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Sharma L, Rahman F, Sharma RA. The emerging role of biotechnological advances and artificial intelligence in tackling gluten sensitivity. Crit Rev Food Sci Nutr 2024:1-17. [PMID: 39145745 DOI: 10.1080/10408398.2024.2392158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Gluten comprises an intricate network of hundreds of related but distinct proteins, mainly "gliadins" and "glutenins," which play a vital role in determining the rheological properties of wheat dough. However, ingesting gluten can trigger severe conditions in susceptible individuals, including celiac disease, wheat allergy, or non-celiac gluten sensitivity, collectively known as gluten-related disorders. This review provides a panoramic view, delving into the various aspects of gluten-triggered disorders, including symptoms, diagnosis, mechanism, and management. Though a gluten-free diet remains the primary option to manage gluten-related disorders, the emerging microbial and plant biotechnology tools are playing a transformative role in reducing the immunotoxicity of gluten. The enzymatic hydrolysis of gluten and the development of gluten-reduced/free wheat lines using RNAi and CRISPR/Cas technology are laying the foundation for creating safer wheat products. In addition to biotechnological interventions, the emerging artificial intelligence technologies are also bringing about a paradigm shift in the diagnosis and management of gluten-related disorders. Here, we provide a comprehensive overview of the latest developments and the potential these technologies hold for tackling gluten sensitivity.
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Affiliation(s)
- Lakshay Sharma
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
| | - Farhanur Rahman
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
| | - Rita A Sharma
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
- National Agri-Food Biotechnology Institute (NABI), Mohali, India
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Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Muruganantham P, Balakrishnan SM. Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00686-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World J Gastroenterol 2022; 28:449-463. [PMID: 35125829 PMCID: PMC8790554 DOI: 10.3748/wjg.v28.i4.449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/16/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
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Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
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Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
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Jones MA, MacCuaig WM, Frickenstein AN, Camalan S, Gurcan MN, Holter-Chakrabarty J, Morris KT, McNally MW, Booth KK, Carter S, Grizzle WE, McNally LR. Molecular Imaging of Inflammatory Disease. Biomedicines 2021; 9:152. [PMID: 33557374 PMCID: PMC7914540 DOI: 10.3390/biomedicines9020152] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/25/2021] [Accepted: 01/31/2021] [Indexed: 02/06/2023] Open
Abstract
Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.
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Affiliation(s)
- Meredith A. Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - William M. MacCuaig
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Alex N. Frickenstein
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Seda Camalan
- Department of Internal Medicine, Wake Forest Baptist Health, Winston-Salem, NC 27157, USA; (S.C.); (M.N.G.)
| | - Metin N. Gurcan
- Department of Internal Medicine, Wake Forest Baptist Health, Winston-Salem, NC 27157, USA; (S.C.); (M.N.G.)
| | - Jennifer Holter-Chakrabarty
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Medicine, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Katherine T. Morris
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Molly W. McNally
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Kristina K. Booth
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Steven Carter
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - William E. Grizzle
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Lacey R. McNally
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
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Molder A, Balaban DV, Jinga M, Molder CC. Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review. Front Pharmacol 2020; 11:341. [PMID: 32372947 PMCID: PMC7179080 DOI: 10.3389/fphar.2020.00341] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 03/09/2020] [Indexed: 02/05/2023] Open
Abstract
Celiac disease (CD) is a chronic autoimmune disease that occurs in genetically predisposed individuals in whom the ingestion of gluten leads to damage of the small bowel. It is estimated to affect 1 in 100 people worldwide, but is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples for the diagnosis of adult CD. In pediatric CD, but in recent years in adults also, nonbioptic diagnostic strategies have become increasingly popular. In this setting, in order to increase the diagnostic rate of this pathology, endoscopy itself has been thought of as a case finding strategy by use of digital image processing techniques. Research focused on computer aided decision support used as database video capsule, endoscopy and even biopsy duodenal images. Early automated methods for diagnosis of celiac disease used feature extraction methods like spatial domain features, transform domain features, scale-invariant features and spatio-temporal features. Recent artificial intelligence (AI) techniques using deep learning (DL) methods such as convolutional neural network (CNN), support vector machines (SVM) or Bayesian inference have emerged as a breakthrough computer technology which can be used for computer aided diagnosis of celiac disease. In the current review we summarize methods used in clinical studies for classification of CD from feature extraction methods to AI techniques.
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Affiliation(s)
- Adriana Molder
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Center of Excellence in Robotics and Autonomous Systems, Military Technical Academy Ferdinand I, Bucharest, Romania
| | - Daniel Vasile Balaban
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Gastroenterology Department, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest, Romania
- *Correspondence: Daniel Vasile Balaban,
| | - Mariana Jinga
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Gastroenterology Department, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest, Romania
| | - Cristian-Constantin Molder
- Center of Excellence in Robotics and Autonomous Systems, Military Technical Academy Ferdinand I, Bucharest, Romania
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Wei JW, Wei JW, Jackson CR, Ren B, Suriawinata AA, Hassanpour S. Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach. J Pathol Inform 2019; 10:7. [PMID: 30984467 PMCID: PMC6437784 DOI: 10.4103/jpi.jpi_87_18] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 01/31/2019] [Indexed: 12/12/2022] Open
Abstract
CONTEXT Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently. SUBJECTS AND METHODS In this study, we trained a deep learning model to detect CD on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. RESULTS Our model identified CD, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was >0.95 for all classes. CONCLUSIONS We have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.
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Affiliation(s)
- Jason W. Wei
- Department of Biomedical Data Science, Dartmouth College, Hanover, New Hampshire, USA
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA
| | - Jerry W. Wei
- Department of Biomedical Data Science, Dartmouth College, Hanover, New Hampshire, USA
| | - Christopher R. Jackson
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Bing Ren
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Arief A. Suriawinata
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Saeed Hassanpour
- Department of Biomedical Data Science, Dartmouth College, Hanover, New Hampshire, USA
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA
- Department of Epidemiology, Dartmouth College, Hanover, New Hampshire, USA
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Marlicz W, Koulaouzidis A. Celiac disease and small-bowel enteropathy - Seeing beyond the haze, the mist and the fog. Comput Biol Med 2018; 104:352-353. [PMID: 30545571 DOI: 10.1016/j.compbiomed.2018.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Wojciech Marlicz
- Department of Gastroenterology, Pomeranian Medical University, Szczecin, Poland.
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