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A Matrix Effect Correction Method for Portable X-ray Fluorescence Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020568] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Portable X-ray fluorescence spectrometry (pXRF) is an analytical technique that can be used for rapid and non-destructive analysis in the field. However, the testing accuracy and precision for trace elements are significantly affected by the matrix effect, which comes mainly from major elements that constitute most of the matrix of a sample. To solve this problem, many methods based on linear regression models have been proposed, but when extreme values or outliers occur, the application of these methods will be greatly affected. In this study, 16 certified reference materials were collected for pXRF analysis, and the major elements most closely related to the elements to be measured were employed as correction indicators to calibrate the analysis results through the application of multiple linear regression analysis. Some statistical parameters were calculated to evaluate the correction results. Compared with the calibration data obtained from simple linear regression analysis without taking major elements into account, those corrected by the new method were of higher quality, especially for elements of Co, Zn, Mo, Ta, Tl, Pb, Cd and Sn. The results show that the new method can effectively suppress the influence of the matrix effect.
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Yang HY, Lee JKW. The Impact of Temperature on the Risk of COVID-19: A Multinational Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18084052. [PMID: 33921381 PMCID: PMC8068915 DOI: 10.3390/ijerph18084052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 11/16/2022]
Abstract
The current understanding of ambient temperature and its link to the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. The objective of this study was to explore the environmental and climatic risk factors for SARS-CoV-2. For this study, we analyzed the data at the beginning of the outbreak (from 20 January to 31 March 2020) to avoid the influence of preventive or control measures. We obtained the number of cases and deaths due to SARS-CoV-2, international tourism, population age, universal health coverage, regional factors, the SARS-CoV-2 testing rate, and population density of a country. A total of 154 countries were included in this study. There were high incidence rates and mortality risks in the countries that had an average ambient temperature between 0 and 10 °C. The adjusted incidence rate for temperatures between 0 and 10 °C was 2.91 (95% CI 2.87–2.95). We randomly divided the data into a training set (80% of data) for model derivation and a test set (20% of data) for validation. Using a random forest statistical model, the model had high accuracy for predicting the high epidemic status of a country (ROC = 95.5%, 95% CI 87.9–100.0%) in the test set. Population age, temperature, and international tourism were the most important factors affecting the risk of SARS-CoV-2 in a country. An understanding the determinants of the SARS-CoV-2 outbreak can help to design better strategies for disease control. This study highlights the need to consider thermal effect in the prevention of emerging infectious diseases.
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Affiliation(s)
- Hsiao-Yu Yang
- Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei 10055, Taiwan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei 10055, Taiwan
- Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei 100225, Taiwan
- Correspondence: ; Tel.: +886-2-3366-8102
| | - Jason Kai Wei Lee
- Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore S117597, Singapore;
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore S117593, Singapore
- Global Asia Institute, National University of Singapore, Singapore S119076, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore S117456, Singapore
- Institute for Digital Medicine, National University of Singapore, Singapore S117456, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore 117609, Singapore
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Richardson AK, Chadha M, Rapp-Wright H, Mills GA, Fones GR, Gravell A, Stürzenbaum S, Cowan DA, Neep DJ, Barron LP. Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:595-606. [PMID: 33427827 DOI: 10.1039/d0ay02013c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balanced (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5 min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.
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Affiliation(s)
- Alexandra K Richardson
- Dept. Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, 150 Stamford Street, London, SE1 9NH, UK
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Hernández-Fernández J. Quantification of oxygenates, sulphides, thiols and permanent gases in propylene. A multiple linear regression model to predict the loss of efficiency in polypropylene production on an industrial scale. J Chromatogr A 2020; 1628:461478. [DOI: 10.1016/j.chroma.2020.461478] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 12/31/2022]
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New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence. Talanta 2020; 219:121238. [PMID: 32887129 DOI: 10.1016/j.talanta.2020.121238] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 01/06/2023]
Abstract
This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R2CV = 0.939, R2P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R2CV = 0.862, R2P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.
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Gamela RR, Costa VC, Sperança MA, Pereira-Filho ER. Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples. Food Res Int 2020; 132:109037. [DOI: 10.1016/j.foodres.2020.109037] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/23/2020] [Accepted: 01/25/2020] [Indexed: 12/23/2022]
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Karadžić Banjac MŽ, Kovačević SZ, Jevrić LR, Podunavac-Kuzmanović SO, Mandić AI. On the characterization of novel biologically active steroids: Selection of lipophilicity models of newly synthesized steroidal derivatives by classical and non-parametric ranking approaches. Comput Biol Chem 2019; 80:23-30. [DOI: 10.1016/j.compbiolchem.2019.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 03/09/2019] [Indexed: 10/27/2022]
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Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS One 2019; 14:e0214808. [PMID: 30946771 PMCID: PMC6448873 DOI: 10.1371/journal.pone.0214808] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 03/20/2019] [Indexed: 12/14/2022] Open
Abstract
Workers processing nephrite, antigorite, or talc may be exposed to paragenetic asbestos minerals. An effective screening method for pneumoconiosis in workers exposed to asbestos-contaminated minerals is still lacking. The objective of this study was to assess the diagnostic accuracy of serum and urinary biomarkers for pneumoconiosis in workers exposed to asbestos-contaminated minerals. We conducted a case-control study in a cohort of stone craft workers in Hualien, where asbestos, nephrite, antigorite, and talc are produced. A total of 140 subjects were screened between March 2013 and July 2014. All subjects received a questionnaire survey and a health examination that included a physical examination; chest X-ray; and tests for standard pulmonary function, fractional exhaled nitric oxide, serum soluble mesothelin-related peptide (SMRP), fibulin-3, carcinoembryonic antigen (CEA), and urinary 8-Oxo-2'-deoxyguanosine (8-OHdG)/creatinine. After excluding subjects with uraemia and chronic obstructive pulmonary disease (COPD), we included 48 subjects with pneumoconiosis and 90 control subjects without pneumoconiosis for analysis. In terms of occupational history, 43/48 (90%) case subjects and 68% (61/90) of the control subjects had processed asbestos-contaminated minerals, including nephrite, antigorite, and talc. The case group had decreased pulmonary function in forced vital capacity (FVC), forced expiratory volume in one second, and forced expiratory flow between 25% and 75% of the FVC. The levels of SMRP, fibulin-3, urinary 8-OHdG/creatinine, and CEA were higher in the case group than in the control group. Subjects exposed to nephrite had significantly higher SMRP levels (0.84 ± 0.52 nM) than subjects exposed to other types of minerals (0.60 ± 0.30 nM). A dose-response relationship was observed between the SMRP level and the severity of pneumoconiosis. Machine learning algorithms, including variables of sex, age, SMRP, fibulin-3, CEA, and 8-OHdG/creatinine, can predict pneumoconiosis with high accuracy. The areas under the receiver operating characteristic curves ranged from 0.7 to 1.0. We suggest that SMRP and fibulin-3 could be used as biomarkers of pneumoconiosis in workers exposed to asbestos-contaminated minerals.
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He G, Wu Y, Lu J. Doping control analysis of 13 steroids and structural-like analytes in human urine using Quadrupole-Orbitrap LC-MS/MS with parallel reaction monitoring (PRM) mode. Steroids 2018; 131:1-6. [PMID: 29274404 DOI: 10.1016/j.steroids.2017.12.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 12/13/2017] [Accepted: 12/18/2017] [Indexed: 01/08/2023]
Abstract
Anabolic androgenic steroids (AASs) and structural-like substances are commonly prohibited substances found in doping control studies that can be difficult to accurately detect. In the present study, 11 AASs and 2 structural-like substances that are commonly detected were examined. Currently, such analytes are detected using low resolution GC-MS/MS or LC-MS/MS, with detection not always possible. Herein, the high resolution Quadrupole-Orbitrap liquid chromatography-tandem mass spectrometry LC-MS/MS system Q Exactive was utilized to increase the specificity. This approach was then combined for the first time with parallel reaction monitoring (PRM) during the screening procedure. The results confirmed high specificity, with the LODs of all 13 analytes being at least 25-fold lower than corresponding MRPLs as defined by WADA. Furthermore, the extraction recoveries were above 70% and the intra- and inter-day precisions were lower than 15%. This approach was successfully applied to analyze over 10,000 samples with no false-positive or false-negative results, thus suggesting that Quadrupole-Orbitrap LC-MS/MS when combined with PRM is an effective method for doping control analysis.
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Affiliation(s)
- Genye He
- National Anti-doping Laboratory, China Anti-Doping Agency, 1st Anding Road, ChaoYang District, Beijing 100029, PR China
| | - Yun Wu
- National Anti-doping Laboratory, China Anti-Doping Agency, 1st Anding Road, ChaoYang District, Beijing 100029, PR China
| | - Jianghai Lu
- National Anti-doping Laboratory, China Anti-Doping Agency, 1st Anding Road, ChaoYang District, Beijing 100029, PR China.
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Multi-perspective evaluation of phytonutrients – Case study on tomato landraces for fresh consumption. J Funct Foods 2017. [DOI: 10.1016/j.jff.2017.03.052] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Park SH, Haddad PR, Talebi M, Tyteca E, Amos RI, Szucs R, Dolan JW, Pohl CA. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model. J Chromatogr A 2017; 1486:68-75. [DOI: 10.1016/j.chroma.2016.12.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 12/14/2016] [Accepted: 12/16/2016] [Indexed: 10/20/2022]
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12
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Comprehensive QSRR modeling as a starting point in characterization and further development of anticancer drugs based on 17α-picolyl and 17(E)-picolinylidene androstane structures. Eur J Pharm Sci 2016; 93:1-10. [DOI: 10.1016/j.ejps.2016.07.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 07/02/2016] [Accepted: 07/10/2016] [Indexed: 01/06/2023]
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Current status and recent advantages in derivatization procedures in human doping control. Bioanalysis 2015; 7:2537-56. [DOI: 10.4155/bio.15.172] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Derivatization is one of the most important steps during sample preparation in doping control analysis. Its main purpose is the enhancement of chromatographic separation and mass spectrometric detection of analytes in the full range of laboratory doping control activities. Its application is shown to broaden the detectable range of compounds, even in LC–MS analysis, where derivatization is not a prerequisite. The impact of derivatization initiates from the stage of the metabolic studies of doping agents up to the discovery of doping markers, by inclusion of the screening and confirmation procedures of prohibited substances in athlete's urine samples. Derivatization renders an unlimited number of opportunities to advanced analyte detection.
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Munro K, Miller TH, Martins CP, Edge AM, Cowan DA, Barron LP. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data. J Chromatogr A 2015; 1396:34-44. [DOI: 10.1016/j.chroma.2015.03.063] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/27/2015] [Accepted: 03/23/2015] [Indexed: 02/07/2023]
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15
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Mizera M, Talaczyńska A, Zalewski P, Skibiński R, Cielecka-Piontek J. Prediction of HPLC retention times of tebipenem pivoxyl and its degradation products in solid state by applying adaptive artificial neural network with recursive features elimination. Talanta 2015; 137:174-81. [DOI: 10.1016/j.talanta.2015.01.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 01/22/2015] [Accepted: 01/23/2015] [Indexed: 02/07/2023]
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Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method. Eur J Clin Pharmacol 2013; 70:265-73. [PMID: 24297344 DOI: 10.1007/s00228-013-1617-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 11/17/2013] [Indexed: 02/05/2023]
Abstract
BACKGROUND The unpredictability of acenocoumarol dose needed to achieve target blood thinning level remains a challenge. We aimed to apply and compare a pharmacogenetic least-squares model (LSM) and artificial neural network (ANN) models for predictions of acenocoumarol dosing. METHODS LSM and ANN models were used to analyze previously collected data on 174 participants (mean age: 67.45 SD 13.49 years) on acenocoumarol maintenance therapy. The models were based on demographics, lifestyle habits, concomitant diseases, medication intake, target INR, and genotyping results for CYP2C9 and VKORC1. LSM versus ANN performance comparisons were done by two methods: by randomly splitting the data as 50 % derivation and 50 % validation cohort followed by a bootstrap of 200 iterations, and by a 10-fold leave-one-out cross-validation technique. RESULTS The ANN-based pharmacogenetic model provided higher accuracy and larger R value than all other LSM-based models. The accuracy percentage improvement ranged between 5 % and 24 % for the derivation cohort and between 12 % and 25 % for the validation cohort. The increase in R value ranged between 6 % and 31 % for the derivation cohort and between 2 % and 31 % for the validation cohort. ANN increased the percentage of accurately dosed subjects (mean absolute error ≤1 mg/week) by 14.1 %, reduced the percentage of mis-dosed subjects (mean absolute error 2-3 mg/week) by 7.04 %, and reduced the percentage of grossly mis-dosed subjects (mean absolute error ≥4 mg/week) by 24 %. CONCLUSIONS ANN-based pharmacogenetic guidance of acenocoumarol dosing reduces the error in dosing to achieve target INR. These results need to be ascertained in a prospective study.
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Chemometric estimation of the RP TLC retention behaviour of some estrane derivatives by using multivariate regression methods. OPEN CHEM 2013. [DOI: 10.2478/s11532-013-0328-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
AbstractQuantitative structure-retention relationship (QSRR) was developed for a series of estrane derivatives, on the basis of their retention data, obtained in reversed-phase thin-layer chromatography (RP TLC), and in silico molecular descriptors. Physicochemical and topological descriptors, as well as molecular bulkiness descriptors, were calculated from the optimized molecular structures. Full geometry optimization was achieved by using Austin Model 1 (AM1) semi-empirical molecular orbital method. In the present study, QSRR analysis was based on principal component analysis (PCA), multiple linear regression (MLR) and partial least squares (PLS) method. PCA was applied in order to reveal similarities or dissimilarities between analytes, and MLR and PLS regression methods were carried out in order to identify the most important in silico molecular descriptors and quantify their influence on the retention behaviour of studied compounds. Physically meaningful and statistically significant structure-retention relationships were established.
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Current status and bioanalytical challenges in the detection of unknown anabolic androgenic steroids in doping control analysis. Bioanalysis 2013; 5:2661-77. [DOI: 10.4155/bio.13.242] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Androgenic anabolic steroids (AAS) are prohibited in sports due to their anabolic effects. Doping control laboratories usually face the screening of AAS misuse by target methods based on MS detection. Although these methods allow for the sensitive and specific detection of targeted compounds and metabolites, the rest remain undetectable. This fact opens a door for cheaters, since different AAS can be synthesized in order to evade doping control tests. This situation was evidenced in 2003 with the discovery of the designer steroid tetrahydrogestrinone. One decade after this discovery, the detection of unknown AAS still remains one of the main analytical challenges in the doping control field. In this manuscript, the current situation in the detection of unknown AAS is reviewed. Although important steps have been made in order to minimize this analytical problem and different analytical strategies have been proposed, there are still some drawbacks related to each approach.
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