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Article ; Online: Inferred inactivation of the Cftr gene in the duodena of mice exposed to hexavalent chromium (Cr(VI)) in drinking water supports its tumor-suppressor status and implies its potential role in Cr(VI)-induced carcinogenesis of the small intestines.

Mezencev, Roman / Auerbach, Scott S

2021 Volume 433, Page(s) 115773

Abstract: Carcinogenicity of hexavalent chromium [Cr (VI)] has been supported by a number of epidemiological and animal studies; however, its carcinogenic mode of action is still incompletely understood. To identify mechanisms involved in cancer development, we ... ...

Abstract	Carcinogenicity of hexavalent chromium [Cr (VI)] has been supported by a number of epidemiological and animal studies; however, its carcinogenic mode of action is still incompletely understood. To identify mechanisms involved in cancer development, we analyzed gene expression data from duodena of mice exposed to Cr(VI) in drinking water. This analysis included (i) identification of upstream regulatory molecules that are likely responsible for the observed gene expression changes, (ii) identification of annotated gene expression data from public repositories that correlate with gene expression changes in duodena of Cr(VI)-exposed mice, and (iii) identification of hallmark and oncogenic signature gene sets relevant to these data. We identified the inactivated CFTR gene among the top scoring upstream regulators, and found positive correlations between the expression data from duodena of Cr(VI)-exposed mice and other datasets in public repositories associated with the inactivation of the CFTR gene. In addition, we found enrichment of signatures for oncogenic signaling, sustained cell proliferation, impaired apoptosis and tissue remodeling. Results of our computational study support the tumor-suppressor role of the CFTR gene. Furthermore, our results support human relevance of the Cr(VI)-mediated carcinogenesis observed in the small intestines of exposed mice and suggest possible groups that may be more vulnerable to the adverse outcomes associated with the inactivation of CFTR by hexavalent chromium or other agents. Lastly, our findings predict, for the first time, the role of CFTR inactivation in chemical carcinogenesis and expand the range of plausible mechanisms that may be operative in Cr(VI)-mediated carcinogenesis of intestinal and possibly other tissues.
MeSH term(s)	Administration, Oral ; Animals ; Cell Transformation, Neoplastic/chemically induced ; Cell Transformation, Neoplastic/genetics ; Cell Transformation, Neoplastic/metabolism ; Cell Transformation, Neoplastic/pathology ; Chromium/administration & dosage ; Chromium/toxicity ; Cystic Fibrosis Transmembrane Conductance Regulator/genetics ; Cystic Fibrosis Transmembrane Conductance Regulator/metabolism ; Databases, Genetic ; Drinking Water ; Duodenal Neoplasms/chemically induced ; Duodenal Neoplasms/genetics ; Duodenal Neoplasms/metabolism ; Duodenal Neoplasms/pathology ; Duodenum/drug effects ; Duodenum/metabolism ; Duodenum/pathology ; Gene Expression Profiling ; Gene Silencing/drug effects ; Mice ; Risk Assessment ; Systems Biology ; Transcriptome ; Tumor Suppressor Proteins/genetics ; Tumor Suppressor Proteins/metabolism ; Water Pollutants, Chemical/administration & dosage ; Water Pollutants, Chemical/toxicity
Chemical Substances	Cftr protein, mouse ; Drinking Water ; Tumor Suppressor Proteins ; Water Pollutants, Chemical ; Chromium (0R0008Q3JB) ; Cystic Fibrosis Transmembrane Conductance Regulator (126880-72-6) ; chromium hexavalent ion (18540-29-9)
Language	English
Publishing date	2021-10-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	204477-8
ISSN	1096-0333 ; 0041-008X
ISSN (online)	1096-0333
ISSN	0041-008X
DOI	10.1016/j.taap.2021.115773
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Article ; Online: Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver.

Pandiri, Arun R / Auerbach, Scott S / Stevens, Jim L / Blomme, Eric A G

Toxicologic pathology

2024 Volume 51, Issue 7-8, Page(s) 470–481

Abstract: Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, ...

Abstract	Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.
MeSH term(s)	Toxicogenetics ; Liver ; Proteomics ; Gene Expression Profiling ; Transcriptome
Language	English
Publishing date	2024-01-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	841009-4
ISSN	1533-1601 ; 0192-6233
ISSN (online)	1533-1601
ISSN	0192-6233
DOI	10.1177/01926233241227942
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Article ; Online: The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization.

Mezencev, Roman / Auerbach, Scott S

PloS one

2020 Volume 15, Issue 5, Page(s) e0232955

Abstract: Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, ... ...

Abstract	Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.
MeSH term(s)	Benchmarking/methods ; Computational Biology/methods ; Gene Expression Profiling/methods ; Genome/genetics ; Humans ; Nucleic Acid Hybridization/methods ; Oligonucleotide Array Sequence Analysis/methods ; Risk Assessment/methods ; Transcriptome/genetics
Language	English
Publishing date	2020-05-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0232955
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Article ; Online: A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure.

Johnson, Kamin J / Auerbach, Scott S / Costa, Eduardo

Toxicological sciences : an official journal of the Society of Toxicology

2020 Volume 176, Issue 1, Page(s) 86–102

Abstract: Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. ... ...

Abstract	Identifying a toxicity point of departure (POD) is a required step in human health risk characterization of crop protection molecules, and this POD has historically been derived from apical endpoints across a battery of animal-based toxicology studies. Using rat transcriptome and apical data for 79 molecules obtained from Open TG-GATES (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) (632 datasets), the hypothesis was tested that a short-term exposure, transcriptome-based liver biological effect POD (BEPOD) could estimate a longer-term exposure "systemic" apical endpoint POD. Apical endpoints considered were body weight, clinical observation, kidney weight and histopathology and liver weight and histopathology. A BMDExpress algorithm using Gene Ontology Biological Process gene sets was optimized to derive a liver BEPOD most predictive of a systemic apical POD. Liver BEPODs were stable from 3 h to 29 days of exposure; the median fold difference of the 29-day BEPOD to BEPODs from earlier time points was approximately 1 (range: 0.7-1.1). Strong positive correlation (Pearson R = 0.86) and predictive accuracy (root mean square difference = 0.41) were observed between a concurrent (29 days) liver BEPOD and the systemic apical POD. Similar Pearson R and root mean square difference values were observed for comparisons between a 29-day systemic apical POD and liver BEPODs derived from 3 h to 15 days of exposure. These data across 79 molecules suggest that a longer-term exposure study apical POD from liver and non-liver compartments can be estimated using a liver BEPOD derived from an acute or subacute exposure study.
MeSH term(s)	Animals ; Benchmarking ; Computational Biology ; Dose-Response Relationship, Drug ; Genomics ; Liver/drug effects ; Prospective Studies ; Rats ; Risk Assessment ; Toxicogenetics ; Transcriptome/drug effects
Language	English
Publishing date	2020-05-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1420885-4
ISSN	1096-0929 ; 1096-6080
ISSN (online)	1096-0929
ISSN	1096-6080
DOI	10.1093/toxsci/kfaa062
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Article ; Online: Tox21BodyMap: a webtool to map chemical effects on the human body.

Borrel, Alexandre / Auerbach, Scott S / Houck, Keith A / Kleinstreuer, Nicole C

Nucleic acids research

2020 Volume 48, Issue W1, Page(s) W472–W476

Abstract: To support rapid chemical toxicity assessment and mechanistic hypothesis generation, here we present an intuitive webtool allowing a user to identify target organs in the human body where a substance is estimated to be more likely to produce effects. ... ...

Abstract	To support rapid chemical toxicity assessment and mechanistic hypothesis generation, here we present an intuitive webtool allowing a user to identify target organs in the human body where a substance is estimated to be more likely to produce effects. This tool, called Tox21BodyMap, incorporates results of 9,270 chemicals tested in the United States federal Tox21 research consortium in 971 high-throughput screening (HTS) assays whose targets were mapped onto human organs using organ-specific gene expression data. Via Tox21BodyMap's interactive tools, users can visualize chemical target specificity by organ system, and implement different filtering criteria by changing gene expression thresholds and activity concentration parameters. Dynamic network representations, data tables, and plots with comprehensive activity summaries across all Tox21 HTS assay targets provide an overall picture of chemical bioactivity. Tox21BodyMap webserver is available at https://sandbox.ntp.niehs.nih.gov/bodymap/.
MeSH term(s)	Gene Expression/drug effects ; High-Throughput Screening Assays ; Humans ; Internet ; Organ Specificity ; Software ; Toxicity Tests/methods
Language	English
Publishing date	2020-05-22
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/gkaa433
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Article: ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis.

Davidson, Sarah E / Wheeler, Matthew W / Auerbach, Scott S / Sivaganesan, Siva / Medvedovic, Mario

Computational toxicology (Amsterdam, Netherlands)

2021 Volume 21

Abstract: Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene' ... ...

Abstract	Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene's dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in 'co-regulated' gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.
Language	English
Publishing date	2021-10-13
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-1113
ISSN	2468-1113
DOI	10.1016/j.comtox.2021.100196
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Article: A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples.

Bushel, Pierre R / Paules, Richard S / Auerbach, Scott S

Frontiers in genetics

2018 Volume 9, Page(s) 485

Abstract: The TempO- ... ...

Abstract	The TempO-Seq
Language	English
Publishing date	2018-10-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2018.00485
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Article: Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data.

Bushel, Pierre R / Ferguson, Stephen S / Ramaiahgari, Sreenivasa C / Paules, Richard S / Auerbach, Scott S

Frontiers in genetics

2020 Volume 11, Page(s) 594

Abstract: Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on ... ...

Abstract	Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.
Language	English
Publishing date	2020-06-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.00594
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Article ; Online: Harnessing In Silico, In Vitro, and In Vivo Data to Understand the Toxicity Landscape of Polycyclic Aromatic Compounds (PACs).

Hsieh, Jui-Hua / Sedykh, Alexander / Mutlu, Esra / Germolec, Dori R / Auerbach, Scott S / Rider, Cynthia V

Chemical research in toxicology

2020 Volume 34, Issue 2, Page(s) 268–285

Abstract: Polycyclic aromatic compounds (PACs) are compounds with a minimum of two six-atom aromatic fused rings. PACs arise from incomplete combustion or thermal decomposition of organic matter and are ubiquitous in the environment. Within PACs, carcinogenicity ... ...

Abstract	Polycyclic aromatic compounds (PACs) are compounds with a minimum of two six-atom aromatic fused rings. PACs arise from incomplete combustion or thermal decomposition of organic matter and are ubiquitous in the environment. Within PACs, carcinogenicity is generally regarded to be the most important public health concern. However, toxicity in other systems (reproductive and developmental toxicity, immunotoxicity) has also been reported. Despite the large number of PACs identified in the environment, research attention to understand exposure and health effects of PACs has focused on a relatively limited subset, namely polycyclic aromatic hydrocarbons (PAHs), the PACs with only carbon and hydrogen atoms. To triage the rest of the vast number of PACs for more resource-intensive testing, we developed a data-driven approach to contextualize hazard characterization of PACs, by leveraging the available data from various data streams (in silico toxicity, in vitro activity, structural fingerprints, and in vivo data availability). The PACs were clustered on the basis of their in silico toxicity profiles containing predictions from 8 different categories (carcinogenicity, cardiotoxicity, developmental toxicity, genotoxicity, hepatotoxicity, neurotoxicity, reproductive toxicity, and urinary toxicity). We found that PACs with the same parent structure (e.g., fluorene) could have diverse in silico toxicity profiles. In contrast, PACs with similar substituted groups (e.g., alkylated-PAHs) or heterocyclics (e.g., N-PACs) with varying ring sizes could have similar in silico toxicity profiles, suggesting that these groups are better candidates for toxicity read-across analysis. The clusters/regions associated with certain in silico toxicity, in vitro activity, and structural fingerprints were identified. We found that genotoxicity/carcinogenicity (in silico toxicity) and xenobiotic homeostasis and stress response (in vitro activity), respectively, dominate the toxicity/activity variation seen in the PACs. The "hot spots" with enriched toxicity/activity in conjunction with availability of in vivo carcinogenicity data revealed regions of either data-poor (hydroxylated-PAHs) or data-rich (unsubstituted, parent PAHs) PACs. These regions offer potential targets for prioritization of further in vivo assessment and for chemical read-across efforts. The analysis results are searchable through an interactive web application (https://ntp.niehs.nih.gov/go/pacs_tableau), allowing for alternative hypothesis generation.
MeSH term(s)	Environmental Monitoring ; Polycyclic Aromatic Hydrocarbons/toxicity ; Principal Component Analysis ; Toxicity Tests
Chemical Substances	Polycyclic Aromatic Hydrocarbons
Language	English
Publishing date	2020-10-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.0c00213
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Article ; Online: Saagar

Sedykh, Alexander Y / Shah, Ruchir R / Kleinstreuer, Nicole C / Auerbach, Scott S / Gombar, Vijay K

Chemical research in toxicology

2020 Volume 34, Issue 2, Page(s) 634–640

Abstract: Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of ...

Abstract	Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures,
MeSH term(s)	Animals ; Anthraquinones/chemistry ; Databases, Factual ; Models, Molecular ; Molecular Structure ; Quantitative Structure-Activity Relationship
Chemical Substances	Anthraquinones
Language	English
Publishing date	2020-12-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.0c00464
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