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Article ; Online: PROX1 induction by autolysosomal activity stabilizes persister-like state of colon cancer via feedback repression of the NOX1-mTORC1 pathway.

Ohata, Hirokazu / Shiokawa, Daisuke / Sakai, Hiroaki / Kanda, Yusuke / Okimoto, Yoshie / Kaneko, Syuzo / Hamamoto, Ryuji / Nakagama, Hitoshi / Okamoto, Koji

Cell reports

2023 Volume 42, Issue 6, Page(s) 112519

Abstract: Cancer chemoresistance is often attributed to slow-cycling persister populations with cancer stem cell (CSC)-like features. However, how persister populations emerge and prevail in cancer remains obscure. We previously demonstrated that while the NOX1- ... ...

Abstract	Cancer chemoresistance is often attributed to slow-cycling persister populations with cancer stem cell (CSC)-like features. However, how persister populations emerge and prevail in cancer remains obscure. We previously demonstrated that while the NOX1-mTORC1 pathway is responsible for proliferation of a fast-cycling CSC population, PROX1 expression is required for chemoresistant persisters in colon cancer. Here, we show that enhanced autolysosomal activity mediated by mTORC1 inhibition induces PROX1 expression and that PROX1 induction in turn inhibits NOX1-mTORC1 activation. CDX2, identified as a transcriptional activator of NOX1, mediates PROX1-dependent NOX1 inhibition. PROX1-positive and CDX2-positive cells are present in distinct populations, and mTOR inhibition triggers conversion of the CDX2-positive population to the PROX1-positive population. Inhibition of autophagy synergizes with mTOR inhibition to block cancer proliferation. Thus, mTORC1 inhibition-mediated induction of PROX1 stabilizes a persister-like state with high autolysosomal activity via a feedback regulation that involves a key cascade of proliferating CSCs.
MeSH term(s)	Humans ; Cell Line, Tumor ; Cell Proliferation ; Colonic Neoplasms/metabolism ; Feedback ; Mechanistic Target of Rapamycin Complex 1/metabolism ; NADPH Oxidase 1
Chemical Substances	Mechanistic Target of Rapamycin Complex 1 (EC 2.7.11.1) ; NADPH Oxidase 1 (EC 1.6.3.-) ; NOX1 protein, human (EC 1.6.3.-) ; prospero-related homeobox 1 protein
Language	English
Publishing date	2023-05-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2023.112519
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analysis of super-enhancer using machine learning and its application to medical biology.

Hamamoto, Ryuji / Takasawa, Ken / Shinkai, Norio / Machino, Hidenori / Kouno, Nobuji / Asada, Ken / Komatsu, Masaaki / Kaneko, Syuzo

Briefings in bioinformatics

2023 Volume 24, Issue 3

Abstract: The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the ... ...

Abstract	The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.
MeSH term(s)	Artificial Intelligence ; Algorithms ; Machine Learning ; Genomics ; Enhancer Elements, Genetic
Language	English
Publishing date	2023-03-24
Publishing country	England
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad107
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Publisher Correction to: C11orf95-RELA fusion drives aberrant gene expression through the unique epigenetic regulation for ependymoma formation.

Ozawa, Tatsuya / Kaneko, Syuzo / Szulzewsky, Frank / Qiao, Zhiwei / Takadera, Mutsumi / Narita, Yoshitaka / Kondo, Tadashi / Holland, Eric C / Hamamoto, Ryuji / Ichimura, Koichi

Acta neuropathologica communications

2021 Volume 9, Issue 1, Page(s) 100

Language	English
Publishing date	2021-05-27
Publishing country	England
Document type	Published Erratum
ZDB-ID	2715589-4
ISSN	2051-5960 ; 2051-5960
ISSN (online)	2051-5960
ISSN	2051-5960
DOI	10.1186/s40478-021-01157-y
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Article ; Online: Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas.

Machino, Hidenori / Dozen, Ai / Konaka, Mariko / Komatsu, Masaaki / Nakamura, Kohei / Ikawa, Noriko / Shozu, Kanto / Asada, Ken / Kaneko, Syuzo / Yoshida, Hiroshi / Kato, Tomoyasu / Nakayama, Kentaro / Saloura, Vassiliki / Kyo, Satoru / Hamamoto, Ryuji

Experimental & molecular medicine

2023 Volume 55, Issue 10, Page(s) 2205–2219

Abstract: High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and ... ...

Abstract	High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and transcription factor dysregulation in HGSOC have not yet been fully elucidated. In this study, we performed integrative omics analyses of a series of stepwise HGSOC model cells originating from human fallopian tube secretory epithelial cells (HFTSECs) to investigate early epigenetic alterations in HGSOC tumorigenesis. Assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) methods were used to analyze HGSOC samples. Additionally, protein expression changes in target genes were confirmed using normal HFTSECs, serous tubal intraepithelial carcinomas (STICs), and HGSOC tissues. Transcription factor motif analysis revealed that the DNA-binding activity of the AP-1 complex and GATA family proteins was dysregulated during early tumorigenesis. The protein expression levels of JUN and FOSL2 were increased, and those of GATA6 and DAB2 were decreased in STIC lesions, which were associated with epithelial-mesenchymal transition (EMT) and proteasome downregulation. The genomic region around the FRA16D site, containing a cadherin cluster region, was epigenetically suppressed by oncogenic signaling. Proteasome inhibition caused the upregulation of chemokine genes, which may facilitate immune evasion during HGSOC tumorigenesis. Importantly, MEK inhibitor treatment reversed these oncogenic alterations, indicating its clinical effectiveness in a subgroup of patients with HGSOC. This result suggests that MEK inhibitor therapy may be an effective treatment option for chemotherapy-resistant HGSOC.
MeSH term(s)	Female ; Humans ; Ovarian Neoplasms/metabolism ; Proteasome Endopeptidase Complex/metabolism ; Cystadenocarcinoma, Serous/genetics ; Cystadenocarcinoma, Serous/metabolism ; Cystadenocarcinoma, Serous/pathology ; Carcinogenesis/genetics ; Transcription Factors/metabolism ; Epigenesis, Genetic ; Mitogen-Activated Protein Kinase Kinases/metabolism
Chemical Substances	Proteasome Endopeptidase Complex (EC 3.4.25.1) ; Transcription Factors ; Mitogen-Activated Protein Kinase Kinases (EC 2.7.12.2)
Language	English
Publishing date	2023-10-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1328915-9
ISSN	2092-6413 ; 1226-3613 ; 0378-8512
ISSN (online)	2092-6413
ISSN	1226-3613 ; 0378-8512
DOI	10.1038/s12276-023-01090-1
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Article ; Online: Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas

Hidenori Machino / Ai Dozen / Mariko Konaka / Masaaki Komatsu / Kohei Nakamura / Noriko Ikawa / Kanto Shozu / Ken Asada / Syuzo Kaneko / Hiroshi Yoshida / Tomoyasu Kato / Kentaro Nakayama / Vassiliki Saloura / Satoru Kyo / Ryuji Hamamoto

Experimental and Molecular Medicine, Vol 55, Iss 10, Pp 2205-

2023 Volume 2219

Abstract: Abstract High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations ... ...

Abstract	Abstract High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and transcription factor dysregulation in HGSOC have not yet been fully elucidated. In this study, we performed integrative omics analyses of a series of stepwise HGSOC model cells originating from human fallopian tube secretory epithelial cells (HFTSECs) to investigate early epigenetic alterations in HGSOC tumorigenesis. Assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) methods were used to analyze HGSOC samples. Additionally, protein expression changes in target genes were confirmed using normal HFTSECs, serous tubal intraepithelial carcinomas (STICs), and HGSOC tissues. Transcription factor motif analysis revealed that the DNA-binding activity of the AP-1 complex and GATA family proteins was dysregulated during early tumorigenesis. The protein expression levels of JUN and FOSL2 were increased, and those of GATA6 and DAB2 were decreased in STIC lesions, which were associated with epithelial-mesenchymal transition (EMT) and proteasome downregulation. The genomic region around the FRA16D site, containing a cadherin cluster region, was epigenetically suppressed by oncogenic signaling. Proteasome inhibition caused the upregulation of chemokine genes, which may facilitate immune evasion during HGSOC tumorigenesis. Importantly, MEK inhibitor treatment reversed these oncogenic alterations, indicating its clinical effectiveness in a subgroup of patients with HGSOC. This result suggests that MEK inhibitor therapy may be an effective treatment option for chemotherapy-resistant HGSOC.
Keywords	Medicine ; R ; Biochemistry ; QD415-436
Subject code	616
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Critical Roles of

Asada, Ken / Bolatkan, Amina / Takasawa, Ken / Komatsu, Masaaki / Kaneko, Syuzo / Hamamoto, Ryuji

Biomolecules

2020 Volume 10, Issue 7

Abstract: Studies have shown that epigenetic abnormalities are involved in various diseases, including cancer. In particular, in order to realize precision medicine, the integrated analysis of genetics and epigenetics is considered to be important; detailed ... ...

Abstract	Studies have shown that epigenetic abnormalities are involved in various diseases, including cancer. In particular, in order to realize precision medicine, the integrated analysis of genetics and epigenetics is considered to be important; detailed epigenetic analysis in the medical field has been becoming increasingly important. In the epigenetics analysis, DNA methylation and histone modification analyses have been actively studied for a long time, and many important findings were accumulated. On the other hand, recently, attention has also been focused on RNA modification in the field of epigenetics; now it is known that RNA modification is associated with various biological functions, such as regulation of gene expression. Among RNA modifications, functional analysis of
MeSH term(s)	Adenosine/analogs & derivatives ; Adenosine/genetics ; Adenosine/metabolism ; Animals ; Epigenesis, Genetic ; Humans ; Neoplasms/genetics ; Neoplasms/metabolism ; RNA/genetics ; RNA/metabolism ; RNA Folding ; RNA Processing, Post-Transcriptional ; RNA Stability ; RNA Transport ; Virus Diseases/genetics ; Virus Diseases/metabolism
Chemical Substances	RNA (63231-63-0) ; N-methyladenosine (CLE6G00625) ; Adenosine (K72T3FS567)
Keywords	covid19
Language	English
Publishing date	2020-07-17
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom10071071
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Article ; Online: Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

Ryuji Hamamoto / Masaaki Komatsu / Ken Takasawa / Ken Asada / Syuzo Kaneko

Biomolecules, Vol 10, Iss 1, p

2019 Volume 62

Abstract: To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core ... ...

Abstract	To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.
Keywords	epigenetics ; precision medicine ; dna methylation ; histone modifications ; machine learning ; deep learning ; Microbiology ; QR1-502
Subject code	006
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine.

Hamamoto, Ryuji / Komatsu, Masaaki / Takasawa, Ken / Asada, Ken / Kaneko, Syuzo

Biomolecules

2019 Volume 10, Issue 1

Abstract	To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.
MeSH term(s)	Artificial Intelligence ; DNA/genetics ; Epigenesis, Genetic/genetics ; Humans ; Precision Medicine ; RNA/genetics
Chemical Substances	RNA (63231-63-0) ; DNA (9007-49-2)
Language	English
Publishing date	2019-12-30
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom10010062
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Article: Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging.

Komatsu, Masaaki / Sakai, Akira / Dozen, Ai / Shozu, Kanto / Yasutomi, Suguru / Machino, Hidenori / Asada, Ken / Kaneko, Syuzo / Hamamoto, Ryuji

Biomedicines

2021 Volume 9, Issue 7

Abstract: Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other ... ...

Abstract	Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.
Language	English
Publishing date	2021-06-23
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines9070720
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Article ; Online: Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability.

Experimental & molecular medicine

2024 Volume 56, Issue 3, Page(s) 646–655

Abstract: DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have ... ...

Abstract	DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.
MeSH term(s)	Humans ; DNA Methylation ; Cross-Sectional Studies ; Algorithms ; Epigenesis, Genetic ; Neoplasms/genetics
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	1328915-9
ISSN	2092-6413 ; 1226-3613 ; 0378-8512
ISSN (online)	2092-6413
ISSN	1226-3613 ; 0378-8512
DOI	10.1038/s12276-024-01173-7
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