Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc.: Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics

Silvia Schöntaler; Christa Nöhammer; Monika Olischar; Angelika Berger; Gregor Kasprian; Georg Langs; Klemens Vierlinger; Nicholas  Nicoletti; Rakesh  Lavu; Wei  Liu; Sreenivas  Karnati; Subhash  Puthuraya; Helen L  Turner; James P  Boardman; Nicola J  Robertson; Ana Laguna  Pradas; Bernhard  Schwaberger; Gerhard Pichler; Mohamed  El-Dib; Seth  Goldstein; John  Sunwoo; Sarah Schlatterer

doi:10.3233/npm-249004

Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics

, Silvia Schöntaler, Christa Nöhammer, Monika Olischar, Angelika Berger, Gregor Kasprian, Georg Langs, Klemens Vierlinger, Nicholas Nicoletti, Rakesh Lavu, Wei Liu, Sreenivas Karnati, Subhash Puthuraya, Helen L Turner, James P Boardman, Nicola J Robertson, Ana Laguna Pradas, Bernhard Schwaberger, Gerhard Pichler, Mohamed El-DibSeth Goldstein, John Sunwoo, Sarah Schlatterer

Molecular Diagnostics

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

Abstract

Preterm neonates with intraventricular hemorrhage (IVH) are at risk for posthemorrhagic ventricular dilatation (PHVD). In recent years targeted proteomics has developed into a powerful protein quantiﬁ cation tool in biomedical research, systems biology, and clinical applications. This study aims to inform therapeutic decision-making and parental counseling using proteomics in this high-risk group.

Originalsprache	Englisch
Aufsatznummer	1
Seiten (von - bis)	S393-S394
Seitenumfang	2
Fachzeitschrift	Journal of Neonatal-Perinatal Medicine
Issue	17
DOIs	https://doi.org/10.3233/npm-249004
Publikationsstatus	Veröffentlicht - 5 Aug. 2024

Research Field

Molecular Diagnostics

Schlagwörter

Machine learning
pediatrics
Biomarker discovery

Web of Science subject categories (JCR Impact Factors)

Medicine, Research & Experimental

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

10.3233/npm-249004

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, Schöntaler, S., Nöhammer, C., Olischar, M., Berger, A., Kasprian, G., Langs, G., Vierlinger, K., Nicoletti, N., Lavu, R., Liu, W., Karnati, S., Puthuraya, S., Turner, H. L., Boardman, J. P., Robertson, N. J., Pradas, A. L., Schwaberger, B., Pichler, G., ... Schlatterer, S. (2024). Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics. Journal of Neonatal-Perinatal Medicine, (17), S393-S394. Artikel 1. https://doi.org/10.3233/npm-249004

Schöntaler, Silvia ; Nöhammer, Christa et al. / Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics. in: Journal of Neonatal-Perinatal Medicine. 2024 ; Nr. 17. S. S393-S394.

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title = "Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc.: Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics",

abstract = "Preterm neonates with intraventricular hemorrhage (IVH) are at risk for posthemorrhagic ventricular dilatation (PHVD). In recent years targeted proteomics has developed into a powerful protein quantiﬁ cation tool in biomedical research, systems biology, and clinical applications. This study aims to inform therapeutic decision-making and parental counseling using proteomics in this high-risk group.",

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doi = "10.3233/npm-249004",

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Schöntaler, S, Nöhammer, C, Olischar, M, Berger, A, Kasprian, G, Langs, G, Vierlinger, K, Nicoletti, N, Lavu, R, Liu, W, Karnati, S, Puthuraya, S, Turner, HL, Boardman, JP, Robertson, NJ, Pradas, AL, Schwaberger, B, Pichler, G, El-Dib, M, Goldstein, S & Sunwoo, J & Schlatterer, S 2024, 'Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics', Journal of Neonatal-Perinatal Medicine, Nr. 17, 1, S. S393-S394. https://doi.org/10.3233/npm-249004

Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics. /; Schöntaler, Silvia; Nöhammer, Christa et al.
in: Journal of Neonatal-Perinatal Medicine, Nr. 17, 1, 05.08.2024, S. S393-S394.

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

TY - JOUR

T1 - Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc.

T2 - Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics

AU - Acun, Ceyda

AU - Aly, Hany

AU - Bauerstätter, Priska

AU - Goeral, Katharina

AU - Schöntaler, Silvia

AU - Nöhammer, Christa

AU - Olischar, Monika

AU - Berger, Angelika

AU - Kasprian, Gregor

AU - Langs, Georg

AU - Vierlinger, Klemens

AU - Nicoletti, Nicholas

AU - Lavu, Rakesh

AU - Liu, Wei

AU - Karnati, Sreenivas

AU - Puthuraya, Subhash

AU - Turner, Helen L

AU - Boardman, James P

AU - Robertson, Nicola J

AU - Pradas, Ana Laguna

AU - Schwaberger, Bernhard

AU - Pichler, Gerhard

AU - El-Dib, Mohamed

AU - Goldstein, Seth

AU - Sunwoo, John

AU - Schlatterer, Sarah

PY - 2024/8/5

Y1 - 2024/8/5

N2 - Preterm neonates with intraventricular hemorrhage (IVH) are at risk for posthemorrhagic ventricular dilatation (PHVD). In recent years targeted proteomics has developed into a powerful protein quantiﬁ cation tool in biomedical research, systems biology, and clinical applications. This study aims to inform therapeutic decision-making and parental counseling using proteomics in this high-risk group.

AB - Preterm neonates with intraventricular hemorrhage (IVH) are at risk for posthemorrhagic ventricular dilatation (PHVD). In recent years targeted proteomics has developed into a powerful protein quantiﬁ cation tool in biomedical research, systems biology, and clinical applications. This study aims to inform therapeutic decision-making and parental counseling using proteomics in this high-risk group.

KW - Machine learning

KW - pediatrics

KW - Biomarker discovery

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UR - https://www.mendeley.com/catalogue/8794d337-774c-36fd-bd1a-e5033f07f28a/

U2 - 10.3233/npm-249004

DO - 10.3233/npm-249004

M3 - Article

SN - 1934-5798

SP - S393-S394

JO - Journal of Neonatal-Perinatal Medicine

JF - Journal of Neonatal-Perinatal Medicine

IS - 17

M1 - 1

ER -

Schöntaler S, Nöhammer C, Olischar M, Berger A, Kasprian G et al. Proceedings of the 15th International Newborn Brain Conference: Other forms of brain monitoring, such as NIRS, fMRI, biochemical, etc. Machine learning predicts outcomes in preterm neonates with intraventricular hemorrhage using targeted proteomics. Journal of Neonatal-Perinatal Medicine. 2024 Aug 5;(17):S393-S394. 1. doi: 10.3233/npm-249004