Jan Moritz Seliger

ORCID: 0000-0003-1337-2850
Publications
Citations
Views
---
Saved
---
About
Contact & Profiles
Research Areas
  • Privacy-Preserving Technologies in Data
  • Hops Chemistry and Applications
  • Medical Imaging Techniques and Applications
  • PI3K/AKT/mTOR signaling in cancer
  • Artificial Intelligence in Healthcare and Education
  • AI in cancer detection
  • Advanced MRI Techniques and Applications
  • Autopsy Techniques and Outcomes
  • Chemotherapy-induced cardiotoxicity and mitigation
  • Lipid metabolism and biosynthesis
  • Aldose Reductase and Taurine
  • Data Quality and Management
  • Cardiovascular Function and Risk Factors
  • Advanced X-ray and CT Imaging
  • Radiomics and Machine Learning in Medical Imaging
  • Bioactive Compounds and Antitumor Agents
  • Synthesis and Reactions of Organic Compounds
  • Cardiac Imaging and Diagnostics
  • Chromatography in Natural Products
  • Biomedical Text Mining and Ontologies
  • Topic Modeling
  • Natural Products and Biological Research
  • Chemotherapy-induced organ toxicity mitigation

Universität Hamburg
2024-2025

German Centre for Cardiovascular Research
2025

University Medical Center Hamburg-Eppendorf
2024-2025

Abstract Objectives Assessment of myocardial strain by feature tracking magnetic resonance imaging (FT-MRI) in human fetuses with and without congenital heart disease (CHD) using cardiac Doppler ultrasound (DUS) gating. Methods A total 43 (gestational age 28–41 weeks) underwent dynamic MRI at 3 T. Cine balanced steady-state free-precession was performed fetal DUS FT-MRI analyzed dedicated post-processing software. Endo- epicardial contours were manually delineated from 4-chamber views,...

10.1007/s00330-023-10551-0 article EN cc-by European Radiology 2024-01-10

Xanthohumol (XN), a prenylated chalcone unique to hops (Humulus lupulus) and two derived prenylflavanones, isoxanthohumol (IX) 8-prenylnaringenin (8-PN) gained increasing attention as potential anti-diabetic cancer preventive compounds. Two enzymes of the aldo-keto reductase (AKR) superfamily are notable pharmacological targets in therapy (AKR1B10) treatment diabetic complications (AKR1B1). Our results show that XN, IX 8-PN potent uncompetitive, tight-binding inhibitors human aldose AKR1B1...

10.1080/14756366.2018.1437728 article EN cc-by Journal of Enzyme Inhibition and Medicinal Chemistry 2018-01-01

Abstract Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This particularly evident the emerging multi-modal learning paradigms, where dataset harmonization including a uniform representation filtering options are of paramount importance. Methods DICOM-structured reports enable standardized linkage arbitrary information beyond imaging...

10.1007/s11548-025-03327-y article EN cc-by International Journal of Computer Assisted Radiology and Surgery 2025-02-03

Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert annotations of labels interest, leaving large portions unlabeled unused. In this study, we conduct the largest federated cardiac CT imaging analysis to date, focusing on datasets ($n=8,124$) Transcatheter Aortic Valve Implantation (TAVI) patients...

10.48550/arxiv.2407.07557 preprint EN arXiv (Cornell University) 2024-07-10

Hop-derived compounds have been subjected to numerous biomedical studies investigating their impact on a wide range of pathologies. Isomerised bitter acids (isoadhumulone, isocohumulone and isohumulone) from hops, used in the brewing process beer, are known inhibit members aldo-keto-reductase superfamily. Aldo-keto-reductase 1B10 (AKR1B10) is upregulated various types cancer has reported promote carcinogenesis. Inhibition AKR1B10 appears be an attractive means specifically treat...

10.3390/molecules23113041 article EN cc-by Molecules 2018-11-21

Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This particularly evident the emerging multi-modal learning paradigms, where dataset harmonization including a uniform representation filtering options are of paramount importance. Methods: DICOM structured reports enable standardized linkage arbitrary information beyond imaging domain can...

10.48550/arxiv.2407.09064 preprint EN arXiv (Cornell University) 2024-07-12
Coming Soon ...