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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
Soggetto topico Three-dimensional printing
Medical technology
ISBN 9781394234233
1394234236
9781394234219
139423421X
9781394234226
1394234228
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Chapter 1 Introduction to 3D Printing in Healthcare -- 1.1 Introduction -- 1.2 The Revolutionary Rise of 3D Printing Technology -- 1.3 3D Printing Revolution Engineering -- 1.4 3D Printer Types for Additive Manufacturing -- 1.5 3D Printing in the Healthcare Industry -- 1.6 Early-Phase Drug Development -- 1.7 Customized Drugs -- 1.8 Advanced Pharmacological Treatments -- 1.9 Community Medicine -- 1.10 Clinical Pharmacy Practice -- 1.11 3D Printing Process and Product Variable Optimization -- 1.12 Recent Trends in 3D Printing Regulation -- 1.13 Conclusion -- References -- Chapter 2 3D Printing in Medical Science -- 2.1 Introduction -- 2.2 Present Clinical Applications -- 2.3 3D-Printed Models in CHD -- 2.4 Cardiovascular Disease Models in 3D Printing -- 2.5 Tumor in 3D-Printed Models -- 2.6 3D-Printed Models in the Development of CT Scanning Procedures -- 2.7 Pharmaceutical 3D-Printing Technologies
Record Nr. UNINA-9911019379603321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances and Insights into AI-Created Disability Supports / / by Rishabha Malviya, Shivam Rajput
Advances and Insights into AI-Created Disability Supports / / by Rishabha Malviya, Shivam Rajput
Autore Malviya Rishabha
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (XIV, 137 p. 18 illus., 16 illus. in color.)
Disciplina 613
614
Collana SpringerBriefs in Modern Perspectives on Disability Research
Soggetto topico Public health
Public Health
ISBN 981-9660-69-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- The History of AI in Transforming Disability Support -- Empowering Disabled People with AI -- AI-Driven Innovations in Assistive Technology for People with Disabilities -- Personalised Support for People with Disabilities Through Generative AI -- Using AI to Empower People with Disabilities' Communication and Autonomy -- The Future of AI in Revolutionizing Support for Disabled Persons.
Record Nr. UNINA-9911002549203321
Malviya Rishabha  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
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-9911018786803321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Intelligence in Neurological Disorders : Management, Diagnosis and Treatment
Artificial Intelligence in Neurological Disorders : Management, Diagnosis and Treatment
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (263 pages)
Disciplina 616.80285/63
Altri autori (Persone) KumarSuraj
SolankeAditya Sushil
GoyalPriyanshi
ChauhanKapil
Soggetto topico Nervous system - Diseases
Artificial intelligence - Medical applications
ISBN 1-394-34753-7
1-394-34752-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9911020178203321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain with Artificial Intelligence for Healthcare : A Synergistic Approach
Blockchain with Artificial Intelligence for Healthcare : A Synergistic Approach
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Bristol : , : Institute of Physics Publishing, , 2023
Descrizione fisica 1 online resource (209 pages)
Disciplina 610.285
Altri autori (Persone) SinghArun Kumar
SundramSonali
BalusamyBalamurugan
KadrySeifedine
Collana IOP Ebooks Series
ISBN 0-7503-5841-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910915779203321
Malviya Rishabha  
Bristol : , : Institute of Physics Publishing, , 2023
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)
Disciplina 615.6028563843
Altri autori (Persone) SundramSonali
MeenakshiDhanalekshmi Unnikrishnan
Soggetto topico Quantum computing
Drug delivery systems
ISBN 9781394159338
1394159331
9781394159321
1394159323
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-9911019543303321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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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 9789819966981
9819966981
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
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Explainable and Responsible Artificial Intelligence in Healthcare
Explainable and Responsible Artificial Intelligence in Healthcare
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (377 pages)
Altri autori (Persone) SundramSonali
Soggetto topico Artificial intelligence - Medical applications
Medical ethics
ISBN 9781394302444
1394302444
9781394302420
1394302428
9781394302437
1394302436
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Chapter 1 Uncapping Explainable Artificial Intelligence–Centered Reinforcement Learning and Natural Language Processing in Smart Healthcare System -- 1.1 Introduction -- 1.1.1 XAI in Healthcare: Relevance and Overview -- 1.1.2 Importance of Explainability in AI -- 1.1.3 Role of Reinforcement Learning and NLP in Smart Healthcare -- 1.1.4 Objectives of the Chapter -- 1.1.5 Structure of the Chapter -- 1.2 XAI-Based Reinforcement Learning in Smart Healthcare Systems -- 1.3 Natural Language Processing in Smart Healthcare Systems -- 1.3.1 Applications of NLP in Healthcare Industry -- 1.3.1.1 Deidentification of Clinical Records -- 1.3.1.2 Medical Text Mining -- 1.3.1.3 Extraction of Medical Information -- 1.3.1.4 Text Data Management -- 1.3.1.5 Health Information Graph -- 1.4 Incorporation of XAI-Based RL and NLP -- 1.5 Synergies Between XAI, RL, and NLP in Healthcare -- 1.5.1 Rule-Based Systems -- 1.5.2 Bayesian Networks -- 1.5.3 Decision Trees -- 1.5.4 Deep Learning Models -- 1.5.5 Enhanced Trust -- 1.5.6 Improved Understanding
Record Nr. UNINA-9911020467103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui