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Biblioteca

MARC Lista (tabellare)
3D Printing in Healthcare : Novel Applications
3D Printing in Healthcare : Novel Applications
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (308 pages)
Altri autori (Persone) SharmaRishav
ISBN 1-394-23421-X
1-394-23422-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910900181603321
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-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)
Disciplina 616.7/1028563
Altri autori (Persone) RajputShivam
VaidyaMakarand
Soggetto topico Bones - Diseases - Data processing
Artificial intelligence - Medical applications
ISBN 1-394-23091-5
1-394-23090-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- Preface -- 1 Artificial Intelligence and Bone Fracture Detection: An Unexpected Alliance -- 1.1 Introduction -- 1.2 Bone Fracture -- 1.3 Deep Learning and Its Significance in Radiology -- 1.4 Role of AI in Bone Fracture Detection and Its Application -- 1.5 Primary Machine Learning-Based Algorithm in Bone Fracture Detection -- 1.6 Deep Learning-Based Techniques for Fracture Detection -- 1.7 Conclusion -- 2 Integrating AI With Tissue Engineering: The Next Step in Bone Regeneration -- 2.1 Introduction -- 2.2 Anatomy and Biology of Bone -- 2.3 Bone Regeneration Mechanism -- 2.4 Understanding AI -- 2.5 Current AI Integration -- 2.6 Applying Deep Learning -- 2.7 Conclusion -- 3 Deep Supervised Learning on Radiological Images to Classify Bone Fractures: A Novel Approach -- 3.1 Introduction -- 3.2 Common Bone Disorder -- 3.3 Deep Supervised Learning's Importance in Orthopedics and Radiology -- 3.4 Perspective From the Past -- 3.5 Essential Deep Learning Methods for Bone Imaging -- 3.6 Strategies for Effective Annotation -- 3.7 Application of Deep Learning to the Detection of Fractures -- 3.8 Conclusion -- 4 Treatment of Osteoporosis and the Use of Digital Health Intervention -- 4.1 Introduction -- 4.2 Opportunistic Diagnosis of Osteoporosis -- 4.3 Predictive Models -- 4.4 Assessment of Fracture Risk and Osteoporosis Diagnosis by Digital Health -- 4.5 Clinical Decision Support Tools, Reminders, and Prompts for Spotting Osteoporosis in Digital Health Settings -- 4.6 The Role of Digital Health in Facilitating Patient Education, Decision, and Conversation -- 4.7 Conclusion -- 5 Utilizing AI to Improve Orthopedic Care -- 5.1 Introduction -- 5.2 What is AI? -- 5.3 Introduction to Machine Learning: Algorithms and Applications -- 5.4 Natural Language Processing -- 5.5 The Internet of Things -- 5.6 Prospective AI Advantages in Orthopedics -- 5.7 Diagnostic Application of AI -- 5.8 Prediction Application With AI -- 5.9 Conclusion -- 6 Significance of Artificial Intelligence in Spinal Disorder Treatment -- 6.1 Introduction -- 6.2 Machine Learning -- 6.3 Methods Derived From Statistics -- 6.4 Applications of Machine Learning in Spine Surgery -- 6.5 Application of AI and ML in Spine Research -- 6.6 Conclusion -- 7 Osteoporosis Biomarker Identification and Use of Machine Learning in Osteoporosis Treatment -- 7.1 Introduction -- 7.2 Biomarkers of Bone Development -- 7.3 Biomarkers for Bone Resorption -- 7.4 Regulators of Bone Turnover -- 7.5 Methods to Identify Osteoporosis -- 7.6 Conclusion -- 8 The Role of AI in Pediatric Orthopedics -- 8.1 Introduction -- 8.2 Strategy Based on Artificial Intelligence -- 8.3 Several Applications of Artificial Intelligence -- 8.4 Conclusion -- 9 Use of Artificial Intelligence in Imaging for Bone Cancer -- 9.1 Introduction -- 9.2 Applications of Machine Learning to Cancer Diagnosis -- 9.3 Artificial Intelligence Methods for Diagnosing Bone Cancer -- 9.4 Methodologies for Constructing Deep Learning Model -- 9.5 Clinical Image Applications of Deep Learning for Bone Tumors -- 9.6 Conclusion -- References -- Index.
Record Nr. UNINA-9910876591503321
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
Drug Delivery Systems Using Quantum Computing
Drug Delivery Systems Using Quantum Computing
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (476 pages)
Altri autori (Persone) SundramSonali
MeenakshiDhanalekshmi Unnikrishnan
ISBN 1-394-15933-1
1-394-15932-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1 Quantum Computational Concepts and Approaches in Drug Discovery, Development and Delivery -- 1.1 Introduction -- 1.2 Algorithms and QC in Pharma -- 1.2.1 Algorithms -- 1.2.2 Supervised Learning -- 1.2.3 Unsupervised Learning -- 1.2.4 Multi-Task Neural Networks -- 1.2.5 Graph Convolution -- 1.3 Potential of QC in Drug Discovery -- 1.3.1 Target Recognition and Validation -- 1.3.2 Production and Validation of Hits -- 1.3.3 Lead Optimization -- 1.3.4 Clinical Trials -- 1.3.5 Linking and Generating Data -- 1.4 QC and Drug Delivery -- 1.5 QC in Drug Delivery Modalities -- 1.5.1 Computational Approach Towards Nano Particulate Drug Delivery -- 1.5.2 Computational Approach for Bone Drug Delivery -- 1.5.3 Computational Approach for Polymeric Drug Delivery -- 1.5.4 Computational Approach for Microsphere Drug Delivery -- 1.5.5 Computational Approach for Dendrimer-Based Drug Delivery -- 1.5.6 Computational Approach for Carbon Nanotube-Based Drug Delivery -- 1.6 QC Applications in Pharma Industry -- 1.7 Challenges or Prospects -- 1.8 Conclusion -- References -- Chapter 2 Quantum-Enabled Drug Discovery Process -- 2.1 Introduction -- 2.2 Usual Challenges in Drug Designing and Discovery -- 2.2.1 Commercial Challenge -- 2.2.2 Modification or Transitional Challenges -- 2.2.3 Unexplored Areas Under Classical Computational Techniques -- 2.3 Medicinal Chemistry Through Quantum Mechanics -- 2.3.1 Underappreciation of Chemical Interactions in Protein-Ligand Complexes -- 2.3.2 Non-Classical Hydrogen Bonding -- 2.3.3 π-π Stacking -- 2.3.4 Hydrophobic Bond Interactions -- 2.3.5 Coordination from Water -- 2.3.6 Explicit Water -- 2.3.7 Implicit Water -- 2.4 Interaction Analysis -- 2.4.1 Fragment Interaction Energy.
2.4.2 Binding Free Energy -- 2.4.3 Fragment Molecular Orbital (FMO) Process and Analysis -- 2.5 Geometric Optimization -- 2.5.1 Analyzing Gradients -- 2.6 GAMESS: A Computational Technique for Biochemical Simulations -- 2.6.1 Introduction to Biochemical Simulations -- 2.6.2 Parametrizing Quantum Mechanics Method for Simulations -- 2.6.3 Quantum Mechanics and Molecular Mechanics Associated with GAMESS -- 2.6.4 Introduction to QuanPol -- 2.6.5 QuanPol: Covalent Boundary Treatment -- 2.6.6 GO and Harmonic Vibration Frequency -- 2.6.7 Molecular Dynamics Simulation -- 2.6.8 Free Energy Perturbation (Deviation of Energy) Simulation -- 2.6.8.1 QuanPol Process Umbrella Sampling -- 2.6.8.2 QuanPol Process Thermodynamic Integration (TI) -- 2.6.9 Setting Up Valuation and Calculation -- 2.6.10 Molecular Modeling and Visualization Software -- 2.7 Conclusion -- References -- Chapter 3 Quantum Computing and Its Promise in Drug Discovery -- 3.1 Introduction -- 3.1.1 Need for Quantum Computing -- 3.2 Quantum Computing's Types, Applications, Generality, and Power -- 3.2.1 Quantum Annealer -- 3.2.2 Analog Quantum -- 3.2.3 Universal Quantum -- 3.3 Drug Discovery and Quantum Computing -- 3.3.1 A Brief History of Drug Discovery -- 3.3.2 Modern Drug Discovery -- 3.4 Role of Quantum Computing in Drug Discovery -- 3.5 Quantum Computing Methodology in Drug Discovery -- 3.5.1 Target Identification and Validation -- 3.5.2 Hit Generation and Validation -- 3.5.3 Lead Optimization -- 3.5.4 Data Linkage and Generation -- 3.5.5 Clinical Trials -- 3.5.6 Molecular Formations -- 3.6 Examples of Companies Using Quantum Theory to Accelerate Drug Discovery -- 3.6.1 Aqemia -- 3.6.2 Hafnium Labs -- 3.6.3 Kuano -- 3.6.4 Menten AI -- 3.6.5 Pharmacelera -- 3.6.6 PharmCADD -- 3.6.7 Polaris Quantum Biotech -- 3.6.8 ProteinQure -- 3.6.9 Riverlane -- 3.6.10 Roivant Discovery -- 3.6.11 XtalPi.
3.6.12 Zapata Computing -- 3.7 Advantages of Quantum Computing -- 3.8 Applications of Quantum Computing in Drug Discovery and Development -- 3.8.1 A Future View of QC and Drug Discovery -- 3.8.2 Current Developments in QC and Drug Discovery -- 3.9 Conclusion -- References -- Chapter 4 Exploring Nano-Based Therapeutics by Quantum Computational Modeling -- 4.1 Introduction to Nano-Based Therapeutics -- 4.2 Introduction to Quantum Computational Modeling with Respect to Nano-Based Therapeutics -- 4.2.1 Particle-Based Models -- 4.2.2 Continuum-Based Models -- 4.3 Exploration of Nano-Based Therapeutics -- 4.4 Design and Development of Nano-Based Therapeutics -- 4.4.1 Prediction of Solubility -- 4.4.2 Prediction of Permeability -- 4.4.3 Selection of Components and Optimization of Formulation of Nano-Based Therapeutics -- 4.4.3.1 Prediction of Therapeutic Loading -- 4.4.4 Prediction of Therapeutic Release or Leakage -- 4.4.5 Prediction of the Pharmacokinetic Profile of Nano-Based Therapeutics -- 4.4.5.1 Prediction of Absorption -- 4.4.5.2 Prediction of Distribution (Protein Corona Formation) -- 4.4.5.3 Prediction of Metabolism -- 4.4.5.4 Prediction of Excretion -- 4.4.6 Understanding Protein Corona Formation -- 4.4.7 Understanding the Interaction of Nanosized Carrying Objects with Bio-Membranes -- 4.4.8 Prediction of the Pharmacodynamic Profile of Nano-Based Therapeutics -- 4.4.9 Prediction of Adverse Drug Reactions and Nanotoxicity -- 4.4.10 Design of Target-Oriented Nano-Based Therapeutics -- 4.5 Conclusion -- References -- Chapter 5 Application of Quantum Computational Simulation in Drug Delivery Strategies with Carbon Nanotubes -- 5.1 Introduction -- 5.2 Properties of CNTs -- 5.2.1 Electrical Properties of CNTs -- 5.2.2 Elastro-Mechanical Properties of CNTs -- 5.2.3 Thermal Properties of CNTs -- 5.2.4 Optical Properties of CNTs.
5.3 Functionalization of CNTs -- 5.3.1 Covalent Functionalization -- 5.3.2 Noncovalent Functionalization of CNTs -- 5.3.3 Encapsulation Inside CNTs -- 5.3.4 "Defect" Functionalization -- 5.4 Significance of CNT in Drug Delivery -- 5.5 Overview of CNT-Based Drug Delivery -- 5.6 Pharmacokinetics of CNTs -- 5.6.1 Absorption -- 5.6.2 Distribution -- 5.6.3 Metabolism and Excretion -- 5.7 Biosafety of Carbon Nanotube -- 5.7.1 Mechanism of CNT Toxicity -- 5.7.2 Scenario to Bypass Carbon Nanotube Toxicity -- 5.8 Quantum Computational -- 5.8.1 Structure-Based Drug Design Methods (SBDD) -- 5.8.2 Ligand-Based Drug Design Methods (LBDD) -- 5.9 Various Simulation Approaches in Drug-CNTs Interaction -- 5.9.1 QM Approaches -- 5.9.1.1 Ab Initio Approach -- 5.9.1.2 Semiempirical Approach -- 5.9.1.3 Hartree-Fock (HF) Approach -- 5.9.2 Molecular Dynamics (MD) Approaches -- 5.9.3 Monte Carlo (MC) Simulation Approaches -- 5.9.4 Hybrid Approaches -- 5.9.4.1 MD and QM Approaches -- 5.9.4.2 MM and QM Approaches -- 5.10 Applications of Quantum Computational Methods -- 5.10.1 Applications of Quantum Computational Methods in DDS -- 5.10.2 Applications of Quantum Computational Methods in Nanobiosensors -- 5.11 Conclusion -- References -- Chapter 6 Quantum Computation Approach for Nanotechnology-Based Targeted Drug Delivery Systems -- 6.1 Introduction -- 6.2 The Types of Quantum Computers -- 6.2.1 Scalable Quantum Computers -- 6.2.2 Noisy Intermediate-Scale Quantum Devices -- 6.2.3 Analog Quantum Devices -- 6.3 Role of QC in Computer-Aided Drug Design -- 6.4 Development of Molecular Formulations -- 6.4.1 QC-Based Development of Nanocarriers -- 6.4.2 QC in Biosensor -- 6.4.3 QC-Based Targeted Drug Delivery -- 6.4.4 Target Identification -- 6.4.5 Target Validation -- 6.4.6 Identification of Hit and Its Validation -- 6.4.7 Optimization of Lead.
6.5 Data Generation, Interpretation, and Co-Relation -- 6.6 Role of QC in Clinical Trials -- 6.7 Future Prospects -- 6.8 Conclusion -- References -- Chapter 7 Role of Quantum Computing Simulations in Targeted Drug Delivery of Liposomes -- 7.1 Introduction -- 7.2 Liposomes -- 7.3 Liposome Classification -- 7.3.1 Based on Preparation Methods -- 7.3.2 Based on Compositional and Structural Characteristics -- 7.4 Methods of Liposome Preparation -- 7.5 Drug Loading Method -- 7.5.1 Passive Loading Technique -- 7.5.1.1 Sonication -- 7.5.1.2 French Pressure Cell -- 7.5.1.3 Freeze-Thawed Liposomes -- 7.5.1.4 Solvent Evaporation with Ether Injection -- 7.5.1.5 Alcohol Infusion -- 7.5.1.6 Method of Reverse Phase Evaporation -- 7.5.1.7 Removal of Non-Encapsulated Material with Detergent -- 7.5.2 Active Loading Technique -- 7.5.2.1 Pro-Liposome -- 7.5.2.2 Lyophilization -- 7.6 Newer Approaches to Liposomes -- 7.6.1 Stealth Liposomes (Improving Circulation Time) -- 7.6.2 Improving Elasticity (Transferosomes) -- 7.6.3 Ethosomes (Improving Skin Penetration) -- 7.6.4 Pharmacosomes (Improvement in Medication Delivery for Poorly Soluble Medicines) -- 7.6.5 Nebulized Liposomes and Stimuli-Responsive Liposomes -- 7.7 Quantum Computing -- 7.8 Computational Modeling for Drug Delivery Process -- 7.9 Correlation Between Quantum Computing Simulation and Targeted Delivery of Liposomes -- 7.10 Computational Simulation of Lipid Membranes -- 7.11 Properties Measured by Simulation -- 7.11.1 Mechanical and Structural Properties -- 7.11.2 Dynamic Properties -- 7.11.3 Molecule Permeation -- 7.12 Liposomal Drug Delivery System -- 7.13 Experimental Techniques and Role of Computational Simulation in Liposomal Drug Delivery System -- 7.13.1 Lipids Membrane -- 7.13.2 Size, Surface Charge, and Zeta Potential -- 7.13.3 Morphology and Lamellarity.
7.14 Computational Simulation Study of Liposomes.
Record Nr. UNINA-9910877056303321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Engineered Biomaterials : Synthesis and Applications / / edited by Rishabha Malviya, Sonali Sundram
Engineered Biomaterials : 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-9910877145303321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Laser Therapy in Healthcare : Advances in Diagnosis and Treatment
Laser Therapy in Healthcare : Advances in Diagnosis and Treatment
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (288 pages)
Altri autori (Persone) MeenakshiDhanalekshmi Unnikrishnan
GoyalPriyanshi
ISBN 1-394-23799-5
1-394-23798-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Chapter 1 Leveraging the Concept of Laser Physics in Healthcare -- 1.1 Introduction -- 1.2 Physics of Laser -- 1.2.1 Principles of Optics -- 1.2.2 Laser Gadget -- 1.2.3 Laser Beam -- 1.2.3.1 Monochromatic -- 1.2.3.2 Coherent -- 1.2.3.3 Collimated -- 1.3 Laser Classification -- 1.4 The Workings of a Laser Therapy -- 1.5 Implications of Lasers on Tissues -- 1.5.1 Photothermal Impacts -- 1.5.2 Photochemical Effects -- 1.5.3 Photomechanical Effects -- 1.6 Spectroscopy Using a Laser-Induced Breakdown Mechanism: Its Use in Medicine and Other Fields -- 1.7 Applications of X-Ray and Computerized Tomography Technology in the Healthcare Industry -- 1.8 Conclusion -- References -- Chapter 2 Laser Surgery in Contemporary Healthcare -- 2.1 Introduction -- 2.2 Applications of Laser Therapy in Different Treatments -- 2.2.1 Laser Lithotripsy -- 2.2.2 Brain Surgery -- 2.2.3 Epilepsy Surgery -- 2.2.4 Cardiovascular Surgery -- 2.2.5 Dermatology -- 2.2.6 Oncology -- 2.2.7 Oral Surgery and Dentistry -- 2.2.8 Cataract Surgery -- 2.2.9 Aesthetic and Reconstructive Surgery -- 2.2.10 Ablation of the Conductive Pathway -- 2.3 Conclusion -- References -- Chapter 3 Management of Genitourinary Syndrome Associated with Dyspareunia with Laser Therapy -- 3.1 Introduction -- 3.2 Pathophysiology -- 3.3 Clinical Manifestation -- 3.3.1 Genital Symptoms -- 3.3.2 Sexual Symptoms -- 3.3.3 Urinary Symptoms -- 3.4 Laser Modalities and Mechanism on Tissue -- 3.5 Laser Therapy for Menopausal Genitourinary Syndrome -- 3.5.1 CO2 Laser -- 3.5.2 Erbium: Yttrium-Aluminum-Garnet -- 3.6 Adverse Effects and Complication -- 3.7 Safety of Laser Therapy -- 3.8 Conclusion -- References -- Chapter 4 Early Detection of Cancer by Laser Therapy -- 4.1 Introduction -- 4.2 Photodynamic Therapy.
4.2.1 Photosensitizer and Its Component -- 4.2.2 Mechanism of Photosensitizer -- 4.3 Laser Therapy Treatment -- 4.3.1 CO2 -- 4.3.2 Argon -- 4.3.3 NAD: YAG -- 4.3.4 Low-Level Laser Treatment -- 4.3.5 Pulse Dye Laser -- 4.3.6 Potassium Titanyl Phosphate Laser -- 4.3.7 Intense Pulse Light -- 4.3.8 Flashlamp-Pumped Dye -- 4.4 Conclusion -- References -- Chapter 5 Use of a Laser for Testing and Treating Sepsis -- 5.1 Introduction -- 5.2 Pathophysiology -- 5.3 Clinical Manifestation -- 5.3.1 Laboratory Method -- 5.3.1.1 Blood Culture -- 5.3.1.2 Cerebrospinal Fluid Culture -- 5.3.1.3 Urine Culture -- 5.3.1.4 Tracheal Aspartate Culture -- 5.3.1.5 Superficial Swab Cultures -- 5.3.1.6 Complete Blood Count Components and Peripheral Smear -- 5.3.1.7 C-Reactive Protein (CRP) -- 5.3.1.8 High Sensitivity-CRP (hs-CRP) -- 5.3.1.9 Procalcitonin (PCT) -- 5.3.1.10 Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF) -- 5.3.2 Molecular Method -- 5.3.1.1 Serum Amyloid A (SAA) -- 5.3.2.2 Lipopolysaccharide Binding Protein (LBP) -- 5.3.2.3 Cytokines and Chemokines -- 5.3.2.4 Interleukin- 6 (IL-6) -- 5.3.2.5 Interleukin-8 (IL-8) -- 5.3.2.6 Tumor Necrosis Factor Alpha (TNF- a) -- 5.3.2.7 Other Chemokines -- 5.4 Laser Treatment for Treating Sepsis -- 5.4.1 Laser Speckle Contrast Imaging -- 5.4.2 Laser Doppler Flowmetry -- 5.5 Conclusion -- References -- Chapter 6 Laser Therapy to Treat Diabetic Macular Edema -- 6.1 Introduction -- 6.2 Pathophysiology -- 6.3 Clinical Characterization -- 6.3.1 Focal Macular Edema -- 6.3.2 Diffuse Macular Edema -- 6.3.3 Macular Ischemia -- 6.4 Diagnosis -- 6.5 Mechanism of Laser Therapy to Treat Diabetic Macular Edema -- 6.6 Laser Therapy for DME -- 6.6.1 Laser Photocoagulation -- 6.6.2 Using Lasers in Conjunction with Anti-VEGF Therapy -- 6.6.3 Navigated Laser Treatment.
6.6.4 Subthreshold Micropulse Laser Therapy -- 6.6.5 Treatment for PDR Brought on by Using Pan-Retinal Photocoagulation -- 6.6.6 Selective Retinal Therapy -- 6.7 Clinical Lasers and Delivery Platforms: Upcoming Innovations -- 6.8 Conclusion -- References -- Chapter 7 Diagnosis and Management of Sleep Bruxism Utilizing Laser Therapy -- 7.1 Introduction -- 7.2 Etiology -- 7.3 Pathophysiology -- 7.4 Diagnosis -- 7.5 Treatment of Sleep Bruxism -- 7.6 Case Study of Sleep Bruxism -- 7.7 Conclusion -- References -- Chapter 8 Treatment for Osteoarthritis with Laser Technology -- 8.1 Introduction -- 8.2 Pathophysiology -- 8.3 Clinical Features -- 8.3.1 Knee Discomfort -- 8.3.2 Joint Stiffness -- 8.3.3 Growth and Swelling of the Bones -- 8.4 Risk Factors -- 8.5 Diagnosis -- 8.6 Laser Therapy for Osteoarthritis -- 8.7 Conclusion -- References -- Chapter 9 Targeting Neurological Disorders with Laser Technology -- 9.1 Introduction -- 9.2 Symptoms of Brain Tumor -- 9.3 Diagnosis -- 9.3.1 Glial Tumor -- 9.3.1.1 Astrocytic Tumor -- 9.3.1.2 Cancers of the Oligodendroglia -- 9.4 Risk Factors -- 9.4.1 Ionizing Radiation -- 9.4.2 Radiofrequency Electromagnetic Radiation -- 9.4.3 Genetic Factors -- 9.4.4 N-Nitroso Compounds (NOCs) -- 9.5 Ablative Methods for Brain Surgery -- 9.5.1 Radiofrequency Thermal Ablation -- 9.5.2 Laser Interstitial Thermal Therapy -- 9.5.3 Stereotactic Radiosurgery -- 9.5.4 Thermal Ablation Using Concentrated Ultrasound -- 9.6 Brain Tumor -- 9.6.1 Radiofrequency/Microwaves -- 9.6.2 Laser Interstitial Thermotherapy -- 9.6.3 Ultrasound -- 9.6.4 Radiosurgery -- 9.7 Epilepsy Surgery -- 9.8 Conclusion -- References -- Chapter 10 Diagnosis of Onychomycosis Using Laser Therapy -- 10.1 Introduction -- 10.2 Etiology and Epidemiology -- 10.3 Clinical Manifestation -- 10.4 Diagnostic Techniques -- 10.5 Complication -- 10.6 Treatment of Onychomycosis.
10.6.1 Topical Antifungal Therapy -- 10.6.2 Laser Therapy -- 10.6.3 Photodynamic Therapy -- 10.6.4 Combination of Laser Therapy with Topical Antifungal Therapy -- 10.7 Prevention -- 10.8 Prognosis -- 10.9 Conclusion -- References -- Chapter 11 Laser Treatment for Wound Healing -- 11.1 Introduction -- 11.2 Effect of Photon on Cell Level -- 11.3 Wound Healing Physiology -- 11.3.1 Physiology of Scar Formation -- 11.3.2 Pathology of Scar Formation -- 11.4 Treatment of Superficial Wounds with Laser Light -- 11.5 Low-Level Laser Therapy for Wound Healing -- 11.6 Laser Devices for Wound Healing -- 11.6.1 Laser Debridement -- 11.6.2 Fractional Photothermolysis -- 11.6.3 Photobiomodulation Therapy -- 11.6.4 Antimicrobial Blue Light -- 11.6.5 Photodynamic Therapy -- 11.6.6 Vascular Laser -- 11.7 Conclusion -- References -- Chapter 12 Laser Therapy in Dentistry -- 12.1 Introduction -- 12.2 Classification of Laser in Dentistry -- 12.2.1 Hard Tissue Laser -- 12.2.2 Low-Level Lasers or Soft Tissue Lasers -- 12.3 Mechanism of Action of Laser -- 12.4 Laser-Tissue Interaction -- 12.4.1 Reflected -- 12.4.2 Absorbed -- 12.4.3 Transmitted -- 12.4.4 Scattered -- 12.5 Advantages and Disadvantages of Laser Therapy -- 12.6 Diagnosis -- 12.7 Clinical Applications -- 12.7.1 Cavity Preparation -- 12.7.2 Caries Removal -- 12.7.3 Restoration Removal -- 12.7.4 Etching -- 12.7.5 Gingiva and Periodontium -- 12.7.6 Pain -- 12.7.7 Oral Surgery -- 12.7.8 Premalignant and Malignant Lesions -- 12.8 Applications of Photodynamic Therapy -- 12.8.1 PDT in Oral Surgery -- 12.8.2 Dental Preventive Treatment -- 12.9 Laser Safety -- 12.10 Conclusion -- References -- Chapter 13 Case Studies of Different Diseases Treated by Laser Therapy -- 13.1 Introduction -- 13.2 Case Studies of Cancer Diseases -- 13.3 Case Studies of Neurological Disorders -- 13.4 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9910877231103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui