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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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Luque-Vilca OM, Paredes-Erquinigo JY, Quille-Quille L, Choque-Rivera TJ, Cabel-Moscoso DJ, Rivera-Ashqui TA, Silva-Paz RJ. Utilization of Sustainable Ingredients (Cañihua Flour, Whey, and Potato Starch) in Gluten-Free Cookie Development: Analysis of Technological and Sensorial Attributes. Foods 2024; 13:1491. [PMID: 38790791 PMCID: PMC11120295 DOI: 10.3390/foods13101491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
In recent years, the consumption of gluten-free products has increased due to the increasing prevalence of celiac disease and the increased preference for gluten-free diets. This study aimed to make cookies using a mixture of cañihua flour, whey, and potato starch. The use of a Box-Behnken design allowed for flexible ingredient proportions and physicochemical properties, centesimal composition, color, texture, and sensory attributes to be evaluated through consumer tests (Sorting and acceptability). The results highlighted significant variations in physicochemical data, composition, color, and texture across formulations. The blend with 38.51% cañihua flour, 10.91% sweet whey, 25.69% potato starch, 8.34% margarine, 11.10% sugar, 0.19% sodium chloride, 0.51% baking powder, 0.51% vanilla essence, and 4.24% egg exhibited superior sensory appeal. This formulation boasted excellent texture, aroma, flavor, color, and appearance, indicating high sensory and physicochemical quality. The use of cañihua flour, sweet whey, and potato starch not only provides a gluten-free option but also delivers a nutritious and sensorily pleasing choice for those with dietary restrictions. Future research could explore the commercial viability of producing these cookies on a larger scale, as well as investigating the potential health benefits of these ingredients.
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Affiliation(s)
- Olivia M. Luque-Vilca
- Facultad de Ingeniería de Procesos Industriales, Universidad Nacional de Juliaca, Av. Nueva Zelandia 631, Juliaca 21101, Peru; (O.M.L.-V.); (J.Y.P.-E.); (L.Q.-Q.); (T.J.C.-R.)
| | - Jover Y. Paredes-Erquinigo
- Facultad de Ingeniería de Procesos Industriales, Universidad Nacional de Juliaca, Av. Nueva Zelandia 631, Juliaca 21101, Peru; (O.M.L.-V.); (J.Y.P.-E.); (L.Q.-Q.); (T.J.C.-R.)
| | - Lenin Quille-Quille
- Facultad de Ingeniería de Procesos Industriales, Universidad Nacional de Juliaca, Av. Nueva Zelandia 631, Juliaca 21101, Peru; (O.M.L.-V.); (J.Y.P.-E.); (L.Q.-Q.); (T.J.C.-R.)
| | - Tania J. Choque-Rivera
- Facultad de Ingeniería de Procesos Industriales, Universidad Nacional de Juliaca, Av. Nueva Zelandia 631, Juliaca 21101, Peru; (O.M.L.-V.); (J.Y.P.-E.); (L.Q.-Q.); (T.J.C.-R.)
| | | | - Thalía A. Rivera-Ashqui
- Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, km 19 Carretera Central, Ñaña, Lima 15457, Peru;
| | - Reynaldo J. Silva-Paz
- Escuela de Ingeniería en Industrias Alimentarias, Departamento de Ingeniería, Universidad Nacional de Barranca, Av. Toribio de Luzuriaga N° 376 Mz J. Urb. La Florida, Barranca 15169, Peru
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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Gruver AM, Lu H, Zhao X, Fulford AD, Soper MD, Ballard D, Hanson JC, Schade AE, Hsi ED, Gottlieb K, Credille KM. Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response. Diagn Pathol 2023; 18:122. [PMID: 37951937 PMCID: PMC10638821 DOI: 10.1186/s13000-023-01412-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. METHODS A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. RESULTS Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. CONCLUSIONS Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.
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Affiliation(s)
- Aaron M Gruver
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Haiyan Lu
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Xiaoxian Zhao
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Angie D Fulford
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Michael D Soper
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Darryl Ballard
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Jeffrey C Hanson
- Research Informatics, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Andrew E Schade
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Eric D Hsi
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Klaus Gottlieb
- Immunology, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Kelly M Credille
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA.
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Faust O, De Michele S, Koh JE, Jahmunah V, Lih OS, Kamath AP, Barua PD, Ciaccio EJ, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107320. [PMID: 36608429 DOI: 10.1016/j.cmpb.2022.107320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP). METHODS Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images. RESULTS For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC. CONCLUSIONS To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers.
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Affiliation(s)
| | - Simona De Michele
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - Joel Ew Koh
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - V Jahmunah
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | | | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Edward J Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Suzanne K Lewis
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Peter H Green
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Govind Bhagat
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA; Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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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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Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images. INFORMATION 2022. [DOI: 10.3390/info13040160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians.
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Stoleru CA, Dulf EH, Ciobanu L. Automated detection of celiac disease using Machine Learning Algorithms. Sci Rep 2022; 12:4071. [PMID: 35260574 PMCID: PMC8904634 DOI: 10.1038/s41598-022-07199-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/14/2022] [Indexed: 12/20/2022] Open
Abstract
Celiac disease is a disorder of the immune system that mainly affects the small intestine but can also affect the skeletal system. The diagnosis relies on histological assessment of duodenal biopsies acquired by upper digestive endoscopy. Immunological tests involve collecting a blood sample to detect if the antibodies have been produced in the body. Endoscopy is invasive and histology is time-consuming. In recent years there have been various algorithms that use artificial intelligence (AI) and neural convolutions (CNN, Convolutional Neural Network) to process images from capsule endoscopy, a non-invasive endoscopy approach, that provides magnified, high qualitative images of the small bowel mucosa, to quickly establish a diagnosis. The proposed innovative approach do not use complex learning algorithms, instead it find some artefacts in the endoscopies using kernels and use classified machine learning algorithms. Each used artefacts have a psychical meaning: atrophies of the mucosa with a visible submucosal vascular pattern; the presence of cracks (depressions) that have an appearance similar to that of dry land; reduction or complete loss of folds in the duodenum; the presence of a submerged appearance at the Kerckring folds and a low number of villi. The results obtained for video capsule endoscopy images processing reveal an accuracy of 94.1% and F1 score of 94%, which is competitive with other complex algorithms. The main goal of the present research was to demonstrate that computer-aided diagnosis of celiac disease is possible even without the use of very complex algorithms, which require expensive hardware and a lot of processing time. The use of the proposed automated images processing acquired noninvasively by capsule endoscopy would be assistive in detecting the subtle presence of villous atrophy not evident by visual inspection. It may also be useful to assess the degree of improvement of celiac. Patients on a gluten-free diet, the main treatment method for stopping the autoimmune process and improving the state of the small intestinal villi. The novelty of the work is that the algorithm uses two modified filters to properly analyse the intestine wall texture. It is proved that using the right filters, the proper diagnostic can be obtained by image processing, without the use of a complicated machine learning algorithm.
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Affiliation(s)
- Cristian-Andrei Stoleru
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului 28, 400014, Cluj-Napoca, Romania
| | - Eva H Dulf
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului 28, 400014, Cluj-Napoca, Romania.
| | - Lidia Ciobanu
- Faculty of Medicine, Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Croitorilor Street 19-21, 400162, Cluj-Napoca, Romania
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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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