top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Artificial Intelligence for Bone Disorder : Diagnosis and Treatment
Artificial Intelligence for Bone Disorder : Diagnosis and Treatment
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (262 pages)
Altri autori (Persone) RajputShivam
VaidyaMakarand
ISBN 1-394-23091-5
1-394-23090-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910829991103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Intelligence for Bone Disorder : Diagnosis and Treatment
Artificial Intelligence for Bone Disorder : Diagnosis and Treatment
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (262 pages)
Altri autori (Persone) RajputShivam
VaidyaMakarand
ISBN 1-394-23091-5
1-394-23090-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910840581003321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Deep Learning for Targeted Treatments : Transformation in Healthcare
Deep Learning for Targeted Treatments : Transformation in Healthcare
Autore Malviya Rishabha
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (458 pages)
Altri autori (Persone) GhineaGheorghita
DhanarajRajesh Kumar
BalusamyBalamurugan
SundramSonali
Soggetto genere / forma Electronic books.
ISBN 1-119-85798-8
1-119-85797-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors.
5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support.
7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction.
9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors.
11.5.1.1 Heredity.
Record Nr. UNINA-9910595599103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Engineered Biomaterials [[electronic resource] ] : Synthesis and Applications / / edited by Rishabha Malviya, Sonali Sundram
Engineered Biomaterials [[electronic resource] ] : Synthesis and Applications / / edited by Rishabha Malviya, Sonali Sundram
Autore Malviya Rishabha
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (623 pages)
Disciplina 610.153
Altri autori (Persone) SundramSonali
Collana Engineering Materials
Soggetto topico Medical physics
Biomaterials
Biomedical engineering
Nanobiotechnology
Cancer - Treatment
Nanoscience
Medical Physics
Biomedical Engineering and Bioengineering
Cancer Therapy
Nanophysics
ISBN 981-9966-98-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Naturally Derived Biomaterials: Advances and Opportunities -- 2. Different Techniques of Genetic Engineering Used for the Development of Novel Biomaterials -- 3. Green methods for the development of bone and tissue engineering based biomaterials -- 4. Genetically Induced Biomaterial Advances in Medical Science -- 5. Biomimetic approach for biomaterials development.
Record Nr. UNINA-9910766881303321
Malviya Rishabha  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Integration of Biomaterials for Gene Therapy
Integration of Biomaterials for Gene Therapy
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (435 pages)
Altri autori (Persone) SundramSonali
JainNeelam
ISBN 1-394-17561-2
1-394-17563-9
1-394-17562-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- Series Page -- Title Page -- Copyright Page -- Foreword -- Preface -- Acknowledgment -- 1 Biocompatible Hydrogels for Gene Therapy -- 1.1 Introduction -- 1.2 Hydrogels Classification -- 1.3 Fabrication of Hydrogels and Its Desirable Technical Features -- 1.4 Factors to be Tuned for Gene Encapsulation in Hydrogels -- 1.5 Recent Advances on Hydrogels for Gene Delivery -- 1.6 Conclusion -- References -- 2 Use of Polysaccharides -- 2.1 Introduction -- 2.2 Cross-Linking Techniques for Engineering Polysaccharides-Based Biomaterials -- 2.3 Approaches to Design Polysaccharide-Derived Biomaterials -- 2.4 Biomedical Applications of Polysaccharide-Derived Biomaterials -- 2.5 Advanced Biomaterials for Wound Dressings -- 2.6 Scaffolds for Tissue Engineering and Development of Bioinks for 3D Bioprinting -- 2.7 Recent Utilization of Polysaccharides -- 2.8 Toxicity Concerns of Polysaccharide-Derived Biomaterials -- 2.9 Preclinical and Clinical Studies on Gene Delivery Using Polysaccharide-Based Biomaterials -- 2.10 Challenges and Future Directions -- 2.11 Future Prospects -- 2.12 Conclusion -- References -- 3 Polysaccharide-Based Biomaterials for Gene Delivery -- 3.1 Background -- 3.2 Introduction -- 3.3 Gene Therapy -- 3.4 Gene Delivery Systems Based on Polysaccharides -- 3.5 Practical Application of Gene Delivery Systems -- 3.6 Polysaccharide-Based Nanoparticles -- 3.7 DNA Delivery -- 3.8 Conclusion -- References -- 4 Hydrogel-Based Gene Therapy -- 4.1 Introduction -- 4.2 Gene Therapy -- 4.3 In Vivo Gene Therapy Using Hydrogels -- 4.4 Encapsulating Cells in Hydrogels for Gene Therapy Delivery -- 4.5 Hydrogels for Integrative Tissue Engineering and Cell Delivery -- 4.6 Biocompatible Hydrogels for Transferring Cells -- 4.7 Using Hydrogels for Gene Therapy in Tissue Engineering-Based Drug.
4.8 Human Gene Therapy that Uses Hydrogel as an Alternative Method of Delivering Genetic Material to Patients -- 4.9 Recent Advancement in Biocompatible Hydrogel -- 4.10 Applications of Hydrogel -- 4.11 Current Hydrogels in Clinical Trials -- 4.12 Conclusions -- References -- 5 Progress and Prospects for Non-Viral Gene Therapy -- 5.1 Introduction -- 5.2 Definition -- 5.3 Technology Overview for Non-Viral Gene Delivery -- 5.4 Chemical Carriers for Gene Transfer: Establishing Effective In Vivo Gene Delivery -- 5.5 Types of Gene Delivery -- 5.6 Reduction of Immunological Responses Through Alteration of Delivery Method or DNA Structure -- 5.7 To Enable Long-Lasting Gene Expression, Self-Replicating, Tissue-Specific, and Integrating Plasmid Expression Systems are Designed -- 5.8 Hybrid Vector Systems to Improve Transfection and Lessen Cytotoxicity -- 5.9 Vehicle Material -- 5.10 Further Effects -- 5.11 Challenges and Prospects -- 5.12 Conclusion -- References -- 6 Nanoparticles for Tumor Gene Therapy -- 6.1 Introduction -- 6.2 Technologies for Gene Delivery -- 6.3 Cancer Treatment with Gene Therapy -- 6.4 Gene Therapy Using Nanotechnology -- 6.5 Challenges and Future Aspects -- References -- 7 Effective Gene Transfer with Non-Viral Vectors -- 7.1 Introduction -- 7.2 System Development for Delivering Genes -- 7.3 Methods for Non-Viral Vector for Delivery of Genes -- 7.4 Delivery System -- 7.5 Current Methods for Nonviral Gene Delivery: Benefits and Drawbacks -- 7.6 Current Barriers for Non-Viral Vectors -- 7.7 Possibilities for Enhancing the Non-Viral Vector Delivery System -- 7.8 Conclusion -- 7.9 Future Relevance -- References -- 8 Utilization of Chitosan for Gene Delivery -- 8.1 Introduction -- 8.2 Cationic Polymers-Based Gene Delivery Systems -- 8.3 Chitosan and Its Derivatives in Gene Delivery Systems -- 8.4 Chitosan as Chemotherapeutic Drugs.
8.5 Conclusion -- References -- 9 Nanoparticles as Gene Vectors in Tumor Therapy -- 9.1 Introduction -- 9.2 Polymer-Based Nanocarriers: Their Technology and Recent Advances -- 9.3 Conclusions -- References -- 10 Progress in Non-Viral Delivery of Nucleic Acid -- 10.1 Introduction -- 10.2 Physical Methods of Non-Viral Nucleic Acid Delivery System -- 10.3 Advantages and Disadvantages of Physical Transfection -- 10.4 Chemical Methods of Non-Viral Nucleic Acid Delivery System -- 10.5 Advantages and Disadvantages of Chemical Transfection -- 10.6 Cellular Barriers for Nucleic Acid Delivery Faced by Non-Viral Vectors -- 10.7 Challenges and Limitations of Non-Viral Nucleic Acid Delivery System -- 10.8 Conclusion -- References -- 11 The Junction of Biomaterials and Gene Therapy - Current Strategies and Future Directions -- 11.1 Introduction -- 11.2 Viral Gene Therapy -- 11.3 DNA Viral Vectors -- 11.4 Adeno-Associated Viral Vectors -- 11.5 Non-Viral Gene Therapy -- 11.6 Recent Advances in the Development of Gene Delivery Systems -- 11.7 Development of Gene Delivery Systems -- 11.8 Viral Vectors Based on DNA for Gene Delivery Systems -- 11.9 Viral Vectors Based on RNA for Gene Delivery Systems -- 11.10 Oncolytic Viral Vectors for Gene Delivery Systems -- 11.11 Practical Application of Gene Delivery Methods -- 11.12 Conclusion -- References -- 12 Utilization of Silk for Gene Delivery -- 12.1 Introduction -- 12.2 Dimensional Structure of Silk -- 12.3 Properties of Silk -- 12.4 Extraction of Fibroin from Silk Worm -- 12.5 Fabrication of Silk in Different Therapeutics Carriers -- 12.6 Utilization of Silk for Gene Therapy -- 12.7 Properties of Silk Fibroin as Biomaterial -- 12.8 Summary of Silk-Based Formulations for Gene Delivery [33] -- 12.9 Examples of Some Delivery Approaches which Utilizes Silk as a Biomaterial for Gene Delivery.
12.10 Some Highlights of Silk Fibroin -- 12.11 Conclusion -- References -- 13 Challenges and Emerging Problems in Nanomedicine Mediated Gene Therapy -- 13.1 Introduction -- 13.2 Why Nanomedicine Over Traditional Drugs? -- 13.3 Nanomedicine for Gene Therapy -- 13.4 Complications in Nanomedicine-Mediated Gene Therapy -- 13.5 Challenges in the Clinical Translation of Nanomedicines -- 13.6 Conclusion -- References -- 14 Biomaterials-Based Vaccination in Cancer Therapy -- 14.1 Introduction -- 14.2 Tumor-Associated Antigens -- 14.3 Vaccine Delivery -- 14.4 Dendritic Cells -- 14.5 In Vitro Generation of Dendritic Cells -- 14.6 Usage of RNA -- 14.7 RNA-Pulsed DCs as Vaccines -- 14.8 RNA Vaccines -- 14.9 Optimization of Immunotherapy -- 14.10 Cancer Treatment Through RNA Interference -- 14.11 Conclusion -- References -- Index -- End User License Agreement.
Record Nr. UNINA-9910830598103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Integration of Biomaterials for Gene Therapy
Integration of Biomaterials for Gene Therapy
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (435 pages)
Altri autori (Persone) SundramSonali
JainNeelam
ISBN 1-394-17561-2
1-394-17563-9
1-394-17562-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- Series Page -- Title Page -- Copyright Page -- Foreword -- Preface -- Acknowledgment -- 1 Biocompatible Hydrogels for Gene Therapy -- 1.1 Introduction -- 1.2 Hydrogels Classification -- 1.3 Fabrication of Hydrogels and Its Desirable Technical Features -- 1.4 Factors to be Tuned for Gene Encapsulation in Hydrogels -- 1.5 Recent Advances on Hydrogels for Gene Delivery -- 1.6 Conclusion -- References -- 2 Use of Polysaccharides -- 2.1 Introduction -- 2.2 Cross-Linking Techniques for Engineering Polysaccharides-Based Biomaterials -- 2.3 Approaches to Design Polysaccharide-Derived Biomaterials -- 2.4 Biomedical Applications of Polysaccharide-Derived Biomaterials -- 2.5 Advanced Biomaterials for Wound Dressings -- 2.6 Scaffolds for Tissue Engineering and Development of Bioinks for 3D Bioprinting -- 2.7 Recent Utilization of Polysaccharides -- 2.8 Toxicity Concerns of Polysaccharide-Derived Biomaterials -- 2.9 Preclinical and Clinical Studies on Gene Delivery Using Polysaccharide-Based Biomaterials -- 2.10 Challenges and Future Directions -- 2.11 Future Prospects -- 2.12 Conclusion -- References -- 3 Polysaccharide-Based Biomaterials for Gene Delivery -- 3.1 Background -- 3.2 Introduction -- 3.3 Gene Therapy -- 3.4 Gene Delivery Systems Based on Polysaccharides -- 3.5 Practical Application of Gene Delivery Systems -- 3.6 Polysaccharide-Based Nanoparticles -- 3.7 DNA Delivery -- 3.8 Conclusion -- References -- 4 Hydrogel-Based Gene Therapy -- 4.1 Introduction -- 4.2 Gene Therapy -- 4.3 In Vivo Gene Therapy Using Hydrogels -- 4.4 Encapsulating Cells in Hydrogels for Gene Therapy Delivery -- 4.5 Hydrogels for Integrative Tissue Engineering and Cell Delivery -- 4.6 Biocompatible Hydrogels for Transferring Cells -- 4.7 Using Hydrogels for Gene Therapy in Tissue Engineering-Based Drug.
4.8 Human Gene Therapy that Uses Hydrogel as an Alternative Method of Delivering Genetic Material to Patients -- 4.9 Recent Advancement in Biocompatible Hydrogel -- 4.10 Applications of Hydrogel -- 4.11 Current Hydrogels in Clinical Trials -- 4.12 Conclusions -- References -- 5 Progress and Prospects for Non-Viral Gene Therapy -- 5.1 Introduction -- 5.2 Definition -- 5.3 Technology Overview for Non-Viral Gene Delivery -- 5.4 Chemical Carriers for Gene Transfer: Establishing Effective In Vivo Gene Delivery -- 5.5 Types of Gene Delivery -- 5.6 Reduction of Immunological Responses Through Alteration of Delivery Method or DNA Structure -- 5.7 To Enable Long-Lasting Gene Expression, Self-Replicating, Tissue-Specific, and Integrating Plasmid Expression Systems are Designed -- 5.8 Hybrid Vector Systems to Improve Transfection and Lessen Cytotoxicity -- 5.9 Vehicle Material -- 5.10 Further Effects -- 5.11 Challenges and Prospects -- 5.12 Conclusion -- References -- 6 Nanoparticles for Tumor Gene Therapy -- 6.1 Introduction -- 6.2 Technologies for Gene Delivery -- 6.3 Cancer Treatment with Gene Therapy -- 6.4 Gene Therapy Using Nanotechnology -- 6.5 Challenges and Future Aspects -- References -- 7 Effective Gene Transfer with Non-Viral Vectors -- 7.1 Introduction -- 7.2 System Development for Delivering Genes -- 7.3 Methods for Non-Viral Vector for Delivery of Genes -- 7.4 Delivery System -- 7.5 Current Methods for Nonviral Gene Delivery: Benefits and Drawbacks -- 7.6 Current Barriers for Non-Viral Vectors -- 7.7 Possibilities for Enhancing the Non-Viral Vector Delivery System -- 7.8 Conclusion -- 7.9 Future Relevance -- References -- 8 Utilization of Chitosan for Gene Delivery -- 8.1 Introduction -- 8.2 Cationic Polymers-Based Gene Delivery Systems -- 8.3 Chitosan and Its Derivatives in Gene Delivery Systems -- 8.4 Chitosan as Chemotherapeutic Drugs.
8.5 Conclusion -- References -- 9 Nanoparticles as Gene Vectors in Tumor Therapy -- 9.1 Introduction -- 9.2 Polymer-Based Nanocarriers: Their Technology and Recent Advances -- 9.3 Conclusions -- References -- 10 Progress in Non-Viral Delivery of Nucleic Acid -- 10.1 Introduction -- 10.2 Physical Methods of Non-Viral Nucleic Acid Delivery System -- 10.3 Advantages and Disadvantages of Physical Transfection -- 10.4 Chemical Methods of Non-Viral Nucleic Acid Delivery System -- 10.5 Advantages and Disadvantages of Chemical Transfection -- 10.6 Cellular Barriers for Nucleic Acid Delivery Faced by Non-Viral Vectors -- 10.7 Challenges and Limitations of Non-Viral Nucleic Acid Delivery System -- 10.8 Conclusion -- References -- 11 The Junction of Biomaterials and Gene Therapy - Current Strategies and Future Directions -- 11.1 Introduction -- 11.2 Viral Gene Therapy -- 11.3 DNA Viral Vectors -- 11.4 Adeno-Associated Viral Vectors -- 11.5 Non-Viral Gene Therapy -- 11.6 Recent Advances in the Development of Gene Delivery Systems -- 11.7 Development of Gene Delivery Systems -- 11.8 Viral Vectors Based on DNA for Gene Delivery Systems -- 11.9 Viral Vectors Based on RNA for Gene Delivery Systems -- 11.10 Oncolytic Viral Vectors for Gene Delivery Systems -- 11.11 Practical Application of Gene Delivery Methods -- 11.12 Conclusion -- References -- 12 Utilization of Silk for Gene Delivery -- 12.1 Introduction -- 12.2 Dimensional Structure of Silk -- 12.3 Properties of Silk -- 12.4 Extraction of Fibroin from Silk Worm -- 12.5 Fabrication of Silk in Different Therapeutics Carriers -- 12.6 Utilization of Silk for Gene Therapy -- 12.7 Properties of Silk Fibroin as Biomaterial -- 12.8 Summary of Silk-Based Formulations for Gene Delivery [33] -- 12.9 Examples of Some Delivery Approaches which Utilizes Silk as a Biomaterial for Gene Delivery.
12.10 Some Highlights of Silk Fibroin -- 12.11 Conclusion -- References -- 13 Challenges and Emerging Problems in Nanomedicine Mediated Gene Therapy -- 13.1 Introduction -- 13.2 Why Nanomedicine Over Traditional Drugs? -- 13.3 Nanomedicine for Gene Therapy -- 13.4 Complications in Nanomedicine-Mediated Gene Therapy -- 13.5 Challenges in the Clinical Translation of Nanomedicines -- 13.6 Conclusion -- References -- 14 Biomaterials-Based Vaccination in Cancer Therapy -- 14.1 Introduction -- 14.2 Tumor-Associated Antigens -- 14.3 Vaccine Delivery -- 14.4 Dendritic Cells -- 14.5 In Vitro Generation of Dendritic Cells -- 14.6 Usage of RNA -- 14.7 RNA-Pulsed DCs as Vaccines -- 14.8 RNA Vaccines -- 14.9 Optimization of Immunotherapy -- 14.10 Cancer Treatment Through RNA Interference -- 14.11 Conclusion -- References -- Index -- End User License Agreement.
Record Nr. UNINA-9910840817503321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui