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Article ; Online: Validation of a clinical and genetic model for predicting severe COVID-19.

Dite, Gillian S / Murphy, Nicholas M / Spaeth, Erika / Allman, Richard

2022 , Page(s) 1–15

Language	English
Publishing date	2022-04-25
Publishing country	England
Document type	Journal Article
ZDB-ID	632982-2
ISSN	1469-4409 ; 0950-2688
ISSN (online)	1469-4409
ISSN	0950-2688
DOI	10.1017/S0950268822000541
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Article ; Online: Concerns About a Dog Model of Dry Eye Disease.

Ofri, Ron / Millichamp, Nicholas J / Keller, Charlotte / McLellan, Gillian J / Komáromy, András M / Morton, David / Matas, Màrian / Michau, Tammy M / Coall, Sarah / Sansom, Jane / Leonard, Brian C

Translational vision science & technology

2024 Volume 13, Issue 3, Page(s) 28

MeSH term(s)	Dogs ; Animals ; Dry Eye Syndromes/diagnosis ; Tears
Language	English
Publishing date	2024-03-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2674602-5
ISSN	2164-2591 ; 2164-2591
ISSN (online)	2164-2591
ISSN	2164-2591
DOI	10.1167/tvst.13.3.28
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Article ; Online: Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk.

Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

The Prostate

2023 Volume 83, Issue 10, Page(s) 962–969

Abstract: Background: Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population-based cohort, we developed and validated a simple model comprising age, family history, and ... ...

Abstract	Background: Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population-based cohort, we developed and validated a simple model comprising age, family history, and a polygenic risk score (PRS) to predict 5-year risk of prostate cancer. Methods: Eligible participants were unaffected Caucasian men aged 40-69 years at their baseline assessment who had genotyping data available and had completed 6 or more weeks of follow-up. Family history was the number of affected first-degree relatives: 0, 1, or 2+. We used 264 single-nucleotide polymorphisms (SNPs) of a previously developed 269-SNP PRS and population standardized the PRS to have a mean of 1. Age was categorized into 10-year groups: 40-49, 50-59, and 60-69. In a 70% training data set, we used Cox regression with age as the time axis to model family history, PRS, and age group. The model estimates were used with prostate cancer incidences to derive 5-year risks of prostate cancer. Using 5 years of follow-up in a 30% testing data set, the model was tested in terms of its association per quintile of risk, discrimination, and calibration. Results: Of the 198 334 eligible participants, 8996 (4.5%) were diagnosed with incident prostate cancer during follow-up and had a mean age of 67.9 (SD = 5.8) years at diagnosis. The best-fitting model included the PRS, family history, 10-year age group, interactions between age and PRS, and age and family history. In the 30% testing data set with follow-up limited to 5 years, the hazard ratio per SD of 5-year risk was 3.058 (95% confidence interval [CI], 2.720-3.438) and the Harrell's C-index was 0.811 (95% CI, 0.800-0.821). Overall, there were 1088 observed and 1159.1 expected prostate cancers, a standardized incidence ratio of 0.939 (95% CI, 0.885-0.996). Conclusions: Men at increased risk of prostate cancer could benefit from informed discussions around the risks and benefits of available options for screening for prostate cancer. Although the model was developed in Caucasian men, it can be used with ethnicity-specific polygenic risk and incidence rates for other populations.
MeSH term(s)	Male ; Humans ; Aged ; Child ; Risk Assessment ; Risk Factors ; Prostatic Neoplasms/epidemiology ; Prostatic Neoplasms/genetics ; Proportional Hazards Models ; Incidence ; Polymorphism, Single Nucleotide ; Genetic Predisposition to Disease
Language	English
Publishing date	2023-04-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	604707-5
ISSN	1097-0045 ; 0270-4137
ISSN (online)	1097-0045
ISSN	0270-4137
DOI	10.1002/pros.24537
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Article ; Online: An integrated clinical and genetic model for predicting risk of severe COVID-19

Gillian S Dite / Nicholas M Murphy / Richard Allman

PLoS ONE, Vol 16, Iss 2, p e

A population-based case-control study.

2021 Volume 0247205

Abstract: Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate ... ...

Abstract	Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors-not age and gender-that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
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Article ; Online: Vinculin Y822 is an important determinant of ligand binding.

DeWane, Gillian / Cronin, Nicholas M / Dawson, Logan W / Heidema, Christy / DeMali, Kris A

Journal of cell science

2023 Volume 136, Issue 12

Abstract: Vinculin is an actin-binding protein present at cell-matrix and cell-cell adhesions, which plays a critical role in bearing force experienced by cells and dissipating it onto the cytoskeleton. Recently, we identified a key tyrosine residue, Y822, whose ... ...

Abstract	Vinculin is an actin-binding protein present at cell-matrix and cell-cell adhesions, which plays a critical role in bearing force experienced by cells and dissipating it onto the cytoskeleton. Recently, we identified a key tyrosine residue, Y822, whose phosphorylation plays a critical role in force transmission at cell-cell adhesions. The role of Y822 in human cancer remains unknown, even though Y822 is mutated to Y822C in uterine cancers. Here, we investigated the effect of this amino acid substitution and that of a phosphodeficient Y822F vinculin in cancer cells. We observed that the presence of the Y822C mutation led to cells that proliferate and migrate more rapidly and contained smaller focal adhesions when compared to cells with wild-type vinculin. In contrast, the presence of the Y822F mutation led to highly spread cells with larger focal adhesions and increased contractility. Furthermore, we provide evidence that Y822C vinculin forms a disulfide bond with paxillin, accounting for some of the elevated phosphorylated paxillin recruitment. Taken together, these data suggest that vinculin Y822 modulates the recruitment of ligands.
MeSH term(s)	Humans ; Vinculin/genetics ; Vinculin/metabolism ; Paxillin/genetics ; Paxillin/metabolism ; Ligands ; Cell Adhesion/genetics ; Cell Communication ; Focal Adhesions/genetics ; Focal Adhesions/metabolism
Chemical Substances	Vinculin (125361-02-6) ; Paxillin ; Ligands
Language	English
Publishing date	2023-06-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2993-2
ISSN	1477-9137 ; 0021-9533
ISSN (online)	1477-9137
ISSN	0021-9533
DOI	10.1242/jcs.260104
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Zs.MG 14: Show issues

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Article ; Online: Melanoma risk prediction based on a polygenic risk score and clinical risk factors.

Wong, Chi Kuen / Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

Melanoma research

2023 Volume 33, Issue 4, Page(s) 293–299

Abstract: Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk ... ...

Abstract	Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N = 54 799) and its performance was tested using a cohort testing dataset ( N = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.
MeSH term(s)	Humans ; Melanoma/genetics ; Melanoma/epidemiology ; Risk Assessment ; Skin Neoplasms/genetics ; Skin Neoplasms/epidemiology ; Risk Factors ; Incidence ; Genetic Predisposition to Disease ; Genome-Wide Association Study
Language	English
Publishing date	2023-04-24
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1095779-0
ISSN	1473-5636 ; 0960-8931
ISSN (online)	1473-5636
ISSN	0960-8931
DOI	10.1097/CMR.0000000000000896
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Article ; Online: A combined clinical and genetic model for predicting risk of ovarian cancer.

Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)

2022 Volume 32, Issue 1, Page(s) 57–64

Abstract: Objective: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score ... ...

Abstract	Objective: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score could predict risk of ovarian cancer in population-based women who would otherwise be considered as being at average risk. Methods: We used the UK Biobank to conduct a prospective cohort study assessing the performance of 10-year ovarian cancer risks based on a polygenic risk score, a clinical risk score and a combined risk score. We used Cox regression to assess association, Harrell's C-index to assess discrimination and Poisson regression to assess calibration. Results: The combined risk model performed best and problems with calibration were overcome by recalibrating the model, which then had a hazard ratio per quintile of risk of 1.338 [95% confidence interval (CI), 1.152-1.553], a Harrell's C-index of 0.663 (95% CI, 0.629-0.698) and overall calibration of 1.000 (95% CI, 0.874-1.145). In the refined model with estimates based on the entire dataset, women in the top quintile of 10-year risk were at 1.387 (95% CI, 1.086-1.688) times increased risk, while women in the top quintile of full-lifetime risk were at 1.527 (95% CI, 1.187-1.866) times increased risk compared with the population. Conclusion: Identification of women who are at high risk of ovarian cancer can allow healthcare providers and patients to engage in joint decision-making discussions around the risks and benefits of screening options or risk-reducing surgery.
MeSH term(s)	Humans ; Female ; Models, Genetic ; Prospective Studies ; Ovarian Neoplasms/diagnosis ; Ovarian Neoplasms/epidemiology ; Ovarian Neoplasms/genetics ; Health Personnel
Chemical Substances	AT-511
Language	English
Publishing date	2022-10-27
Publishing country	England
Document type	Journal Article
ZDB-ID	1137033-6
ISSN	1473-5709 ; 0959-8278
ISSN (online)	1473-5709
ISSN	0959-8278
DOI	10.1097/CEJ.0000000000000771
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Article ; Online: Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.

Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

Epidemiology and infection

2021 Volume 149, Page(s) e162

Abstract: Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) ... ...

Abstract	Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64-1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708-0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = -0.08; 95% CI -0.21-0.05) and no evidence or over-or under-dispersion of risk (β = 0.90, 95% CI 0.80-1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.
MeSH term(s)	Aged ; Aged, 80 and over ; COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/physiopathology ; Comorbidity ; Female ; Humans ; Male ; Models, Genetic ; Models, Statistical ; Polymorphism, Single Nucleotide/genetics ; ROC Curve ; Reproducibility of Results ; Risk Assessment/methods ; SARS-CoV-2 ; Severity of Illness Index
Language	English
Publishing date	2021-07-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632982-2
ISSN	1469-4409 ; 0950-2688
ISSN (online)	1469-4409
ISSN	0950-2688
DOI	10.1017/S095026882100145X
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Article ; Online: An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

PloS one

2021 Volume 16, Issue 2, Page(s) e0247205

Abstract	Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors-not age and gender-that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.
MeSH term(s)	Age Factors ; Aged ; COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/pathology ; Comorbidity ; Female ; Humans ; Male ; Middle Aged ; Polymorphism, Single Nucleotide ; Race Factors ; Severity of Illness Index ; Sex Factors
Language	English
Publishing date	2021-02-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0247205
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Article ; Online: Interactions of the anti-FcRn monoclonal antibody, rozanolixizumab, with Fcγ receptors and functional impact on immune cells

Qureshi, Omar S / Sutton, Emma J / Bithell, Rosemary F / West, Shauna M / Cutler, Rona M / McCluskey, Gillian / Craggs, Graham / Maroof, Asher / Barnes, Nicholas M / Humphreys, David P / Rapecki, Stephen / Smith, Bryan J / Shock, Anthony

mAbs

2024 Volume 16, Issue 1, Page(s) 2300155

Abstract: Rozanolixizumab is a humanized anti-neonatal Fc receptor (FcRn) monoclonal antibody (mAb) of the immunoglobulin G4 (IgG4) sub-class, currently in clinical development for the treatment of IgG autoantibody-driven diseases. This format is frequently used ... ...

Abstract	Rozanolixizumab is a humanized anti-neonatal Fc receptor (FcRn) monoclonal antibody (mAb) of the immunoglobulin G4 (IgG4) sub-class, currently in clinical development for the treatment of IgG autoantibody-driven diseases. This format is frequently used for therapeutic mAbs due to its intrinsic lower affinity for Fc gamma receptors (FcγR) and lack of C1q engagement. However, with growing evidence suggesting that no Fc-containing agent is truly "silent" in this respect, we explored the engagement of FcγRs and potential functional consequences with rozanolixizumab. In the study presented here, rozanolixizumab was shown to bind to FcγRs in both protein-protein and cell-based assays, and the kinetic data were broadly as expected based on published data for an IgG4 mAb. Rozanolixizumab was also able to mediate antibody bipolar bridging (ABB), a phenomenon that led to a reduction of labeled FcγRI from the surface of human macrophages in an FcRn-dependent manner. However, the presence of exogenous human IgG, even at low concentrations, was able to prevent both binding and ABB events. Furthermore, data from
MeSH term(s)	Humans ; Receptors, IgG ; Receptors, Fc ; Antibodies, Monoclonal, Humanized/metabolism ; Antibodies, Monoclonal ; Immunoglobulin G ; Histocompatibility Antigens Class I
Chemical Substances	Receptors, IgG ; rozanolixizumab (P7186074QC) ; Receptors, Fc ; Antibodies, Monoclonal, Humanized ; Antibodies, Monoclonal ; Immunoglobulin G ; Histocompatibility Antigens Class I
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2537838-7
ISSN	1942-0870 ; 1942-0870
ISSN (online)	1942-0870
ISSN	1942-0870
DOI	10.1080/19420862.2023.2300155
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