LIVIVO - Search results -

Search results

Result 1 - 10 of total 46

Search options

Article: Systems level analysis of sex-dependent gene expression changes in Parkinson's disease.

Tranchevent, Léon-Charles / Halder, Rashi / Glaab, Enrico

2023 Volume 9, Issue 1, Page(s) 8

Abstract: Parkinson's disease (PD) is a heterogeneous disorder, and among the factors which influence the symptom profile, biological sex has been reported to play a significant role. While males have a higher age-adjusted disease incidence and are more frequently ...

Abstract	Parkinson's disease (PD) is a heterogeneous disorder, and among the factors which influence the symptom profile, biological sex has been reported to play a significant role. While males have a higher age-adjusted disease incidence and are more frequently affected by muscle rigidity, females present more often with disabling tremors. The molecular mechanisms involved in these differences are still largely unknown, and an improved understanding of the relevant factors may open new avenues for pharmacological disease modification. To help address this challenge, we conducted a meta-analysis of disease-associated molecular sex differences in brain transcriptomics data from case/control studies. Both sex-specific (alteration in only one sex) and sex-dimorphic changes (changes in both sexes, but with opposite direction) were identified. Using further systems level pathway and network analyses, coordinated sex-related alterations were studied. These analyses revealed significant disease-associated sex differences in mitochondrial pathways and highlight specific regulatory factors whose activity changes can explain downstream network alterations, propagated through gene regulatory cascades. Single-cell expression data analyses confirmed the main pathway-level changes observed in bulk transcriptomics data. Overall, our analyses revealed significant sex disparities in PD-associated transcriptomic changes, resulting in coordinated modulations of molecular processes. Among the regulatory factors involved, NR4A2 has already been reported to harbor rare mutations in familial PD and its pharmacological activation confers neuroprotective effects in toxin-induced models of Parkinsonism. Our observations suggest that NR4A2 may warrant further research as a potential adjuvant therapeutic target to address a subset of pathological molecular features of PD that display sex-associated profiles.
Language	English
Publishing date	2023-01-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819218-7
ISSN	2373-8057
ISSN	2373-8057
DOI	10.1038/s41531-023-00446-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article ; Online: A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.

Tranchevent, Léon-Charles / Azuaje, Francisco / Rajapakse, Jagath C

BMC medical genomics

2019 Volume 12, Issue Suppl 8, Page(s) 178

Abstract: Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important ... ...

Abstract	Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods: We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results: We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions: Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.
MeSH term(s)	Computational Biology/methods ; Deep Learning ; Gene Expression Profiling ; Humans ; Neuroblastoma/diagnosis ; Neuroblastoma/genetics ; Prognosis
Language	English
Publishing date	2019-12-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1755-8794
ISSN (online)	1755-8794
DOI	10.1186/s12920-019-0628-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Computational tools for prioritizing candidate genes: boosting disease gene discovery.

Moreau, Yves / Tranchevent, Léon-Charles

Nature reviews. Genetics

2012 Volume 13, Issue 8, Page(s) 523–536

Abstract: At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of ... ...

Abstract	At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
MeSH term(s)	Animals ; Computational Biology/methods ; Databases, Genetic ; Genetic Association Studies/methods ; Genetic Association Studies/statistics & numerical data ; Genetic Predisposition to Disease ; Haploinsufficiency/genetics ; Humans ; Mice ; Models, Genetic
Language	English
Publishing date	2012-07-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2035157-4
ISSN	1471-0064 ; 1471-0056
ISSN (online)	1471-0064
ISSN	1471-0056
DOI	10.1038/nrg3253
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 5474: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 40: Show issues

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article: iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease.

Badanjak, Katja / Mulica, Patrycja / Smajic, Semra / Delcambre, Sylvie / Tranchevent, Leon-Charles / Diederich, Nico / Rauen, Thomas / Schwamborn, Jens C / Glaab, Enrico / Cowley, Sally A / Antony, Paul M A / Pereira, Sandro L / Venegas, Carmen / Grünewald, Anne

Frontiers in cell and developmental biology

2021 Volume 9, Page(s) 740758

Abstract: Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the ... ...

Abstract	Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain. With increasing age, microglia become dysfunctional and release pro-inflammatory factors into the extracellular space, which promote neuronal cell death. Accordingly, neuroinflammation has also been described as a feature of PD. So far, studies exploring inflammatory pathways in IPD patient samples have primarily focused on blood-derived immune cells or brain sections, but rarely investigated patient microglia
Language	English
Publishing date	2021-11-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2737824-X
ISSN	2296-634X
ISSN	2296-634X
DOI	10.3389/fcell.2021.740758
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.

Cancer gene therapy

2023 Volume 30, Issue 10, Page(s) 1330–1345

Abstract: Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting ... ...

Abstract	Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.
MeSH term(s)	Humans ; Multiomics ; Cell Line, Tumor ; Melanoma/drug therapy ; Melanoma/genetics ; Protein Kinase Inhibitors/pharmacology ; Protein Kinase Inhibitors/therapeutic use ; Insulins/therapeutic use ; Cellular Senescence/genetics ; Membrane Proteins/genetics ; GTP Phosphohydrolases/genetics ; GTP Phosphohydrolases/therapeutic use
Chemical Substances	Protein Kinase Inhibitors ; Insulins ; NRAS protein, human (EC 3.6.1.-) ; Membrane Proteins ; GTP Phosphohydrolases (EC 3.6.1.-)
Language	English
Publishing date	2023-07-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1212513-1
ISSN	1476-5500 ; 0929-1903
ISSN (online)	1476-5500
ISSN	0929-1903
DOI	10.1038/s41417-023-00640-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 4125: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

Article ; Online: Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

Tranchevent, Léon-Charles / Nazarov, Petr V / Kaoma, Tony / Schmartz, Georges P / Muller, Arnaud / Kim, Sang-Yoon / Rajapakse, Jagath C / Azuaje, Francisco

Biology direct

2018 Volume 13, Issue 1, Page(s) 12

Abstract: Background: One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high- ... ...

Abstract	Background: One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. Results: We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. Conclusion: We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. Reviewers: This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Gene Regulatory Networks/genetics ; Gene Regulatory Networks/physiology ; Genomics/methods ; Humans ; Neuroblastoma/genetics
Language	English
Publishing date	2018-06-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2221028-3
ISSN	1745-6150 ; 1745-6150
ISSN (online)	1745-6150
ISSN	1745-6150
DOI	10.1186/s13062-018-0214-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Beegle: from literature mining to disease-gene discovery.

ElShal, Sarah / Tranchevent, Léon-Charles / Sifrim, Alejandro / Ardeshirdavani, Amin / Davis, Jesse / Moreau, Yves

Nucleic acids research

2015 Volume 44, Issue 2, Page(s) e18

Abstract: Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature ... ...

Abstract	Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle, an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes. Beegle is publicly available at http://beegle.esat.kuleuven.be/.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Data Mining ; Genetic Association Studies/statistics & numerical data ; Humans ; Probability ; Search Engine ; Software
Language	English
Publishing date	2015-09-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkv905
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1067: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Gene prioritization and clustering by multi-view text mining

De Moor Bart / Tranchevent Leon-Charles / Yu Shi / Moreau Yves

BMC Bioinformatics, Vol 11, Iss 1, p

2010 Volume 28

Abstract: Abstract Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text ... ...

Abstract	Abstract Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2010-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Interplay between coding and exonic splicing regulatory sequences.

Fontrodona, Nicolas / Aubé, Fabien / Claude, Jean-Baptiste / Polvèche, Hélène / Lemaire, Sébastien / Tranchevent, Léon-Charles / Modolo, Laurent / Mortreux, Franck / Bourgeois, Cyril F / Auboeuf, Didier

Genome research

2019 Volume 29, Issue 5, Page(s) 711–722

Abstract: The inclusion of exons during the splicing process depends on the binding of splicing factors to short low-complexity regulatory sequences. The relationship between exonic splicing regulatory sequences and coding sequences is still poorly understood. We ... ...

Abstract	The inclusion of exons during the splicing process depends on the binding of splicing factors to short low-complexity regulatory sequences. The relationship between exonic splicing regulatory sequences and coding sequences is still poorly understood. We demonstrate that exons that are coregulated by any given splicing factor share a similar nucleotide composition bias and preferentially code for amino acids with similar physicochemical properties because of the nonrandomness of the genetic code. Indeed, amino acids sharing similar physicochemical properties correspond to codons that have the same nucleotide composition bias. In particular, we uncover that the TRA2A and TRA2B splicing factors that bind to adenine-rich motifs promote the inclusion of adenine-rich exons coding preferentially for hydrophilic amino acids that correspond to adenine-rich codons. SRSF2 that binds guanine/cytosine-rich motifs promotes the inclusion of GC-rich exons coding preferentially for small amino acids, whereas SRSF3 that binds cytosine-rich motifs promotes the inclusion of exons coding preferentially for uncharged amino acids, like serine and threonine that can be phosphorylated. Finally, coregulated exons encoding amino acids with similar physicochemical properties correspond to specific protein features. In conclusion, the regulation of an exon by a splicing factor that relies on the affinity of this factor for specific nucleotide(s) is tightly interconnected with the exon-encoded physicochemical properties. We therefore uncover an unanticipated bidirectional interplay between the splicing regulatory process and its biological functional outcome.
MeSH term(s)	Alternative Splicing ; Amino Acids/chemistry ; Base Composition/genetics ; Cell Line ; Exons/genetics ; Genetic Code ; Heterogeneous-Nuclear Ribonucleoproteins/metabolism ; Humans ; Introns/genetics ; Nucleotide Motifs/genetics ; RNA Splice Sites/genetics ; RNA Splicing Factors/metabolism ; Sequence Analysis, Protein ; Sequence Analysis, RNA ; Serine-Arginine Splicing Factors/metabolism
Chemical Substances	Amino Acids ; Heterogeneous-Nuclear Ribonucleoproteins ; RNA Splice Sites ; RNA Splicing Factors ; Serine-Arginine Splicing Factors (170974-22-8)
Language	English
Publishing date	2019-04-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.241315.118
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 3729: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 8: Show issues

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Candidate gene prioritization with Endeavour.

Tranchevent, Léon-Charles / Ardeshirdavani, Amin / ElShal, Sarah / Alcaide, Daniel / Aerts, Jan / Auboeuf, Didier / Moreau, Yves

Nucleic acids research

2016 Volume 44, Issue W1, Page(s) W117–21

Abstract: Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ... ...

Abstract	Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using 'gold standard' gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene-phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.
MeSH term(s)	Algorithms ; Animals ; Benchmarking ; Genetic Association Studies ; Genetic Predisposition to Disease ; Genotype ; Humans ; Internet ; Phenotype ; Software
Language	English
Publishing date	2016-07-08
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkw365
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Full text

In stock of ZB MED Cologne/Königswinter

Zs.A 1067: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

To top

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito