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
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
3.9.2 Individualized treatment 3-26
3.9.3 Proactive patient monitoring 3-27
3.9.4 Decision support systems 3-27
3.9.5 Biomarker discovery and therapeutic development 3-27
3.9.6 Patient outcomes and quality of life 3-27
3.9.7 Regulation and ethical issues 3-27
3.10 Conclusion 3-27
References 3-28
4 Novel drug delivery systems for inflammatory bowel disease 4-1
Ashutosh Kumar, Pratistha Singh, Rajesh Kumar and Sunil Dutt
4.1 Introduction 4-2
4.2 Challenges and barriers in drug delivery 4-3
4.3 Drugs used in IBD 4-3
4.4 Novel drug delivery system for inflammatory bowel disease 4-4
4.4.1 Vesicular delivery system 4-5
4.4.2 Nanoparticle drug delivery system 4-5
4.5 pH-dependent nano-delivery systems 4-6
4.6 Inorganic nanoparticles 4-7
4.7 Prodrugs based 4-7
4.8 Hybrid drug delivery systems 4-8
4.9 Enteric coated formulations 4-8
4.10 RNA interference-based novel drug delivery 4-9
4.11 Toxicity profiling of IBD 4-10
4.11.1 Corticosteroids 4-10
4.11.2 Immuno modulators 4-11
4.11.3 Biologic therapies 4-11
4.11.4 JAK inhibitors 4-12
4.11.5 Immune dysregulation in IBD 4-12
4.11.6 Gastrointestinal effects 4-13
4.11.7 Antibiotics 4-13
4.11.8 Cyclosporine 4-13
4.11.9 Nutritional deficiencies 4-13
4.11.10 Surgery-related complications 4-14
4.11.11 Increased risk of colorectal cancer 4-14
4.12 Current prospective of IBD 4-14
4.12.1 Personalized medicine and immunological therapies 4-14
x
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
4.12.2 Disease monitoring and surgical advances 4-15
4.12.3 Development of IL-6 signaling inhibitors 4-16
4.12.4 Genome-wide association studies (GWAS) 4-16
4.12.5 Rare variant analysis 4-16
4.12.6 Functional genomics and gene expression studies 4-17
4.12.7 Therapeutic targets 4-17
4.12.8 Gene-environment interactions 4-18
4.13 Future prospective of IBD 4-18
4.13.1 Targeted drug delivery and site-specific release 4-19
4.13.2 Microparticles-based delivery systems 4-19
4.13.3 Biological therapies 4-19
4.13.4 Combination therapies 4-19
4.14 Conclusion 4-20
References 4-20
5 Nanotechnology in gastrointestinal endoscopy 5-1
Rajesh Kumar, Sunil Dutt, Ankush Goyal, Ashutosh Kumar
and Brijesh Kumar
5.1 Introduction 5-2
5.2 Nanotechnology 5-5
5.3 Nanoparticles 5-6
5.4 Classification of nanoparticles 5-7
5.4.1 Polymer-based nanoparticles 5-7
5.4.2 Solid nanoparticles 5-8
5.4.3 Carbon-based nanoparticles 5-8
5.4.4 Lipid-based nanoparticles 5-9
5.4.5 Nanoemulsions 5-9
5.4.6 Nanoparticles in biomedical applications 5-10
5.4.7 Characteristics of nanoparticles 5-10
5.4.8 Characterization of nanoparticles 5-11
5.5 Intestinal endoscopy 5-11
5.6 Medical nanotechnology 5-12
5.6.1 Diagnosis 5-12
5.6.2 Nanotechnology in the early diagnosis 5-13
5.6.3 Theragnostic 5-13
5.6.4 Tissue engineering 5-14
5.6.5 Targeted imaging and therapeutic in colorectal cancer 5-14
5.6.6 Gene therapy delivery 5-15
xi
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
5.6.7 Colitis therapy 5-15
5.6.8 Oral delivery of vaccines 5-16
5.6.9 Mitigation 5-16
5.6.10 Role in targeted drug delivery 5-16
5.7 Role of nanotechnology in intestinal tract 5-17
5.8 Nanotechnological aids 5-18
5.8.1 Nanopowder 5-18
5.8.2 Plastic stents 5-18
5.8.3 Capsule endoscopy 5-19
5.9 Quality control of nanotechnology 5-20
5.10 Artificial intelligence in gastrointestinal endoscopy 5-21
5.11 Future perspectives 5-22
5.12 Limitations of nanotechnology 5-23
5.13 Conclusion 5-25
References 5-26
6 Nano-biotechnology in gastrointestinal cancer 6-1
Mohammad Zafaryab, Mazharul Haque and Komal Vig
6.1 Introduction 6-2
6.2 Global burden of gastric cancer 6-2
6.3 Gastric cancer risk factors 6-3
6.3.1 Infection with Helicobacter pylori 6-3
6.3.2 Age and sex 6-4
6.3.3 Cigarette smoking 6-4
6.3.4 Obesity and metabolic dysfunction 6-4
6.3.5 Dietary factors 6-5
6.3.6 Alcohol use 6-5
6.3.7 Medications 6-5
6.3.8 Host genetics 6-5
6.4 Other risk factors 6-6
6.4.1 Epstein–Barr virus infection 6-6
6.4.2 Autoimmune disorders 6-6
6.4.3 Ménétrier’s disease 6-7
6.5 Nanotechnology in cancer diagnostic and therapeutics 6-7
6.6 Nanotechnology and gastric cancer diagnostic 6-8
6.6.1 Fluorescence imaging and gastric cancer detection 6-8
6.6.2 Photoacoustic imaging and gastric cancer detection 6-9
xii
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
6.6.3 Computed tomography and gastric cancer detection 6-10
6.6.4 Magnetic resonance imaging and gastric cancer detection 6-10
6.6.5 Multimodal imaging and gastric cancer detection 6-10
6.7 Nanotechnology and gastric cancer management 6-11
6.7.1 Nanomaterial and chemotherapy 6-11
6.7.2 Nanomedicine and radiotherapy 6-12
6.7.3 Phototherapy and gastric cancer detection 6-13
6.7.4 Combination therapies and theranostics for gastric cancer
6-14
detection
6.8 Challenges and prospectives 6-14
Acknowledgments 6-16
References 6-16
7 Role of nanoparticles for the treatment of gastric cancer 7-1
Ravi Kumar Yadav, Shefali Singh, Zeba Azim, Niraj Kumar Goswami
and Navneet Yadav
7.1 Introduction 7-1
7.2 Nanoparticles as drug delivery systems 7-3
7.2.1 Advantages of nanoparticles for drug delivery 7-3
7.2.2 Types of nanoparticles used in gastric cancer treatment 7-3
7.2.3 Targeted drug delivery to gastric cancer cells 7-3
7.3 Nanoparticles for imaging and diagnosis 7-4
7.3.1 Nanoparticles in gastric cancer imaging 7-4
7.3.2 Contrast agents and theranostic nanoparticles 7-4
7.3.3 Molecular imaging and targeting approaches 7-5
7.4 Therapeutic applications of nanoparticles in gastric cancer 7-5
7.4.1 Chemotherapy with nanoparticle formulations 7-5
7.4.2 Photothermal and photodynamic therapy 7-6
7.4.3 Immunotherapy and nanoparticles 7-7
7.4.4 RNA interference (RNAi) and gene therapy 7-7
7.5 Nanoparticles for combination therapy 7-8
7.5.1 Synergistic effects of nanoparticle-based combination therapies 7-8
7.5.2 Sequential and simultaneous delivery of therapeutics 7-9
7.6 Challenges and limitations of nanoparticle-based therapy 7-9
7.6.1 Biocompatibility and toxicity concerns 7-9
7.6.2 Nanoparticle clearance and stability 7-10
7.6.3 Regulatory aspects and clinical translation 7-10
7.7 Preclinical and clinical studies with nanoparticles for gastric cancer 7-10
xiii
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
7.7.1 Preclinical studies and animal models 7-11
7.7.2 Clinical trials and human studies 7-11
7.7.3 Promising results and future directions 7-11
7.8 Nanoparticles in personalized medicine for gastric cancer 7-11
7.8.1 Biomarker-driven nanoparticle therapies 7-12
7.8.2 Individualized treatment approaches 7-12
7.9 Nanoparticle-based theranostics for gastric cancer 7-13
7.9.1 Diagnostic and therapeutic integration 7-13
7.9.2 Multifunctional nanoparticle platforms 7-14
7.10 Future perspectives and concluding remarks 7-14
Acknowledgments 7-14
References 7-14
8 Artificial intelligence in hepatitis and chronic liver disease 8-1
Akbar Hamid, Gira Sulabh and Vinod Kumar
8.1 Introduction 8-1
8.2 Artificial intelligence role in hepatitis disease 8-3
8.2.1 Limitations of the traditional method for the diagnosis and
treatment of hepatitis disease
8.2.2 Artificial intelligence in hepatitis diagnosis 8-6
8.2.3 Treatment and management of hepatitis with artificial intelligence 8-7
8.2.4 Artificial intelligence-enabled hepatitis disease surveillance and
prevention
8.3 Artificial intelligence role in non-alcoholic fatty liver disease 8-9
8.3.1 Limitations of the traditional method for the diagnosis
and treatment of non-alcoholic fatty liver disease
8.3.2 Artificial intelligence in non-alcoholic fatty liver disease
diagnosis
8.3.3 Treatment and management of non-alcoholic fatty liver disease
with artificial intelligence
8.3.4 Artificial intelligence-enabled non-alcoholic fatty liver
disease surveillance and prevention
8.4 Artificial intelligence role in hepatocellular carcinoma 8-15
8.4.1 Limitations of the traditional method for the diagnosis and
treatment of hepatocellular carcinoma
8.4.2 Artificial intelligence with hepatocellular carcinoma diagnosis 8-17
8.4.3 Treatment and management of hepatocellular carcinoma with
artificial intelligence
8-5
8-8
8-10
8-12
8-13
8-14
8-16
8-19
xiv
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
8.4.4 Artificial intelligence-enabled hepatocellular carcinoma disease
8-20
surveillance and prevention
8.5 Conclusion 8-21
References 8-22
9 Artificial intelligence applications for clinical decisions support 9-1
Bhaskar Sharma, Renu Negi, Anjali Yadav, Yogesh Sharma
and Vivek K Chaturvedi
9.1 Introduction 9-1
9.2 Overview of clinical decision support 9-3
9.2.1 Role of artificial intelligence (AI) in enhancing CDS 9-3
9.2.2 Medical imaging and diagnostic services 9-3
9.2.3 Virtual patient care 9-4
9.2.4 Patient safety 9-4
9.2.5 Diagnostic support 9-5
9.2.6 Medical research and drug discovery 9-5
9.2.7 Rehabilitation 9-6
9.2.8 Administrative applications 9-6
9.3 Types of AI algorithms in CDS 9-6
9.3.1 Machine learning algorithms 9-6
9.3.2 Bayesian Gaussian regression 9-7
9.4 Supervised learning 9-7
9.4.1 Diagnosis and treatment prediction 9-8
9.5 Unsupervised learning 9-8
9.6 Deep learning and neural networks 9-9
9.7 Natural language processing (NLP) techniques 9-10
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 9-13
9.9 Challenges and considerations 9-13
9.9.1 Current AI-based CDS systems 9-13
9.10 Regulatory and ethical issues (HIPAA, GDPR, etc) 9-14
9.11 Challenges for clinical translation 9-15
9.12 Obstacles, restrictions, and missing knowledge 9-18
9.13 Future trends 9-19
9.14 Future trends and developments 9-19
9.14.1 Advancements in AI algorithms 9-19
9-10
9-10
xv
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
9.15 Expansion to point-of-care devices 9-20
9.16 AI-driven drug discovery 9-21
9.17 AI in public health and epidemiology 9-21
9.18 Conclusion 9-22
References 9-22
10 Role of artificial intelligence in an early diagnosis and
10-1
prediction of gastric cancer as an advanced therapeutic
technique
Juhi Singh and Vinod Kumar Dixit
10.1 Introduction 10-2
10.2 Developing history of AI 10-3
10.3 AI’s role in the early detection of GC 10-4
10.3.1 Screening of GC by AI 10-5
10.3.2 Accuracy of sampling from early endoscopic diagnosis 10-6
10.3.3 Digital pathological diagnosis 10-6
10.4 Role of AI from endoscopic diagnosis to treatment 10-8
10.5 Artificial intelligence in surgery 10-9
10.6 Molecules and genes 10-9
10.7 AI models’ function in prognosis prediction 10-12
10.7.1 Metastasis and staging prediction 10-14
10.7.2 AI aided treatment decisions 10-15
10.7.3 Clinical massive data analysis and prognostic prediction 10-15
10.8 Survival analysis 10-15
10.9 Conclusion and future prospects 10-19
References 10-24
11 Nanomedicines in liver fibrosis 11-1
Saras Tiwari, Bhaskar Sharma, Jugasmita Deka, Prabhakar Singh
and Vivek K Chatruvedi
11.1 Introduction 11-1
11.2 Stages of liver fibrosis 11-2
11.3 Etiology of liver fibrosis 11-3
11.3.1 Chronic viral hepatitis 11-3
11.3.2 Alcohol-related liver disease (ALD) 11-3
11.3.3 Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic
steatohepatitis (NASH)
xvi
11-3
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
11.4 Pathogenesis 11-4
11.5 Symptoms 11-5
11.6 Diagnosis 11-7
11.7 Invasive approach 11-8
11.7.1 Liver biopsy 11-8
11.7.2 Limitations of liver biopsy 11-8
11.8 Non-invasive approach 11-9
11.8.1 Ultrasonographic based 11-9
11.9 Non-surgical tests 11-11
11.9.1 Serum biomarkers 11-11
11.10 Treatment 11-11
11.11 Limitations of antifibrotic therapy 11-12
11.12 Role of nanomedicines in the treatment of hepatic fibrosis 11-13
11.13 Type of nanoparticles currently in use for LF 11-13
11.13.1 Phytochemical compound for LF 11-13
11.13.2 Synthetic antifibrotic nano formulations 11-14
11.13.3 siRNA derived NPs 11-15
11.13.4 Mesenchymal stem cells coated nanoparticles in hepatic
11-17
fibrosis
11.14 HSC targeted nanoparticle delivery 11-17
11.15 Advantage of nanomedicine for LF 11-19
11.15.1 Specific targeting and minimized side effects 11-19
11.15.2 Enhanced drug delivery 11-19
11.15.3 Modulation of inflammatory and oxidative stress
11-19
pathways
11.15.4 Reduced adverse effects 11-20
11.15.5 Improved pharmacokinetic properties 11-20
11.16 Challenges of nm for LF 11-20
11.17 Future of nm in the treatment of LF 11-21
References 11-21
12 Artificial intelligence (AI) based colonoscopy 12-1
Akbar Hamid, Rajesh Kumar, Vivek K Chaturvedi, Sunil Dutt,
Gira Sulabh, Vinod Kumar and D P Yadav
12.1 Introduction 12-1
12.2 Medical requirement for colonoscopy 12-3
12.3 Limitation of colonoscopy 12-4
12.4 Advancement of colonoscopy 12-5
xvii
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
12.5 High-definition and ultra-high-definition imaging technology 12-6
12.6 Computed tomography 12-7
12.7 Artificial intelligence and machine learning 12-8
12.8 Advancement in patient experience 12-8
12.9 Capsule endoscopy 12-9
12.10 Simulated detection systems 12-10
12.11 Improved training and workshop programs 12-11
12.12 Future of colonoscopy 12-12
12.13 Multi-spectral imaging 12-12
12.14 Machine learning algorithms integration 12-13
12.15 Robotic-assisted colonoscopy 12-14
12.16 Virtual colonoscopy 12-15
12.17 Tailoring colonoscopy screening 12-15
12.18 Patient-compatible techniques 12-16
12.19 Remote monitoring and consultations 12-17
12.20 Alternative bowel preparation methods 12-17
12.21 Preventive measures enhancement 12-18
12.22 Conclusion 12-19
References 12-19
xviii
Preface
Nanotechnology and artificial intelligence (AI) has transformed numerous fields of
research as well as daily life spans during the past 20 years. It has shown a promising
ability to treat the majority of medical complications, including gastrointestinal
(GI), infectious, cancer, genetic, and neurological diseases. One of the most
promising fields in nanotechnology and AI-based personalized nanomedicine
systems is defined as a highly specialized medical intervention for the diagnosis,
prevention, and treatment of GI diseases, which helps medical practitioners increase
the level of diagnostic accuracy and efficiency. The diagnosis and treatment of
gastroenterological illnesses are expected to be significantly impacted by the
combination of nanotechnology and AI. The essential aspect of AI-based nanomedicine is ‘drug delivery,’ which is one of the most exciting applications of
nanotechnology and can manipulate molecules and supramolecular structures to
make devices with pre-programmed functionality. The present drug delivery
methods are divided into nano- and microscale systems, which primarily make
use of nanoparticles, liposomes, polymeric micelles (nanovehicles), dendrimers,
nanocrystals, microchips, microtherapeutic systems, and innovative 100 nm-sized
microparticles. Future advancements in these technologies will create effective nano/
microdrug delivery systems that will meet healthcare problems for the detection and
treatment of infectious diseases, with a special focus on those microorganisms that
are developing drug resistance. This book contains 13 chapters that are broadly
focused on recent developments in nanotechnology and AI-based drug delivery
systems, diagnosis, and the role of various nanomaterials in the management of GI,
such as abdominal pain, bowel obstruction, diarrhoea, pancreatitis, upper gastrointestinal bleeding (UGIB), non-alcoholic fatty liver diseases (NAFLD), intestinal
tuberculosis (ITB), celiac disease, and duodenal ulcer. The role of nanotechnology
and AI in GI illnesses is covered in chapter 1 as a breakthrough advancement in the
fields of drug delivery, disease diagnostics, and treatment. The use of various nanobiosensors for the diagnosis and treatment of gastrointestinal tract illnesses is
covered in chapter 2. Chapter 3 focuses on the role of nanoscience in controlled
drug delivery in the GI tract, and management of GI disorders. Chapter 4
emphasizes one of the most emerging fields of GI tract-based novel drug delivery
systems for inflammatory bowel disease. The use of nanotechnology in GI endoscopy is examined in chapter 5. Nowadays, superparamagnetic iron oxide nanoparticles and other magnetic nanoparticles attract a great deal of attention from
researchers all over the world due to their strong magnetic properties, which provide
an added advantage when they are used in GI endoscopy. The contribution of
nanobiotechnology to GI cancer and its use in drug delivery are discussed in chapter
6. The reader’s comprehension of new ideas and the application of drug delivery
carriers in GI delivery will be improved by a thorough explanation provided on the
difficult barriers for drug delivery that combine the difficulties brought on by solid
tumours, the physiologic environment of the GI tract, and the tight epithelial tissue
barriers. Chapter 7 is also about the revolutionary applications of the role of
xix