SERS-based detection of haptoglobin in ovarian cyst fluid as a point-of-care diagnostic assay for epithelial ovarian cancer.

PURPOSE: To evaluate haptoglobin (Hp) in ovarian cyst fluid as a diagnostic biomarker for epithelial ovarian cancers (EOCs) using surface-enhanced Raman spectroscopy (SERS)-based in vitro diagnostic assay for use in an intraoperative setting. METHODS: SERS-based method was used to detect and quantify Hp in archived ovarian cyst fluids collected from suspicious ovarian cysts and differentiate benign tumors from EOCs. The diagnostic performance of SERS-based assay was verified against the histopathology conclusions and compared with the results of CA125 test and frozen sections. RESULTS: Hp concentration present in the clinical cyst fluid measured by SERS was normalized to 3.3 mg/mL of standard Hp. Normalized mean values for patients with benign cysts were 0.65 (n=57) and malignant cysts were 1.85 (n=54), demonstrating a significantly (P<0.01) higher Hp in malignant samples. Verified against histology, Hp measurements using SERS had a sensitivity of 94% and specificity of 91%. Receiver operating characteristic curve analysis of SERS-based Hp measurements resulted in area under the curve of 0.966±0.03, establishing the robustness of the method. CA125 test on the same set of patients had a sensitivity of 85% and specificity of 90%, while frozen section analysis on 65 samples had 100% sensitivity and specificity. CONCLUSION: With a total execution time of <10 minutes and consistent performance across different stages of cancer, the SERS-based Hp detection assay can serve as a promising intra-operative EOC diagnostic test.

Identification of mycolic acid forms using surface-enhanced Raman scattering as a fast detection method for tuberculosis.

BACKGROUND: Tuberculosis (TB) is the ninth leading cause of death worldwide and the leading cause from a single infectious agent, based on the WHO Global Tuberculosis Report in 2017. TB causes massive health care burdens in many parts of the world, specifically in the resource constrained developing world. Most deaths from TB could be prevented with cost effective early diagnosis and appropriate treatment. PURPOSE: Conventional TB detection methods are either too slow as it takes a few weeks for diagnosis or they lack the specificity and accuracy. Thus the objective of this study was to develop a fast and efficient detection for TB using surface enhanced Raman scattering (SERS) technique. METHODS: SERS spectra for different forms of mycolic acids (MAs) that are both synthetic origin and actual extracts from the mycobacteria species were obtained by label-free direct detection mode. Similarly, we collected SERS spectra for γ-irradiated whole bacteria (WB). Measurements were done using silver (Ag) coated silicon nanopillar (Ag SNP) as SERS substrate. RESULTS: We report the SERS based detection of MA, which is a biomarker for mycobacteria species including Mycobacterium tuberculosis. For the first time, we also establish the SERS spectral characterization of the three major forms of MA - αMA, methoxy-MA, and keto-MA, in bacterial extracts and also in γ-irradiated WB. We validated our findings by mass spectrometry. SERS detection of these three forms of MA could be useful in differentiating pathogenic and nonpathogenic Mycobacterium spp. CONCLUSIONS: We have demonstrated the direct detection of three major forms of MA - αMA, methoxy-MA, and keto-MA, in two different types of MA extracts from MTB bacteria, namely delipidated MA and undelipidated MA and finally in γ-irradiated WB. In the near future, this study could pave the way for a fast and efficient detection method for TB, which is of high clinical significance.

Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation.

The aim of this study is to solve a problem of denoising and artifact removal from in vivo multispectral photoacoustic imaging when the level of noise is not known a priori. The study analyzes Wiener filtering in Fourier domain when a family of anisotropic shape filters is considered. The unknown noise and signal power spectral densities are estimated using spectral information of images and the autoregressive of the power 1 ( AR(1)) model. Edge preservation is achieved by detecting image edges in the original and the denoised image and superimposing a weighted contribution of the two edge images to the resulting denoised image. The method is tested on multispectral photoacoustic images from simulations, a tissue-mimicking phantom, as well as in vivo imaging of the mouse, with its performance compared against that of the standard Wiener filtering in Fourier domain. The results reveal better denoising and fine details preservation capabilities of the proposed method when compared to that of the standard Wiener filtering in Fourier domain, suggesting that this could be a useful denoising technique for other multispectral photoacoustic studies.

Non-invasive and label-free detection of oral squamous cell carcinoma using saliva surface-enhanced Raman spectroscopy and multivariate analysis.

Reported here is the application of silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) as a label-free, non-invasive technique for detection of oral squamous cell cancer (OSCC) using saliva and desquamated oral cells. A total of 180 SERS spectra were acquired from saliva and 120 SERS spectra from oral cells collected from normal healthy individuals and from confirmed oropharyngeal cancer patients. Notable biochemical peaks in the SERS spectra were tentatively assigned to various components. Data were subjected to multivariate statistical techniques including principal component analysis, linear discriminate analysis (PCA-LDA) and logistic regression (LR) revealing a sensitivity of 89% and 68% and a diagnostic accuracy of 73% and 60% for saliva and oral cells, respectively. The results from this study demonstrate the potential of saliva and oral cell SERS combined with PCA-LDA or PCA-LR diagnostic algorithms as a promising clinical adjunct for the non-invasive detection of oral cancer.

Integrating global data into local health data base.

Data analysis traditionally belong to scientist's field of interest. Usually scientists gatherer and analyze their own data. Challenges of 21st century require more global point view in decision making process from each worker in local workplace. Local public health sector is strongly related with global world. So, right managerial decision-making in local workplaces depends on changes in global environment. The speed of changes is very high. Therefore, it is necessary to encourage workers of public health sector to make daily managerial decisions evaluating both global and local data. Information technologies (IT) supports decision making process. It is easier to analyze homogeneous data. Analysis of global data has shown that data formats were often incompatible. Limited IT competencies of public health workers make analysis of data from different sources difficult. This article describes a method for integration of global heterogeneous data in local databases.