A surface-enhanced Raman spectroscopy (SERS) approach was utilized for urine biochemical analysis with the aim to develop a label-free and non-invasive optical diagnostic method for esophagus cancer detection. SERS spectrums were acquired from 31 normal urine samples and 47 malignant esophagus cancer (EC) urine samples. Tentative assignments of urine SERS bands demonstrated esophagus cancer specific changes, including an increase in the relative amounts of urea and a decrease in the percentage of uric acid in the urine of normal compared with EC. The empirical algorithm integrated with linear discriminant analysis (LDA) were employed to identify some important urine SERS bands for differentiation between healthy subjects and EC urine. The empirical diagnostic approach based on the ratio of the SERS peak intensity at 527 to 1002 cm-1 and 725 to 1002 cm-1 coupled with LDA yielded a diagnostic sensitivity of 72.3% and specificity of 96.8%, respectively. The area under the receive operating characteristic (ROC) curve was 0.954, which further evaluate the performance of the diagnostic algorithm based on the ratio of the SERS peak intensity combined with LDA analysis. This work demonstrated that the urine SERS spectra associated with empirical algorithm has potential for noninvasive diagnosis of esophagus cancer.
Surface-enhanced Raman scattering (SERS) spectroscopy combined with membrane electrophoresis (ME) was firstly employed to detect albumin variation in type II diabetic development. Albumin was first purified from human serum by ME and then mixed with silver nanoparticles to perform SERS spectral analysis. SERS spectra were obtained from blood albumin samples of 20 diabetic patients and 19 healthy volunteers. Subtle but discernible changes in the acquired mean spectra of the two groups were observed. Tentative assignment of albumin SERS bands indicated specific structural changes of albumin molecule with diabetic development. Meanwhile, PCA-LDA diagnostic algorithms were employed to classify the two kinds of albumin SERS spectra, yielding the diagnostic sensitivity of 90% and specificity of 94.7%. The results from this exploratory study demonstrated that the EM-SERS method in combination with multivariate statistical analysis has great potential for the label-free detection of albumin variation for improving type II diabetes screening.
Micro-Raman spectroscopy is widely used for non-invasive tissue diagnosis and detection, as it provides detailed information about biomolecular composition, structure, and interaction of tissue. In this work, micro-Raman spectroscopy was used to investigate non-cancerous and cancerous nasopharyngeal tissues. The obtained nasopharyngeal tissue samples in vitro are divided into two groups: cancerous (n=12, undifferentiated non-keratinizing carcinomas) and non-cancerous (n=10, 7 chronic inflammations, 2 lymphomas and 1 lymphocytosis). Firstly, we analyzed the Raman spectra in the fingerprint (FP, 400-1800cm-1) region acquired. Preliminary results showed that there are some spectral differences in different pathological conditions. Furthermore, Raman spectra from cancerous and non-cancerous nasopharyngeal tissue in the high wavenumber region (HW, 2800-3100cm-1) were also reported for the first time. After detailed analysis, we achieved significant differences in Raman bands at 2854, 2874, 2934, and 3067cm-1 between cancerous and non-cancerous nasopharyngeal tissues. This study demonstrates that both fingerprint and high wavenumber regions of micro-Raman spectroscopy have the potential for the early detection of nasopharyngeal carcinomas.
Raman spectroscopy is a rapidly non-invasive technique with great potential for biomedical research. The aim of this
study was to evaluate the feasibility of using Raman spectroscopy of human saliva for acute myocardial infarction (AMI)
detection. Raman spectroscopy measurements were performed on two groups of saliva samples: one group from patients
(n=30) with confirmed AMI and the other group from healthy controls (n=31). The diagnostic performance for
differentiating AMI saliva from normal saliva was evaluated by multivariate statistical analysis. The combination of
principal component analysis (PCA) and linear discriminate analysis (LDA) of the measured Raman spectra separated
the spectral features of the two groups into two distinct clusters with little overlaps, rendering the sensitivity of 80.0%
and specificity of 80.6%. The results from this exploratory study demonstrated that Raman spectroscopy of human saliva
can serve as a potentially clinical tool for rapid AMI detection and screening.