Dataset Information

Multi-mycotoxin analysis using dried blood spots and dried serum spots.

ABSTRACT: In this study, a rapid multi-mycotoxin approach was developed for biomonitoring and quantification of 27 important mycotoxins and mycotoxin metabolites in human blood samples. HPLC-MS/MS detection was used for the analysis of dried serum spots (DSS) and dried blood spots (DBS). Detection of aflatoxins (AFB₁, AFB₂, AFG₁, AFG₂, AFM₁), trichothecenes (deoxynivalenol, DON; DON-3-glucoronic acid, DON-3-GlcA; T-2; HT-2; and HT-2-4-GlcA), fumonisin B₁ (FB₁), ochratoxins (OTA and its thermal degradation product 2'R-OTA; OT?; 10-hydroxychratoxin A, 10-OH-OTA), citrinin (CIT and its urinary metabolite dihydrocitrinone, DH-CIT), zearalenone and zearalanone (ZEN, ZAN), altenuene (ALT), alternariols (AOH; alternariol monomethyl ether, AME), enniatins (EnA, EnA₁, EnB, EnB₁) and beauvericin (Bea) was validated for two matrices, serum (DSS), and whole blood (DBS). HPLC-MS/MS analysis showed signal suppression as well as signal enhancement due to matrix effects. However, for most analytes LOQs in the lower pg/mL range and excellent recovery rate were achieved using matrix-matched calibration. Besides validation of the method, the analyte stability in DBS and DSS was also investigated. Stability is a main issue for some analytes when the dried samples are stored under common conditions at room temperature. Nevertheless, the developed method was applied to DBS samples of a German cohort (n?=?50). Besides positive findings of OTA and 2'R-OTA, all samples were positive for EnB. This methodical study establishes a validated multi-mycotoxin approach for the detection of 27 mycotoxins and metabolites in dried blood/serum spots based on a fast sample preparation followed by sensitive HPLC-MS/MS analysis. Graphical Abstract ?.

SUBMITTER: Osteresch B

PROVIDER: S-EPMC5395583 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-mycotoxin analysis using dried blood spots and dried serum spots.

Osteresch Bernd B Viegas Susana S Cramer Benedikt B Humpf Hans-Ulrich HU

Analytical and bioanalytical chemistry 20170315 13

In this study, a rapid multi-mycotoxin approach was developed for biomonitoring and quantification of 27 important mycotoxins and mycotoxin metabolites in human blood samples. HPLC-MS/MS detection was used for the analysis of dried serum spots (DSS) and dried blood spots (DBS). Detection of aflatoxins (AFB1, AFB2, AFG1, AFG2, AFM1), trichothecenes (deoxynivalenol, DON; DON-3-glucoronic acid, DON-3-GlcA; T-2; HT-2; and HT-2-4-GlcA), fumonisin ...[more]

PMID: 28299415

Similar Datasets

Project description:BackgroundEpidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.MethodsFilter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS.ResultsLogistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen.ConclusionThese results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

Dataset Information

Multi-mycotoxin analysis using dried blood spots and dried serum spots.

Publications

Multi-mycotoxin analysis using dried blood spots and dried serum spots.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets