection etiology in febrile pediatric oncology patients using infrared spectroscopy of leukocytes.

机构信息

Department of Physics, Ben-Gurion University, Beer-Sheva, Israel.

Department of Hematology, Soroka University Medical Center, Beer-Sheva, Israel.

出版信息

J Biophotonics. 2020 Feb;13(2):e201900215. doi: 10.1002/jbio.201900215. Epub 2019 Dec 17.

DOI:10.1002/jbio.201900215
PMID:31566906
Abstract

Rapid diagnosis of the etiology of infection is highly important for an effective treatment of the infected patients. Bacterial and viral infections are serious diseases that can cause death in many cases. The human immune system deals with many viral and bacterial infections that cause no symptoms and pass quietly without treatment. However, oncology patients undergoing chemotherapy have a very weak immune system caused by leukopenia, and even minor pathogen infection threatens their lives. For this reason, physicians tend to prescribe immediately several types of antibiotics for febrile pediatric oncology patients (FPOPs). Uncontrolled use of antibiotics is one of the major contributors to the development of resistant bacteria. Therefore, for oncology patients, a rapid and objective diagnosis of the etiology of the infection is extremely critical. Current identification methods are time-consuming (>24 h). In this study, the potential of midinfrared spectroscopy in tandem with machine learning algorithms is evaluated for rapid and objective diagnosis of the etiology of infections in FPOPs using simple peripheral blood samples. Our results show that infrared spectroscopy enables the diagnosis of the etiology of infection as bacterial or viral within 70 minutes after the collection of the blood sample with 93% sensitivity and 88% specificity.

摘要

快速诊断感染的病因对于有效治疗感染患者非常重要。细菌和病毒感染是严重的疾病,在许多情况下可导致死亡。人类免疫系统可应对许多无明显症状的病毒和细菌感染,这些感染未经治疗可悄然过去。然而,正在接受化疗的肿瘤患者因白细胞减少而免疫系统非常薄弱,即使是轻微的病原体感染也会威胁到他们的生命。出于这个原因,医生往往会立即为发热儿科肿瘤患者(FPOPs)开几种类型的抗生素。抗生素的不合理使用是耐药菌发展的主要原因之一。因此,对于肿瘤患者来说,快速、客观地诊断感染的病因极其关键。目前的鉴定方法耗时较长(>24 小时)。在这项研究中,我们评估了中红外光谱与机器学习算法相结合,使用简单的外周血样本,快速、客观地诊断 FPOPs 感染的病因的潜力。我们的研究结果表明,红外光谱能够在采集血液样本后 70 分钟内,以 93%的灵敏度和 88%的特异性,诊断出感染的病因是细菌或病毒。

相似文献

1
Rapid diagnosis of infection etiology in febrile pediatric oncology patients using infrared spectroscopy of leukocytes.应用白细胞红外光谱技术快速诊断发热性儿科肿瘤患者的感染病因。
J Biophotonics. 2020 Feb;13(2):e201900215. doi: 10.1002/jbio.201900215. Epub 2019 Dec 17.
2
Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms.利用白细胞红外显微镜和机器学习算法诊断难以触及部位的感染
Analyst. 2020 Oct 26;145(21):6955-6967. doi: 10.1039/d0an00752h.
3
Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning.应用白细胞红外光谱机器学习监测发热性儿科肿瘤合并菌血症患者抗生素治疗的疗效。
Talanta. 2024 Apr 1;270:125619. doi: 10.1016/j.talanta.2023.125619. Epub 2024 Jan 5.
4
Differential Diagnosis of the Etiologies of Bacterial and Viral Infections Using Infrared Microscopy of Peripheral Human Blood Samples and Multivariate Analysis.利用外周血样本的红外显微镜和多元分析技术对细菌性和病毒性感染病因的鉴别诊断。
Anal Chem. 2018 Jul 3;90(13):7888-7895. doi: 10.1021/acs.analchem.8b00017. Epub 2018 Jun 13.
5
Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria.利用红外光谱和多变量分析检测耐抗生素大肠杆菌
Anal Chem. 2017 Sep 5;89(17):8782-8790. doi: 10.1021/acs.analchem.7b01025. Epub 2017 Aug 11.
6
Serum interleukin-6 in bacterial and nonbacterial acute otitis media.细菌性和非细菌性急性中耳炎中的血清白细胞介素-6
Pediatrics. 1998 Aug;102(2 Pt 1):296-9. doi: 10.1542/peds.102.2.296.
7
[Usefulness of clinical data and rapid diagnostic tests to identify bacterial etiology in adult respiratory infections].[临床数据和快速诊断检测在成人呼吸道感染中确定细菌病因的实用性]
Medwave. 2015 Jan 19;15(1):e6067. doi: 10.5867/medwave.2015.01.6067.
8
Evaluation of febrile, nonneutropenic pediatric oncology patients with central venous catheters who are not given empiric antibiotics.评估未给予经验性抗生素的发热、非中性粒细胞减少性儿科肿瘤患者的中心静脉导管。
J Pediatr. 2015 Jan;166(1):157-62. doi: 10.1016/j.jpeds.2014.09.008. Epub 2014 Oct 14.
9
Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.快速可靠地测定大肠杆菌对抗生素的敏感性:红外显微镜与机器学习算法联用。
J Biophotonics. 2019 Jul;12(7):e201800478. doi: 10.1002/jbio.201800478. Epub 2019 Apr 10.
10
Pathogen Analysis of Central Nervous System Infections in a Chinese Teaching Hospital from 2012-2018: A Laboratory-based Retrospective Study.2012-2018 年中国教学医院中枢神经系统感染的病原体分析:一项基于实验室的回顾性研究。
Curr Med Sci. 2019 Jun;39(3):449-454. doi: 10.1007/s11596-019-2058-7. Epub 2019 Jun 17.

引用本文的文献

1
Differentiating viral and bacterial infections: A machine learning model based on routine blood test values.区分病毒感染和细菌感染:一种基于常规血液检测值的机器学习模型。
Heliyon. 2024 Apr 9;10(8):e29372. doi: 10.1016/j.heliyon.2024.e29372. eCollection 2024 Apr 30.
2
A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells.机器学习方法近期应用于傅里叶变换红外光谱数据以分析人类血细胞的综述。
Micromachines (Basel). 2023 May 29;14(6):1145. doi: 10.3390/mi14061145.
3
Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer's diseases using peripheral blood samples and machine learning algorithms.
利用外周血样本和机器学习算法,红外显微镜在鉴别路易体痴呆和阿尔茨海默病方面的潜力。
J Biomed Opt. 2020 Apr;25(4):1-15. doi: 10.1117/1.JBO.25.4.046501.