Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease

Acri, Giuseppe; Venuti, Valentina; Costa, Stéfano; Testagrossa, Barbara; Pellegrino, Salvatore; Crupi, V.; Majolino, D.

doi:10.3390/app10196974

Cited by 16 publications

(18 citation statements)

References 52 publications

(33 reference statements)

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“…Recently, Acri et al [156] proposed a method to diagnose the pediatric onset of inflammatory dowel diseases, such as ulcerative colitis and Crohn's disease. Their approach involved taking fecal samples to be analyzed with Raman spectroscopy and compared with the fingerprints of the various pathologies.…”

Section: Biological Applicationsmentioning

confidence: 99%

A Comprehensive Review on Raman Spectroscopy Applications

et al. 2021

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Raman spectroscopy is a very powerful tool for material analysis, allowing for exploring the properties of a wide range of different materials. Since its discovery, Raman spectroscopy has been used to investigate several features of materials such carbonaceous and inorganic properties, providing useful information on their phases, functions, and defects. Furthermore, techniques such as surface and tip enhanced Raman spectroscopy have extended the field of application of Raman analysis to biological and analytical fields. Additionally, the robustness and versatility of Raman instrumentations represent a promising solution for performing on-field analysis for a wide range of materials. Recognizing the many hot applications of Raman spectroscopy, we herein overview the main and more recent applications for the investigation of a wide range of materials, such as carbonaceous and biological materials. We also provide a brief but exhaustive theoretical background of Raman spectroscopy, also providing deep insight into the analytical achievements.

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Section: Biological Applicationsmentioning

confidence: 99%

A Comprehensive Review on Raman Spectroscopy Applications

et al. 2021

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“…Deconvolution is the process by which pressures are assigned for each gas considered to be present in the MS. It is one of the most widely used techniques in the reconstruction and analysis of samples obtained by spectrometry and spectroscopy in many different areas of study, including Raman spectrometry for pediatric diagnoses [26], time-resolved fluorescence spectroscopy [27], nuclear instruments MS [28] and spectrometry analytics [29,30], among others.…”

Section: Iterative Deconvolutionmentioning

confidence: 99%

Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms

et al. 2021

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In ultra-high vacuum systems, obtaining the composition of a mass spectrum is often a challenging task due to the highly overlapping nature of the individual profiles of the gas species that contribute to that spectrum, as well as the high differences in terms of degree of contribution (several orders of magnitude). This problem is even more complex when not only the presence but also a quantitative estimation of the contribution (partial pressure) of each species is required. This paper aims at estimating the relative contribution of each species in a target mass spectrum by combining a state-of-the-art machine learning method (multilabel classifier) to obtain a pool of candidate species based on a threshold applied to the probability scores given by the classifier with a genetic algorithm that aims at finding the partial pressure at which each one of the species contributes to the target mass spectrum. For this purpose, we use a dataset of synthetically generated samples. We explore different acceptance thresholds for the generation of initial populations, and we establish comparative metrics against the most novel method to date for automatically obtaining partial pressure contributions. Our results show a clear advantage in terms of the integral error metric (up to 112 times lower for simpler spectra) and computational times (up to 4 times lower for complex spectra) in favor of the proposed method, which is considered a substantial improvement for this task.

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“…This technique could probe the changes in the secondary structures of proteic extract and distinguish IBD from HC subjects and CD from UC patients. 19 Another research group measured colon tissue biopsies in vitro from 9 CD and 12 UC patients using Raman fiber-optic probes, and detected characteristic Raman features potentially to distinguish CD from UC. 20 However, these studies usually had small sample sizes, and sample collection was invasive and obtained in an uneasy way.…”

Section: Introductionmentioning

confidence: 99%

The Specific Changes of Urine Raman Spectra Can Serve as Novel Diagnostic Tools for Disease Characteristics in Patients with Crohn’s Disease

Wu¹,

Wang²,

Xing³

et al. 2022

JIR

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Purpose: Crohn's disease (CD) is a chronic recurrent intestinal inflammatory disease that requires repeated invasive examinations. Convenient and noninvasive diagnostic tools for CD are lacking. Surface-enhanced Raman spectroscopy (SERS) can rapidly provide specific metabolite information in various samples. Our previous study has showed urine Raman spectrum can distinguish CD patients from healthy controls noninvasively. In this study, we further investigated the value of urine Raman spectra on identifying the disease characterizations in patients with CD. Patients and Methods: Urine samples were analyzed by SERS to acquire specific changes of the spectra from 100 active CD (aCD) patients and 88 inactive CD (iCD) patients. The accuracy of classifier models yielded by SERS was assessed by principal component analysis and support vector machine (PCA-SVM) to investigate spectral differences and disease characterizations. Results: Given a panel of 16 specific Raman spectra, the classifier model was established to predict disease activity between patients with aCD and iCD and achieved higher efficacy than fecal calprotectin (AUC value, 0.864 vs 0.596, P=0.02). After leave-one-patientout cross-validation, the classifier model still obtained 75.5% of accuracy. The correlation analysis showed it had negative correlation with endoscopic results (r=−0.616, P<0.0001). We further established the classifier model in identifying disease location to discriminate colonic-type from ileal-type CD with 63.6% of accuracy with the significantly increased intensity of 1643 cm −1 band, and the model to predict the spectra changes of before and after treatment in tumor necrosis factor inhibitor responders with 91.2% of accuracy with a panel of 11 specific spectra. The metabolic changes of amino acids, proteins, lipids, and other compounds in urine levels were noted by SERS in patients with CD. Conclusion:The specific changes of urine Raman spectra can reflect changes in urine metabolism. It has the potential value on being the promising diagnostic tool for disease characterizations in CD patients by a convenient and noninvasive way.

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Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease

Cited by 16 publications

References 52 publications

A Comprehensive Review on Raman Spectroscopy Applications

A Comprehensive Review on Raman Spectroscopy Applications

Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms

The Specific Changes of Urine Raman Spectra Can Serve as Novel Diagnostic Tools for Disease Characteristics in Patients with Crohn’s Disease

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