LIVIVO - Search results -

Search results

Result 1 - 10 of total 253

Search options

Article ; Online: Hovenia acerba Lindl.: An insight into botany, phytochemistry, bioactivity, quality control, and exploitation.

Ma, Xiaogen / Zhou, Huiyan

2022 , Page(s) e14434

Abstract: Hovenia acerba Lindl. is not only a popular fruit with rich nutrients, but also a traditional Chinese medicine with multiple clinical values. It possesses therapeutic properties of clearing away heat and diuresis, relieving alcohol, protecting liver, ... ...

Abstract	Hovenia acerba Lindl. is not only a popular fruit with rich nutrients, but also a traditional Chinese medicine with multiple clinical values. It possesses therapeutic properties of clearing away heat and diuresis, relieving alcohol, protecting liver, quenching thirst, and eliminating annoyance. There are structurally diverse components of H. acerba Lindl., which mainly including flavonoids (1-39) (58.2%), triterpenoid saponins (40-47) (12.0%), organic acids (48-60) (19.4%), other compounds (61-67) (10.4%), and their structural characteristics were summarized and analyzed in this review. The extracts or monomer compounds of H. acerba Lindl. had been reported to exert various pharmacological activities, such as anti-alcoholism, hepatoprotective, anti-oxidant, hypoglycemic, immunomodulatory, and other activities are summarized and discussed in this review. In addition, the quality control, present exploitation, and developed products of this plant have also been analyzed and summarized, which provide valuable references for in-depth research and development of H. acerba Lindl. in this review. PRACTICAL APPLICATIONS: Hovenia acerba Lindl. is an edible and medical fruit with many functional properties. An insight into botany, phytochemistry, bioactivity, quality control, and exploitation study of H. acerba Lindl. was carried out to summarize and analyze in this review. This review will provide a strong foundation for the further studies of H. acerba Lindl. focusing on its development and utilization.
Language	English
Publishing date	2022-10-02
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	433846-7
ISSN	1745-4514 ; 0145-8884
ISSN (online)	1745-4514
ISSN	0145-8884
DOI	10.1111/jfbc.14434
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Bonn / Germany

Z 3310: Show issues

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.

Details ▾

Article ; Online: DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.

Zhang, Ziying / Cai, Yaxian / Zhang, Biao / Zheng, Wei / Freddolino, Lydia / Zhang, Guijun / Zhou, Xiaogen

Briefings in bioinformatics

2024 Volume 25, Issue 2

Abstract: The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In ... ...

Abstract	The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.
MeSH term(s)	Cryoelectron Microscopy/methods ; Models, Molecular ; Protein Conformation
Language	English
Publishing date	2024-03-22
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/bbae113
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 6262: Show issues

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

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Hovenia acerba Lindl.: An insight into botany, phytochemistry, bioactivity, quality control, and exploitation

Ma, Xiaogen / Zhou, Huiyan

Journal of Food Biochemistry. 2022 Dec., v. 46, no. 12 p.e14434-

2022

Abstract	Hovenia acerba Lindl. is not only a popular fruit with rich nutrients, but also a traditional Chinese medicine with multiple clinical values. It possesses therapeutic properties of clearing away heat and diuresis, relieving alcohol, protecting liver, quenching thirst, and eliminating annoyance. There are structurally diverse components of H. acerba Lindl., which mainly including flavonoids (1–39) (58.2%), triterpenoid saponins (40–47) (12.0%), organic acids (48–60) (19.4%), other compounds (61–67) (10.4%), and their structural characteristics were summarized and analyzed in this review. The extracts or monomer compounds of H. acerba Lindl. had been reported to exert various pharmacological activities, such as anti‐alcoholism, hepatoprotective, anti‐oxidant, hypoglycemic, immunomodulatory, and other activities are summarized and discussed in this review. In addition, the quality control, present exploitation, and developed products of this plant have also been analyzed and summarized, which provide valuable references for in‐depth research and development of H. acerba Lindl. in this review. PRACTICAL APPLICATIONS: Hovenia acerba Lindl. is an edible and medical fruit with many functional properties. An insight into botany, phytochemistry, bioactivity, quality control, and exploitation study of H. acerba Lindl. was carried out to summarize and analyze in this review. This review will provide a strong foundation for the further studies of H. acerba Lindl. focusing on its development and utilization.
Keywords	Hovenia acerba ; Oriental traditional medicine ; alcohols ; antioxidants ; bioactive properties ; diuresis ; flavonoids ; fruits ; heat ; liver ; quality control ; research and development ; therapeutics ; thirst ; triterpenoid saponins
Language	English
Dates of publication	2022-12
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	REVIEW
ZDB-ID	433846-7
ISSN	1745-4514 ; 0145-8884
ISSN (online)	1745-4514
ISSN	0145-8884
DOI	10.1111/jfbc.14434
Database	NAL-Catalogue (AGRICOLA)

In stock of ZB MED Bonn / Germany

Z 3310: Show issues

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 ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning

Yuhao Xia / Kailong Zhao / Dong Liu / Xiaogen Zhou / Guijun Zhang

Communications Biology, Vol 6, Iss 1, Pp 1-

2023 Volume 17

Abstract: Abstract Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi- ... ...

Abstract	Abstract Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Nature Portfolio
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: Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Xia, Yuhao / Zhao, Kailong / Liu, Dong / Zhou, Xiaogen / Zhang, Guijun

Communications biology

2023 Volume 6, Issue 1, Page(s) 1221

Abstract: Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain ... ...

Abstract	Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
MeSH term(s)	Deep Learning ; Proteins/chemistry ; Algorithms
Chemical Substances	Proteins
Language	English
Publishing date	2023-12-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05610-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

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

Article ; Online: Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation.

Zhou, Xiaogen / Tong, Tong / Zhong, Zhixiong / Fan, Haoyi / Li, Zuoyong

Computers in biology and medicine

2023 Volume 154, Page(s) 106551

Abstract: Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of ...

Abstract	Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of multi-task, such as the salient object detection (SOD) task and the image segmentation task. In this paper, we propose a novel dual-task framework for white blood cell (WBC) and skin lesion (SL) saliency detection and segmentation in biomedical images, called Saliency-CCE. Saliency-CCE consists of a preprocessing of hair removal for skin lesions images, a novel colour contextual extractor (CCE) module for the SOD task and an improved adaptive threshold (AT) paradigm for the image segmentation task. In the SOD task, we perform the CCE module to extract hand-crafted features through a novel colour channel volume (CCV) block and a novel colour activation mapping (CAM) block. We first exploit the CCV block to generate a target object's region of interest (ROI). After that, we employ the CAM block to yield a refined salient map as the final salient map from the extracted ROI. We propose a novel adaptive threshold (AT) strategy in the segmentation task to automatically segment the WBC and SL from the final salient map. We evaluate our proposed Saliency-CCE on the ISIC-2016, the ISIC-2017, and the SCISC datasets, which outperform representative state-of-the-art SOD and biomedical image segmentation approaches. Our code is available at https://github.com/zxg3017/Saliency-CCE.
MeSH term(s)	Algorithms ; Color ; Image Interpretation, Computer-Assisted/methods ; Diagnosis, Computer-Assisted
Language	English
Publishing date	2023-01-20
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.106551
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

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

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.

Zhao, Kailong / Xia, Yuhao / Zhang, Fujin / Zhou, Xiaogen / Li, Stan Z / Zhang, Guijun

Communications biology

2023 Volume 6, Issue 1, Page(s) 243

Abstract: Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. ... ...

Abstract	Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.
MeSH term(s)	Humans ; Protein Folding ; Recognition, Psychology
Language	English
Publishing date	2023-03-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-04605-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

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

Article ; Online: A widely conserved protein Rof inhibits transcription termination factor Rho and promotes Salmonella virulence program.

Zhang, Jing / Zhang, Shuo / Zhou, Wei / Zhang, Xiang / Li, Guanjin / Li, Ruoxuan / Lin, Xingyu / Chen, Ziying / Liu, Fang / Shen, Pan / Zhou, Xiaogen / Gao, Yue / Chen, Zhenguo / Chao, Yanjie / Wang, Chengyuan

Nature communications

2024 Volume 15, Issue 1, Page(s) 3187

Abstract: Transcription is crucial for the expression of genetic information and its efficient and accurate termination is required for all living organisms. Rho-dependent termination could rapidly terminate unwanted premature RNAs and play important roles in ... ...

Abstract	Transcription is crucial for the expression of genetic information and its efficient and accurate termination is required for all living organisms. Rho-dependent termination could rapidly terminate unwanted premature RNAs and play important roles in bacterial adaptation to changing environments. Although Rho has been discovered for about five decades, the regulation mechanisms of Rho-dependent termination are still not fully elucidated. Here we report that Rof is a conserved antiterminator and determine the cryogenic electron microscopy structure of Rho-Rof antitermination complex. Rof binds to the open-ring Rho hexamer and inhibits the initiation of Rho-dependent termination. Rof's N-terminal α-helix undergoes conformational changes upon binding with Rho, and is key in facilitating Rof-Rho interactions. Rof binds to Rho's primary binding site (PBS) and excludes Rho from binding with PBS ligand RNA at the initiation step. Further in vivo analyses in Salmonella Typhimurium show that Rof is required for virulence gene expression and host cell invasion, unveiling a physiological function of Rof and transcription termination in bacterial pathogenesis.
MeSH term(s)	Transcription Factors/metabolism ; Virulence/genetics ; Rho Factor/genetics ; Rho Factor/metabolism ; Gene Expression Regulation, Bacterial ; Transcription, Genetic ; Bacteria/genetics ; Salmonella typhimurium/genetics ; Salmonella typhimurium/metabolism
Chemical Substances	Transcription Factors ; Rho Factor
Language	English
Publishing date	2024-04-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-47438-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Zhou, Xiaogen / Peng, Chunxiang / Zheng, Wei / Li, Yang / Zhang, Guijun / Zhang, Yang

Nucleic acids research

2022 Volume 50, Issue W1, Page(s) W235–W245

Abstract: Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents ...

Abstract	Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents a significantly improved method, DEMO2, which integrates analogous template structural alignments with deep-learning techniques for high-accuracy domain structure assembly. Starting from individual domain models, inter-domain spatial restraints are first predicted with deep residual convolutional networks, where full-length structure models are assembled using L-BFGS simulations under the guidance of a hybrid energy function combining deep-learning restraints and analogous multi-domain template alignments searched from the PDB. The output of DEMO2 contains deep-learning inter-domain restraints, top-ranked multi-domain structure templates, and up to five full-length structure models. DEMO2 was tested on a large-scale benchmark and the blind CASP14 experiment, where DEMO2 was shown to significantly outperform its predecessor and the state-of-the-art protein structure prediction methods. By integrating with new deep-learning techniques, DEMO2 should help fill the rapidly increasing gap between the improved ability of tertiary structure determination and the high demand for the high-quality multi-domain protein structures. The DEMO2 server is available at https://zhanggroup.org/DEMO/.
MeSH term(s)	Algorithms ; Deep Learning ; Proteins/chemistry ; Protein Conformation ; Computational Biology/methods ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2022-05-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; 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/gkac340
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1067: Show issues

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

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Zheng, Wei / Wuyun, Qiqige / Zhou, Xiaogen / Li, Yang / Freddolino, Peter L / Zhang, Yang

Nucleic acids research

2022 Volume 50, Issue W1, Page(s) W454–W464

Abstract: Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function ... ...

Abstract	Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)-(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.
MeSH term(s)	Algorithms ; Deep Learning ; Protein Conformation ; Proteins/chemistry ; Sequence Alignment ; Sequence Analysis, Protein/methods ; Software ; Models, Chemical
Chemical Substances	Proteins
Language	English
Publishing date	2022-04-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; 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/gkac248
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1067: Show issues

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

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

To top

Full text online

More links

Kategorien

In stock of ZB MED Bonn / Germany

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Bonn / Germany

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito