LIVIVO - Search results -

Search results

Result 1 - 10 of total 36

Search options

Article: Design and In Silico Validation of a Novel MZF-1-Based Multi-Epitope Vaccine to Combat Metastatic Triple Negative Breast Cancer.

Krishnamoorthy, HemaNandini Rajendran / Karuppasamy, Ramanathan

2023 Volume 11, Issue 3

Abstract: Immunotherapy is emerging as a potential therapeutic strategy for triple negative breast cancer (TNBC) owing to the immunogenic landscape of its tumor microenvironment. Interestingly, peptide-based cancer vaccines have garnered a lot of attention as one ... ...

Abstract	Immunotherapy is emerging as a potential therapeutic strategy for triple negative breast cancer (TNBC) owing to the immunogenic landscape of its tumor microenvironment. Interestingly, peptide-based cancer vaccines have garnered a lot of attention as one of the most promising cancer immunotherapy regimens. Thus, the present study intended to design a novel, efficacious peptide-based vaccine against TNBC targeting myeloid zinc finger 1 (MZF1), a transcription factor that has been described as an oncogenic inducer of TNBC metastasis. Initially, the antigenic peptides from MZF1 were identified and evaluated based on their likelihood to induce immunological responses. The promiscuous epitopes were then combined using a suitable adjuvant (50S ribosomal L7/L12 protein) and linkers (AAY, GPGPG, KK, and EAAAK) to reduce junctional immunogenicity. Furthermore, docking and dynamics analyses against TLR-4 and TLR-9 were carried out to understand more about their structural stability and integrity. Finally, the constructed vaccine was subjected to in silico cloning and immune simulation studies. Overall, the findings imply that the designed chimeric vaccine could induce strong humoral and cellular immune responses in the desired organism. In light of these findings, the final multi-epitope vaccine could be used as an effective prophylactic treatment for TNBC and may pave the way for future research.
Language	English
Publishing date	2023-03-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2703319-3
ISSN	2076-393X
ISSN	2076-393X
DOI	10.3390/vaccines11030577
Database	MEDical Literature Analysis and Retrieval System OnLINE

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.

Article ; Online: Imidazole and Biphenyl Derivatives as Anti-cancer Agents for Glioma Therapeutics: Computational Drug Repurposing Strategy.

Murali, Poornimaa / Karuppasamy, Ramanathan

Anti-cancer agents in medicinal chemistry

2023 Volume 23, Issue 9, Page(s) 1085–1101

Abstract: Background: Targeting mutated isocitrate dehydrogenase 1 (mIDH1) is one of the key therapeutic strategies for the treatment of glioma. Few inhibitors, such as ivosidenib and vorasidenib, have been identified as selective inhibitors of mIDH1. However, ... ...

Abstract	Background: Targeting mutated isocitrate dehydrogenase 1 (mIDH1) is one of the key therapeutic strategies for the treatment of glioma. Few inhibitors, such as ivosidenib and vorasidenib, have been identified as selective inhibitors of mIDH1. However, dose-dependent toxicity and limited brain penetration of the blood-brain barrier remain the major limitations of the treatment procedures using these inhibitors. Objective: In the present study, computational drug repurposing strategies were employed to identify potent mIDH1- specific inhibitors from the 11,808 small molecules listed in the DrugBank repository. Methods: Tanimoto coefficient (Tc) calculations were initially used to retrieve compounds with structurally similar scaffolds to ivosidenib. The resultant compounds were then subjected to molecular docking to discriminate the binders from the non-binders. The binding affinities and pharmacokinetic properties of the screened compounds were examined using prime Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) and QikProp algorithm, respectively. The conformational stability of these molecules was validated using 100 ns molecular dynamics simulation. Results: Together, these processes led to the identification of three-hit molecules, namely DB12001, DB08026, and DB03346, as potential inhibitors of the mIDH1 protein. Of note, the binding free energy calculations and MD simulation studies emphasized the greater binding affinity and structural stability of the hit compounds towards the mIDH1 protein. Conclusion: The collective evidence from our study indicates the activity of DB12001 against recurrent glioblastoma, which, in turn, highlights the accuracy of our adapted strategy. Hence, we hypothesize that the identified lead molecules could be translated for the development of mIDH1 inhibitors in the near future.
MeSH term(s)	Humans ; Molecular Docking Simulation ; Drug Repositioning ; Neoplasm Recurrence, Local ; Antineoplastic Agents/pharmacology ; Imidazoles ; Glioma/drug therapy ; Molecular Dynamics Simulation
Chemical Substances	diphenyl (2L9GJK6MGN) ; Antineoplastic Agents ; Imidazoles
Language	English
Publishing date	2023-01-21
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2217610-X
ISSN	1875-5992 ; 1871-5206
ISSN (online)	1875-5992
ISSN	1871-5206
DOI	10.2174/1871520623666230125090815
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 5583: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: A multitier virtual screening of antagonists targeting PD-1/PD-L1 interface for the management of triple-negative breast cancer.

Krishnamoorthy, HemaNandini Rajendran / Karuppasamy, Ramanathan

Medical oncology (Northwood, London, England)

2023 Volume 40, Issue 11, Page(s) 312

Abstract: Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been ... ...

Abstract	Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been proven to be an efficient and potent strategy to combat triple-negative breast cancer. To date, various anti-PD-1/anti-PD-L1 antibodies have been approved. However, the intrinsic constraints of these therapeutic antibodies significantly limit their application, making small molecules a potentially significant option for PD-1/PD-L1 inhibition. In light of this, the current study aims to use a high-throughput virtual screening technique to identify potential repurposed candidates as PD-L1 inhibitors. Thus, the present study explored binding efficiency of 2509 FDA-approved compounds retrieved from the drug bank database against PD-L1 protein. The binding affinity of the compounds was determined using the glide XP docking programme. Furthermore, prime-MM/GBSA, DFT calculations, and RF score were used to precisely re-score the binding free energy of the docked complexes. In addition, the ADME and toxicity profiles for the lead compounds were also examined to address PK/PD characteristics. Altogether, the screening process identified three molecules, namely DB01238, DB06016 and DB01167 as potential therapeutics for the PD-L1 protein. To conclude, a molecular dynamic simulation of 100 ns was run to characterise the stability and inhibitory action of the three lead compounds. The results from the simulation study confirm the robust structural and thermodynamic stability of DB01238 than other investigated molecules. Thus, our findings hypothesize that DB01238 could serve as potential PD-L1 inhibitor in the near future for triple-negative breast cancer patients.
MeSH term(s)	Humans ; B7-H1 Antigen/antagonists & inhibitors ; B7-H1 Antigen/metabolism ; Early Detection of Cancer ; Molecular Dynamics Simulation ; Programmed Cell Death 1 Receptor/antagonists & inhibitors ; Triple Negative Breast Neoplasms/metabolism ; Immune Checkpoint Inhibitors/chemistry ; Immune Checkpoint Inhibitors/pharmacology
Chemical Substances	B7-H1 Antigen ; Programmed Cell Death 1 Receptor ; Immune Checkpoint Inhibitors
Language	English
Publishing date	2023-09-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	1201189-7
ISSN	1559-131X ; 0736-0118 ; 1357-0560
ISSN (online)	1559-131X
ISSN	0736-0118 ; 1357-0560
DOI	10.1007/s12032-023-02183-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 1899: 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 ▾

Article ; Online: Exploration of natural product database for the identification of potent inhibitor against IDH2 mutational variants for glioma therapy

Murali, Poornimaa / Karuppasamy, Ramanathan

J Mol Model. 2023 Jan., v. 29, no. 1, p. 6

2023 , Page(s) 6

Abstract: Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting ... ...

Abstract	Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting toxicity and modest brain penetrating capability restrict its use as a plausible mIDH2 inhibitor. Furthermore, secondary site mutations (Q316E and I319M) were identified in patients with enasidenib treatments resulting in acquired therapeutic resistance. Hence, in the present investigation, we aimed to identify novel and potent drug-like compounds to overcome the existing drawbacks using an integrated in-silico strategy. A sum of 1574 natural compounds from the naturally occurring plant-based anti-cancerous compound activity target (NPACT) database was proclaimed and subjected to molecular docking. The binding affinities of the resultant natural compounds were rescored using MM-GBSA scoring functions. The resultant lead molecules were subjected to anticancer activity prediction using the machine-learning model. Furthermore, the toxicity and drug-likeliness of the lead compounds were investigated using ADMET properties. Eventually, the integrated in silico approach resulted in a lead molecule, namely squalene (NPACT00954) against mIDH2 protein. The screened compound was subjected to mutational analysis accomplishing second-site mutations. Interestingly, squalene exhibited appreciable binding affinity alongside good brain penetrating potential than enasidenib. Indeed, the reproducibility and significance of our results are examined by running 3 replicas of 100-ns simulations per system using the random initial velocities of the atoms generated by Maxwell distribution at a given temperature. Thus, we hypothesize from our results that further optimization of squalene could be beneficial for the treatment and management of glioma in the near future.
Keywords	antineoplastic activity ; artificial intelligence ; brain ; computer simulation ; databases ; drugs ; glioma ; isocitrate dehydrogenase ; models ; mutation ; mutational analysis ; prediction ; squalene ; temperature ; therapeutics ; toxicity
Language	English
Dates of publication	2023-01
Size	p. 6
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	1284729-X
ISSN	0948-5023 ; 1610-2940
ISSN (online)	0948-5023
ISSN	1610-2940
DOI	10.1007/s00894-022-05409-z
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Article ; Online: Mining of soil data for predicting the paddy productivity by machine learning techniques

Antony, Ajitha / Karuppasamy, Ramanathan

Paddy Water Environ. 2023 Apr., v. 21, no. 2 p.231-242

2023

Abstract: Crop yield prediction is a challenging task towards precision agriculture. In particular, paddy is one of the world’s significant cereal crops and thus crucial for crop management and decision making. Despite the number of crop yield prediction models, ... ...

Abstract	Crop yield prediction is a challenging task towards precision agriculture. In particular, paddy is one of the world’s significant cereal crops and thus crucial for crop management and decision making. Despite the number of crop yield prediction models, better performance in paddy yield prediction is still desirable. Keeping this in mind, the present study aimed to determine the most influencing features that impact paddy production. We employed a machine learning algorithm alongside the best data sources for paddy yield prediction in this study. A total of 5 regression machine learning algorithms were developed using the 16 input variables obtained from the soil health card. Note that we have carried out multiple approaches to improving the model performances. The model results were also validated using Monte Carlo methods. The result from our analysis depicts that XG boost ensembled random forest has demonstrated the highest prediction accuracy of 86% of the other models investigated in our study. It is worth mentioning that this is the first study on paddy crop yield prediction from the features of a soil health card. Indeed, farmers and agronomists could use this model to plan their paddy cultivation and procure the maximum yield.
Keywords	algorithms ; crop management ; crop yield ; paddies ; precision agriculture ; prediction ; soil ; soil quality ; water ; yield forecasting
Language	English
Dates of publication	2023-04
Size	p. 231-242.
Publishing place	Springer Nature Singapore
Document type	Article ; Online
ZDB-ID	2168266-5
ISSN	1611-2490
ISSN	1611-2490
DOI	10.1007/s10333-023-00924-y
Database	NAL-Catalogue (AGRICOLA)

In stock of ZB MED Bonn / Germany

Z 7524: 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: Bioherbicides for sustainable barnyard grass management in paddy field: an in-silico perspective.

Antony, Ajitha / Karuppasamy, Ramanathan

Natural product research

2022 Volume 37, Issue 22, Page(s) 3857–3861

Abstract: Paddy ( ...

Abstract	Paddy (
Language	English
Publishing date	2022-12-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2185747-7
ISSN	1478-6427 ; 1478-6419
ISSN (online)	1478-6427
ISSN	1478-6419
DOI	10.1080/14786419.2022.2152449
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Exploration of natural product database for the identification of potent inhibitor against IDH2 mutational variants for glioma therapy.

Murali, Poornimaa / Karuppasamy, Ramanathan

Journal of molecular modeling

2022 Volume 29, Issue 1, Page(s) 6

Abstract	Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting toxicity and modest brain penetrating capability restrict its use as a plausible mIDH2 inhibitor. Furthermore, secondary site mutations (Q316E and I319M) were identified in patients with enasidenib treatments resulting in acquired therapeutic resistance. Hence, in the present investigation, we aimed to identify novel and potent drug-like compounds to overcome the existing drawbacks using an integrated in-silico strategy. A sum of 1574 natural compounds from the naturally occurring plant-based anti-cancerous compound activity target (NPACT) database was proclaimed and subjected to molecular docking. The binding affinities of the resultant natural compounds were rescored using MM-GBSA scoring functions. The resultant lead molecules were subjected to anticancer activity prediction using the machine-learning model. Furthermore, the toxicity and drug-likeliness of the lead compounds were investigated using ADMET properties. Eventually, the integrated in silico approach resulted in a lead molecule, namely squalene (NPACT00954) against mIDH2 protein. The screened compound was subjected to mutational analysis accomplishing second-site mutations. Interestingly, squalene exhibited appreciable binding affinity alongside good brain penetrating potential than enasidenib. Indeed, the reproducibility and significance of our results are examined by running 3 replicas of 100-ns simulations per system using the random initial velocities of the atoms generated by Maxwell distribution at a given temperature. Thus, we hypothesize from our results that further optimization of squalene could be beneficial for the treatment and management of glioma in the near future.
Language	English
Publishing date	2022-12-09
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1284729-X
ISSN	0948-5023 ; 1610-2940
ISSN (online)	0948-5023
ISSN	1610-2940
DOI	10.1007/s00894-022-05409-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

Details ▾
- Full text online
- Order with fees

Article ; Online: Forecasting determinants of recurrence in lung cancer patients exploiting various machine learning models.

Thirunavukkarasu, Muthu Kumar / Karuppasamy, Ramanathan

Journal of biopharmaceutical statistics

2022 Volume 33, Issue 3, Page(s) 257–271

Abstract: Lung cancer recurrence seems to be the most leading cause of death as well as deterioration of lifespan. Proper assessment of the probability of recurrence in early-stage lung cancer is necessary to push up the treatment progress. We therefore employed ... ...

Abstract	Lung cancer recurrence seems to be the most leading cause of death as well as deterioration of lifespan. Proper assessment of the probability of recurrence in early-stage lung cancer is necessary to push up the treatment progress. We therefore employed machine-learning technologies to forecast post-operative recurrence risks using 174 lung cancer patient records. Six classification algorithms logistic regression, SVM, decision tree classification, random forest classification, XGBoost and lightGBM were used to predict the cancer recurrence. The patient samples were divided into training and test group with the split ratio of 3:1 for model generation and the accuracy were validated using k-fold cross-validation method. It is worth noting that the logistic regression model outperformed all the models in both training (Accuracy = 0.82) and test set (Accuracy = 0.79) on k-fold validation. Further, the optimal features (n = 7) identified using the RFE method is certainly helpful to improve the model in a high precision. The imperative risk factors associated with recurrence were identified using three feature selection methods. Importantly, our research showed that age is an important prognostic factor to be considered during the recurrence prediction. Indeed, severe concern on the identified risk factors combined with predictive models assists the physician to reduce the cancer recurrence rate in patients with lung cancer.
MeSH term(s)	Humans ; Neoplasm Recurrence, Local/epidemiology ; Lung Neoplasms/diagnosis ; Lung Neoplasms/epidemiology ; Machine Learning ; Forecasting ; Algorithms
Language	English
Publishing date	2022-11-17
Publishing country	England
Document type	Journal Article
ZDB-ID	1131763-2
ISSN	1520-5711 ; 1054-3406
ISSN (online)	1520-5711
ISSN	1054-3406
DOI	10.1080/10543406.2022.2148162
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 3511: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer.

Thirunavukkarasu, Muthu Kumar / Ramesh, Priyanka / Karuppasamy, Ramanathan / Veerappapillai, Shanthi

Journal of applied genetics

2024

Abstract: Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are ... ...

Abstract	Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.
Language	English
Publishing date	2024-03-05
Publishing country	England
Document type	Journal Article
ZDB-ID	1235302-4
ISSN	2190-3883 ; 1234-1983
ISSN (online)	2190-3883
ISSN	1234-1983
DOI	10.1007/s13353-024-00847-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Bonn / Germany

Einzelsign.: 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: Searching of Novel Herbicides for Paddy Field Weed Management—A Case Study with Acetyl-CoA Carboxylase

Antony, Ajitha / Karuppasamy, Ramanathan

Agronomy. 2022 July 08, v. 12, no. 7

2022

Abstract: Weed management is the major biological constraint in paddy (Oryza sativa L.) producing areas. Predominantly, barnyard grass (Echinochloa crus-galli) is a rice-mimicking weed that causes 57% of yield loss in rice production. Conventionally, herbicides ... ...

Abstract	Weed management is the major biological constraint in paddy (Oryza sativa L.) producing areas. Predominantly, barnyard grass (Echinochloa crus-galli) is a rice-mimicking weed that causes 57% of yield loss in rice production. Conventionally, herbicides are the site-specific weed inhibitors often used to suppress E. crus-galli growth. Acetyl-CoA carboxylase (ACCase) is an important target for developing novel herbicides with remarkable selectivity against gramineous weeds. Notably, fenoxaprop-P-ethyl (FPPE) is a selective ACCase herbicide extensively used in paddy fields to inhibit barnyard grass. However, prolonged use of FPPE herbicide elicits phytotoxicity in cultivated rice and herbicide resistance in weeds. Recently, phytotoxins are emerging as an alternative to commercial herbicides with safer environmental profiles. Nevertheless, discovering natural herbicides through in vivo and in vitro techniques is time-consuming and expensive. Therefore, high-end computational screening strategies including Tanimoto similarity, docking, binding free energy, and herbicide-likeness were used to pinpoint the lead molecule. Finally, molecular dynamics and MM/PBSA calculations were employed to validate the binding kinetics of the hit compound. Indeed, sinigrin was identified as a promising phytotoxic inhibitor against the ACCase enzyme. The findings of our study were well correlated with the existing experimental results. Overall, the current work will aid in the development of commercializing phytotoxin herbicides in foreseeable future.
Keywords	Echinochloa crus-galli ; Gibbs free energy ; Oryza sativa ; acetyl-CoA carboxylase ; agronomy ; case studies ; herbicide resistance ; herbicides ; molecular dynamics ; paddies ; phytotoxicity ; phytotoxins ; rice ; sinigrin ; weed control ; weeds
Language	English
Dates of publication	2022-0708
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2607043-1
ISSN	2073-4395
ISSN	2073-4395
DOI	10.3390/agronomy12071635
Database	NAL-Catalogue (AGRICOLA)

Order via subito

To top

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Bonn / Germany

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

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 Bonn / Germany

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito