Breast cancer detection based on serum sample surface enhanced Raman spectroscopy.

Raman spectroscopy is a vibrational technique which provides information about the chemical structure. Nevertheless, since many chemicals are present in a sample at very low concentration, the Raman signal observed is extremely weak. In surface enhanced Raman scattering (SERS), Raman signals can be enhanced by many orders of magnitude when nanoparticles are used. To the best of our knowledge, this is the first report in the breast cancer detection based on serum SERS. The serum samples were obtained from 12 patients who were clinically diagnosed with advanced breast cancer and 15 controls. In the same proportion, the serum samples were mixed with colloidal gold nanoparticles of 40 nm using sonication. At least 10 spectra were collected of each serum sample using a Jobin-Yvon LabRAM Raman Spectrometer with a laser of 830 nm. Raw spectra were processed by carrying baseline correction, smoothing, and normalization and then analyzed using principle component analysis (PCA) and linear discriminant analysis (LDA). Raman spectra showed strongly enhanced bands in the 600-1800 cm (-1) range due to the nanoparticle colloidal clusters observed. These Raman bands allowed identifying biomolecules present at low concentration as amide I and III, ß carotene, glutathione, tryptophan, tyrosine, and phenylalanine. Preliminary results demonstrated that SERS and PCA-LDA can be used to discriminate between control and cancer samples with high sensitivity and specificity. SERS allowed short exposures and required a minimal sample preparation. The preliminary results suggest that SERS and PCA-LDA could be an excellent support technique for the breast cancer detection using serum samples.

Early detection of dental fluorosis using Raman spectroscopy and principal component analysis.

Raman spectroscopic technique has the potential to provide vibrational spectra of minerals by analyzing scattered light caused by monochromatic laser excitation. In this paper, recent applications of Raman spectroscopy in the study of dental hard tissues are reported. Special attention is given to mineral components in enamel and to calcium fluoride formed in/on enamel. The criteria used to classify the dental hard samples were according to the Dean Index (DI), which consists into healthy or control, mild, moderate, and severe, indicating the amount of dental fluorosis observed on enamel. A total of 39 dental samples (9 control, 9 mild, 10 moderate, and 11 severe) were analyzed in the study. Dental samples were positioned under an Olympus microscope, and around 10 points were chosen for Raman measurement. All spectra were collected by a Horiba Jobin-Yvon LabRAM HR800 Raman Spectrometer with a laser of 830-nm and 17-mW power irradiation. Raw spectra were processed by carrying out baseline correction, smoothing, and normalization to remove noise, florescence, and shot noise and then analyzed using principal component analysis (PCA). In the spectra of dental samples, we observed the main bands as the broad band due to CO[Formula: see text] (240-300 cm (-1)), CaF 2 (322 cm (-1)), PO[Formula: see text] vibrations (437 and 450 cm (-1)), PO[Formula: see text] vibrations (582, 598, and 609 cm (-1)), PO[Formula: see text] vibrations (960 cm (-1)), PO[Formula: see text] vibrations (1,045 cm (-1)), and CO[Formula: see text] vibration (1,073 cm (-1)). Nevertheless, the intensity of the band at 960 cm (-1) associated to symmetric stretch of phosphate, PO[Formula: see text], decreases as the amount of dental fluorosis increases, suggesting that the intensity of this band could be used to quantitatively measure the level of fluorosis on a dental sample. On the other hand, PCA allowed to identify two large clusters discriminating between control, and severe and moderate samples with high sensitivity and specificity. PCA was able to discriminate mild from moderate samples with 100 % sensitivity and 89 % specificity and mild from severe samples with 91 % sensitivity and 100 % specificity. In addition, PCA was also able to discriminate between mild samples and group formed by the moderate and severe samples with 95 % sensitivity and 89 % specificity. Finally, PCA allowed us to define the wavelength differences between the spectral bands of the healthy teeth with sound enamel and those with fluorosis by confirming that the main chemical differences among control and severe fluorosis samples were associated to the vibrational modes of phosphate (PO[Formula: see text], PO[Formula: see text], PO[Formula: see text], and PO[Formula: see text] and carbonate (CO[Formula: see text]) ions. The preliminary results suggest that Raman-PCA technique has the potential to be a noninvasive real-time tool for the early detection and monitoring evolution of dental fluorosis.

Monitoring of chemotherapy leukemia treatment using Raman spectroscopy and principal component analysis.

In this research, we used the Raman spectroscopy to distinguish between normal and leukemia blood serum and identify the different types of leukemia based on serum biochemistry. In addition, monitoring of patients under chemotherapy leukemia treatment (CHLT) was studied. Blood samples were obtained from seven patients who were clinically diagnosed with three leukemia types and 21 healthy volunteers. In addition, other five leukemia patients were monitored during the CHLT, two patients were declared healthy, one patient suspended it; the health of the other two patients worsened, and no improvement was observed along CHLT. The serum samples were put under an Olympus microscope integrated to the Raman system, and several points were chosen for the Raman measurement. The Horiba Jobin Yvon LabRAM HR800 Raman system is equipped with a liquid nitrogen-cooled detector and a laser of 830 nm with a power irradiation of 17 mW. It is shown that the serum samples from patient with leukemia and from the control group can be discriminated when multivariate statistical methods of principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to their Raman spectra obtaining two large clusters corresponding to the control and leukemia serum samples and three clusters inside the leukemia group associated with the three leukemia types. The major differences between leukemia and control spectra were at 1,338 (Trp, α-helix, phospholipids), 1,447 (lipids), 1,523 (ß-carotene), 1,556 (Trp), 1,587 (protein, Tyr), 1,603 (Tyr, Phe), and 1,654 (proteins, amide I, α-helix, phospholipids) cm(-1), where these peaks were less intense in the leukemia spectrum. Minor differences occurred at 661 (glutathione), 890 (glutathione), 973 (glucosamine), 1,126 (protein, phospholipid C-C str), 1,160 (ß-carotene), 1,174 (Trp, Phe), 1,208 (Trp), 1,246 (amide III), 1,380 (glucosamine), and 1,404 (glutathione) cm(-1). Leukemia spectrum showed a peak at 917 cm(-1) associated with glutathione, but it was absent in the control spectrum. The results suggest that the Raman spectroscopy and PCA could be a technique with a strong potential of support for current techniques to detect and identify the different leukemia types by using a serum sample. Nevertheless, with the construction of a data library integrated with a large number of leukemia and control Raman spectra obtained from a wide range of healthy and leukemic population, the Raman-PCA technique could be converted into a new technique for minimally invasive real-time diagnosis of leukemia from serum samples. In addition, complementary results suggest that using these techniques is possible to monitor CHLT.

Cervical cancer detection based on serum sample Raman spectroscopy.

The use of Raman spectroscopy to analyze the biochemical composition of serum samples and hence distinguish between normal and cervical cancer serum samples was investigated. The serum samples were obtained from 19 patients who were clinically diagnosed with cervical cancer, 3 precancer, and 20 healthy volunteer controls. The imprint was put under an Olympus microscope, and around points were chosen for Raman measurement.All spectra were collected at a Horiba Jobin-Yvon LabRAM HR800 Raman Spectrometer with a laser of 830-nm wavelength and 17-mW power irradiation. Raw spectra were processed by carrying out baseline correction, smoothing, and normalization to remove noise, florescence, and shot noise and then analyzed using principal component analysis (PCA). The control serum spectrum showed the presence of higher amounts of carotenoids indicated by peaks at 1,002, 1,160, and 1,523 cm(-1)and intense peaks associated with protein components at 754, 853, 938, 1,002, 1,300-1,345, 1,447, 1,523, 1,550, 1,620, and 1,654 cm(-1). The Raman bands assigned to glutathione (446, 828, and 1,404 cm(-1)) and tryptophan (509, 1,208, 1,556, 1,603, and 1,620 cm(-1)) in cervical cancer were higher than those of control samples, suggesting that their presence may also play a role in cervical cancer. Furthermore, weak bands in the control samples attributed to tryptophan (545, 760, and 1,174 cm(-1)) and amide III (1,234-1,290 cm(-1)) seem to disappear and decrease in the cervical cancer samples, respectively. It is shown that the serum samples from patients with cervical cancer and from the control group can be discriminated with high sensitivity and specificity when the multivariate statistical methods of PCA is applied to Raman spectra. PCA allowed us to define the wavelength differences between the spectral bands of the control and cervical cancer groups by confirming that the main molecular differences among the control and cervical cancer samples were glutathione, tryptophan, ß carotene, and amide III. The preliminary results suggest that Raman spectroscopy could be a highly effective technique with a strong potential of support for current techniques as Papanicolaou smear by reducing the number of these tests; nevertheless, with the construction of a data library integrated with a large number of cervical cancer and control Raman spectra obtained from a wide range of healthy and cervical cancer population, Raman-PCA technique could be converted into a new technique for noninvasive real-time diagnosis of cervical cancer from serum samples.