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Use of attenuated total reflectance Fourier transform infrared spectroscopy to identify microbial metabolic products on carbonate mineral surfaces.

ABSTRACT: This paper demonstrates the use of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to detect microbial metabolic products on carbonate mineral surfaces. By creating an ATR-FTIR spectral database for specific organic acids using ATR-FTIR spectroscopy we were able to distinguish metabolic acids on calcite surfaces following Escherichia coli growth. The production of these acids by E. coli was verified using high-performance liquid chromatography with refractive index detection. The development of this technique has allowed us to identify microbial metabolic products on carbonate surfaces in nutrient-limited cave environments.

SUBMITTER: Bullen HA

PROVIDER: S-EPMC2493160 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Use of attenuated total reflectance Fourier transform infrared spectroscopy to identify microbial metabolic products on carbonate mineral surfaces.

Bullen Heather A HA Oehrle Stuart A SA Bennett Ariel F AF Taylor Nicholas M NM Barton Hazel A HA

Applied and environmental microbiology 20080523 14

This paper demonstrates the use of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to detect microbial metabolic products on carbonate mineral surfaces. By creating an ATR-FTIR spectral database for specific organic acids using ATR-FTIR spectroscopy we were able to distinguish metabolic acids on calcite surfaces following Escherichia coli growth. The production of these acids by E. coli was verified using high-performance liquid chromatography with refractive inde ...[more]

PMID: 18502924

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Project description:BACKGROUND:The topical HIV prevention (microbicides) field is in acute need of a method to rapidly and objectively measure adherence to product use in clinical trials. Infrared (IR) spectroscopy has been used in many pharmaceutical and forensic applications but has yet to be applied to adherence monitoring. In this study, we report on efforts to test the feasibility of using IR spectroscopy as a means to measure residual active or placebo vaginal product, semen exposure and vaginal insertion from a single swab. METHODS:A portable IR spectrometer equipped with diamond attenuated total reflectance (ATR) was used to capture spectra of unused vs. vaginally-used swabs, vaginal swabs containing semen, and vaginal swabs to which either tenofovir-containing or matching placebo products (vaginal gel or insert) were added. Spectral data obtained from swabs placed directly on the spectrometer were divided into calibration and testing sets for developing and validating discriminant models set up to provide yes/no predictions of: vaginal vs. non-vaginal use, presence vs. no presence of each test product, and presence vs. no presence of semen. Further validation of models was performed using vaginal swabs collected from a clinical study evaluating vaginally administered placebo insert formulations. RESULTS:For each discriminant model developed to predict vaginal vs. non-vaginal use, presence vs. no presence of each test product, and presence vs. no presence of semen, classified validation samples not included in the model development were correctly identified into their respective classes with minimal prediction error. Clinically obtained vaginal swabs collected 15-60 minutes after placebo insert use were also correctly identified, further validating the models. CONCLUSION:Our findings demonstrate the proof of concept that IR spectroscopy can be a method for rapid detection and characterization of microbicide products and biological fluids present in vaginal swabs. This novel method has potential to support real-time, on-site adherence monitoring in clinical or field settings.

Project description:In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700-1500 cm-1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700-1500 cm-1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800-900 cm-1. Exosome spectra in the 1700-1500 and combined 3000-2800 and 1800-900 cm-1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800-900 cm-1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

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Use of attenuated total reflectance Fourier transform infrared spectroscopy to identify microbial metabolic products on carbonate mineral surfaces.

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Use of attenuated total reflectance Fourier transform infrared spectroscopy to identify microbial metabolic products on carbonate mineral surfaces.

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