Determination of Fatty Acids in Human Sweat during Fasting Using GC/MS

Nunome, Yoko; Tsuda, Toshitaka; Kitagawa, Kuniyuki

doi:10.2116/analsci.26.917

Cited by 13 publications

(15 citation statements)

References 13 publications

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“…Artificial human axillary sweat medium was adapted from that described by Callewaert et al (26) with several modifications (Table S2). In place of fatty acids hydrolyzed from human abdominal subcutaneous fat, we included a mix of the three most abundance fatty acids found in human sweat (lauric, myristic, and palmitic fatty acids) (26,27). In light of this, lower concentrations of fatty acids were added to ensure solubility.…”

Section: Methodsmentioning

confidence: 99%

Candida auris Forms High-Burden Biofilms in Skin Niche Conditions and on Porcine Skin

Horton

Johnson

Kernien

et al. 2020

mSphere

115

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Emerging pathogen Candida auris causes nosocomial outbreaks of lifethreatening invasive candidiasis. It is unclear how this species colonizes skin and spreads in health care facilities. Here, we analyzed C. auris growth in synthetic sweat medium designed to mimic axillary skin conditions. We show that C. auris demonstrates a high capacity for biofilm formation in this milieu, well beyond that observed for the most commonly isolated Candida sp., Candida albicans. The C. auris biofilms persist in environmental conditions expected in the hospital setting. To model C. auris skin colonization, we designed an ex vivo porcine skin model. We show that C. auris proliferates on porcine skin in multilayer biofilms. This capacity to thrive in skin niche conditions helps explain the propensity of C. auris to colonize skin, persist on medical devices, and rapidly spread in hospitals. These studies provide clinically relevant tools to further characterize this important growth modality. IMPORTANCE The emerging fungal pathogen Candida auris causes invasive infections and is spreading in hospitals worldwide. Why this species exhibits the capacity to transfer efficiently among patients is unknown. Our findings reveal that C. auris forms high-burden biofilms in conditions mimicking sweat on the skin surface. These adherent biofilm communities persist in environmental conditions expected in the hospital setting. Using a pig skin model, we show that C. auris also forms high-burden biofilm structures on the skin surface. Identification of this mode of growth sheds light on how this recently described pathogen persists in hospital settings and spreads among patients.

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Section: Methodsmentioning

confidence: 99%

Candida auris Forms High-Burden Biofilms in Skin Niche Conditions and on Porcine Skin

Horton

Johnson

Kernien

et al. 2020

mSphere

115

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“…Only a few methods for the determination of FA in human sweat have been developed [3,[5][6][7][8]. However, there are no reports about a relation between sweat and plasma FA and FA stability.…”

Section: Introductionmentioning

confidence: 99%

Determination of selected fatty acids in dried sweat spot using gas chromatography with flame ionization detection

Kanďár

Drábková

Andrlová

et al. 2016

J of Separation Science

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A method is described for the determination of fatty acids in dried sweat spot and plasma samples using gas chromatography with flame ionization detection. Plasma and dried sweat spot samples were obtained from a group of blood donors. The sweat was collected from each volunteer during exercise. Sweat was spotted onto collection paper containing butylated hydroxytoluene. Fatty acids were derivatized with acetyl chloride in methanol to form methyl esters of fatty acids. The fatty acids in dried sweat spot samples treated with butylated hydroxytoluene and stored at -20°C were stable for 3 months. Our results indicate that sweat contains, among fatty acids with short chain, also fatty acids with long chain and unsaturated fatty acids. Linear relationships between percentage content of selected fatty acids in dried sweat spot and plasma were observed.

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“…According to our personal experiences, breath gas is largely influenced by volatile substances produced or consumed by oral microbiota, whereas the skin gas is devoid of these factors. Although acquisition of the skin gas was more tedious than that of breath gas, we previously developed a method to reproducibly obtain gas samples from the skin [7][8][9][10].…”

Section: Discussionmentioning

confidence: 99%

“…Serum biomarkers in PD have also been analyzed by mass spectrometry. In PD, serum levels of tryptophan, caffeine, bilirubin, and ergothioneine were lower than controls, and serum levels of levodopa and biliverdin were higher than controls [8]. The same group later reported that serum levels of caffeine and nine of its downstream metabolites were decreased, which was irrelevant to total caffeine intake or disease severity [9].…”

Section: Introductionmentioning

confidence: 98%

Application of Skin Gas GC/MS Analysis for Prediction of the Severity Scale of Parkinson’s Disease

et al. 2019

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Parkinson's disease (PD) is diagnosed by neurological examinations, as well as by scintigraphy for the dopamine transporter and metaiodobenzylguanidine (MIBG). We studied possible application of the skin gas in diagnosis of PD. We analyzed chemical substances emanated from the skin by gas chromatograph/mass spectrometer (GC/MS) after on-line-pre-concentrator. We analyzed the skin gas in 61 PD patients and 61 controls. The GC/MS chromatograms were sectionalized every 30 sec. The retention time drift was shifted every 5 sec, and a similarity coefficient (Z score) between a reference chromatogram and each shifted chromatogram was calculated. Chromatograms with high Z scores were excluded from our analysis. Models were made with partial least square (PLS), support vector machine (SVM), and support vector regression (SVR) analyses. PLS modeling to predict the Unified Parkinson's Disease Rating Scale part 3 (UPDRS3), representing motor deficits in PD, with all the detected mass numbers yielded 50 mass numbers with high PLS coefficients. Among the 50 mass numbers, nine mass numbers (m/e 48, 63, 67, 70, 81, 93, 96, 104, and 105) had dependable signal-to-noise ratios. We then generated an SVM model to differentiate PD and controls. Our SVM model had a sensitivity of 90.2% and a specificity of 85.2% by leave-one-out cross-validation (LOOCV) analysis. We next generated an SVR model to predict UPDRS3 with the nine mass numbers, and obtained a Pearson's correlation coefficient of 0.834. LOOCV analysis of our SVR model similarly gave rise to a correlation coefficient of 0.710. We propose that chemical substances in the skin gas potentially serve as biomarkers for PD.

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Determination of Fatty Acids in Human Sweat during Fasting Using GC/MS

Cited by 13 publications

References 13 publications

Candida auris Forms High-Burden Biofilms in Skin Niche Conditions and on Porcine Skin

Candida auris Forms High-Burden Biofilms in Skin Niche Conditions and on Porcine Skin

Determination of selected fatty acids in dried sweat spot using gas chromatography with flame ionization detection

Application of Skin Gas GC/MS Analysis for Prediction of the Severity Scale of Parkinson’s Disease

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