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Article ; Online: Isotopic Distribution Calibration for Mass Spectrometry.

Maus, Anthony D / Kemp, Jennifer V / Hoffmann, Todd J / Ramsay, Steven L / Grebe, Stefan K G

2021 Volume 93, Issue 37, Page(s) 12532–12540

Abstract: Mass spectrometry (MS) is widely used in science and industry. It allows accurate, specific, sensitive, and reproducible detection and quantification of a huge range of analytes. Across MS applications, quantification by MS has grown most dramatically, ... ...

Abstract	Mass spectrometry (MS) is widely used in science and industry. It allows accurate, specific, sensitive, and reproducible detection and quantification of a huge range of analytes. Across MS applications, quantification by MS has grown most dramatically, with >50 million experiments/year in the USA alone. However, quantification performance varies between instruments, compounds, different samples, and within- and across runs, necessitating normalization with analyte-similar internal standards (IS) and use of IS-corrected multipoint external calibration curves for each analyte, a complicated and resource-intensive approach, which is particularly ill-suited for multi-analyte measurements. We have developed an internal calibration method that utilizes the natural isotope distribution of an IS for a given analyte to provide internal multipoint calibration. Multiple isotope distribution calibrators for different targets in the same sample facilitate multiplex quantification, while the emerging random-access automated MS platforms should also greatly benefit from this approach. Finally, isotope distribution calibration allows mathematical correction for suboptimal experimental conditions. This might also enable quantification of hitherto difficult, or impossible to quantify, targets, if the distribution is adjusted
MeSH term(s)	Calibration ; Isotopes ; Reference Standards ; Tandem Mass Spectrometry
Chemical Substances	Isotopes
Language	English
Publishing date	2021-09-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.1c01672
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Article ; Online: Pitfalls in Diagnosing Hypoglycemia Due to Exogenous Insulin: Validation and Utility of an Insulin Analog Assay.

Egan, Aoife M / Galior, Kornelia D / Maus, Anthony D / Fatica, Erica / Simha, Vinaya / Shah, Pankaj / Singh, Ravinder J / Vella, Adrian

Mayo Clinic proceedings

2022 Volume 97, Issue 11, Page(s) 1994–2004

Abstract: Objective: To overcome the limitations of commercially available insulin immunoassays which have variable detection of analog insulin and can lead to clinically discordant results and misdiagnosis in the workup of factitious hypoglycemia.: Patients ... ...

Abstract	Objective: To overcome the limitations of commercially available insulin immunoassays which have variable detection of analog insulin and can lead to clinically discordant results and misdiagnosis in the workup of factitious hypoglycemia. Patients and methods: We performed analytical validation of a liquid chromatography high resolution accurate mass (LC-HRAM) immunoassay to detect insulin analogs. We completed clinical assessment using a large cohort of human serum samples from 78 unique individuals, and subsequently used the assay in the evaluation of eight individuals with high diagnostic suspicion for factitious hypoglycemia. Results: The performance characteristics show that the LC-HRAM immunoassay can be applied to detect five commonly used synthetic insulin analogs (lispro, glulisine, aspart, glargine metabolite, and detemir) in human serum. Our clinical cases show that this assay could be used in the diagnosis of factitious hypoglycemia by identifying the analog insulin(s) in question. Conclusion: The LC-HRAM immunoassay reported here overcomes a gap in our diagnostic pathway for hypoglycemia. The results obtained from our studies suggest that this method is appropriate for use in clinical laboratories when factitious hypoglycemia is considered as a differential diagnosis.
MeSH term(s)	Humans ; Insulin/adverse effects ; Insulin/analysis ; Hypoglycemia/chemically induced ; Hypoglycemia/diagnosis ; Immunoassay/methods ; Hypoglycemic Agents/adverse effects
Chemical Substances	Insulin ; Hypoglycemic Agents
Language	English
Publishing date	2022-10-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	124027-4
ISSN	1942-5546 ; 0025-6196
ISSN (online)	1942-5546
ISSN	0025-6196
DOI	10.1016/j.mayocp.2022.07.021
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Article: Isotopic Distribution Calibration for Mass Spectrometry

Maus, Anthony D. / Kemp, Jennifer V. / Hoffmann, Todd J. / Ramsay, Steven L. / Grebe, Stefan K. G.

Analytical chemistry. 2021 Sept. 07, v. 93, no. 37

2021

Abstract	Mass spectrometry (MS) is widely used in science and industry. It allows accurate, specific, sensitive, and reproducible detection and quantification of a huge range of analytes. Across MS applications, quantification by MS has grown most dramatically, with >50 million experiments/year in the USA alone. However, quantification performance varies between instruments, compounds, different samples, and within- and across runs, necessitating normalization with analyte-similar internal standards (IS) and use of IS-corrected multipoint external calibration curves for each analyte, a complicated and resource-intensive approach, which is particularly ill-suited for multi-analyte measurements. We have developed an internal calibration method that utilizes the natural isotope distribution of an IS for a given analyte to provide internal multipoint calibration. Multiple isotope distribution calibrators for different targets in the same sample facilitate multiplex quantification, while the emerging random-access automated MS platforms should also greatly benefit from this approach. Finally, isotope distribution calibration allows mathematical correction for suboptimal experimental conditions. This might also enable quantification of hitherto difficult, or impossible to quantify, targets, if the distribution is adjusted in silico to mimic the analyte. The approach works well for high resolution, accurate mass MS for analytes with at least a modest-sized isotopic envelope. As shown herein, the approach can also be applied to lower molecular weight analytes, but the reduction in calibration points does reduce quantification performance.
Keywords	analytical chemistry ; calibration ; chemical species ; computer simulation ; industry ; isotopes ; mass spectrometry ; molecular weight
Language	English
Dates of publication	2021-0907
Size	p. 12532-12540.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.1c01672
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Article: Correction: A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

Mangalaparthi, Kiran K / Chavan, Sandip / Madugundu, Anil K / Renuse, Santosh / Vanderboom, Patrick M / Maus, Anthony D / Kemp, Jennifer / Kipp, Benjamin R / Grebe, Stefan K / Singh, Ravinder J / Pandey, Akhilesh

Clinical proteomics

2022 Volume 19, Issue 1, Page(s) 11

Language	English
Publishing date	2022-05-04
Publishing country	England
Document type	Published Erratum
ZDB-ID	2205154-5
ISSN	1542-6416
ISSN	1542-6416
DOI	10.1186/s12014-022-09355-z
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Article ; Online: Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Vanderboom, Patrick M / Renuse, Santosh / Maus, Anthony D / Madugundu, Anil K / Kemp, Jennifer V / Gurtner, Kari M / Singh, Ravinder J / Grebe, Stefan K / Pandey, Akhilesh / Dasari, Surendra

Journal of proteome research

2022 Volume 21, Issue 8, Page(s) 2045–2054

Abstract: Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved ... ...

Abstract	Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.
MeSH term(s)	COVID-19 ; COVID-19 Testing ; Humans ; Machine Learning ; Mass Spectrometry/methods ; SARS-CoV-2 ; Sensitivity and Specificity
Language	English
Publishing date	2022-07-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.2c00156
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Article: Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Vanderboom, Patrick M. / Renuse, Santosh / Maus, Anthony D. / Madugundu, Anil K. / Kemp, Jennifer V. / Gurtner, Kari M. / Singh, Ravinder J. / Grebe, Stefan K. / Pandey, Akhilesh / Dasari, Surendra

Journal of proteome research. 2022 July 18, v. 21, no. 8

2022

Abstract	Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.
Keywords	Severe acute respiratory syndrome coronavirus 2 ; biomarkers ; detection limit ; mass spectrometry ; models ; peptides ; proteome ; research
Language	English
Dates of publication	2022-0718
Size	p. 2045-2054.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.2c00156
Database	NAL-Catalogue (AGRICOLA)

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Article: A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

Clinical proteomics

2021 Volume 18, Issue 1, Page(s) 25

Abstract: SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral ... ...

Abstract	SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.
Language	English
Publishing date	2021-10-22
Publishing country	England
Document type	Letter
ZDB-ID	2205154-5
ISSN	1542-6416
ISSN	1542-6416
DOI	10.1186/s12014-021-09331-z
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Article ; Online: A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

Renuse, Santosh / Vanderboom, Patrick M / Maus, Anthony D / Kemp, Jennifer V / Gurtner, Kari M / Madugundu, Anil K / Chavan, Sandip / Peterson, Jane A / Madden, Benjamin J / Mangalaparthi, Kiran K / Mun, Dong-Gi / Singh, Smrita / Kipp, Benjamin R / Dasari, Surendra / Singh, Ravinder J / Grebe, Stefan K / Pandey, Akhilesh

EBioMedicine

2021 Volume 69, Page(s) 103465

Abstract: Background: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based ...

Abstract	Background: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time. Methods: Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data. Findings: The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method. Interpretation: Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures. Funding: This study was supported by DBT/Wellcome Trust India Alliance Margdarshi Fellowship grant IA/M/15/1/502023 awarded to AP and the generosity of Eric and Wendy Schmidt.
MeSH term(s)	Animals ; Antigens, Viral/chemistry ; Antigens, Viral/immunology ; Automation, Laboratory/methods ; Automation, Laboratory/standards ; COVID-19 Serological Testing/methods ; COVID-19 Serological Testing/standards ; Chlorocebus aethiops ; Coronavirus Nucleocapsid Proteins/chemistry ; Coronavirus Nucleocapsid Proteins/immunology ; Humans ; Immunoassay/methods ; Immunoassay/standards ; Machine Learning ; Mass Spectrometry/methods ; Mass Spectrometry/standards ; Phosphoproteins/chemistry ; Phosphoproteins/immunology ; Sensitivity and Specificity
Chemical Substances	Antigens, Viral ; Coronavirus Nucleocapsid Proteins ; Phosphoproteins ; nucleocapsid phosphoprotein, SARS-CoV-2
Language	English
Publishing date	2021-07-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2851331-9
ISSN	2352-3964
ISSN (online)	2352-3964
DOI	10.1016/j.ebiom.2021.103465
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Article ; Online: Development of mass spectrometry-based targeted assay for direct detection of novel SARS-CoV-2 coronavirus from clinical specimens

Renuse, Santosh / Vanderboom, Patrick M / Maus, Anthony D. / Kemp, Jennifer V. / Gurtner, Kari M. / Madugundu, Anil K. / Chavan, Sandip / Peterson, Jane A. / Madden, Benjamin J. / Mangalaparthi, Kiran K. / Mun, Dong-Gi / Singh, Smrita / Kipp, Benjamin R. / Dasari, Surendra / Singh, Ravinder J. / Grebe, Stefan K. / Pandey, Akhilesh

medRxiv

Abstract: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostics including RT-PCR-based assays, antigen ... ...

Abstract	The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostics including RT-PCR-based assays, antigen detection by lateral flow assays and antibody-based assays have been developed and deployed in a short time. However, many of these assays are lacking in sensitivity and/or specificity. Here, we describe an immunoaffinity purification followed by high resolution mass spectrometry-based targeted assay capable of detecting viral antigen in nasopharyngeal swab samples of SARS-CoV-2 infected individuals. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assays on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was created using fragment ion intensities in the PRM data. This resulted in 97.8% sensitivity and 100% specificity with RT-PCR-based molecular testing as the gold standard. Our results demonstrate that direct detection of infectious agents from clinical samples by mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories.
Keywords	covid19
Language	English
Publishing date	2020-08-06
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.08.05.20168948
Database	COVID19

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