Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 27 entries
Sorted by: Best Match Show Resources per page
Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring.

Analytical chemistry

Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M.
PMID: 34859676
Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance....

Cite
Ghorasaini M, Mohammed Y, Adamski J, et al. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021;93(49):16369-16378doi: 10.1021/acs.analchem.1c02826.
Ghorasaini, M., Mohammed, Y., Adamski, J., Bettcher, L., Bowden, J. A., Cabruja, M., Contrepois, K., Ellenberger, M., Gajera, B., Haid, M., Hornburg, D., Hunter, C., Jones, C. M., Klein, T., Mayboroda, O., Mirzaian, M., Moaddel, R., Ferrucci, L., Lovett, J., Nazir, K., Pearson, M., Ubhi, B. K., Raftery, D., Riols, F., Sayers, R., Sijbrands, E. J. G., Snyder, M. P., Su, B., Velagapudi, V., Williams, K. J., de Rijke, Y. B., & Giera, M. (2021). Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Analytical chemistry, 93(49), 16369-16378. https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini, Mohan, et al. "Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring." Analytical chemistry vol. 93,49 (2021): 16369-16378. doi: https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03. PMID: 34859676; PMCID: PMC8674878.
Copy Download .nbib Format: NLM
Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy.

Circulation

Ranjbarvaziri S, Kooiker KB, Ellenberger M, Fajardo G, Zhao M, Vander Roest AS, Woldeyes RA, Koyano TT, Fong R, Ma N, Tian L, Traber GM, Chan F, Perrino J, Reddy S, Chiu W, Wu JC, Woo JY, Ruppel KM, Spudich JA, Snyder MP, Contrepois K, Bernstein D.
PMID: 34672721
Circulation. 2021 Nov 23;144(21):1714-1731. doi: 10.1161/CIRCULATIONAHA.121.053575. Epub 2021 Oct 21.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant...

Cite
Ranjbarvaziri S, Kooiker KB, Ellenberger M, et al. Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy. Circulation. 2021;144(21):1714-1731doi: 10.1161/CIRCULATIONAHA.121.053575.
Ranjbarvaziri, S., Kooiker, K. B., Ellenberger, M., Fajardo, G., Zhao, M., Vander Roest, A. S., Woldeyes, R. A., Koyano, T. T., Fong, R., Ma, N., Tian, L., Traber, G. M., Chan, F., Perrino, J., Reddy, S., Chiu, W., Wu, J. C., Woo, J. Y., Ruppel, K. M., Spudich, J. A., Snyder, M. P., Contrepois, K., & Bernstein, D. (2021). Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy. Circulation, 144(21), 1714-1731. https://doi.org/10.1161/CIRCULATIONAHA.121.053575
Ranjbarvaziri, Sara, et al. "Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy." Circulation vol. 144,21 (2021): 1714-1731. doi: https://doi.org/10.1161/CIRCULATIONAHA.121.053575
Ranjbarvaziri S, Kooiker KB, Ellenberger M, Fajardo G, Zhao M, Vander Roest AS, Woldeyes RA, Koyano TT, Fong R, Ma N, Tian L, Traber GM, Chan F, Perrino J, Reddy S, Chiu W, Wu JC, Woo JY, Ruppel KM, Spudich JA, Snyder MP, Contrepois K, Bernstein D. Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy. Circulation. 2021 Nov 23;144(21):1714-1731. doi: 10.1161/CIRCULATIONAHA.121.053575. Epub 2021 Oct 21. PMID: 34672721; PMCID: PMC8608736.
Copy Download .nbib Format: NLM
Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease.

Metabolites

Chaby LE, Lasseter HC, Contrepois K, Salek RM, Turck CW, Thompson A, Vaughan T, Haas M, Jeromin A.
PMID: 34564425
Metabolites. 2021 Sep 08;11(9). doi: 10.3390/metabo11090609.

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate...

Cite
Chaby LE, Lasseter HC, Contrepois K, et al. Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease. Metabolites. 2021;11(9)doi: 10.3390/metabo11090609.
Chaby, L. E., Lasseter, H. C., Contrepois, K., Salek, R. M., Turck, C. W., Thompson, A., Vaughan, T., Haas, M., & Jeromin, A. (2021). Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease. Metabolites, 11(9), . https://doi.org/10.3390/metabo11090609
Chaby, Lauren E, et al. "Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease." Metabolites vol. 11,9 (2021). doi: https://doi.org/10.3390/metabo11090609
Chaby LE, Lasseter HC, Contrepois K, Salek RM, Turck CW, Thompson A, Vaughan T, Haas M, Jeromin A. Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease. Metabolites. 2021 Sep 08;11(9). doi: 10.3390/metabo11090609. PMID: 34564425; PMCID: PMC8466258.
Copy Download .nbib Format: NLM
Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring.

Analytical chemistry

Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M.
PMID: 34859676
Anal Chem. 2021 Dec 03; doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance....

Cite
Ghorasaini M, Mohammed Y, Adamski J, et al. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021;doi: 10.1021/acs.analchem.1c02826.
Ghorasaini, M., Mohammed, Y., Adamski, J., Bettcher, L., Bowden, J. A., Cabruja, M., Contrepois, K., Ellenberger, M., Gajera, B., Haid, M., Hornburg, D., Hunter, C., Jones, C. M., Klein, T., Mayboroda, O., Mirzaian, M., Moaddel, R., Ferrucci, L., Lovett, J., Nazir, K., Pearson, M., Ubhi, B. K., Raftery, D., Riols, F., Sayers, R., Sijbrands, E. J. G., Snyder, M. P., Su, B., Velagapudi, V., Williams, K. J., de Rijke, Y. B., & Giera, M. (2021). Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Analytical chemistry, . https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini, Mohan, et al. "Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring." Analytical chemistry vol. (2021). doi: https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021 Dec 03; doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03. PMID: 34859676.
Copy Download .nbib Format: NLM
metID: a R package for automatable compound annotation for LC-MS-based data.

Bioinformatics (Oxford, England)

Shen X, Wu S, Liang L, Chen S, Contrepois K, Zhu ZJ, Snyder M.
PMID: 34432001
Bioinformatics. 2021 Aug 25; doi: 10.1093/bioinformatics/btab583. Epub 2021 Aug 25.

SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used...

Cite
Shen X, Wu S, Liang L, et al. metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab583.
Shen, X., Wu, S., Liang, L., Chen, S., Contrepois, K., Zhu, Z. J., & Snyder, M. (2021). metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab583
Shen, Xiaotao, et al. "metID: a R package for automatable compound annotation for LC-MS-based data." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab583
Shen X, Wu S, Liang L, Chen S, Contrepois K, Zhu ZJ, Snyder M. metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics. 2021 Aug 25; doi: 10.1093/bioinformatics/btab583. Epub 2021 Aug 25. PMID: 34432001; PMCID: PMC8722759.
Copy Download .nbib Format: NLM
metID: a R package for automatable compound annotation for LC-MS-based data.

Bioinformatics (Oxford, England)

Shen X, Wu S, Liang L, Chen S, Contrepois K, Zhu ZJ, Snyder M.
PMID: 34432001
Bioinformatics. 2021 Aug 25; doi: 10.1093/bioinformatics/btab583. Epub 2021 Aug 25.

SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used...

Cite
Shen X, Wu S, Liang L, et al. metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab583.
Shen, X., Wu, S., Liang, L., Chen, S., Contrepois, K., Zhu, Z. J., & Snyder, M. (2021). metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab583
Shen, Xiaotao, et al. "metID: a R package for automatable compound annotation for LC-MS-based data." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab583
Shen X, Wu S, Liang L, Chen S, Contrepois K, Zhu ZJ, Snyder M. metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics. 2021 Aug 25; doi: 10.1093/bioinformatics/btab583. Epub 2021 Aug 25. PMID: 34432001.
Copy Download .nbib Format: NLM
Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation.

Nature aging

Lu RJ, Taylor S, Contrepois K, Kim M, Bravo JI, Ellenberger M, Sampathkumar NK, Benayoun BA.
PMID: 34514433
Nat Aging. 2021 Aug;1(8):715-733. doi: 10.1038/s43587-021-00086-8. Epub 2021 Jul 19.

Neutrophils are the most abundant human white blood cell and constitute a first line of defense in the innate immune response. Neutrophils are short-lived cells, and thus the impact of organismal aging on neutrophil biology, especially as a function...

Cite
Lu RJ, Taylor S, Contrepois K, et al. Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation. Nat Aging. 2021;1(8):715-733doi: 10.1038/s43587-021-00086-8.
Lu, R. J., Taylor, S., Contrepois, K., Kim, M., Bravo, J. I., Ellenberger, M., Sampathkumar, N. K., & Benayoun, B. A. (2021). Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation. Nature aging, 1(8), 715-733. https://doi.org/10.1038/s43587-021-00086-8
Lu, Ryan J, et al. "Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation." Nature aging vol. 1,8 (2021): 715-733. doi: https://doi.org/10.1038/s43587-021-00086-8
Lu RJ, Taylor S, Contrepois K, Kim M, Bravo JI, Ellenberger M, Sampathkumar NK, Benayoun BA. Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation. Nat Aging. 2021 Aug;1(8):715-733. doi: 10.1038/s43587-021-00086-8. Epub 2021 Jul 19. PMID: 34514433; PMCID: PMC8425468.
Copy Download .nbib Format: NLM
Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring.

Analytical chemistry

Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M.
PMID: 34859676
Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance....

Cite
Ghorasaini M, Mohammed Y, Adamski J, et al. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021;93(49):16369-16378doi: 10.1021/acs.analchem.1c02826.
Ghorasaini, M., Mohammed, Y., Adamski, J., Bettcher, L., Bowden, J. A., Cabruja, M., Contrepois, K., Ellenberger, M., Gajera, B., Haid, M., Hornburg, D., Hunter, C., Jones, C. M., Klein, T., Mayboroda, O., Mirzaian, M., Moaddel, R., Ferrucci, L., Lovett, J., Nazir, K., Pearson, M., Ubhi, B. K., Raftery, D., Riols, F., Sayers, R., Sijbrands, E. J. G., Snyder, M. P., Su, B., Velagapudi, V., Williams, K. J., de Rijke, Y. B., & Giera, M. (2021). Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Analytical chemistry, 93(49), 16369-16378. https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini, Mohan, et al. "Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring." Analytical chemistry vol. 93,49 (2021): 16369-16378. doi: https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03. PMID: 34859676; PMCID: PMC8674878.
Copy Download .nbib Format: NLM
Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring.

Analytical chemistry

Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M.
PMID: 34859676
Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance....

Cite
Ghorasaini M, Mohammed Y, Adamski J, et al. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021;93(49):16369-16378doi: 10.1021/acs.analchem.1c02826.
Ghorasaini, M., Mohammed, Y., Adamski, J., Bettcher, L., Bowden, J. A., Cabruja, M., Contrepois, K., Ellenberger, M., Gajera, B., Haid, M., Hornburg, D., Hunter, C., Jones, C. M., Klein, T., Mayboroda, O., Mirzaian, M., Moaddel, R., Ferrucci, L., Lovett, J., Nazir, K., Pearson, M., Ubhi, B. K., Raftery, D., Riols, F., Sayers, R., Sijbrands, E. J. G., Snyder, M. P., Su, B., Velagapudi, V., Williams, K. J., de Rijke, Y. B., & Giera, M. (2021). Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Analytical chemistry, 93(49), 16369-16378. https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini, Mohan, et al. "Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring." Analytical chemistry vol. 93,49 (2021): 16369-16378. doi: https://doi.org/10.1021/acs.analchem.1c02826
Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem. 2021 Dec 14;93(49):16369-16378. doi: 10.1021/acs.analchem.1c02826. Epub 2021 Dec 03. PMID: 34859676; PMCID: PMC8674878.
Copy Download .nbib Format: NLM
Impact of acute lymphoblastic leukemia induction therapy: findings from metabolomics on non-fasted plasma samples from a biorepository.

Metabolomics : Official journal of the Metabolomic Society

Saito T, Wei Y, Wen L, Srinivasan C, Wolthers BO, Tsai CY, Harris MH, Stevenson K, Byersdorfer C, Oparaji JA, Fernandez C, Mukherjee A, Abu-El-Haija M, Agnihotri S, Schmiegelow K, Showalter MR, Fogle PW, McCulloch S, Contrepois K, Silverman LB, Ding Y, Husain SZ.
PMID: 34175981
Metabolomics. 2021 Jun 27;17(7):64. doi: 10.1007/s11306-021-01814-2.

INTRODUCTION: Acute lymphoblastic leukemia (ALL) is among the most common cancers in children. With improvements in combination chemotherapy regimens, the overall survival has increased to over 90%. However, the current challenge is to mitigate adverse events resulting from the...

Cite
Saito T, Wei Y, Wen L, et al. Impact of acute lymphoblastic leukemia induction therapy: findings from metabolomics on non-fasted plasma samples from a biorepository. Metabolomics. 2021;17(7):64doi: 10.1007/s11306-021-01814-2.
Saito, T., Wei, Y., Wen, L., Srinivasan, C., Wolthers, B. O., Tsai, C. Y., Harris, M. H., Stevenson, K., Byersdorfer, C., Oparaji, J. A., Fernandez, C., Mukherjee, A., Abu-El-Haija, M., Agnihotri, S., Schmiegelow, K., Showalter, M. R., Fogle, P. W., McCulloch, S., Contrepois, K., Silverman, L. B., Ding, Y., & Husain, S. Z. (2021). Impact of acute lymphoblastic leukemia induction therapy: findings from metabolomics on non-fasted plasma samples from a biorepository. Metabolomics : Official journal of the Metabolomic Society, 17(7), 64. https://doi.org/10.1007/s11306-021-01814-2
Saito, Toshie, et al. "Impact of acute lymphoblastic leukemia induction therapy: findings from metabolomics on non-fasted plasma samples from a biorepository." Metabolomics : Official journal of the Metabolomic Society vol. 17,7 (2021): 64. doi: https://doi.org/10.1007/s11306-021-01814-2
Saito T, Wei Y, Wen L, Srinivasan C, Wolthers BO, Tsai CY, Harris MH, Stevenson K, Byersdorfer C, Oparaji JA, Fernandez C, Mukherjee A, Abu-El-Haija M, Agnihotri S, Schmiegelow K, Showalter MR, Fogle PW, McCulloch S, Contrepois K, Silverman LB, Ding Y, Husain SZ. Impact of acute lymphoblastic leukemia induction therapy: findings from metabolomics on non-fasted plasma samples from a biorepository. Metabolomics. 2021 Jun 27;17(7):64. doi: 10.1007/s11306-021-01814-2. PMID: 34175981.
Copy Download .nbib Format: NLM
The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension.

Chest

Haddad F, Contrepois K, Amsallem M, Denault AY, Bernardo RJ, Jha A, Taylor S, Ataam JA, Mercier O, Kouznetsova T, Noordegraaf AV, Zamanian RT, Sweatt AJ.
PMID: 34774527
Chest. 2021 Nov 10; doi: 10.1016/j.chest.2021.10.045. Epub 2021 Nov 10.

BACKGROUND: Prognostic in pulmonary arterial hypertension (PAH) is closely related to indices of right ventricular function. A better understanding of their relationship may provide important implications for risk stratification in PAH.RESEARCH QUESTIONS: Can clinical network graphs inform risk stratification...

Cite
Haddad F, Contrepois K, Amsallem M, et al. The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension. Chest. 2021;doi: 10.1016/j.chest.2021.10.045.
Haddad, F., Contrepois, K., Amsallem, M., Denault, A. Y., Bernardo, R. J., Jha, A., Taylor, S., Ataam, J. A., Mercier, O., Kouznetsova, T., Noordegraaf, A. V., Zamanian, R. T., & Sweatt, A. J. (2021). The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension. Chest, . https://doi.org/10.1016/j.chest.2021.10.045
Haddad, Francois, et al. "The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension." Chest vol. (2021). doi: https://doi.org/10.1016/j.chest.2021.10.045
Haddad F, Contrepois K, Amsallem M, Denault AY, Bernardo RJ, Jha A, Taylor S, Ataam JA, Mercier O, Kouznetsova T, Noordegraaf AV, Zamanian RT, Sweatt AJ. The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension. Chest. 2021 Nov 10; doi: 10.1016/j.chest.2021.10.045. Epub 2021 Nov 10. PMID: 34774527.
Copy Download .nbib Format: NLM
H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1.

Proteomes

Contrepois K, Mann C, Fenaille F.
PMID: 34205514
Proteomes. 2021 Jun 21;9(2). doi: 10.3390/proteomes9020030.

Cellular senescence is a state of terminal proliferative arrest that plays key roles in aging by preventing stem cell renewal and by inducing the expression of a series of inflammatory factors including many secreted proteins with paracrine effects. The...

Cite
Contrepois K, Mann C, Fenaille F. H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1. Proteomes. 2021;9(2)doi: 10.3390/proteomes9020030.
Contrepois, K., Mann, C., & Fenaille, F. (2021). H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1. Proteomes, 9(2), . https://doi.org/10.3390/proteomes9020030
Contrepois, Kévin, et al. "H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1." Proteomes vol. 9,2 (2021). doi: https://doi.org/10.3390/proteomes9020030
Contrepois K, Mann C, Fenaille F. H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1. Proteomes. 2021 Jun 21;9(2). doi: 10.3390/proteomes9020030. PMID: 34205514; PMCID: PMC8293446.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 27 entries