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Ver términos de la licenciaSánchez Rojo, S. A., et al. (2016). Cervical cancer detection based on serum sample surface enhanced Raman spectroscopy. Revista Mexicana de Física; Vol 62, No 3 May-Jun: 213-0. Recuperado de https://repositorio.unam.mx/contenidos/4108372
Autor(es)
Sánchez Rojo, S. A.; Martínez Zerega, B. E.; Velázquez Pedroza, E. F.; Martínez Espinosa, J. C.; Torres González, L. A.; Aguilar Lemarro, A.; Jave Suárez, L. F.; Palomares Anda, P.; González Solís, J. L.
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Cervical cancer detection based on serum sample surface enhanced Raman spectroscopy
Fecha
2016-01-01
Resumen
In the presence of nanoparticles, the Raman signal is enhanced to the levels sufficient to detect a single molecule, therefore spectroscopy Surface-Enhanced Raman Scattering (SERS) is currently recognized as a detection technique extremely sensitive with high levels of molecular specificity. This is the first report in the cervical cancer detection based on serum SERS. The serum samples were obtained from 14 patients who were clinically diagnosed with cancer and 14 healthy volunteer controls. The serum samples were mixed with colloidal silver nanoparticles of 40 nm in the same proportion, using sonication. About 10 spectra were collected of each serum sample using a Horiba Jobin-Yvon LabRAM Raman Spectrometer with a laser of 830 nm. The enhanced Raman bands allowed identifying biomolecules present at low concentration as amide I and III, carotene, glutathione, tryptophan, tyrosine and phenylalanine. Subsequently, the processed SERS spectra were analyzed using multivariate statistical analysis including principal component analysis and linear discriminant analysis (LDA). Preliminary results showed that SERS and PCA-LDA can be used to discriminate between cervical cancer and control samples with high sensitivity and specificity, forming an excellent support technique for current detection techniques.
Tema
Cervical cancer; blood serum; surface enhanced Raman scattering; principal component analysis; linear discriminant analysis
Idioma
eng
ISSN
2683-2224 (digital); 0035-001X (impresa)
