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Validated differentiation of Listeria monocytogenes serogroups by FTIR spectroscopy using an Artificial Neural Network based classifier in an accredited official food control laboratory 在官方认可的食品控制实验室,使用基于人工神经网络的分类器,通过FTIR光谱验证了单核细胞增生李斯特菌血清群的分化
Pub Date : 2023-10-27 DOI: 10.1016/j.clispe.2023.100030
Helene Oberreuter, Martin Dyk, Jörg Rau

Listeria monocytogenes is a well-known human pathogen, and especially the young, the elderly, otherwise immunocompromised individuals or pregnant women might suffer severe health consequences from listeriosis. Up to date, Fourier-Transform Infrared (FTIR) spectroscopical methods have been established for decades as a valuable means to differentiate between microbiological specimens at different taxonomical levels. In recent years, machine-based learning methods using Artificial Neural Networks (ANN) have highly advanced the discriminatory power of distinguishing spectrally closely related units such as serogroups of a given species. The present report describes the classification performance evaluation of a manufacturer (Bruker Daltonics, Bremen, Germany) - provided L. monocytogenes serogroup classifier by means of a formalized external validation carried out in a single laboratory. N = 630 absorption spectra from n = 94 food L. monocytogenes isolates pertaining to n = 11 serotypes / n = 3 serogroups were recorded on the IR Biotyper (Bruker Daltonics) and subsequently typed by the given classifier. The quantitative evaluation of inclusivity and exclusivity was performed following the principles of the Guidelines for Validating Species Identifications Using MALDI-TOF-MS issued by the German Federal Office of Consumer Protection (BVL) for a targeted identification. The FTIR classifier allocated all n = 486 spectra from n = 71 serogroup 1/2 and 4 isolates correctly to their respective serogroups, resulting in a true-positive rate of 100%. All remaining n = 144 spectra from n = 23 isolates of serogroup 3 were correctly allocated to an arbitrarily combined class entity of serogroups 3 and 7, likewise yielding both inclusivity and exclusivity rates of 100%. Consequently, in our official food control laboratory, this validated IR Biotyper method has been integrated into the accredited workflow for L. monocytogenes analysis in food samples according to ISO 11290, followed by MALDI-TOF MS confirmation on the species level to subsequent serogrouping and pre-selection by FTIR spectroscopy for Whole Genome Sequencing (WGS). This study confirmed that FTIR spectroscopy in combination with Artificial Neural Networks proves to be a reliable and thus valuable tool for the differentiation of the most common serogroups from Listeria monocytogenes. The application of FTIR spectroscopy saves valuable resources with respect to labor and time and thus facilitates outbreak analyses of the clinically relevant severe food-borne disease listeriosis where potentially a high number of isolates are involved.

单核细胞增生李斯特菌是一种众所周知的人类病原体,特别是年轻人、老年人、免疫功能低下的个体或孕妇可能因李斯特菌病而遭受严重的健康后果。迄今为止,傅里叶变换红外(FTIR)光谱方法已经建立了几十年,作为一种有价值的手段来区分不同分类水平的微生物标本。近年来,利用人工神经网络(ANN)的机器学习方法在识别谱上密切相关的单位(如特定物种的血清群)方面取得了很大的进步。本报告描述了一家制造商(Bruker Daltonics, Bremen, Germany)的分类性能评估——通过在单个实验室进行的形式化外部验证,提供了单核增生乳杆菌血清群分类器。在IR生物分型仪(Bruker Daltonics)上记录了N = 11个血清型/ N = 3个血清群的N = 630个食品单核增生乳杆菌的吸收光谱,并使用该分类器进行了分型。根据德国联邦消费者保护办公室(BVL)发布的《使用MALDI-TOF-MS验证物种鉴定指南》的原则进行了包容性和排他性的定量评估。FTIR分类器将来自n = 71个血清组1/2和4个分离株的所有n = 486个光谱正确分配到各自的血清组中,真阳性率为100%。来自n = 23株血清群3的所有剩余n = 144个光谱被正确地分配到血清群3和7的任意组合类实体中,同样获得100%的包容性和排他率。因此,在我们的官方食品控制实验室,这种经过验证的IR生物分型方法已根据ISO 11290整合到食品样品中单核细胞生长乳杆菌分析的认可工作流程中,随后在物种水平上进行MALDI-TOF MS确认,随后通过FTIR光谱进行血清分组和全基因组测序(WGS)预选。本研究证实,FTIR光谱结合人工神经网络被证明是鉴别单核细胞增生李斯特菌最常见血清群的可靠和有价值的工具。FTIR光谱的应用节省了宝贵的人力和时间资源,从而促进了临床上相关的严重食源性疾病李斯特菌病的爆发分析,其中可能涉及大量分离株。
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引用次数: 0
Rapid detection of heart failure using a spectroscopic liquid biopsy 使用光谱液体活检快速检测心力衰竭
Pub Date : 2023-10-21 DOI: 10.1016/j.clispe.2023.100029
Loren Christie , Alexandra Sala , James M. Cameron , Justin J.A. Conn , David S. Palmer , William J. McGeown , Jane A. Cannon , John Sharp , Matthew J. Baker

Heart disease is growing annually across the globe with numbers expected to rise to 46% of the population by 2030. Early detection is vital for several reasons, firstly it improves the long-term prognosis of the patient by admitting them through the appropriate pathway faster, secondly it reduces healthcare costs by streamlining diagnosis and finally, in combination with management or treatment, it can prevent the progression of the disease which in turn improves the patient’s quality of life. Therefore, there lies an increasing need to develop assays which can rapidly detect heart disease at an early stage. The Dxcover® liquid biopsy platform employs infrared spectroscopy and artificial intelligence, to quickly analyse minute amounts of patient serum. In this study, discrimination between healthy controls and diseased patients was obtained with an area under the receiver operating characteristic curve (AUC) of 0.89. When assessing the heart failure vs all patients, which is most akin to what would be observed in a triage setting, the model when tuned to a minimum of 45% specificity yielded a sensitivity of 89% and an NPV of 0.996, conversely when sensitivity was set at a 45% minimum, the specificity was 96%, giving an NPV of 0.991 when using a 1.5% prevalence. Other models were assessed in parallel, but the performance of the ORFPLS model was overall superior to the other models tested. In this large scale (n = 404) proof-of-concept study, we have shown that the Dxcover liquid biopsy platform has the potential to be a viable triage tool in emergency and routine situations for the diagnosis of heart failure.

心脏病在全球范围内每年都在增长,预计到2030年,心脏病患者人数将上升到人口的46%。早期发现是至关重要的,有几个原因,首先,它通过通过适当的途径更快地入院,从而改善患者的长期预后,其次,它通过简化诊断来降低医疗成本,最后,与管理或治疗相结合,它可以防止疾病的进展,从而提高患者的生活质量。因此,越来越需要开发能够在早期阶段快速检测心脏病的检测方法。Dxcover®液体活检平台采用红外光谱和人工智能,快速分析微量患者血清。在本研究中,受试者工作特征曲线下面积(AUC)为0.89,获得了健康对照与患病患者的区别。当评估心力衰竭与所有患者的对比时,这与在分诊设置中观察到的最相似,当调整到最低45%的特异性时,模型的灵敏度为89%,NPV为0.996,相反,当灵敏度设置为最低45%时,特异性为96%,当使用1.5%的患病率时,NPV为0.991。其他模型被并行评估,但ORFPLS模型的性能总体上优于其他模型。在这项大规模(n = 404)概念验证研究中,我们已经证明Dxcover液体活检平台有潜力成为紧急和常规情况下诊断心力衰竭的可行分诊工具。
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引用次数: 0
Raman spectroscopic analysis of human serum samples of convalescing COVID-19 positive patients COVID-19阳性康复患者血清样本的拉曼光谱分析
Pub Date : 2023-10-07 DOI: 10.1016/j.clispe.2023.100028
Naomi Jackson , Jaythoon Hassan , Hugh J. Byrne

Rapid screening, detection and monitoring of viral infection is of critical importance, as exemplified by the rapid spread of SARS-CoV-2, leading to the worldwide pandemic of COVID-19. This is equally the case for the stages of patient convalescence as for the initial stages of infection, to understand the medium and long terms effects, as well as the efficacy of therapeutic interventions. Optical spectroscopic techniques potentially offer an alternative to currently employed techniques of screening for the presence, or the response to infection. In this study, the ability of Raman spectroscopy to distinguish between samples of the serum of convalescent COVID-19 positive patients and COVID-19 negative serum samples, and to further analyse and quantify systemic responses, was explored. The study included serum samples of patients who had been tested for SARS-CoV-2 specific IgG and IgM responses between 25 and 134 days after the infection was identified. Both COVID-19 positive and negative groups included males and females who ranged in age from 21 to 81 years old. No correlation was apparent between the specified SARS-CoV-2 specific IgG and IgM immunoglobulin levels of the positive group, their sex, or age. Raman spectroscopic measurements were performed at 785 nm, in liquid serum, thawed from frozen, and spectra were pre-processed to remove the contribution of water, normalising to the water content. Principal components analysis of the spectral dataset over the range 400–1800 cm-1 provided no clear indication of a difference between normal serum and SARS-CoV-2 positive serum. A selection of 5 of the samples, which were available in sufficient volume, were fractionated by centrifugal filtration, and the 100 kDa, 50 kDa, 30 kDa, and 10 kDa concentrates similarly analysed by Raman spectroscopy. Partial least squares regression analysis revealed a negative correlation between the spectral profile of the 30 kDa fractions and SARS-CoV-2 specific IgG antibody levels, potentially indicating an association with depleted glutathione levels. The study supports a potential role of Raman screening of blood serum for monitoring of SARS-CoV-2 infection, but also in longitudinal studies of disease progression, long term effects, and therapeutic interventions.

对病毒感染的快速筛查、检测和监测至关重要,例如导致新冠肺炎全球大流行的严重急性呼吸系统综合征冠状病毒2型的快速传播。患者恢复期和感染初期的情况同样如此,以了解中长期影响以及治疗干预的疗效。光学光谱技术可能为目前使用的筛查感染的存在或反应的技术提供一种替代方案。在本研究中,探讨了拉曼光谱区分恢复期新冠肺炎阳性患者血清样本和新冠肺炎阴性血清样本的能力,以及进一步分析和量化全身反应的能力。该研究包括在发现感染后25至134天内接受过严重急性呼吸系统综合征冠状病毒2型特异性IgG和IgM反应检测的患者的血清样本。新冠肺炎阳性和阴性组均包括年龄在21岁至81岁之间的男性和女性。阳性组的特定严重急性呼吸系统综合征冠状病毒2型特异性IgG和IgM免疫球蛋白水平、性别或年龄之间没有明显相关性。拉曼光谱测量在785nm下在液体血清中进行,从冷冻中解冻,并对光谱进行预处理以去除水的贡献,使其归一化为水含量。对400–1800 cm-1范围内的光谱数据集的主成分分析没有明确表明正常血清和严重急性呼吸系统综合征冠状病毒2型阳性血清之间存在差异。通过离心过滤对足够体积的5个样品进行分级,并通过拉曼光谱对100kDa、50kDa、30kDa和10kDa浓缩物进行类似分析。偏最小二乘回归分析显示,30kDa组分的光谱图谱与严重急性呼吸系统综合征冠状病毒2型特异性IgG抗体水平呈负相关,这可能表明与谷胱甘肽耗竭水平有关。该研究支持血清拉曼筛查在监测严重急性呼吸系统综合征冠状病毒2型感染方面的潜在作用,也支持在疾病进展、长期影响和治疗干预的纵向研究中的潜在作用。
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引用次数: 0
Application of optical spectroscopy in diagnosing and monitoring breast cancers: A technical review 光谱学在乳腺癌诊断和监测中的应用技术综述
Pub Date : 2023-10-07 DOI: 10.1016/j.clispe.2023.100027
Afshan Shirkavand , Mozhdeh Babadi , Leila Ataie Fashtami , Ezeddin Mohajerani

Breast cancer is one of the most prevalent cancers among the global women population. It is due to the development of malignant cells in the breast tissue based on external or internal causes. The stages of detecting breast cancer include screening, diagnosis, and prognosis. Multiple imaging modalities, including digital Mammography, Ultrasonography, breast MRI, CT scan, and PET are applied for screening, diagnosis, identifying the stage of the tumor, classifying the developmental trend of the disease, and monitoring the treatment response. These modalities are commonly used in most fields of medicine, and have their merits and drawbacks. There are some optical technologies which have been developed in the diagnostic field. Optical imaging, and spectroscopy are known as real-time, sensitive, and non-invasive detecting approaches for human cancers in inaccessible locations, which use light propagation through the tissue to assess the optical properties. Optical techniques are used to measure optical and physiological properties of healthy breast tissue to discriminate abnormalities. Optical spectroscopy and fluorescence spectroscopy are some of the technologies for breast cancer detection. Such technologies can be combined with other modalities based on the capability of light guidance using optical fibers. Moreover, optical imaging offers potency for image-guided surgery. We review and discuss the broad range of methodologies and applications. Through a brief review of breast physiology, we discuss the various instrumental techniques and the related methods of optical spectroscopy and data analysis.

癌症是全球女性中最常见的癌症之一。这是由于外部或内部原因导致乳腺组织中恶性细胞的发育。检测癌症的阶段包括筛查、诊断和预后。多种成像方式,包括数字乳腺造影、超声、乳腺MRI、CT扫描和PET,用于筛查、诊断、确定肿瘤分期、分类疾病发展趋势和监测治疗反应。这些模式通常用于大多数医学领域,有其优点和缺点。在诊断领域已经发展了一些光学技术。光学成像和光谱学是一种实时、灵敏和非侵入性的检测方法,用于在无法到达的位置检测人类癌症,它利用光在组织中的传播来评估光学特性。光学技术用于测量健康乳房组织的光学和生理特性,以识别异常。光学光谱和荧光光谱是检测癌症的一些技术。这种技术可以与基于使用光纤的光引导能力的其他模式相结合。此外,光学成像为图像引导手术提供了潜力。我们回顾并讨论了广泛的方法和应用。通过对乳腺生理学的简要回顾,我们讨论了各种仪器技术以及光谱和数据分析的相关方法。
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引用次数: 0
Deep UV resonance Raman spectroscopy for sensitive detection and quantification of the fluoroquinolone antibiotic drug moxifloxacin and the β-lactam meropenem in human plasma 深紫外共振拉曼光谱法对人血浆中氟喹诺酮类抗生素莫西沙星和β-内酰胺美罗培南的灵敏检测和定量
Pub Date : 2023-09-26 DOI: 10.1016/j.clispe.2023.100026
Christian Domes , Juergen Popp , Stefan Hagel , Mathias W. Pletz , Torsten Frosch

Quantification of antibiotics in body fluids is of major clinical interest. A sensitive detection of the fluoroquinolone moxifloxacin and the β-lactam meropenem in the complex matrix human blood plasma was achieved with help of deep UV resonance Raman spectroscopy. Multivariate curve resolution was applied for quantification and low limits of detection in order of magnitude of the clinical concentration were detected in human plasma. Moxifloxacin and meropenem were detected down to minimum concentrations of 4 µM (2 mg/L) and 2 µM (1 mg/L). The acquired results and the benefits of enhanced Raman spectroscopy, i.e., short analysis time, small sample volume, and high sensitivity with the potential for multicomponent detection based on multivariate quantification, will pave the path as future point-of-care approach for sensitive detection of antibiotics in complex body fluids.

体液中抗生素的定量是主要的临床兴趣。利用深紫外共振拉曼光谱法对复杂基质人血浆中的氟喹诺酮类药物莫西沙星和β-内酰胺类药物美罗培南进行了灵敏检测。应用多变量曲线分辨率进行定量,并在人体血浆中检测到临床浓度的低检测限。莫西沙星和美罗培南的最低检测浓度分别为4µM(2 mg/L)和2µM(1 mg/L)。所获得的结果和增强拉曼光谱的优点,即分析时间短、样品体积小、灵敏度高,具有基于多元定量的多组分检测潜力,将为未来复杂体液中抗生素的灵敏检测铺平道路。
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引用次数: 0
Detection of several respiratory viruses with Surface-Enhanced Raman Spectroscopy coupled with Artificial Intelligence 结合人工智能的表面增强拉曼光谱检测几种呼吸道病毒
Pub Date : 2023-07-12 DOI: 10.1016/j.clispe.2023.100025
Delphine Garsuault , Sanaa El Messaoudi , Mookkan Prabakaran , Ian Cheong , Anthony Boulanger , Marion Schmitt-Boulanger

Diagnoses of viral infections are a challenge when facing a crisis like COVID-19, where their speed and reliability are critical to minimize diseases spread. The gold standard of diagnostics, quantitative Polymerase Chain Reaction, is time- and reagent-consuming and requires qualified personnel. Therefore, it is necessary to find new detection techniques to overcome these barriers. Surface Enhanced Raman Spectroscopy (SERS) is a detection method, based on light and metallic particles admixed with the samples, already used in different fields of research. In this study, we discriminate three respiratory viruses using a combination of SERS and Artificial Intelligence (AI). Our technique appears to be fast, reproducible, and reliable, achieving between 95 % and 100 % of accuracy, standing out as a powerful tool usable for viral diagnostics.

在面临新冠肺炎等危机时,病毒感染的诊断是一个挑战,其速度和可靠性对于最大限度地减少疾病传播至关重要。诊断学的黄金标准,定量聚合酶链式反应,需要时间和试剂,需要合格的人员。因此,有必要找到新的检测技术来克服这些障碍。表面增强拉曼光谱(SERS)是一种基于光和金属颗粒与样品混合的检测方法,已用于不同的研究领域。在这项研究中,我们使用SERS和人工智能(AI)的组合来区分三种呼吸道病毒。我们的技术看起来快速、可重复、可靠,准确率在95%到100%之间,是一种可用于病毒诊断的强大工具。
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引用次数: 0
Biochemical characterization and discrimination of Leishmania major parasites and infected macrophages with Raman spectroscopy and chemometrics 用拉曼光谱和化学计量学对利什曼原虫主要寄生虫和感染巨噬细胞的生化特征和鉴别
Pub Date : 2023-06-07 DOI: 10.1016/j.clispe.2023.100024
Thulya Chakkumpulakkal Puthan Veettil , Rebekah N. Duffin , Supti Roy , Philip C. Andrews , Bayden R. Wood

Leishmaniasis is classified as one of the neglected tropical disease (NTD), which are caused by a group of parasitic protozoans called Leishmania. The high case load and severity of the disease make Leishmaniasis second only to malaria in terms of both severity and infectivity. However, due to the low economic interest in research and development, it may become a major world-wide health threat. Current diagnostics including serological assessment of infected tissue by either light microscopy, or antibody tests or by the culturing of potential infection via in vitro or in vivo animal inoculation, parasitological tests using samples aspirated from the spleen and bone marrow, Immunological tests such as the Montenegro test, Fluorescence assays, and polymerase chain reaction (PCR) techniques are suffer from several limitations including time and expense. Herein, we first apply Raman microscopy to distinguish the two L. Major parasitic forms namely promastigotes and amastigotes and secondly, distinguish infected from non-infected macrophages using multivariate data analysis including Principal Component Analysis (PCA) and unsupervised hierarchical cluster imaging analysis (UHCA). The maximum variance between infected and uninfected macrophage groups are visible in the lipid region (92.20 %) as compared to the fingerprint region (46.13 %) along PC1. The contributions from nucleic acids can be found at 805 cm−1 (phosphodiester - Z-marker), 767 cm−1 (pyrimidine ring breathing mode), 742 cm−1 (ring breathing mode of DNA/RNA bases), and 568 cm−1 (cytosine/guanine). These amplified nucleic acid signals in infected macrophages indicate the presence of infection compared to the uninfected macrophage group. Similarly, the maximum variance between amastigotes and promastigotes groups are observed in the lipid region (88.45%) as compared to the fingerprint region (28.34 %). Moreover, the UHCA of infected macrophages revealed the accumulation of lipid bodies or droplets inside or close proximity parasitophorous vacuole, which is consistent with the reported literature. Once established macrophages were infected with Leishmania in vitro and the differences between infected and non-infected established with high reproducibility. The reported spectral differences between infected and non-infected macrophages lays the ground work for developing a diagnostic tool for detection of leishmaniasis in a buffy coat preparation and also offers the potential of monitoring the effects of new therapeutics.

利什曼病是一种被忽视的热带疾病,由一组寄生原生动物利什曼原虫引起。该疾病的高病例数和严重程度使利什曼病在严重程度和传染性方面仅次于疟疾。然而,由于对研究和开发的经济兴趣较低,它可能会成为世界范围内的主要健康威胁。目前的诊断包括通过光学显微镜或抗体测试或通过体外或体内动物接种培养潜在感染对感染组织进行血清学评估,使用从脾脏和骨髓抽取的样本进行寄生虫学测试,免疫学测试如黑山测试,荧光测定,并且聚合酶链式反应(PCR)技术受到包括时间和费用在内的若干限制。在此,我们首先应用拉曼显微镜来区分两种主要寄生形式,即前鞭毛体和无鞭毛体,其次,使用包括主成分分析(PCA)和无监督分级聚类成像分析(UHCA)在内的多变量数据分析来区分感染和未感染的巨噬细胞。与沿着PC1的指纹区(46.13%)相比,感染和未感染巨噬细胞组之间的最大差异在脂质区(92.20%)可见。核酸的贡献可以在805 cm−1(磷酸二酯-Z标记)、767 cm−2(嘧啶环呼吸模式)、742 cm−3(脱氧核糖核酸/核糖核酸碱基的环状呼吸模式)和568 cm−4(胞嘧啶/鸟嘌呤)处发现。与未感染的巨噬细胞组相比,感染的巨噬细胞中这些扩增的核酸信号表明存在感染。类似地,与指纹区(28.34%)相比,在脂质区(88.45%)观察到无鞭毛体和前鞭毛体组之间的最大差异。此外,感染巨噬细胞的UHCA显示脂质体或液滴在寄生液泡内或附近积聚,这与报道的文献一致。一旦建立的巨噬细胞在体外感染利什曼原虫,并且感染和未感染之间的差异具有高再现性。据报道,感染和未感染巨噬细胞之间的光谱差异为开发一种诊断工具来检测血沉棕皮制剂中的利什曼病奠定了基础,也为监测新疗法的效果提供了潜力。
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引用次数: 0
Contributions of vibrational spectroscopy to virology: A review 振动光谱学对病毒学的贡献综述
Pub Date : 2022-12-01 DOI: 10.1016/j.clispe.2022.100022
Iqra Chaudhary , Naomi Jackson , Denise Denning , Luke O’Neill , Hugh J. Byrne

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

振动光谱技术,包括红外吸收和拉曼散射,是高精度、无标签的分析技术,在分析化学、药理学、法医学和考古学等领域都有应用,近年来,在生物医学应用方面引起了越来越多的关注。作为分析技术,它们早在20世纪70年代就被应用于病毒的表征,并且在2019冠状病毒病(COVID-19)大流行的背景下,作为一种新方法,应世界卫生组织的要求,已被探索作为一种新方法,以帮助全球努力实施和改进病毒感染的快速筛查。这篇综述考虑了振动光谱技术在病毒形态和化学成分表征、它们的附着、被细胞摄取和在细胞内复制方面的应用历史,以及它们在人群筛查和感染反应监测应用中检测病毒的潜力。特别要考虑到最近在检测严重急性呼吸综合征冠状病毒2和监测COVID-19方面所做的努力。
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引用次数: 7
Improved tissue preparation for multimodal vibrational imaging of biological tissues 生物组织多模态振动成像的改进组织制备
Pub Date : 2022-12-01 DOI: 10.1016/j.clispe.2022.100021
Callum Gassner , John A. Adegoke , Sheila K. Patel , Varun J. Sharma , Kamila Kochan , Louise M. Burrell , Jaishankar Raman , Bayden R. Wood

The complementary nature of Infrared (IR) and Raman spectroscopies enables a thorough understanding of biological tissue – so called multimodal vibrational spectroscopic imaging. However, new approaches in terms of sample preparation and data analysis are required to release the full potential of multimodal spectroscopy. Herein, we propose an inexpensive and relatively simple sample preparation technique incorporating mirror-finished stainless-steel slides and polyethylene glycol as an embedding medium that is compatible for both infrared and Raman spectroscopy of tissue sections. K-Means Clustering and Principal Component Analysis (PCA) were used to evaluate the performance of multimodal vibrational spectroscopic imaging compared with IR and Raman spectroscopic imaging individually using a rat kidney as a model. The K-Means cluster maps generated with the multimodal dataset showed the best correlation between different tissue types identified by an adjacent section stained with Masson’s Trichrome compared to either Raman or IR spectroscopy analysed independently. PCA score maps of the multimodal dataset produced a clear separation of individual tissue types along the first three Principal Components. Additionally, PCA permitted the correlation of IR and Raman peaks arising mainly from collagen vibrational modes. Finally, polyethylene glycol embedding is shown as an attractive alternative to paraffin embedding for spectroscopic analyses, due to significantly less fluorescence in Raman measurements and retention of lipids in the tissue, without any retention of the medium within the tissue.

红外(IR)和拉曼光谱的互补性使我们能够彻底了解生物组织——即所谓的多模态振动光谱成像。然而,需要在样品制备和数据分析方面的新方法来释放多模态光谱的全部潜力。在此,我们提出了一种廉价且相对简单的样品制备技术,将镜面加工的不锈钢载玻片和聚乙二醇作为包埋介质,可用于组织切片的红外和拉曼光谱。以大鼠肾脏为模型,采用k均值聚类和主成分分析(PCA)对多模态振动光谱成像与红外和拉曼光谱成像的性能进行比较。由多模态数据集生成的K-Means聚类图显示,与独立分析的拉曼光谱或红外光谱相比,马松三色染色的相邻切片识别的不同组织类型之间的相关性最好。多模态数据集的PCA得分图沿着前三个主成分产生了个体组织类型的明确分离。此外,PCA允许主要由胶原蛋白振动模式产生的IR和拉曼峰的相关性。最后,聚乙二醇包埋被证明是一种有吸引力的替代石蜡包埋光谱分析,因为在拉曼测量中荧光明显减少,组织中脂质保留,而组织内没有任何介质保留。
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引用次数: 3
An observational cohort study to evaluate the use of serum Raman spectroscopy in a rapid diagnosis center setting 一项评价血清拉曼光谱在快速诊断中心使用的观察性队列研究
Pub Date : 2022-12-01 DOI: 10.1016/j.clispe.2022.100020
Freya E.R. Woods , Susan Chandler , Natalia Sikora , Rachel Harford , Ahmad Souriti , Helen Gray , Heather Wilkes , Catherine Lloyd-Bennett , Dean A. Harris , Peter R. Dunstan

Cancer presenting with non-specific vague symptoms remains a clinical challenge. The purpose of this study was to assess the feasibility of serum Raman spectroscopy for cancer detection in a rapid diagnosis center (RDC) setting. The primary aim was to identify significant spectral peaks of change in sera from cancer patients and the secondary aim was to assign molecular species at Raman peaks.

In this prospective observation study of a secondary care RDC, patients referred with vague cancer-related symptoms were recruited. Raman spectra of blood sera of 54 patients was obtained. Of these, 10 patients were diagnosed with cancer, and 44 no significant pathology (control). Common spectral increase/decrease between control and cancer was seen in spectral peaks 830 cm−1, 878 cm−1, 1031 cm−1, 1174 cm−1, 1397 cm−1 tentatively attributed to amino acids, carbohydrates, fatty acids, and proteins. Individual differences between cancer and control via statistical analysis identifies 3 peaks with significance for all 10 of the cancer patients. The peaks are 878 cm−1, 1449 cm−1 and 1519 cm−1, tentatively attributed to proteins, amino acids, lipids, fatty acids, glycoproteins, carbohydrates, and carotenoids. Differences are also seen for at least 9 of the cancers in the peaks at 830 cm−1, 851 cm−1, 1127 cm−1, 1174 cm−1, 1270 cm−1, and 1656 cm−1, tentatively attributed to amino acids, lactate, lipids, triglycerides, carbohydrates, and proteins.

Raman spectroscopy has the potential to enhance RDC referral criteria through the detection of peak differences seen commonly with different cancer types. Development of Artificial Intelligence (AI) based models could enable rapid detection and discrimination of different cancer types with more data availability.

以非特异性模糊症状表现的癌症仍然是一个临床挑战。本研究的目的是评估血清拉曼光谱在快速诊断中心(RDC)检测癌症的可行性。主要目的是确定癌症患者血清变化的显著光谱峰,次要目的是在拉曼峰上分配分子种类。在这项二级护理RDC的前瞻性观察研究中,招募了有模糊癌症相关症状的患者。获得54例患者血清拉曼光谱。其中,10名患者被诊断为癌症,44名患者无明显病理(对照组)。在830 cm−1、878 cm−1、1031 cm−1、1174 cm−1、1397 cm−1的光谱峰中,可以看到对照组和癌症之间的光谱增减,初步归因于氨基酸、碳水化合物、脂肪酸和蛋白质。通过统计分析,癌症患者和对照组之间的个体差异确定了所有10名癌症患者的3个显著峰值。峰分别为878 cm−1、1449 cm−1和1519 cm−1,暂定为蛋白质、氨基酸、脂类、脂肪酸、糖蛋白、碳水化合物和类胡萝卜素。在830 cm−1、851 cm−1、1127 cm−1、1174 cm−1、1270 cm−1和1656 cm−1的峰值中,至少9种癌症也存在差异,初步归因于氨基酸、乳酸、脂类、甘油三酯、碳水化合物和蛋白质。拉曼光谱通过检测不同癌症类型常见的峰值差异,有可能提高RDC转诊标准。基于人工智能(AI)的模型的发展可以通过更多的数据可用性来快速检测和区分不同类型的癌症。
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引用次数: 3
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Clinical Spectroscopy
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