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Serum Based Diagnosis of Asthma Using Raman Spectroscopy: An Early Phase Pilot Study
The currently prescribed tests for asthma diagnosis require compulsory patient compliance, and are usually not sensitive to mild asthma. Development of an objective test using minimally invasive samples for diagnosing and monitoring of the response of asthma may help better management of the disease...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3826756/ https://www.ncbi.nlm.nih.gov/pubmed/24250817 http://dx.doi.org/10.1371/journal.pone.0078921 |
Sumario: | The currently prescribed tests for asthma diagnosis require compulsory patient compliance, and are usually not sensitive to mild asthma. Development of an objective test using minimally invasive samples for diagnosing and monitoring of the response of asthma may help better management of the disease. Raman spectroscopy (RS) has previously shown potential in several biomedical applications, including pharmacology and forensics. In this study, we have explored the feasibility of detecting asthma and determining treatment response in asthma patients, through RS of serum. Serum samples from 44 asthma subjects of different grades (mild, moderate, treated severe and untreated severe) and from 15 reference subjects were subjected to Raman spectroscopic analysis and YKL-40 measurements. The force expiratory volume in 1 second (FEV1) values were used as gold standard and the serum YKL-40 levels were used as an additional parameter for diagnosing the different grades of asthma. For spectral acquisition, serum was placed on a calcium fluoride (CaF(2)) window and spectra were recorded using Raman microprobe. Mean and difference spectra comparisons indicated significant differences between asthma and reference spectra. Differences like changes in protein structure, increase in DNA specific bands and increased glycosaminoglycans-like features were more prominent with increase in asthma severity. Multivariate tools using Principal-component-analysis (PCA) and Principal-component based-linear-discriminant analysis (PC-LDA) followed by Leave-one-out-cross-validation (LOOCV), were employed for data analyses. PCA and PC-LDA results indicate separation of all asthma groups from the reference group, with minor overlap (19.4%) between reference and mild groups. No overlap was observed between the treated severe and untreated severe groups, indicating that patient response to treatment could be determined. Overall promising results were obtained, and a large scale validation study on random subjects is warranted before the routine clinical usage of this technique. |
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