Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5533_Библиотеки_им_академика_М_И_Перельмана.pdf
X
- •Acknowledgements
- •Chapter 3
- •Chapter 4
- •Chapter 5
- •Chapter 6
- •Chapter 7
- •Editor biographies
- •Vivek Kumar Chaturvedi
- •Anurag Kumar Singh
- •Jay Singh
- •Dawesh Prakash Yadav
- •Short description about chapters
- •Chapter 1
- •Chapter 2
- •Chapter 8
- •Chapter 9
- •Chapter 10
- •Chapter 11
- •Chapter 12
- •List of contributors
- •Introduction
- •1.1 Introduction
- •1.2 Nanotechnology in medical science
- •1.2.1 Nanomaterials in drug delivery
- •1.2.2 Use of nanomaterials in designing diagnostic nanosensors
- •1.2.3 Nanomaterials as theranostics
- •1.3 Artificial intelligence in medical science
- •1.3.1 Machine learning in diagnostics
- •1.3.2 Natural language processing in healthcare
- •1.3.3 Predictive analytics in patient care
- •1.4.1 Nanoscience in controlled drug release in the GI tract
- •1.4.3 Nanotechnology in gastrointestinal endoscopy
- •1.4.4 Nano-biotechnology in gastrointestinal cancer
- •1.5 Role of nanoparticles for the treatment of gastric cancer
- •1.6 Artificial intelligence in hepatitis and chronic liver disease
- •1.7 Artificial intelligence applications for clinical decisions support
- •1.9 Nanomedicines for liver fibrosis
- •1.10 Artificial intelligence-based colonoscopy
- •1.12 Summary and conclusions
- •Acknowledgments
- •References
- •2.1 Introduction
- •2.2 Causes
- •2.3 Mechanism
- •2.4 Diagnosis
- •2.5 Prognosis
- •2.6 Present methods of detection
- •2.7 Biosensors
- •2.7.1 Components of biosensors
- •2.7.2 Types of biosensors
- •2.7.3 Enzyme based biosensors
- •2.7.5 Immunosensors
- •2.7.6 Microbial biosensors
- •2.7.7 DNA-based biosensors
- •2.7.8 Phage sensors
- •2.7.9 Optical biosensors
- •2.7.10 Cantilever-based biosensors
- •2.7.11 Bio-MEMS
- •2.8 Physical biosensors
- •2.8.1 Thermometric biosensors
- •2.8.2 Acoustic biosensors
- •2.8.3 Magnetic biosensors
- •2.8.4 Wearable skins as biosensors
- •2.9 Electrochemical biosensors
- •2.9.1 Potentiometric
- •2.9.2 Coulometry methods
- •2.9.3 Conductometry methods
- •2.9.4 Potentiometric titration
- •2.10 Materials for biosensors
- •2.10.1 Nanomaterials for biosensors
- •2.10.2 Gastrointestinal diseases (GIDs) biosensor
- •2.11 Summary and future perspectives
- •3.1 Introduction
- •3.2 Challenges in drug delivery to the GI tract
- •3.2.1 Residence time
- •3.2.4 Metabolism in the GI tract
- •3.3 Role of nanoscience in drug delivery
- •3.3.2 Targeted drug delivery
- •3.3.3 Increased bioavailability
- •3.3.4 Reduced toxicity and side effects
- •3.3.5 Imaging and diagnostic capabilities
- •3.3.6 Drug designing
- •3.3.7 Delivery system
- •3.4 Methods of nanomedicine formulation
- •3.5 Drug release strategies
- •3.5.1 Active targeting strategies
- •3.5.2 Stimuli-based delivery strategy
- •3.5.3 pH-dependent drug release
- •3.5.4 ROS-dependent drug release
- •3.5.5 Time-dependent dosage forms
- •3.5.6 Gastro retentive strategies
- •3.5.7 Photothermal and photodynamic approach
- •3.6 Types of nanoparticles in drug delivery
- •3.6.1 Liposomes
- •3.8 Application of AI in GI disease
- •3.9 Future perspectives and challenges
- •3.6.2 Polymeric nanoparticles
- •3.6.3 Metallic nanoparticles
- •3.6.4 Quantum dots
- •3.7 Approved nanomedicines
- •3.9.1 Diagnostics
- •3.9.2 Individualized treatment
- •3.9.3 Proactive patient monitoring
- •3.9.4 Decision support systems
- •3.9.5 Biomarker discovery and therapeutic development
- •3.9.6 Patient outcomes and quality of life
- •3.9.7 Regulation and ethical issues
- •3.10 Conclusion
- •References
- •4.1 Introduction
- •4.2 Challenges and barriers in drug delivery
- •4.3 Drugs used in IBD
- •4.4 Novel drug delivery system for inflammatory bowel disease
- •4.4.1 Vesicular delivery system
- •4.4.2 Nanoparticle drug delivery system
- •4.5 pH-dependent nano-delivery systems
- •4.6 Inorganic nanoparticles
- •4.7 Prodrugs based
- •4.8 Hybrid drug delivery systems
- •4.9 Enteric coated formulations
- •4.10 RNA interference-based novel drug delivery
- •4.11 Toxicity profiling of IBD
- •4.11.1 Corticosteroids
- •4.11.2 Immuno modulators
- •4.11.3 Biologic therapies
- •4.11.4 JAK inhibitors
- •4.11.5 Immune dysregulation in IBD
- •4.11.6 Gastrointestinal effects
- •4.11.7 Antibiotics
- •4.11.8 Cyclosporine
- •4.11.10 Surgery-related complications
- •4.11.11 Increased risk of colorectal cancer
- •4.12 Current prospective of IBD
- •4.12.1 Personalized medicine and immunological therapies
- •4.12.2 Disease monitoring and surgical advances
- •4.12.3 Development of IL-6 signaling inhibitors
- •4.12.4 Genome-wide association studies (GWAS)
- •4.12.5 Rare variant analysis
- •4.12.6 Functional genomics and gene expression studies
- •4.12.7 Therapeutic targets
- •4.12.8 Gene-environment interactions
- •4.13 Future prospective of IBD
- •4.13.2 Microparticles-based delivery systems
- •4.13.3 Biological therapies
- •4.13.4 Combination therapies
- •4.14 Conclusion
- •References
- •5.1 Introduction
- •5.2 Nanotechnology
- •5.3 Nanoparticles
- •5.4 Classification of nanoparticles
- •5.4.1 Polymer-based nanoparticles
- •5.4.2 Solid nanoparticles
- •5.4.3 Carbon-based nanoparticles
- •5.4.4 Lipid-based nanoparticles
- •5.4.5 Nanoemulsions
- •5.4.6 Nanoparticles in biomedical applications
- •5.4.7 Characteristics of nanoparticles
- •5.4.8 Characterization of nanoparticles
- •5.5 Intestinal endoscopy
- •5.6 Medical nanotechnology
- •5.6.1 Diagnosis
- •5.6.2 Nanotechnology in the early diagnosis
- •5.6.3 Theragnostic
- •5.6.4 Tissue engineering
- •5.6.5 Targeted imaging and therapeutic in colorectal cancer
- •5.6.6 Gene therapy delivery
- •5.6.7 Colitis therapy
- •5.6.8 Oral delivery of vaccines
- •5.6.9 Mitigation
- •5.6.10 Role in targeted drug delivery
- •5.7 Role of nanotechnology in intestinal tract
- •5.8 Nanotechnological aids
- •5.8.1 Nanopowder
- •5.8.2 Plastic stents
- •5.8.3 Capsule endoscopy
- •5.9 Quality control of nanotechnology
- •5.10 Artificial intelligence in gastrointestinal endoscopy
- •5.11 Future perspectives
- •5.12 Limitations of nanotechnology
- •5.13 Conclusion
- •6.1 Introduction
- •6.2 Global burden of gastric cancer
- •6.3 Gastric cancer risk factors
- •6.3.1 Infection with Helicobacter pylori
- •6.3.2 Age and sex
- •6.3.3 Cigarette smoking
- •6.3.4 Obesity and metabolic dysfunction
- •6.3.5 Dietary factors
- •6.3.6 Alcohol use
- •6.3.7 Medications
- •6.3.8 Host genetics
- •6.4 Other risk factors
- •6.4.1 Epstein–Barr virus infection
- •6.4.2 Autoimmune disorders
- •6.4.3 Ménétrier’s disease
- •6.5 Nanotechnology in cancer diagnostic and therapeutics
- •6.6 Nanotechnology and gastric cancer diagnostic
- •6.6.1 Fluorescence imaging and gastric cancer detection
- •6.6.2 Photoacoustic imaging and gastric cancer detection
- •6.6.3 Computed tomography and gastric cancer detection
- •6.6.4 Magnetic resonance imaging and gastric cancer detection
- •6.6.5 Multimodal imaging and gastric cancer detection
- •6.7 Nanotechnology and gastric cancer management
- •6.7.1 Nanomaterial and chemotherapy
- •6.7.2 Nanomedicine and radiotherapy
- •6.7.3 Phototherapy and gastric cancer detection
- •6.7.4 Combination therapies and theranostics for gastric cancer detection
- •6.8 Challenges and prospectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2 Nanoparticles as drug delivery systems
- •7.2.1 Advantages of nanoparticles for drug delivery
- •7.2.2 Types of nanoparticles used in gastric cancer treatment
- •7.2.3 Targeted drug delivery to gastric cancer cells
- •7.3 Nanoparticles for imaging and diagnosis
- •7.3.1 Nanoparticles in gastric cancer imaging
- •7.3.2 Contrast agents and theranostic nanoparticles
- •7.3.3 Molecular imaging and targeting approaches
- •7.4 Therapeutic applications of nanoparticles in gastric cancer
- •7.4.1 Chemotherapy with nanoparticle formulations
- •7.4.2 Photothermal and photodynamic therapy
- •7.4.3 Immunotherapy and nanoparticles
- •7.4.4 RNA interference (RNAi) and gene therapy
- •7.5 Nanoparticles for combination therapy
- •7.5.1 Synergistic effects of nanoparticle-based combination therapies
- •7.5.2 Sequential and simultaneous delivery of therapeutics
- •7.6 Challenges and limitations of nanoparticle-based therapy
- •7.6.1 Biocompatibility and toxicity concerns
- •7.6.2 Nanoparticle clearance and stability
- •7.6.3 Regulatory aspects and clinical translation
- •7.7.1 Preclinical studies and animal models
- •7.7.2 Clinical trials and human studies
- •7.7.3 Promising results and future directions
- •7.8 Nanoparticles in personalized medicine for gastric cancer
- •7.8.1 Biomarker-driven nanoparticle therapies
- •7.8.2 Individualized treatment approaches
- •7.9 Nanoparticle-based theranostics for gastric cancer
- •7.9.1 Diagnostic and therapeutic integration
- •7.9.2 Multifunctional nanoparticle platforms
- •7.10 Future perspectives and concluding remarks
- •Acknowledgments
- •References
- •8.1 Introduction
- •8.2 Artificial intelligence role in hepatitis disease
- •8.3 Artificial intelligence role in non-alcoholic fatty liver disease
- •8.4 Artificial intelligence role in hepatocellular carcinoma
- •8.5 Conclusion
- •References
- •9.1 Introduction
- •9.2 Overview of clinical decision support
- •9.2.2 Medical imaging and diagnostic services
- •9.2.3 Virtual patient care
- •9.2.4 Patient safety
- •9.2.5 Diagnostic support
- •9.2.6 Medical research and drug discovery
- •9.2.7 Rehabilitation
- •9.2.8 Administrative applications
- •9.3 Types of AI algorithms in CDS
- •9.3.1 Machine learning algorithms
- •9.3.2 Bayesian Gaussian regression
- •9.4 Supervised learning
- •9.4.1 Diagnosis and treatment prediction
- •9.5 Unsupervised learning
- •9.6 Deep learning and neural networks
- •9.7 Natural language processing (NLP) techniques
- •9.7.1 Convolutional neural networks (CNNs) for medical image analysis
- •9.7.2 Recurrent neural networks (RNNs) for signal processing
- •9.8 Current AI-based clinical data support system
- •9.9 Challenges and considerations
- •9.9.1 Current AI-based CDS systems
- •9.10 Regulatory and ethical issues (HIPAA, GDPR, etc)
- •9.11 Challenges for clinical translation
- •References
- •9.12 Obstacles, restrictions, and missing knowledge
- •9.13 Future trends
- •9.14 Future trends and developments
- •9.14.1 Advancements in AI algorithms
- •9.15 Expansion to point-of-care devices
- •9.16 AI-driven drug discovery
- •9.17 AI in public health and epidemiology
- •9.18 Conclusion
- •10.1 Introduction
- •10.2 Developing history of AI
- •10.3 AI’s role in the early detection of GC
- •10.3.1 Screening of GC by AI
- •10.3.2 Accuracy of sampling from early endoscopic diagnosis
- •10.3.3 Digital pathological diagnosis
- •10.4 Role of AI from endoscopic diagnosis to treatment
- •10.5 Artificial intelligence in surgery
- •10.6 Molecules and genes
- •10.7 AI models’ function in prognosis prediction
- •10.7.1 Metastasis and staging prediction
- •10.7.2 AI aided treatment decisions
- •10.7.3 Clinical massive data analysis and prognostic prediction
- •10.8 Survival analysis
- •10.9 Conclusion and future prospects
- •References
- •11.1 Introduction
- •11.2 Stages of liver fibrosis
- •11.3 Etiology of liver fibrosis
- •11.3.1 Chronic viral hepatitis
- •11.3.2 Alcohol-related liver disease (ALD)
- •11.4 Pathogenesis
- •11.5 Symptoms
- •11.6 Diagnosis
- •11.7 Invasive approach
- •11.7.1 Liver biopsy
- •11.7.2 Limitations of liver biopsy
- •11.8 Non-invasive approach
- •11.8.1 Ultrasonographic based
- •11.9 Non-surgical tests
- •11.9.1 Serum biomarkers
- •11.10 Treatment
- •11.11 Limitations of antifibrotic therapy
- •11.12 Role of nanomedicines in the treatment of hepatic fibrosis
- •11.13 Type of nanoparticles currently in use for LF
- •11.13.1 Phytochemical compound for LF
- •11.13.3 siRNA derived NPs
- •11.14 HSC targeted nanoparticle delivery
- •11.15 Advantage of nanomedicine for LF
- •11.15.2 Enhanced drug delivery
- •11.15.4 Reduced adverse effects
- •11.15.5 Improved pharmacokinetic properties
- •11.16 Challenges of nm for LF
- •11.17 Future of nm in the treatment of LF
- •References
- •12.1 Introduction
- •12.2 Medical requirement for colonoscopy
- •12.3 Limitation of colonoscopy
- •12.4 Advancement of colonoscopy
- •12.5 High-definition and ultra-high-definition imaging technology
- •12.6 Computed tomography
- •12.7 Artificial intelligence and machine learning
- •12.8 Advancement in patient experience
- •12.9 Capsule endoscopy
- •12.10 Simulated detection systems
- •12.11 Improved training and workshop programs
- •12.12 Future of colonoscopy
- •12.13 Multi-spectral imaging
- •12.14 Machine learning algorithms integration
- •12.15 Robotic-assisted colonoscopy
- •12.16 Virtual colonoscopy
- •12.17 Tailoring colonoscopy screening
- •12.18 Patient-compatible techniques
- •12.19 Remote monitoring and consultations
- •12.20 Alternative bowel preparation methods
- •12.21 Preventive measures enhancement
- •12.22 Conclusion
- •References

IOP Series in Articial Intelligence in the Biomedical Sciences
Nanobiotechnology and
Articial Intelligence in
Gastrointestinal Diseases
Edited by
Vivek K Chaturvedi
Anurag Kumar Singh
Jay Singh
Dawesh P Yadav

Nanobiotechnology and
Artificial Intelligence in
Gastrointestinal Diseases
Online at: https://doi.org/10.1088/978-0-7503-6134-7

IOP Series in Artificial Intelligence in the Biomedical Sciences
Series Editor
Ge Wang, Clark and Crossan Endowed Chair Professor,
Rensselaer Polytechnic Institute, Troy New York, USA
About the Series
The IOP Series in Artificial Intelligence in the Biomedical Sciences aims to develop a
library of key texts and reference works encompassing the broad range of artificial
intelligence, machine learning, deep learning and neural networks within all
applicable fields of biomedicine. There is now significant focus in using advancements in the field of AI to improve diagnosis, management, and better therapeutic
options of various diseases. Some examples and applications incorporated would be
AI in cancer diagnosis/prognosis, implementing artificial intelligence, data mining of
electronic health records data, ambient intelligence in hospitals, AI in virus
detection, AI in infectious diseases, biomarkers and genomics utilizing machine
learning and clinical decision support with AR. These are just a few of the many
applications that AI and related technologies can bring to the biomedical sciences.
The series contains two broad types of approach. Those addressing a particular field
of application and reviewing the numerous relevant artificial intelligence methods
applicable to the field, and those that focus on a specific AI method which will
permit a greater in-depth review of the theory and appropriate technology.
A full list of titles p ublished in this series can be found here:
https://iopscience.iop.org/bookListInfo/iop-series-in-artificial-intelligence-in-thebiomedical-sciences#series.

Nanobiotechnology and
Artificial Intelligence in
Gastrointestinal Diseases
Edited by
Vivek K Chaturvedi
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi, Uttar Pradesh 221005, India
Anurag Kumar Singh
Department of Pharmaceutical Engineering and Technology-Indian Institute of
Technology, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
Cancer Biology Research and Training, Department of Biological Sciences, Alabama
State University, 915 S Jackson Street, Montgomery AL 36101-0271, USA
Jay Singh
Department of Chemistry, Institute of Science, Banaras Hindu University, Varanasi,
Uttar Pradesh 221005, India
Dawesh P Yadav
Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu
University, Varanasi, Uttar Pradesh 221005, India
IOP Publishing, Bristol, UK

ª IOP Publishing Ltd 2024
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system
or transmitted in any form or by any means, electronic, mechanical, photocopying, recording
or otherwise, without the prior permission of the publisher, or as expressly permitted by law or
under terms agreed with the appropriate rights organization. Multiple copying is permitted in
accordance with the terms of licences issued by the Copyright Licensing Agency, the Copyright
Clearance Centre and other reproduction rights organizations.
Permission to make use of IOP Publishing content other than as set out above may be sought
at permissions@ioppublishing.org.
Vivek K Chaturvedi, Anurag Kumar Singh, Jay Singh and Dawesh P Yadav have asserted their
right to be identified as the editors of this work in accordance with sections 77 and 78 of the
Copyright, Designs and Patents Act 1988.
ISBN 978-0-7503-6134-7 (ebook)
ISBN 978-0-7503-6132-3 (print)
ISBN 978-0-7503-6135-4 (myPrint)
ISBN 978-0-7503-6133-0 (mobi)
DOI 10.1088/978-0-7503-6134-7
Version: 20240801
IOP ebooks
British Library Cataloguing-in-Publication Data: A catalogue record for this book is available
from the British Library.
Published by IOP Publishing, wholly owned by The Institute of Physics, London
IOP Publishing, No.2 The Distillery, Glassfields, Avon Street, Bristol, BS2 0GR, UK
US Office: IOP Publishing, Inc., 190 North Independence Mall West, Suite 601, Philadelphia,
PA 19106, USA

To those who care, conserve, and protect but do not destroy the beauty
and unique characteristics of
Kshit (Earth)
Jal (Water)
Pawak (Fire)
Gagan (Sky)
and
Sameera (Air).


Contents
Preface xix
Acknowledgements xxi
Editor biographies xxii
Short description about chapters xxv
List of contributors xxxiv
Introduction xxxviii
1 Nanotechnology and artificial intelligence 1-1
Anshu Singh, Vivek K Chaturvedi, Anurag K Singh, Jay Singh,
Kshitij R B Singh and Dawesh P Yadav
1.1 Introduction 1-1
1.2 Nanotechnology in medical science 1-3
1.2.1 Nanomaterials in drug delivery 1-3
1.2.2 Use of nanomaterials in designing diagnostic nanosensors 1-4
1.2.3 Nanomaterials as theranostics 1-4
1.3 Artificial intelligence in medical science 1-5
1.3.1 Machine learning in diagnostics 1-6
1.3.2 Natural language processing in healthcare 1-7
1.3.3 Predictive analytics in patient care 1-7
1.4 Synergies between nanotechnology and AI and their application in the
medical field
1.4.1 Nanoscience in controlled drug release in the GI tract 1-8
1.4.2 Novel drug delivery systems for inflammatory bowel disease 1-9
1.4.3 Nanotechnology in gastrointestinal endoscopy 1-10
1.4.4 Nano-biotechnology in gastrointestinal cancer 1-11
1.5 Role of nanoparticles for the treatment of gastric cancer 1-11
1.6 Artificial intelligence in hepatitis and chronic liver disease 1-12
1.7 Artificial intelligence applications for clinical decisions support 1-13
1.8 Role of artificial intelligence in an early diagnosis and prediction of
gastric cancer as an advanced therapeutic technique
1.9 Nanomedicines for liver fibrosis 1-15
1.10 Artificial intelligence-based colonoscopy 1-15
1.11 Clinical validation of artificial intelligence for gastrointestinal diseases 1-16
1.12 Summary and conclusions 1-16
Acknowledgments 1-17
References 1-17
1-8
1-14
vii

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
2 Nano-biosensors for diagnosis of gastrointestinal diseases 2-1
Mazharul Haque, Mohammad Zafaryab and Komal Vig
2.1 Introduction 2-2
2.2 Causes 2-3
2.3 Mechanism 2-3
2.4 Diagnosis 2-4
2.5 Prognosis 2-4
2.6 Present methods of detection 2-5
2.7 Biosensors 2-5
2.7.1 Components of biosensors 2-6
2.7.2 Types of biosensors 2-6
2.7.3 Enzyme based biosensors 2-7
2.7.4 Affinity biosensors 2-7
2.7.5 Immunosensors 2-8
2.7.6 Microbial biosensors 2-8
2.7.7 DNA-based biosensors 2-9
2.7.8 Phage sensors 2-9
2.7.9 Optical biosensors 2-9
2.7.10 Cantilever-based biosensors 2-9
2.7.11 Bio-MEMS 2-10
2.8 Physical biosensors 2-10
2.8.1 Thermometric biosensors 2-10
2.8.2 Acoustic biosensors 2-10
2.8.3 Magnetic biosensors 2-10
2.8.4 Wearable skins as biosensors 2-10
2.9 Electrochemical biosensors 2-11
2.9.1 Potentiometric 2-11
2.9.2 Coulometry methods 2-11
2.9.3 Conductometry methods 2-11
2.9.4 Potentiometric titration 2-11
2.10 Materials for biosensors 2-14
2.10.1 Nanomaterials for biosensors 2-14
2.10.2 Gastrointestinal diseases (GIDs) biosensor 2-15
2.11 Summary and future perspectives 2-20
References 2-20
viii

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
3 Nanoscience in controlled drug release in the
3-1
gastrointestinal tract
Ritu, Bharmjeet, Nida-e-Falak, Asmita Das, Rahul Gupta
and Prakash Chandra
3.1 Introduction 3-1
3.2 Challenges in drug delivery to the GI tract 3-3
3.2.1 Residence time 3-3
3.2.2 Influence of the GI environment 3-3
3.2.3 Intestinal fluid volume 3-4
3.2.4 Metabolism in the GI tract 3-4
3.3 Role of nanoscience in drug delivery 3-5
3.3.1 Importance of nanotechnology-based techniques in controlled
drug release in the GI tract
3.3.2 Targeted drug delivery 3-5
3.3.3 Increased bioavailability 3-5
3.3.4 Reduced toxicity and side effects 3-5
3.3.5 Imaging and diagnostic capabilities 3-5
3.3.6 Drug designing 3-6
3.3.7 Delivery system 3-6
3.4 Methods of nanomedicine formulation 3-7
3.5 Drug release strategies 3-8
3.5.1 Active targeting strategies 3-8
3.5.2 Stimuli-based delivery strategy 3-9
3.5.3 pH-dependent drug release 3-9
3.5.4 ROS-dependent drug release 3-10
3.5.5 Time-dependent dosage forms 3-10
3.5.6 Gastro retentive strategies 3-11
3.5.7 Photothermal and photodynamic approach 3-12
3.6 Types of nanoparticles in drug delivery 3-12
3.6.1 Liposomes 3-12
3.6.2 Polymeric nanoparticles 3-15
3.6.3 Metallic nanoparticles 3-17
3.6.4 Quantum dots 3-18
3.7 Approved nanomedicines 3-18
3.8 Application of AI in GI disease 3-19
3.9 Future perspectives and challenges 3-23
3.9.1 Diagnostics 3-26
3-5
ix
Соседние файлы в папке Библиотека им академика М.И. Перельмана
