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Cancer growth and metastasis
| Cancer growth and metastasis |
| Pubbl/distr/stampa | Auckland, N.Z., : Libertas Academica, 2008- |
| Disciplina | 616.99400285 |
| Soggetto topico |
Cancer
Metastasis Neoplasm Metastasis Medical Oncology |
| Soggetto genere / forma |
Periodical
Periodicals. |
| Soggetto non controllato | Oncology |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996208370903316 |
| Auckland, N.Z., : Libertas Academica, 2008- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Cancer growth and metastasis
| Cancer growth and metastasis |
| Pubbl/distr/stampa | Auckland, N.Z., : Libertas Academica, 2008- |
| Disciplina | 616.99400285 |
| Soggetto topico |
Cancer
Metastasis Neoplasm Metastasis Medical Oncology Métastases |
| Soggetto genere / forma |
Periodical
Periodicals. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910143083103321 |
| Auckland, N.Z., : Libertas Academica, 2008- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Methods of mathematical oncology : Fusion of Mathematics and Biology, Osaka, Japan, October 26-28, 2020 / / Takashi Suzuki [and three others] editors
| Methods of mathematical oncology : Fusion of Mathematics and Biology, Osaka, Japan, October 26-28, 2020 / / Takashi Suzuki [and three others] editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 616.99400285 |
| Collana | Springer Proceedings in Mathematics & Statistics |
| Soggetto topico |
Oncology - Mathematics
Oncologia Matemàtica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 981-16-4866-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- List of Participants -- Organizer -- Session Chairs -- Speakers -- Contents -- Mathematical Modeling -- Constitutive Modelling of Soft Biological Tissue from Ex Vivo to in Vivo: Myocardium as an Example -- 1 Introduction -- 2 Constitutive Modelling of Soft Biological Tissue -- 3 Ex Vivo Calibration -- 4 Move to in Vivo -- 5 Biomechanical Study to Cancer -- 6 Conclusion -- References -- Mathematical Modeling of Gastro-Intestinal Metastasis Resistance to Tyrosine Kinase Inhibitors -- 1 Introduction -- 2 The Model -- 3 Main Results and Interpretation -- 4 Preliminary Results -- 4.1 Estimates for Operator V -- 4.2 Estimates for Operators U, N -- 4.3 Estimate for M -- 4.4 Estimates for -- 5 Local Existence and Uniqueness for Problems ([eqspsmodel]3) and ([eqspsmodelWithoutNecrosis]4) -- 6 Asymptotic Behavior of the Solution as Decreases to 0 -- 6.1 Uniform Bound on the Final Time of Existence -- 6.2 The Limit Case for 0 -- 7 Conclusion -- References -- Mathematical Modeling and Experimental Verification of the Proneural Wave -- 1 Introduction -- 2 Mathematical Model of the PW -- 3 Noise Canceling Function Using JAK/STAT -- 4 Continuation Method from Spatially Discretized Models -- 4.1 Scalar Equation in One-Dimensional Space -- 4.2 Singular Limit Analysis -- 4.3 Application to the Discrete Model of PW -- 4.4 Radially Symmetric Kernel -- 4.5 Description of Discrete Model on Sphere Surface -- 5 Discussion -- References -- Exploring Similarity Between Embedding Dimension of Time-Series Data and Flows of an Ecological Population Model -- 1 Introduction -- 2 What is Empirical Dynamic Modeling? -- 3 Inference of Causal Relationships: Example -- 4 Pattern Similarity Between Optimal Embedding Dimension and Flow -- 5 Concluding Remarks -- References -- Mathematical Modeling for Angiogenesis -- 1 Introduction.
2 Discrete Dynamics System Model for Angiogenesis -- 3 A Two-Dimensional Model Considering the Anisotropic Nature of Two-Body Interactions -- 4 Concluding Remarks -- References -- Floating Potential Boundary Condition in Smooth Domains in an Electroporation Context -- 1 Introduction -- 1.1 Preliminary Numerical Observations on Concentric Disks -- 1.2 Outline of the Paper -- 2 Analysis and Computation of the Floating Potential Problem -- 2.1 Existence and Uniqueness of Floating Potential Problem -- 2.2 Numerical Strategy to Solve the Floating Potential Problem -- 3 Asymptotic Analysis and Generalization of the Floating Potential Problem for Thin Highly Conductive Sheets -- 3.1 The Conductivity Problem -- 3.2 Local Coordinates and Laplace Operator -- 3.3 Formal Expansion -- 4 Conclusion -- References -- Free Boundary Problem of Cell Deformation and Invasion -- 1 Introduction -- 2 Cell-Deformation-ECM Degradation -- 3 Individual Cell Model -- 3.1 Classical Solution Scheme -- 3.2 Free Boundary Problem -- 4 Numerical Scheme -- 4.1 Weak Form Derivation -- 4.2 Stefan Problems - Enthaply Formulation -- 4.3 Degenerate Parabolic Equations -- 5 Numerical Simulation and Results -- 5.1 Level Set Methods -- 5.2 Cell Deformation: Free Boundary Conversion -- 5.3 Enthalpy Formulation -- 6 Conclusion -- References -- Multi-level Mathematical Models for Cell Migration in Confined Environments -- 1 Introduction -- 2 Mechanical and Geometrical Conditions for Cell Penetration in Narrow Channels -- 3 Cell Migration Speed in Constrained Environments -- 4 Nesting Cell-Level Information in Multiphase Models of Tumour Growth -- 5 Tumour Invasion Across Membranes -- References -- Mathematical Modeling of Cancer Signaling Addressing Tumor Heterogeneity -- 1 Introduction -- 2 Modeling of Non-genetic Cell-to-Cell Variability in Cancer Signaling -- 2.1 Modeling of Extrinsic Noise. 2.2 Modeling of Intrinsic Noise -- 3 Modeling of Variability Among Cancer Subtypes -- 4 Conclusion -- References -- Mathematical Modelling of Cancer Invasion: A Review -- 1 Introduction -- 2 Biological Background -- 3 Mathematical Models of Cancer Invasion -- 3.1 Early ODE and PDE Models -- 3.2 A Hybrid Continuum-Discrete Model -- 3.3 A Model of Trophoblast Invasion -- 3.4 An Individual-Based Cellular Potts Model -- 3.5 A Model of the Urokinase-Plasminogen uPA System -- 3.6 Modelling the Role of Acidity in Invasion -- 3.7 Modelling the Role of Cell-Cell Adhesion Using PDEs -- 3.8 Multiscale Moving Boundary Models of Cancer Invasion -- 3.9 A Framework for Modelling the Metastatic Spread of Cancer -- 3.10 A Novel Hybrid Continuum-Discrete Multiscale Model of Invasion -- 4 Discussion and Conclusion -- References -- The First Step Towards the Mathematical Understanding of the Role of Matrix Metalloproteinase-8 in Cancer Invasion -- 1 Introduction -- 2 Model Description -- 3 Parametrisation, Simulations, and Results -- 4 Discussion -- 5 Phase Transition Operators -- 5.1 Individual-Cell-to-Density Transition Operator -- 5.2 Density-to-Individual-Cell Transition Operator -- 5.3 Hybrid Formulation of Cancer Cells -- 6 Construction of the Initially Randomly Structured ECM and PMN -- References -- Biological Prediction -- Mathematical Modeling of the Dimerization of EGFR and ErbB3 in Lung Adenocarcinoma -- 1 Introduction -- 2 Mathematical Modeling of the Dimerization of EGFR and ErbB3 -- 3 Quantification of Cell-Surface EGFR and ErbB3 -- 4 Estimation of the Reaction Constants by Dimensional Analysis -- 5 Simulation Results -- 6 Discussion -- References -- Selective Regulation of the Insulin-Akt Pathway by Simultaneous Processing of Blood Insulin Pattern in the Liver -- 1 Temporal Patterns of the Blood Insulin Level -- 2 Obtaining Experimental Data. 2.1 Animal Experiments -- 2.2 Different Temporal Patterns of Insulin-Akt Pathway Molecules -- 3 Developing the Insulin-Akt Pathway Model -- 3.1 Developing the Insulin-Akt Pathway Model -- 3.2 Selective Regulation Mechanisms of the Insulin-Akt Pathway -- 3.3 Decoding of the Blood Insulin Level In Vivo -- 4 Simulation of the T2DM Condition -- 5 Conclusions -- References -- Mathematical Simulation of Linear Ubiquitination in T Cell Receptor-Mediated NF-κB Activation Pathway -- 1 Involvement of LUBAC in TCR-Mediated NF-κB Activation -- 1.1 Linear Ubiquitination of CBM Complex by LUBAC Induces TCR-Mediated NF-κB Activation -- 1.2 OTULIN Is the Predominant Regulator of TCR-Mediated NF-κB Activation -- 2 Mathematical Simulation of Linear Ubiquitination-Mediated IKK Activation in TCR Pathway -- 3 Mathematical Simulation for Linear Ubiquitination of CBM Complex Components -- 4 Conclusion -- References -- Time Changes in the VEGF-A Concentration Gradient Lead Neovasculature to Engage in Stair-Like Growth -- 1 Introduction -- 2 Quantification and Fluctuations of VEGF in Our Angiogenesis Animal Model -- 2.1 Tissue Collection and VEGF Quantification -- 2.2 Dampened Oscillation of VEGF Concentration at F4/5 -- 3 Mathematical Modeling -- 3.1 VEGF Concentration and Sprouting Tip Cells in This Model -- 3.2 VEGF Concentration Equation -- 3.3 Sprouting Tip Cell Distribution Equation -- 3.4 Numerical Simulation -- 4 The Histological Measurement of New Blood Vessels in Our Angiogenesis Models -- 5 Discussion -- References -- Mathematical Modeling of Tumor Malignancy in Bone Microenvironment -- 1 Introduction -- 2 Modeling (1) -- 3 Analysis via Simulations (1) -- 4 Modeling (2) -- 5 Analylsis via Simulations (2) -- 6 Conclusion -- 7 Discussions -- References -- Signaling Networks Involved in the Malignant Transformation of Breast Cancer -- 1 Subtypes of Breast Cancer. 2 Differentiation and Malignant Transformation of Normal Mammary Epithelial Cells -- 3 Signaling Networks that Maintain Cancer Stem Cells (CSCs) -- 3.1 Breast CSCs -- 3.2 Constitutive Activation of NF-B in Breast Cancer -- 3.3 Maintenance of Breast CSCs via JAG1-Notch Signaling Induced by Constitutive NF-B Activation -- 4 Signaling Network Regulating Intratumoral Bidirectional Transitions Between Epithelial and Mesenchymal Cells in TNBC -- 5 Conclusion -- References -- Data Science -- Cell-Free Based Protein Array Technology -- 1 Importance of Protein-Protein Interactome -- 1.1 Wheat Cell-Free Protein Production -- 1.2 Cell-Free Based Protein Array -- 2 E3 Ubiquitin Ligase Protein Array -- 3 CF-PA2Vtech (Cell-Free Based Protein Array for Antibody Validation) -- 4 Conclusion -- References -- Omics Data Analysis Tools for Biomarker Discovery and the Tutorial -- 1 Introduction -- 2 Downloading and Analysis of Public RNA-Seq Data -- 2.1 Gene Expression Omnibus -- 2.2 How to Download RNA-Seq Data from GEO -- 2.3 RNA-Seq Analyses -- 3 Disease Ontology -- 4 Metabolic Networks -- 5 Conclusion -- References -- Integrative Network Analysis of Cancer Cell Signaling by High-Resolution Proteomics -- 1 Shotgun Proteomics Enables Comprehensive Identification and Quantification of the Focused Protein Modifications -- 2 Global Regulation of Cancer Cell Signaling Networks by Post-Translational Modification Dynamics -- 2.1 Phosphoproteomics -- 2.2 Lysine Modification Proteomics -- 2.3 Mathematical Modeling of Cellular Signaling Networks Based on PTM-Directed Quantitative Proteomic Data -- 3 Future Prospects -- References -- Distance-Matrix-Based Extraction of Motility Features from Functionally Heterogeneous Cell Populations -- 1 Introduction -- 2 Problem Formulation -- 3 The Proposed Method -- 3.1 Step 1 -- 3.2 Step 2 -- 3.3 Step 3 -- 4 Results. 4.1 Simulated Dataset. |
| Record Nr. | UNISA-996466414403316 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Methods of Mathematical Oncology : Fusion of Mathematics and Biology, Osaka, Japan, October 26–28, 2020 / / edited by Takashi Suzuki, Clair Poignard, Mark Chaplain, Vito Quaranta
| Methods of Mathematical Oncology : Fusion of Mathematics and Biology, Osaka, Japan, October 26–28, 2020 / / edited by Takashi Suzuki, Clair Poignard, Mark Chaplain, Vito Quaranta |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 616.99400285 |
| Collana | Springer Proceedings in Mathematics & Statistics |
| Soggetto topico |
Neural networks (Computer science)
Mathematical models Mathematical analysis Mathematical Models of Cognitive Processes and Neural Networks Mathematical Modeling and Industrial Mathematics Analysis |
| ISBN | 981-16-4866-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | PART 1: Mathematical Modeling: D. Guan, X. Luo, and H. Gao, Constitutive Modelling of Soft Biological Tissue from Ex Vivo to In Vivo: Myocardium as an Example -- T. Colin, T. Michel, and C. Poignard, Mathematical Modeling of Gastro-intestinal Metastasis Resistance to Tyrosine Kinase Inhibitors -- Y. Tanaka and T. Yasugi, Mathematical Modeling and Experimental Verification of the Proneural Wave -- D. Kumakura and S. Nakaoka, Exploring Similarity between Embedding Dimension of Time-series Data and Flows of an Ecological Population Model -- T. Hayashi, Mathematical Modeling for Angiogenesis -- S. Collin, Corridore and C. Poignard, Floating Potential Boundary Condition in Smooth Domains in an Electroporation Context -- N. L. Othman and T. Suzuki, Free Boundary Problem of Cell Deformation and Invasion -- L. Preziosi and M. Scianna, Multi-level Mathematical Models for Cell Migration in Confined Environments -- S. Magi, Mathematical Modeling of Cancer Signaling Addressing Tumor Heterogeneity -- N. Sfakianakis and Mark A.J. Chaplain, Mathematical Modelling of Cancer Invasion: A Review -- T. Williams, A. Wilson, and N. Sfakianakis, The First Step towards the Mathematical Understanding of the Role of Matrix Metalloproteinase-8 in Cancer Invasion -- PART II: Biological Prediction: T. Ito, T. Suzuki, and Y. Murakami, Mathematical Modeling of the Dimerization of EGFR and ErbB3 in Lung Adenocarcinoma -- H. Kubota, Selective Regulation of the Insulin-Akt Pathway by Simultaneous Processing of Blood Insulin Pattern in the Liver -- D. Oikawa, N. Hatanaka, T. Suzuki, and F. Tokunaga, Mathematical Simulation of Linear Ubiquitination in T Cell Receptor-mediated NF-κB Activation Pathway -- Y. Ito, D. Minerva, S. Tasaki, M. Yoshida, T. Suzuki, and A. Goto, Time Changes in the VEGF-A Concentration Gradient Lead Neovasculature to Engage in Stair-like Growth -- N. Hatanaka, M. Futakuchi, and T. Suzuki, Mathematical Modeling of Tumor Malignancy in Bone Microenvironment -- M. Yamamoto and Jun-ichiro Inoue, Signaling Networks Involved in the Malignant Transformation of Breast Cancer -- PART III: Data Science: R. Morishita, H. Takahashi, and T. Sawasaki, Cell-free Based Protein Array Technology -- Y. Nojima and Y. Takeda, Omics Data Analysis Tools for Biomarker Discovery and the Tutorial -- M. Oyama and H. Kozuka-Hata, Integrative Network Analysis of Cancer Cell Signaling by High-resolution Proteomics -- N. Nakamura and R. Yamada, Distance-matrix-based Extraction of Motility Features from Functionally Heterogeneous Cell Populations -- S. Kawasaki, H. Hayashi, and Y. Tominaga, Data Analytic Study of Genetic Mechanism of Ovarian Carcinoma from Single Cell RNA-seq Data. |
| Record Nr. | UNINA-9910495245503321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Microfluidics and Biosensors in Cancer Research : Applications in Cancer Modeling and Theranostics / / edited by David Caballero, Subhas C. Kundu, Rui L. Reis
| Microfluidics and Biosensors in Cancer Research : Applications in Cancer Modeling and Theranostics / / edited by David Caballero, Subhas C. Kundu, Rui L. Reis |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (598 pages) : illustrations |
| Disciplina |
610.28
616.99400285 |
| Collana | Advances in Experimental Medicine and Biology |
| Soggetto topico |
Cancer
Biochemical markers Microfluidics Pharmacology Cancer - Treatment Cancer Biology Biomarkers Cancer Therapy |
| ISBN | 3-031-04039-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Fundamentals of Microfluidics and Biosensors -- Chapter 1. Fundamentals of Biosensors and Detection Methods -- Chapter 2. How to Get Away with Gradients -- Chapter 3. Sensors and Biosensors in Organs-on-a-chip Platforms -- Chapter 4. Current Trends in Microfluidics and Biosensors for Cancer Research Applications -- Part 2. Modelling the Tumor Microenvironment and Its Dynamic Events -- Chapter 5. The Tumor Microenvironment — an Introduction for the Development of Microfluidic Devices -- Chapter 6. Biomaterials for Mimicking and Modelling Tumor Micro-environment -- Chapter 7. Advancing Tumor Microenvironment Research by Combining Organs-on-chips and Biosensors -- Chapter 8. Microfluidic-driven Biofabrication and the Engineering of Cancer-like Microenvironments -- Chapter 9. Advances in 3d Vascularized Tumor-on-a-chip Technology -- Part 3. Cancer Detection and Diagnosis -- Chapter 10. Biosensors Advances: Contributions to Cancer Diagnostics and Treatment -- Chapter 11. Flexible Sensing Systemsfor Cancer Diagnostics -- Chapter 12. Coupling Micro-physiological Systems and Biosensors for Improving Cancer Biomarkers Detection -- Chapter 13. Microfluidic Biosensor-based Devices for Rapid Diagnosis and Effective Anti-cancer Therapeutic Monitoring for Breast Cancer Metastasis -- Chapter 14. Liquid Biopsies: Flowing Biomarkers -- Chapter 15. From Exosomes to Circulating Tumor Cells: Using Microfludics to Detect High Predictive Cancer Biomarkers -- Chapter 16. Microfluidics for the Isolation and Detection of Circulating Tumor Cells -- Chapter 17. Evolution in Automatized Detection of Cancer Cells: Advances in Magnetic Microcytometers -- Chapter 18. Droplet-based Microfluidic Chip Design, Fabrication and Use for Ultrahigh-throughput DNA Analysis and Quantification -- Chapter 19. Emerging Microfluidic and Biosensor Technologies for Improved Cancer Theranostics -- Part 4. Clinical Applications: Towards Personalized Medicine -- Chapter 20. Microfluidics for Cancer Biomarker Discovery, Research and Clinical Application -- Chapter 21. Methods for the Detection of Circulating Biomarkers in Cancer Patients -- Chapter 22. Advances in Microfluidics for the Implementation of Liquid Biopsy in Clinical Routine. |
| Record Nr. | UNINA-9910733712203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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