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Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others]
| Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (458 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Deep learning (Machine learning) |
| 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-9910642709403321 |
| Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others]
| Deep learning for targeted treatments : transformation in healthcare / / edited by Rishabha Malviya [and four others] |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] |
| Descrizione fisica | 1 online resource (458 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Deep learning (Machine learning) |
| 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-9910831186303321 |
| Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Human-Machine Interface : Making Healthcare Digital / / edited by Rishabha Malviya [and three others]
| Human-Machine Interface : Making Healthcare Digital / / edited by Rishabha Malviya [and three others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2024] |
| Descrizione fisica | 1 online resource (519 pages) |
| Disciplina | 621.39 |
| Soggetto topico |
Computer vision
Human-computer interaction |
| ISBN |
1-394-20034-X
1-394-20033-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Preface -- Acknowledgement -- Part I: Advanced Patient Care with HMI -- Chapter 1 Introduction to Human-Machine Interface -- 1.1 Introduction -- 1.2 Types of HMI -- 1.2.1 The Pushbutton Replacer -- 1.2.2 The Data Handler -- 1.2.3 The Overseer -- 1.3 Transformation of HMI -- 1.4 Importance and COVID Relevance With HMI -- 1.5 Applications -- 1.5.1 Biological Applications -- 1.5.1.1 HMI Signal Detection and Procurement Method -- 1.5.1.2 Healthcare and Rehabilitation -- 1.5.1.3 Magnetoencephalography -- 1.5.1.4 Flexible Hybrid Electronics (FHE) -- 1.5.1.5 Robotic-Assisted Surgeries -- 1.5.1.6 Flexible Microstructural Pressure Sensors -- 1.5.1.7 Biomedical Applications -- 1.5.1.8 CB-HMI -- 1.5.1.9 HMI in Medical Devices -- 1.5.2 Industrial Applications -- 1.5.2.1 Metal Industries -- 1.5.2.2 Video Game Industry -- 1.5.2.3 Aerospace and Defense -- 1.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS) -- 1.5.2.5 Virtual and Haptic Interfaces -- 1.5.2.6 Space Crafts -- 1.5.2.7 Car Wash System -- 1.5.2.8 Pharmaceutical Processing and Industries -- 1.6 Challenges -- 1.7 Conclusion and Future Prospects -- References -- Chapter 2 Improving Healthcare Practice by Using HMI Interface -- 2.1 Background of Human-Machine Interaction -- 2.2 Introduction -- 2.2.1 Healthcare Practice -- 2.2.2 Human-Machine Interface System in Healthcare -- 2.3 Evolution of HMI Design -- 2.3.1 HMI Design 1.0 -- 2.3.2 HMI Design 2.0 -- 2.3.3 HMI Design 3.0 -- 2.3.4 HMI Design 4.0 -- 2.4 Anatomy of Human Brain -- 2.5 Signal Associated With Brain -- 2.5.1 Evoked Signals -- 2.5.2 Spontaneous Signals -- 2.5.3 Hybrid Signals -- 2.6 HMI Signal Processing and Acquisition Methods -- 2.7 Human-Machine Interface-Based Healthcare System -- 2.7.1 Healthcare Practice System.
2.7.1.1 Healthcare Practice -- 2.7.1.2 Current State of Healthcare Provision -- 2.7.1.3 Concerns With Domestic Healthcare -- 2.7.2 Medical Education System -- 2.7.2.1 Traditional and Modern Way of Providing Medical Education -- 2.8 Working Model of HMI -- 2.9 Challenges and Limitations of HMI Design -- 2.10 Role of HMI in Healthcare Practice -- 2.10.1 Simple to Clean -- 2.10.2 High Chemical Tolerance -- 2.10.3 Transportable and Light -- 2.10.4 Enhancing Communication -- 2.11 Application of HMI Technology in Medical Fields -- 2.11.1 Medical and Rehabilitative Engineering Using HMI -- 2.11.2 Controls for Robotic Surgery and Human Prosthetics -- 2.11.3 Sensory Replacement Mechanism -- 2.11.4 Wheelchairs and Moving Robots Along With Neurological Interface -- 2.11.5 Cognitive Improvement -- 2.12 Conclusion and Future Perspective -- References -- Chapter 3 Human-Machine Interface and Patient Safety -- 3.1 Introduction -- 3.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact -- 3.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena -- 3.2.2 Consequences of Errors -- 3.2.3 Lessons From Other Industries -- 3.2.4 The Double-Human Interface -- 3.2.5 The Culture of Denial and Effort -- 3.2.6 Poor Labeling -- 3.3 Systematic Approaches to Improve Patient Safety During Anesthesia -- 3.3.1 Design Principles -- 3.3.2 Evidence of Safety Gains -- 3.3.3 Consistent Color-Coding -- 3.3.4 The Codonics Label System -- 3.4 The Triumph of Software -- 3.4.1 Software in Hospitals -- 3.4.2 Software in Anesthesia -- 3.4.3 The Alarm Problem -- 3.5 Environments that Audit Themselves -- 3.6 New Risks and Dangers -- 3.7 Conclusion -- References -- Chapter 4 Human-Machine Interface Improving Quality of Patient Care -- 4.1 Introduction -- 4.2 An Advanced Framework for Human-Machine Interaction. 4.2.1 A Simulated Workplace Safety and Health Program -- 4.3 Human-Computer Interaction (HCI) -- 4.4 Multimodal Processing -- 4.5 Integrated Multimodality at a Lower Order (Stimulus Orientation) -- 4.6 Higher-Order Multimodal Integration (Perceptual Binding) -- 4.7 Gains in Performance From Multisensory Stimulation -- 4.8 Amplitude Envelope and Alarm Design -- 4.9 Recent Trends in Alarm Tone Design for Medical Devices -- 4.10 Percussive Tone Integration in Multimodal User Interfaces -- 4.11 Software in Hospitals -- 4.12 Brain-Machine Interface (BCI) Outfit -- 4.13 BCI Sensors and Techniques -- 4.13.1 EEG -- 4.13.2 ECoG -- 4.13.3 ECG -- 4.13.4 EMG -- 4.13.5 MEG -- 4.13.6 FMRI -- 4.14 New Generation Advanced Human-Machine Interface -- 4.15 Conclusion -- References -- Chapter 5 Smart Patient Engagement through Robotics -- 5.1 Introduction -- 5.1.1 Robotics in Healthcare -- 5.1.2 Patient Engagement Tasks (Front End) -- 5.1.2.1 Robotics in Nursing, Patient Handling, and Support -- 5.1.2.2 Robotics in Patient Reception -- 5.1.2.3 Robotics in Ambulance Services -- 5.1.2.4 Robotics in Serving (Food and Medicine) -- 5.1.2.5 Robotics in Surgery and Surgical Assistance -- 5.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting -- 5.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation (Exoskeletons) -- 5.1.2.8 Robotics in Tele-Presence -- 5.1.2.9 Robotics in Hospital Kitchen and Pantry Management -- 5.1.2.10 Robotics in Outdoor Medicine Delivery -- 5.1.2.11 Robotics in Home Healthcare -- 5.1.3 Documentation and Other Hospital Management Tasks (Back End) -- 5.1.3.1 Robotics in Patient Data Feeding and Storing -- 5.1.3.2 Robotics in Data Mining -- 5.1.3.3 Robotics in Job Allocation to Hospital Staffs -- 5.1.3.4 Robotics in Payroll Management -- 5.1.3.5 Robotics in Medicine and Medical Equipment Logistics. 5.1.3.6 Robotics in Medical Waste Residual Management -- 5.2 Theoretical Framework -- 5.3 Objectives -- 5.4 Research Methodology -- 5.5 Primary and Secondary Data -- 5.6 Factors for Consideration -- 5.6.1 Patient Demographics -- 5.6.2 Hospital/Health Institutes Demographics -- 5.6.3 Patient Perception Factors -- 5.6.4 Hospital's Feasibility Factors and Hospital's Economic Factors for Implementation -- 5.7 Robotics Implementation -- 5.8 Tools for Analysis -- 5.9 Analysis of Patient's Perception -- 5.10 Review of Literature -- 5.11 Hospitals Considered for the Study (Through Indirect Sources) -- 5.12 Analysis and Interpretation -- 5.12.1 Crosstabulation -- 5.12.2 Regression and Model Fit -- 5.12.3 Factor Analysis -- 5.12.4 Regression Analysis -- 5.12.5 Descriptive Statistics -- 5.13 Conclusion -- References -- Annexure -- Chapter 6 Accelerating Development of Medical Devices Using Human-Machine Interface -- 6.1 Introduction -- 6.2 HMI Machineries -- 6.3 Brain-Computer Interface and HMI -- 6.4 HMI for a Mobile Medical Exoskeleton -- 6.5 Human Artificial Limb and Robotic Surgical Treatment by HMI -- 6.6 Cognitive Enhancement by HMI -- 6.7 Soft Electronics for the Skin Using HMI -- 6.8 Safety Considerations -- 6.9 Conclusion -- References -- Chapter 7 The Role of a Human-Machine Interaction (HMI) System on the Medical Devices -- 7.1 Introduction -- 7.2 Machine Learning for HCI Systems -- 7.3 Patient Experience -- 7.4 Cognitive Science -- 7.5 HCI System Based on Image Processing -- 7.5.1 Patient's Facial Expression -- 7.5.2 Gender and Age -- 7.5.3 Emotional Intelligence -- 7.6 Blockchain -- 7.7 Virtual Reality -- 7.8 The Challenges in Designing HCI Systems for Medical Devices -- 7.9 Conclusion -- References -- Chapter 8 Human-Machine Interaction in Leveraging the Concept of Telemedicine -- 8.1 Introduction. 8.2 Innovative Development in HMI Technologies and Its Use in Telemedicine -- 8.2.1 Nanotechnology -- 8.2.2 The Internet of Things (IoT) -- 8.2.3 Internet of Medical Things (IoMT) -- 8.2.3.1 Motion Detection Sensors -- 8.2.3.2 Pressure Sensors -- 8.2.3.3 Temperature Sensors -- 8.2.3.4 Monitoring Cardiovascular Disease -- 8.2.3.5 Glucose Level Monitoring -- 8.2.3.6 Asthma Monitoring -- 8.2.3.7 GPS Smart Soles and Motion Detection Sensors -- 8.2.3.8 Wireless Fetal Monitoring -- 8.2.3.9 Smart Clothing -- 8.2.4 AI -- 8.2.5 Machine Learning Techniques -- 8.2.6 Deep Learning -- 8.2.7 Home Monitoring Devices, Augmented and Virtual -- 8.2.8 Drone Technology -- 8.2.9 Robotics -- 8.2.9.1 Robotics in Healthcare -- 8.2.9.2 History of Robotics -- 8.2.9.3 Tele-Surgery/Remote Surgery -- 8.2.10 5G Technology -- 8.2.11 6G -- 8.2.12 Big Data -- 8.2.13 Cloud Computing -- 8.2.14 Blockchain -- 8.2.14.1 Clinical Trials -- 8.2.14.2 Patient Records -- 8.2.14.3 Drug Tracking -- 8.2.14.4 Device Tracking -- 8.3 Advantages of Utilizing HMI in Healthcare for Telemedicine -- 8.3.1 Emotive Telemedicine -- 8.3.2 Ambient Assisted Living -- 8.3.2.1 Wearable Sensors for AAL -- 8.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing -- 8.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence -- 8.3.5 Personalized and Connected Healthcare -- 8.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine -- 8.4.1 Data Inconsistency and Disintegration -- 8.4.2 Standards and Interoperability are Lacking -- 8.4.3 Intermittent or Non-Existent Network Connectivity -- 8.4.4 Sensor Data Unreliability and Invalidity -- 8.4.5 Privacy, Confidentiality, and Data Consistency -- 8.4.6 Scalability Issues -- 8.4.7 Health Consequences -- 8.4.8 Clinical Challenges -- 8.4.9 Nanosensors and Biosensors Offer Health Risks. 8.4.10 Limited Computing Capability and Inefficient Energy Use. |
| Record Nr. | UNINA-9910747099103321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-drug resistance in cancer : mechanism and treatment strategies / / edited by Rishabha Malviya, Arun Kumar Singh, and Deepika Yadav
| Multi-drug resistance in cancer : mechanism and treatment strategies / / edited by Rishabha Malviya, Arun Kumar Singh, and Deepika Yadav |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, , [2023] |
| Descrizione fisica | 1 online resource (216 pages) |
| Disciplina | 614.5999 |
| Soggetto topico |
Multidrug resistance
Drug resistance in cancer cells Drug interactions |
| Soggetto non controllato |
Pharmacology
Medical |
| ISBN |
1-394-20986-X
1-394-20985-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Multi-Drug Rmesistance in Cancer: Understanding of Treatment Strategies -- 1.1 Introduction -- 1.2 Both Congenital and Developed Resistance to Drugs -- 1.2.1 Intrinsic Resistance -- 1.2.2 Acquired Resistance -- 1.3 Drug-Resistance Mechanisms -- 1.3.1 Increased Efflux of Drugs -- 1.3.2 Impact on Medication Target -- 1.3.3 Improved DNA-Damage Repair -- 1.4 Senescence Escape -- 1.5 Epigenetic Alterations -- 1.6 Tumor Heterogeneity -- 1.7 Tumor Microenvironment -- 1.8 Epithelial to Mesenchymal Transition -- 1.9 Conclusion -- References -- Chapter 2 Understanding Different Mechanisms Involved in Cancer Drug Resistance: Proposing Novel Strategies to Overcome MDR -- 2.1 Introduction -- 2.2 Drug Resistance: Internal and External Variables -- 2.2.1 Phenotypic Variation of Tumors -- 2.2.2 Tumor Microenvironment -- 2.2.3 Cancer Stem Cells -- 2.2.4 Inactivation of the Anticancer Drugs -- 2.2.5 Multi-Drug Resistance -- 2.2.6 Increasing the Release of Drugs Outside the Cell -- 2.2.7 Reducing the Absorption of the Drugs -- 2.2.8 Inhibition of Cell Death (Apoptosis Pathway Blocking) -- 2.3 Improving the Pharmacokinetics -- 2.4 Changing the Aim of the Chemotherapy Agents -- 2.5 Improving the DNA Repair Process -- 2.5.1 Augmentation of a Gene -- 2.5.2 Epigenetic Altering Caused Drug Resistance -- 2.6 MicroRNA in Cancer Drug Resistance -- 2.7 Conclusion -- References -- Chapter 3 Molecular Mechanism of Multi-Drug Resistant Cancer Cells -- 3.1 Introduction -- 3.2 Types of Drug Resistance -- 3.3 Mechanisms of Drug Resistance -- 3.3.1 Drug Efflux via ABC Transporters -- 3.3.2 Permeability Glycoprotein/MDR-1 -- 3.3.3 Multi-Drug Resistance Protein -- 3.3.4 Breast Cancer Resistance Protein -- 3.4 Reduction in Drug Activity and Cellular Absorption.
3.5 Instability in the Genome and Medication Resistance -- 3.5.1 Mutation and Medication Target Alteration -- 3.5.2 Restoration of DNA Integrity -- 3.5.3 Resistant Genes and Epigenetic Modifications -- 3.5.4 Drug Resistance and Programmed Cell Death -- 3.6 RNA Interference Therapy -- 3.7 Methods of Physical Intervention to Treat MDR -- 3.8 Conclusion -- References -- Chapter 4 Natural Products for Clinical Management of Drug Resistant Cancer Cells -- 4.1 Introduction -- 4.2 Resistance Mechanisms -- 4.3 Antitumor Plants for Multi-Drug-Resistant Cells -- 4.4 Qualea Species and Their Medical Applications -- 4.5 Antitumor Activity of Qualea Grandiflora and Qualea Multiflora -- 4.6 Conclusion -- References -- Chapter 5 Understanding of Autophagy to Combat MDR During Anticancer Therapy -- 5.1 Introduction -- 5.2 Mechanisms of Autophagy -- 5.2.1 Phagophore Assembly -- 5.2.2 Autophagosome Formation and Maturation -- 5.2.3 Autolysosome Degradation -- 5.2.4 Core Regulator of Autophagy -- 5.3 Mechanisms of MDR -- 5.4 Correlation Between Autophagy and Multi-Drug Resistance -- 5.5 The Cytoprotective Effect of Autophagy in the Regulation of Multi-Drug Resistance -- 5.6 Increased Autophagy Facilitates Multi-Drug Resistance -- 5.7 Autophagy Inhibition Improves Chemotherapy in MDR Cancers -- 5.8 Overcoming MDR With Autophagic Cell Death -- 5.9 Autophagy Kills Apoptosis-Deficient MDR Cancer Cells -- 5.10 Autophagy Promotes Chemosensitivity -- 5.11 Conclusion -- References -- Chapter 6 Transporter Inhibitors: A Chemotherapeutic Regimen to Improve the Clinical Outcome of Colorectal Cancer -- 6.1 Introduction -- 6.2 CRC Transporters or ATP-Binding Cassette -- 6.2.1 ABC Transporter Family -- 6.2.2 ABC Transporters and CRC Initiation -- 6.2.3 ABC Transporters and the Resistance of Cancer Cells to Chemotherapy. 6.3 Clinical Evidence for the Function of ABC Transporters in CRC MDR -- 6.3.1 Intrinsic Drug Resistance in Colon Cancer Upregulation of P-gp at Detection -- 6.3.2 Proliferating Tumor Cells Have MRP1 on Their Surface -- 6.4 General Approaches -- 6.5 By Blocking Tyrosine Kinase Inhibitors from Inhibiting MDR Transporters -- 6.6 Components Produced from Natural Sources that Inhibit MDR Transporters -- 6.7 Inhibiting ABC Transporters in Other Ways for CRC MDR Circumvention -- 6.8 Challenges and Future Prospective -- 6.9 Conclusion -- References -- Chapter 7 Epithelial to Mesenchymal Transition (EMT): Major Contribution to Cancer Drug Therapy Resistance -- 7.1 Introduction -- 7.2 EMT and Tumor Resistance: In Vitro, In Vivo, and Clinical Trials -- 7.3 Tumor Microenvironment Regulates EMT -- 7.3.1 Hypoxia -- 7.3.2 The Extracellular Matrix -- 7.3.3 The Inflammatory and Immune Microenvironment -- 7.3.4 EMT Microenvironment: Medication Resistance -- 7.4 Drug Resistance and EMT Bioinformatics -- 7.4.1 Bioinformatics and Pharmacogenomics to Optimize Drugs and Targets and Identify Medication Resistance -- 7.4.2 Drug Resistance: Hereditary or Acquired -- 7.4.3 Therapies for EMT-Induced Medication Resistance -- 7.5 Conclusion -- References -- Chapter 8 Advances in Metallodrug-Driven Combination Therapy for Treatment of Cancer -- 8.1 Introduction -- 8.2 Cancer Treatment Using Combination Therapy -- 8.3 Combined Treatment with Metallodrugs for Cancer Treatment -- 8.3.1 Platinum Metallodrugs -- 8.4 Nonplatinum Metallodrugs -- 8.5 Conclusion -- References -- Chapter 9 Novel Strategies Preventing Emergence of MDR in Breast Cancer -- 9.1 Introduction -- 9.2 Breast Cancer Categorization and Epidemiological Studies -- 9.2.1 Treatment Options for Women With Breast Cancer -- 9.3 Multi-Drug Resistance in Breast Cancer -- 9.3.1 Breast Cancer Chemoresistance. 9.3.2 Multi-Drug Resistance and ABC Channels in Breast Cancer -- 9.4 Drug Efflux Transporters in Breast Cancer -- 9.4.1 Exocytosis Transporters in the Stem Cell Population of Breast Cancer -- 9.4.2 Drug Efflux Channel Upregulation in Breast Cancer -- 9.4.3 Techniques for Breast Cancer MDR Reversal -- 9.4.4 Direct Pharmacologic Inhibition With MDR Inhibitors -- 9.5 Excessive Synthesis or Overexpression of Transporters for the Expulsion of Drugs -- 9.6 Nanotherapeutic Approach for MDR Reversal -- 9.7 Breast Cancer's MDR Cure Problems and Future Outlook -- 9.8 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830428803321 |
| Hoboken, NJ : , : John Wiley & Sons, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Targeted Cancer Therapy in Biomedical Engineering [[electronic resource] /] / edited by Rishabha Malviya, Sonali Sundram
| Targeted Cancer Therapy in Biomedical Engineering [[electronic resource] /] / edited by Rishabha Malviya, Sonali Sundram |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xxii, 941 pages, 211 illustrations, 204 illustrations in color) : illustrations |
| Disciplina | 616.99406 |
| Collana | Biological and Medical Physics, Biomedical Engineering |
| Soggetto topico |
Medical physics
Cancer - Treatment Biomedical engineering Computer-aided engineering Nanobiotechnology Cancer - Imaging Neoplasms - drug therapy Neoplasms - diagnostic imaging Biomarkers, Tumor Biomedical Engineering Tissue Engineering Medical Physics Cancer Therapy Biomedical Engineering and Bioengineering Computer-Aided Engineering (CAD, CAE) and Design Cancer Imaging |
| ISBN | 981-19-9786-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Strategies for Cancer Targeting: Novel Drug Delivery Systems Opportunities and Future Challenges -- Implementation of Biomedical Engineering Tools in Targeted Cancer Therapy: Challenges and Opportunities -- Exploration of tissue-engineered systems for cancer research -- Advancement of Tissue Engineered in Cancer treatment -- Immunotherapy: Targeting Cancer Cells -- Bioinformatics Tools to Discover and Validate Cancer Biomarkers -- Application of Biomaterials in Cancer Research -- Engineered Tissue in Cancer Research: Techniques, Challenges and Current status -- CADD for Cancer Therapy: Current and Future Perspective -- Leveraging Advancement in Robotics in the Treatment of Cancer -- Innovative Biomedical Equipment for Diagnosis of Cancer -- Detection of Cancer Biomarker by Advanced Biosensor. |
| Record Nr. | UNINA-9910686477303321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||