Dataset Information

Determination of sialic acid in saliva by means of surface-enhanced Raman spectroscopy as a marker in adnexal mass patients: ovarian cancer vs benign cases.

ABSTRACT:

Background

To demonstrate the use of surface-enhanced Raman spectroscopy (SERS) to determine sialic acid (SA) levels in saliva using silver nanoparticles as substrates, in adnexal mass patients scheduled for surgical intervention to remove invasive masses, with the aim to compare SA levels in benign tumor vs ovarian cancer patients.

Methods

Quantification of SA levels was accomplished by measuring their SERS and calibrating with analytical reagent SA. The mean SA concentration in saliva from 37 benign adnexal mass resulted smaller (5.1 mg/dL) than the mean concentration in 15 Ovarium cancer patients (23 mg/dL). The cancer condition was determined by biopsy of the removed adnexal mass. The CA-125 biomarker was also measured. The predictive potential of both biomarkers is discussed, together with the malignity risk index (MRI).

Results

Our results showed a sensitivity/specificity of 80%/100% with a cutoff to distinguish between benign/cancer cases of SA 15.5 mg/dL, as established from a ROC analysis. Our results suggest that SA may be a more useful biomarker than CA-125 to detect ovarian cancer.

Conclusions

Our results suggest that the SA levels measured from saliva may be as good predictors as the MRI index for the presence of ovarian cancer in sensitivity/negative predictive value and outperforms it in specificity/positive predictive value.

SUBMITTER: Zermeno-Nava JJ

PROVIDER: S-EPMC6058371 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Determination of sialic acid in saliva by means of surface-enhanced Raman spectroscopy as a marker in adnexal mass patients: ovarian cancer vs benign cases.

Zermeño-Nava José de Jesús JJ Martínez-Martínez Marco Ulises MU Rámirez-de-Ávila Ana Laura AL Hernández-Arteaga Aida Catalina AC García-Valdivieso Ma Guadalupe MG Hernández-Cedillo Alondra A José-Yacamán Miguel M Navarro-Contreras Hugo Ricardo HR

Journal of ovarian research 20180724 1

<h4>Background</h4>To demonstrate the use of surface-enhanced Raman spectroscopy (SERS) to determine sialic acid (SA) levels in saliva using silver nanoparticles as substrates, in adnexal mass patients scheduled for surgical intervention to remove invasive masses, with the aim to compare SA levels in benign tumor vs ovarian cancer patients.<h4>Methods</h4>Quantification of SA levels was accomplished by measuring their SERS and calibrating with analytical reagent SA. The mean SA concentration in ...[more]

PMID: 30041680

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Project description:Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Dataset Information

Determination of sialic acid in saliva by means of surface-enhanced Raman spectroscopy as a marker in adnexal mass patients: ovarian cancer vs benign cases.

Background

Methods

Results

Conclusions

Publications

Determination of sialic acid in saliva by means of surface-enhanced Raman spectroscopy as a marker in adnexal mass patients: ovarian cancer vs benign cases.

Similar Datasets

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