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Article ; Online: Improved cancer biomarkers identification using network-constrained infinite latent feature selection.

Cai, Lihua / Wu, Honglong / Zhou, Ke

2021 Volume 16, Issue 2, Page(s) e0246668

Abstract: Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that ... ...

Abstract	Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.
MeSH term(s)	Algorithms ; Biomarkers, Tumor/genetics ; Female ; Gene Expression Profiling/methods ; Gene Ontology ; Gene Regulatory Networks ; Humans ; Male ; Neoplasms/genetics ; Oligonucleotide Array Sequence Analysis ; Support Vector Machine
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2021-02-11
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.0246668
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Article ; Online: FRMC: a fast and robust method for the imputation of scRNA-seq data.

Wu, Honglong / Wang, Xuebin / Chu, Mengtian / Xiang, Ruizhi / Zhou, Ke

RNA biology

2021 Volume 18, Issue sup1, Page(s) 172–181

Abstract: The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the ...

Abstract	The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish 'true zeros' from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.
MeSH term(s)	Gene Expression Profiling ; Humans ; RNA-Seq/methods ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software ; Whole Exome Sequencing/methods
Language	English
Publishing date	2021-08-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	2159587-2
ISSN	1555-8584 ; 1555-8584
ISSN (online)	1555-8584
ISSN	1555-8584
DOI	10.1080/15476286.2021.1960688
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Article ; Online: FRMC: a fast and robust method for the imputation of scRNA-seq data

Wu, Honglong / Wang, Xuebin / Chu, Mengtian / Xiang, Ruizhi / Zhou, Ke

RNA Biology. 2021 Oct. 15, v. 18, no. S1 p.172-181

2021

Abstract	The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish ‘true zeros’ from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.
Keywords	RNA ; computer software ; gene expression ; sequence analysis ; transcriptome ; Imputation1 ; scRNA-seq2 ; dropout event3 ; low-rank matrix optimization4 ; singular value thresholding iteration5 ; sparsity6
Language	English
Dates of publication	2021-1015
Size	p. 172-181.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2159587-2
ISSN	1555-8584
ISSN	1555-8584
DOI	10.1080/15476286.2021.1960688
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Article: HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data.

Wu, Honglong / Wang, Xuebin / Chu, Mengtian / Li, Dongfang / Cheng, Lixin / Zhou, Ke

Computational and structural biotechnology journal

2021 Volume 19, Page(s) 2637–2645

Abstract: The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated ... ...

Abstract	The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated domains, A/B compartments, and other biologically relevant signals. Normalization is a critical pre-processing step of downstream analyses for the elimination of systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and restriction fragment lengths. Especially, the problem of high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and efficient method for Hi-C data normalization. Recently, some matrix balancing methods have been developed to normalize Hi-C data, such as the Knight-Ruiz (KR) algorithm, but it failed to normalize contact matrices with high sparsity. Here, we presented an algorithm, Hi-C Matrix Balancing (HCMB), based on an iterative solution of equations, combining with linear search and projection strategy to normalize the Hi-C original interaction data. Both the simulated and experimental data demonstrated that HCMB is robust and efficient in normalizing Hi-C data and preserving the biologically relevant Hi-C features even facing very high sparsity. HCMB is implemented in Python and is freely accessible to non-commercial users at GitHub: https://github.com/HUST-DataMan/HCMB.
Language	English
Publishing date	2021-04-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2021.04.064
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Article: Diagnostic value and clinical application of next-generation sequencing for infections in immunosuppressed patients with corticosteroid therapy.

Wang, Sen / Ai, Jingwen / Cui, Peng / Zhu, Yimin / Wu, Honglong / Zhang, Wenhong

Annals of translational medicine

2020 Volume 8, Issue 5, Page(s) 227

Abstract: Background: Next-generation sequencing (NGS) is a comprehensive approach for sequence-based identification of pathogens. However, reports on the use of NGS in patients with immunosuppression are scarce, especially in subjects with negative ... ...

Abstract	Background: Next-generation sequencing (NGS) is a comprehensive approach for sequence-based identification of pathogens. However, reports on the use of NGS in patients with immunosuppression are scarce, especially in subjects with negative microbiological results. Methods: In this study, NGS was performed on samples obtained from 108 anonymized patients with suspected infection undergoing immunosuppressive corticosteroid therapy. A panel of conventional microbiological tests (CMT) was performed in parallel with NGS. Results: Of these 108 subjects, 36 were diagnosed with infections by clinical and microbiological criteria (Group I), 41 were exclusively diagnosed clinically (Group II), and 31 exhibited no evidence of infection (Group III). In Group I, NGS was concordant with CMT results from 29 patients (80.6%). A total of 4 samples had positive NGS results in Group III. NGS showed a sensitivity of 80.6% (95% CI, 64.7% to 90.6%) and specificity of 87.1% (95% CI, 70.5% to 95.5%). NGS also played an important role in optimizing antibiotic regimens in patients with negative results for CMT (Group II). The treatment success rate (TSR) of patients using NGS-guided antibiotic regimens (81.8%, 18/22) was significantly higher than that of patients using empirical antibiotics (52.6%, 10/19) (P<0.0001). NGS results were not affected by the degree of immunosuppression. Conclusions: NGS of clinical samples from immunosuppressed patients demonstrated promising diagnostic potential in identifying clinically relevant pathogens. Consequently NGS stands to become a standard tool for infection detection and control, providing valuable information to optimize antibiotic therapy in immunosuppressed patients.
Language	English
Publishing date	2020-04-16
Publishing country	China
Document type	Journal Article
ZDB-ID	2893931-1
ISSN	2305-5847 ; 2305-5839
ISSN (online)	2305-5847
ISSN	2305-5839
DOI	10.21037/atm.2020.01.30
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Article ; Online: Angiostrongyliasis detected by next-generation sequencing in a ELISA-negative eosinophilic meningitis: A case report.

Zou, Yueli / Guan, Hongzhi / Wu, Honglong / Bu, Hui / Yan, Litian / Zhu, Yifei / He, Junying

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

2020 Volume 97, Page(s) 177–179

Abstract: Next-generation sequencing (NGS) is an emerging method with the potential of pan-pathogen screening. This study described a case of eosinophilic meningitis (EoM) with enzyme-linked immunosorbent assay (ELISA)-negative results for Angiostrongylus ... ...

Abstract	Next-generation sequencing (NGS) is an emerging method with the potential of pan-pathogen screening. This study described a case of eosinophilic meningitis (EoM) with enzyme-linked immunosorbent assay (ELISA)-negative results for Angiostrongylus cantonensis (A. cantonensis), Trichinella spiralis and Paragonimus westermani and a positive identification of A. cantonensis by NGS in the cerebrospinal fluid.
MeSH term(s)	Angiostrongylus cantonensis ; Animals ; Enzyme-Linked Immunosorbent Assay ; Eosinophilia/parasitology ; High-Throughput Nucleotide Sequencing ; Humans ; Male ; Meningitis/cerebrospinal fluid ; Meningitis/diagnosis ; Meningitis/parasitology ; Strongylida Infections/cerebrospinal fluid ; Strongylida Infections/diagnosis ; Strongylida Infections/parasitology
Language	English
Publishing date	2020-06-02
Publishing country	Canada
Document type	Case Reports
ZDB-ID	1331197-9
ISSN	1878-3511 ; 1201-9712
ISSN (online)	1878-3511
ISSN	1201-9712
DOI	10.1016/j.ijid.2020.05.108
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Article ; Online: Pathogen quantitative efficacy of different spike-in internal controls and clinical application in central nervous system infection with metagenomic sequencing.

Fu, Zhangfan / Ai, Jingwen / Zhang, Haocheng / Cui, Peng / Xu, Tao / Zhang, Yumeng / Zhang, Yi / Wu, Honglong / Shen, Ao / Lin, Ke / Zhang, Miaoqu / Qiu, Chao / Jiang, Ning / Zhou, Yang / Zhang, Wenhong

Microbiology spectrum

2023 Volume 11, Issue 6, Page(s) e0113923

Abstract: Importance: Metagenomic next-generation sequencing (mNGS) has been used broadly for pathogens detection of infectious diseases. However, there is a lack of method for the absolute quantitation of pathogens by mNGS. We compared the quantitative ... ...

Abstract	Importance: Metagenomic next-generation sequencing (mNGS) has been used broadly for pathogens detection of infectious diseases. However, there is a lack of method for the absolute quantitation of pathogens by mNGS. We compared the quantitative efficiency of three mNGS internal controls (ICs)
MeSH term(s)	Humans ; Central Nervous System Infections ; High-Throughput Nucleotide Sequencing ; Bacteriophages ; Metagenome ; Metagenomics
Language	English
Publishing date	2023-11-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.01139-23
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Article ; Online: GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Wang, Xuebin / Wang, Taifu / Xie, Zhihao / Zhang, Youjin / Xia, Shiqiang / Sun, Ruixue / He, Xinqiu / Xiang, Ruizhi / Zheng, Qiwen / Liu, Zhencheng / Wang, Jin'An / Wu, Honglong / Jin, Xiangqian / Chen, Weijun / Li, Dongfang / He, Zengquan

Briefings in bioinformatics

2023 Volume 24, Issue 2

Abstract: Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a ... ...

Abstract	Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.
MeSH term(s)	Metagenome ; Microbiota ; High-Throughput Nucleotide Sequencing/methods ; Metagenomics/methods ; Sensitivity and Specificity
Language	English
Publishing date	2023-03-13
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/bbad092
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Article ; Online: Evaluations of Clinical Utilization of Metagenomic Next-Generation Sequencing in Adults With Fever of Unknown Origin.

Fu, Zhang-Fan / Zhang, Hao-Cheng / Zhang, Yi / Cui, Peng / Zhou, Yang / Wang, Hong-Yu / Lin, Ke / Zhou, Xian / Wu, Jing / Wu, Hong-Long / Zhang, Wen-Hong / Ai, Jing-Wen

Frontiers in cellular and infection microbiology

2022 Volume 11, Page(s) 745156

Abstract: Introduction: The diagnosis of infection-caused fever of unknown origin (FUO) is still challenging, making it difficult for physicians to provide an early effective therapy. Therefore, a novel pathogen detection platform is needed. Metagenomic next- ... ...

Abstract	Introduction: The diagnosis of infection-caused fever of unknown origin (FUO) is still challenging, making it difficult for physicians to provide an early effective therapy. Therefore, a novel pathogen detection platform is needed. Metagenomic next-generation sequencing (mNGS) provides an unbiased, comprehensive technique for the sequence-based identification of pathogenic microbes, but the study of the diagnostic values of mNGS in FUO is still limited. Methods: In a single-center retrospective cohort study, 175 FUO patients were enrolled, and clinical data were recorded and analyzed to compare mNGS with culture or traditional methods including as smears, serological tests, and nucleic acid amplification testing (NAAT) (traditional PCR, Xpert MTB/RIF, and Xpert MTB/RIF Ultra). Results: The blood mNGS could increase the overall rate of new organisms detected in infection-caused FUO by roughly 22.9% and 19.79% in comparison to culture (22/96 vs. 0/96; OR, ∞; p = 0.000) and conventional methods (19/96 vs. 3/96; OR, 6.333; p = 0.001), respectively. Bloodstream infection was among the largest group of those identified, and the blood mNGS could have a 38% improvement in the diagnosis rate compared to culture (19/50 vs. 0/50; OR, ∞; p = 0.000) and 32.0% compared to conventional methods (16/50 vs. 3/50; OR, 5.333; p = 0.004). Among the non-blood samples in infection-caused FUO, we observed that the overall diagnostic performance of mNGS in infectious disease was better than that of conventional methods by 20% (9/45 vs. 2/45; OR, 4.5; p = 0.065), and expectedly, the use of non-blood mNGS in non-bloodstream infection increased the diagnostic rate by 26.2% (8/32 vs. 0/32; OR, ∞; p = 0.008). According to 175 patients' clinical decision-making, we found that the use of blood mNGS as the first-line investigation could effectively increase 10.9% of diagnosis rate of FUO compared to culture, and the strategy that the mNGS of suspected parts as the second-line test could further benefit infectious patients, improving the diagnosis rate of concurrent infection by 66.7% and 12.5% in non-bloodstream infection, respectively. Conclusion: The application of mNGS in the FUO had significantly higher diagnostic efficacy than culture or other conventional methods. In infection-caused FUO patients, application of blood mNGS as the first-line investigation and identification of samples from suspected infection sites as the second-line test could enhance the overall FUO diagnosis rate and serve as a promising optimized diagnostic protocol in the future.
MeSH term(s)	Adult ; Fever of Unknown Origin/diagnosis ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Metagenome ; Metagenomics/methods ; Retrospective Studies ; Sensitivity and Specificity
Language	English
Publishing date	2022-01-21
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2619676-1
ISSN	2235-2988 ; 2235-2988
ISSN (online)	2235-2988
ISSN	2235-2988
DOI	10.3389/fcimb.2021.745156
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Article ; Online: Clinical application and evaluation of metagenomic next-generation sequencing in suspected adult central nervous system infection.

Zhang, Yi / Cui, Peng / Zhang, Hao-Cheng / Wu, Hong-Long / Ye, Ming-Zhi / Zhu, Yi-Min / Ai, Jing-Wen / Zhang, Wen-Hong

Journal of translational medicine

2020 Volume 18, Issue 1, Page(s) 199

Abstract: Background: Accurate etiology diagnosis is crucial for central nervous system infections (CNS infections). The diagnostic value of metagenomic next-generation sequencing (mNGS), an emerging powerful platform, remains to be studied in CNS infections.: ... ...

Abstract	Background: Accurate etiology diagnosis is crucial for central nervous system infections (CNS infections). The diagnostic value of metagenomic next-generation sequencing (mNGS), an emerging powerful platform, remains to be studied in CNS infections. Methods: We conducted a single-center prospective cohort study to compare mNGS with conventional methods including culture, smear and etc. 248 suspected CNS infectious patients were enrolled and clinical data were recorded. Results: mNGS reported a 90.00% (9/10) sensitivity in culture-positive patients without empirical treatment and 66.67% (6/9) in empirically-treated patients. Detected an extra of 48 bacteria and fungi in culture-negative patients, mNGS provided a higher detection rate compared to culture in patients with (34.45% vs. 7.56%, McNemar test, p < 0.0083) or without empirical therapy (50.00% vs. 25.00%, McNemar test, p > 0.0083). Compared to conventional methods, positive percent agreement and negative percent agreement was 75.00% and 69.11% separately. mNGS detection rate was significantly higher in patients with cerebrospinal fluid (CSF) WBC > 300 * 10 Conclusion: mNGS showed a satisfying diagnostic performance in CNS infections and had an overall superior detection rate to culture. mNGS may held diagnostic advantages especially in empirically treated patients. CSF laboratory results were statistically relevant to mNGS detection rate, and mNGS could dynamically monitor disease progression.
MeSH term(s)	Adult ; Central Nervous System Infections/diagnosis ; High-Throughput Nucleotide Sequencing ; Humans ; Metagenomics ; Prospective Studies ; Sensitivity and Specificity
Keywords	covid19
Language	English
Publishing date	2020-05-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1479-5876
ISSN (online)	1479-5876
DOI	10.1186/s12967-020-02360-6
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