LIVIVO - Search results -

Search results

Result 1 - 10 of total 40

Search options

Article ; Online: Machine learning based refined differential gene expression analysis of pediatric sepsis.

Abbas, Mostafa / El-Manzalawy, Yasser

BMC medical genomics

2020 Volume 13, Issue 1, Page(s) 122

Abstract: Background: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially ...

Abstract	Background: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods: In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results: Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions: Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.
MeSH term(s)	Biomarkers/analysis ; Child ; Computational Biology/methods ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Machine Learning ; ROC Curve ; Sepsis/genetics ; Sepsis/pathology ; Transcriptome
Chemical Substances	Biomarkers
Keywords	covid19
Language	English
Publishing date	2020-08-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1755-8794
ISSN (online)	1755-8794
DOI	10.1186/s12920-020-00771-4
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.

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: Machine learning based refined differential gene expression analysis of pediatric sepsis

Mostafa Abbas / Yasser EL-Manzalawy

BMC Medical Genomics, Vol 13, Iss 1, Pp 1-

2020 Volume 10

Abstract: Abstract Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are ... ...

Abstract	Abstract Background Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. Methods In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Results Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Conclusions Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.
Keywords	Biomarkers discovery ; Differential expression analysis ; Refined differential gene expression analysis ; Feature selection ; Internal medicine ; RC31-1245 ; Genetics ; QH426-470
Subject code	004
Language	English
Publishing date	2020-08-01T00:00:00Z
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: Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States.

Abbas, Mostafa / Morland, Thomas B / Hall, Eric S / El-Manzalawy, Yasser

International journal of environmental research and public health

2021 Volume 18, Issue 9

Abstract: We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data ... ...

Abstract	We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
MeSH term(s)	COVID-19 ; Forecasting ; Humans ; SARS-CoV-2 ; Search Engine ; United States/epidemiology
Language	English
Publishing date	2021-04-25
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph18094560
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

Details ▾
- Full text online
- Order with fees

Article: Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder.

Matta, John / Dobrino, Daniel / Yeboah, Dacosta / Howard, Swade / El-Manzalawy, Yasser / Obafemi-Ajayi, Tayo

Frontiers in human neuroscience

2022 Volume 16, Page(s) 960991

Abstract: Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the ... ...

Abstract	Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.
Language	English
Publishing date	2022-10-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2425477-0
ISSN	1662-5161
ISSN	1662-5161
DOI	10.3389/fnhum.2022.960991
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Sex Differences in Fish Oil and Olanzapine Effects on Gut Microbiota in Diet-Induced Obese Mice.

Abbas, Mostafa M / Soto, Paul / Ramalingam, Latha / El-Manzalawy, Yasser / Bensmail, Halima / Moustaid-Moussa, Naima

Nutrients

2022 Volume 14, Issue 2

Abstract: Children are prescribed second-generation antipsychotic (SGA) medications, such as olanzapine (OLZ) for FDA-approved and "off-label" indications. The long-term impact of early-life SGA medication exposure is unclear. Olanzapine and other SGA medications ... ...

Abstract	Children are prescribed second-generation antipsychotic (SGA) medications, such as olanzapine (OLZ) for FDA-approved and "off-label" indications. The long-term impact of early-life SGA medication exposure is unclear. Olanzapine and other SGA medications are known to cause excessive weight gain in young and adult patients, suggesting the possibility of long-term complications associated with the use of these drugs, such as obesity, diabetes, and heart disease. Further, the weight gain effects of OLZ have previously been shown to depend on the presence of gut bacteria and treatment with OLZ, which shifts gut bacteria toward an "obesogenic" profile. The purpose of the current study was to evaluate changes in gut bacteria in adult mice following early life treatment with OLZ and being fed either a high-fat diet or a high-fat diet supplemented with fish oil, which has previously been shown to counteract gut dysbiosis, weight gain, and inflammation produced by a high-fat diet. Female and male C57Bl/6J mice were fed a high fat diet without (HF) or with the supplementation of fish oil (HF-FO) and treated with OLZ from postnatal day (PND) 37-65 resulting in four groups of mice: mice fed a HF diet and treated with OLZ (HF-OLZ), mice fed a HF diet and treated with vehicle (HF), mice fed a HF-FO diet and treated with OLZ (HF-FO-OLZ), and mice fed a HF-FO diet and treated with vehicle (HF-FO). Following euthanasia at approximately 164 days of age, we determined changes in gut bacteria populations and serum LPS binding protein, an established marker of gut inflammation and dysbiosis. Our results showed that male HF-FO and HF-FO-OLZ mice had lower body weights, at sacrifice, compared to the HF group, with a comparable body weight across groups in female mice. HF-FO and HF-FO-OLZ male groups also exhibited lower serum LPS binding protein levels compared to the HF group, with no differences across groups in female mice. Gut microbiota profiles were also different among the four groups; the Bacteroidetes-to-Firmicutes (B/F) ratio had the lowest value of 0.51 in the HF group compared to 0.6 in HF-OLZ, 0.9 in HF-FO, and 1.1 in HF-FO-OLZ, with no differences in female mice. In conclusion, FO reduced dietary obesity and its associated inflammation and increased the B/F ratio in male mice but did not benefit the female mice. Although the weight lowering effects of OLZ were unexpected, FO effects persisted in the presence of olanzapine, demonstrating its potential protective effects in male subjects using antipsychotic drugs.
MeSH term(s)	Animals ; Body Weight ; Diet, High-Fat/adverse effects ; Dietary Supplements ; Female ; Fish Oils/administration & dosage ; Gastrointestinal Microbiome/drug effects ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Obese ; Obesity/etiology ; Obesity/therapy ; Olanzapine/adverse effects ; Sex Characteristics ; Weight Gain/drug effects
Chemical Substances	Fish Oils ; Olanzapine (N7U69T4SZR)
Language	English
Publishing date	2022-01-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2518386-2
ISSN	2072-6643 ; 2072-6643
ISSN (online)	2072-6643
ISSN	2072-6643
DOI	10.3390/nu14020349
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

Abbas, Mostafa / Morland, Thomas B. / Hall, Eric S. / El-Manzalawy, Yasser

medRxiv

Abstract: We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data ... ...

Abstract	We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
Keywords	covid19
Language	English
Publishing date	2021-02-24
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.02.22.21252254
Database	COVID19

Full text online

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: Building classifier ensembles for B-cell epitope prediction.

EL-Manzalawy, Yasser / Honavar, Vasant

Methods in molecular biology (Clifton, N.J.)

2014 Volume 1184, Page(s) 285–294

Abstract: Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or ... ...

Abstract	Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics.Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.
MeSH term(s)	Animals ; Artificial Intelligence ; Computational Biology/methods ; Epitopes, B-Lymphocyte/chemistry ; Epitopes, B-Lymphocyte/immunology ; Humans ; Models, Immunological ; Software
Chemical Substances	Epitopes, B-Lymphocyte
Language	English
Publishing date	2014-07-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-1115-8_15
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

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.

Details ▾
- Full text online
- Order with fees

Article ; Online: Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

Mostafa Abbas / Thomas B. B. Morland / Eric S. S. Hall / Yasser EL-Manzalawy

International Journal of Environmental Research and Public Health, Vol 18, Iss 4560, p

2021 Volume 4560

Abstract	We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
Keywords	COVID-19 spread and mortality in US ; functional data analysis ; SARS-COV-2 ; Google COVID-19 search trends symptoms ; Medicine ; R
Subject code	310
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	MDPI AG
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: Partner-specific prediction of RNA-binding residues in proteins: A critical assessment.

Jung, Yong / El-Manzalawy, Yasser / Dobbs, Drena / Honavar, Vasant G

Proteins

2018 Volume 87, Issue 3, Page(s) 198–211

Abstract: RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA ... ...

Abstract	RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.
MeSH term(s)	Amino Acid Sequence/genetics ; Base Sequence/genetics ; Binding Sites/genetics ; Computational Biology ; Models, Molecular ; Protein Binding/genetics ; Protein Conformation ; Proteins/chemistry ; Proteins/genetics ; RNA/chemistry ; RNA/genetics ; RNA-Binding Motifs/genetics ; RNA-Binding Proteins/chemistry ; RNA-Binding Proteins/genetics ; Sequence Analysis, Protein ; Software
Chemical Substances	Proteins ; RNA-Binding Proteins ; RNA (63231-63-0)
Language	English
Publishing date	2018-12-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.25639
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 2291: 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: A PheWAS Model of Autism Spectrum Disorder.

Matta, John / Dobrino, Daniel / Howard, Swade / Yeboah, Dacosta / Kopel, Jonathan / El-Manzalawy, Yasser / Obafemi-Ajayi, Tayo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 2110–2114

Abstract: Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with ... ...

Abstract	Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with their unique genetic variants. There is a need for a better understanding of the connections between genotype information and the phenotypes to sort out the heterogeneity of ASD. In this study, single nucleotide polymorphism (SNP) and phenotype data obtained from a simplex ASD sample are combined using a PheWAS-inspired approach to construct a phenotype-phenotype network. The network is clustered, yielding groups of etiologically related phenotypes. These clusters are analyzed to identify relevant genes associated with each set of phenotypes. The results identified multiple discriminant SNPs associated with varied phenotype clusters such as ASD aberrant behavior (self-injury, compulsiveness and hyperactivity), as well as IQ and language skills. Overall, these SNPs were linked to 22 significant genes. An extensive literature search revealed that eight of these are known to have strong evidence of association with ASD. The others have been linked to related disorders such as mental conditions, cognition, and social functioning.Clinical relevance- This study further informs on connections between certain groups of ASD phenotypes and their unique genetic variants. Such insight regarding the heterogeneity of ASD would support clinicians to advance more tailored interventions and improve outcomes for ASD patients.
MeSH term(s)	Autism Spectrum Disorder/genetics ; Cognition ; Humans ; Phenotype ; Polymorphism, Single Nucleotide
Language	English
Publishing date	2021-12-07
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9629533
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.

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED