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Book ; Online: Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases

Cai, Yudong / Huang, Tao / Jia, Peilin

2020

Keywords	Science: general issues ; Medical genetics ; artificial intelligence ; NGS - next generation sequencing ; non-invasive prenatal testing (NIPT) ; WGS - whole-genome sequencing ; WES - whole-exome sequencing
Size	1 electronic resource (234 pages)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021230444
ISBN	9782889662746 ; 2889662748
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Benchmark of embedding-based methods for accurate and transferable prediction of drug response.

Jia, Peilin / Hu, Ruifeng / Zhao, Zhongming

Briefings in bioinformatics

2023 Volume 24, Issue 3

Abstract: Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of ...

Abstract	Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of them train models by using the drug-response screening data generated from cell lines and then use these models to predict response in cancer patient data. In this study, we focus on and evaluate deep learning methods using transcriptome data for the long-standing question of personalized drug-response prediction. We developed an embedding-based approach for drug-response prediction and benchmarked similar methods for their performance. For all methods, we used pretreatment transcriptome data to train models and then conducted a comprehensive evaluation and comparison of the models using cross-panels, cross-datasets and target genes. We further validated the methods using three independent datasets assessing multiple compounds for their predictive capability of drug response, survival outcome and cell line status. As a result, the methods building on gene embeddings had an overall competitive performance with reduced overfitting when we applied evaluation parameters for model fitting as well as the correlation with clinical outcomes in the validation data. We further developed an ensemble model to combine the results from the three most competitive methods for an overall prediction. Finally, we developed DrVAEN (https://bioinfo.uth.edu/drvaen), a user-friendly and easy-accessible web-server that hosts all these methods for drug-response prediction and model comparison for broad use in cancer research, method evaluation and drug development.
MeSH term(s)	Humans ; Benchmarking ; Neoplasms/drug therapy ; Neoplasms/genetics ; Precision Medicine/methods
Language	English
Publishing date	2023-03-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad098
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Article ; Online: A Method for Bridging Population-Specific Genotypes to Detect Gene Modules Associated with Alzheimer's Disease.

Dai, Yulin / Jia, Peilin / Zhao, Zhongming / Gottlieb, Assaf

Cells

2022 Volume 11, Issue 14

Abstract: Background: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. ... ...

Abstract	Background: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success. Methods: Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules. Results: We demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD. Conclusions: Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.
MeSH term(s)	Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Gene Regulatory Networks ; Genome-Wide Association Study/methods ; Genotype ; Humans ; Phenotype
Language	English
Publishing date	2022-07-16
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2661518-6
ISSN	2073-4409 ; 2073-4409
ISSN (online)	2073-4409
ISSN	2073-4409
DOI	10.3390/cells11142219
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Article ; Online: scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.

Jia, Peilin / Hu, Ruifeng / Yan, Fangfang / Dai, Yulin / Zhao, Zhongming

Genome biology

2022 Volume 23, Issue 1, Page(s) 220

Abstract: Background: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively ... ...

Abstract	Background: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. Results: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. Conclusions: We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.
MeSH term(s)	Animals ; Genome-Wide Association Study ; Humans ; Mice ; Phenotype ; Single-Cell Analysis/methods ; Transcriptome
Language	English
Publishing date	2022-10-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-022-02785-w
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Article ; Online: Charting the proteome landscape in major psychiatric disorders: From biomarkers to biological pathways towards drug discovery.

Fernandes, Brisa S / Dai, Yulin / Jia, Peilin / Zhao, Zhongming

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology

2022 Volume 61, Page(s) 43–59

Abstract: Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying ... ...

Abstract	Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying biomarkers are urgent needs for developing objective diagnostic tests and new therapeutics. In this study, we performed a systematic review and then extracted, curated, and analyzed proteomics data from published studies, aiming to assess the proteome in peripheral blood of individuals with SZ, BD, or MDD. Then, we performed pathway and network analyses to illuminate the biological themes concatenated by the differentially expressed proteins by systematically interrogating the literature to uncover biological pathways with more robust biological meaning. We identified 486 differentially expressed proteins from 51 studies across the three disorders with 9,423 participants. The great majority of pathways were common to SZ, BD, and MDD. They were related to the immune system, including signaling by interleukins, Toll-like receptor signaling pathway, and complement cascade, and to signal transduction, notably MAPK1/MAPK3 signaling, PI3K-Akt Signaling Pathway, Focal Adhesion-PI3K-Akt-mTOR-signaling, rhodopsin-like receptors, GPCR signaling, and the JAK-STAT signaling pathway. Other shared pathways included advanced glycosylation end-product receptor signaling, Regulation of Insulin-like Growth Factor, cholesterol metabolism, and IL-17 signaling pathway. Pathways shared between SZ and BD were integrin cell-surface interactions, GRB2:SOS provides linkage to MAPK signaling for integrins, and syndecan interactions. Shared between BD and MDD were the NRF2 pathway and signaling by EGFR pathways. Our findings advance our understanding of the protein variations and associations with these disorders, which are useful for accelerating biomarker development and drug discovery.
MeSH term(s)	Biomarkers ; Depressive Disorder, Major/drug therapy ; Depressive Disorder, Major/metabolism ; Drug Discovery ; Humans ; Mental Disorders/drug therapy ; Phosphatidylinositol 3-Kinases ; Proteome ; Proto-Oncogene Proteins c-akt
Chemical Substances	Biomarkers ; Proteome ; Proto-Oncogene Proteins c-akt (EC 2.7.11.1)
Language	English
Publishing date	2022-06-25
Publishing country	Netherlands
Document type	Journal Article ; Systematic Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1082947-7
ISSN	1873-7862 ; 0924-977X
ISSN (online)	1873-7862
ISSN	0924-977X
DOI	10.1016/j.euroneuro.2022.06.001
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3288: 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)
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Article: Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis.

Fan, Huihui / Jia, Peilin / Zhao, Zhongming

Cancers

2021 Volume 13, Issue 23

Abstract: Fibrosis is a major cause of mortality. Key profibrotic mechanisms are common pathways involved in tumorigenesis. Characterizing the profibrotic phenotype will help reveal the underlying mechanisms of early development and progression of a variety of ... ...

Abstract	Fibrosis is a major cause of mortality. Key profibrotic mechanisms are common pathways involved in tumorigenesis. Characterizing the profibrotic phenotype will help reveal the underlying mechanisms of early development and progression of a variety of human diseases, such as fibrosis and cancer. Fibroblasts have been center stage in response to various stimuli, such as viral infections. However, a comprehensive catalog of cell types involved in this process is currently lacking. Here, we deployed single-cell transcriptomic data across multi-organ systems (i.e., heart, kidney, liver, and lung) to identify novel profibrotic cell populations based on ECM pathway activity at single-cell resolution. In addition to fibroblasts, we also reported that epithelial, endothelial, myeloid, natural killer T, and secretory cells, as well as proximal convoluted tubule cells of the nephron, were significantly actively involved. Cell-type-specific gene signatures were enriched in viral infection pathways, enhanced glycolysis, and carcinogenesis, among others; they were validated using independent datasets in this study. By projecting the signatures into bulk TCGA tumor samples, we could predict prognosis in the patients using profibrotic scores. Our profibrotic cellular phenotype is useful for identifying new mechanisms and potential drug targets at the cell-type level for a wide range of diseases involved in ECM pathway activation.
Language	English
Publishing date	2021-11-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers13236024
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Article: A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades.

Kang, Hongen / Zhu, Xiuli / Cui, Ying / Xiong, Zhuang / Zong, Wenting / Bao, Yiming / Jia, Peilin

Cancers

2023 Volume 15, Issue 16

Abstract: Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB ... ...

Abstract	Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.
Language	English
Publishing date	2023-08-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15164094
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Article ; Online: PharmGWAS: a GWAS-based knowledgebase for drug repurposing.

Kang, Hongen / Pan, Siyu / Lin, Shiqi / Wang, Yin-Ying / Yuan, Na / Jia, Peilin

Nucleic acids research

2023 Volume 52, Issue D1, Page(s) D972–D979

Abstract: Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount ...

Abstract	Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.
MeSH term(s)	Drug Repositioning/methods ; Genome-Wide Association Study/methods ; Databases, Pharmaceutical
Language	English
Publishing date	2023-10-12
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/gkad832
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Article ; Online: DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.

Pei, Guangsheng / Hu, Ruifeng / Jia, Peilin / Zhao, Zhongming

Nucleic acids research

2021 Volume 49, Issue W1, Page(s) W131–W139

Abstract: More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the ... ...

Abstract	More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the functional activity of non-coding genetic variants. This new server is built on a convolutional neural network (CNN) framework that has been extensively evaluated. Specifically, we collected chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles covering 225 tissues or cell types. With such comprehensive epigenomics annotations, DeepFun expands the functionality of existing non-coding variant prioritizing tools to provide a more specific functional assessment on non-coding variants in a tissue- and cell type-specific manner. By using the datasets from various GWAS studies, we conducted independent validations and demonstrated the functions of the DeepFun web server in predicting the effect of a non-coding variant in a specific tissue or cell type, as well as visualizing the potential motifs in the region around variants. We expect our server will be widely used in genetics, functional genomics, and disease studies.
MeSH term(s)	Chromatin/metabolism ; Computer Simulation ; Deep Learning ; Genetic Variation ; Genome, Human ; Genome-Wide Association Study ; Histone Code ; Humans ; Mutagenesis ; Organ Specificity ; Software ; Transcription Factors/metabolism
Chemical Substances	Chromatin ; Transcription Factors
Language	English
Publishing date	2021-05-28
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
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/gkab429
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Article ; Online: A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis.

Manuel, Astrid M / Dai, Yulin / Jia, Peilin / Freeman, Leorah A / Zhao, Zhongming

Human molecular genetics

2023 Volume 32, Issue 6, Page(s) 998–1009

Abstract: Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have ... ...

Abstract	Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have pinpointed differentially methylated CpG sites in MS patients. However, the interplay between genetic risk factors and epigenetic regulation remains elusive. Here, we employed a network model to integrate GWAS summary statistics of 14 802 MS cases and 26 703 controls with DNA methylation profiles from 140 MS cases and 139 controls and the human interactome. We identified differentially methylated genes by aggregating additive effects of differentially methylated CpG sites within promoter regions. We reconstructed a gene regulatory network (GRN) using literature-curated transcription factor knowledge. Colocalization of the MS GWAS and methylation quantitative trait loci (mQTL) was performed to assess the GRN. The resultant MS-associated GRN highlighted several single nucleotide polymorphisms with GWAS-mQTL colocalization: rs6032663, rs6065926 and rs2024568 of CD40 locus, rs9913597 of STAT3 locus, and rs887864 and rs741175 of CIITA locus. Moreover, synergistic mQTL and expression QTL signals were identified in CD40, suggesting gene expression alteration was likely induced by epigenetic changes. Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA) indicated that the GRN was enriched in T follicular helper cells (P-value = 0.0016). Drug target enrichment analysis of annotations from the Therapeutic Target Database revealed the GRN was also enriched with drug target genes (P-value = 3.89 × 10-4), revealing repurposable candidates for MS treatment. These candidates included vorinostat (HDAC1 inhibitor) and sivelestat (ELANE inhibitor), which warrant further investigation.
MeSH term(s)	Humans ; Epigenesis, Genetic/genetics ; Gene Regulatory Networks ; Genome-Wide Association Study ; Multiple Sclerosis/drug therapy ; Multiple Sclerosis/genetics ; DNA Methylation/genetics ; Quantitative Trait Loci/genetics
Language	English
Publishing date	2023-02-01
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddac265
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