ArticleBiological trace element research2021
Diagnosis of Systemic Diseases Using Infrared Spectroscopy: Detection of Iron Overload in Plasma-Preliminary Study.
Article in Biological trace element research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 26 citations in OpenAlex.
- One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.Analytical chemistry · 2025Article
- Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.BMC pregnancy and childbirth · 2025Article
- Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study.ACS omega · 2025Article
- Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study.Orphanet journal of rare diseases · 2024Article
- Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization.Cancers · 2022Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the important role of iron in cellular homeostasis, iron overload (IO) is associated with systemic and tissue deposits which damage several organs. In order to reduce the impact caused by IO, invasive diagnosis exams (e.g., biopsies) and minimally invasive methods were developed including computed tomography and magnetic resonance imaging. However, current diagnostic methods are still time-consuming and expensive. A cost-effective solution is using Fourier-transform infrared spectroscopy (FTIR) for real-time and molecular-sensitive biofluid analysis during conventional laboratory exams. In this study, we performed the first evaluation of the accuracy of FTIR for IO diagnosis. The study was performed by collecting FTIR spectra of plasma samples of five rats intravenously injected with iron-dextran and five control rats. We developed a classification model based on principal component analysis and supervised methods including J48, random forest, multilayer perceptron, and radial basis function network. We achieved 100% accuracy for the classification of the IO status and provided a list of possible biomolecules related to the vibrational modes detected. In this preliminary study, we give a first step towards real-time diagnosis for acute IO or intoxication. Furthermore, we have expanded the literature knowledge regarding the pathophysiological changes induced by iron overload.
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