scholarly journals Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francesco Piccialli ◽  
Francesco Calabrò ◽  
Danilo Crisci ◽  
Salvatore Cuomo ◽  
Edoardo Prezioso ◽  
...  
Keyword(s):  
Machine Learning ◽  
Celiac Disease ◽  
Precision Medicine ◽  
Clinical Symptoms ◽  
Morphological Changes ◽  
Selection Procedure ◽  
Clinical Problem ◽  
Mucosal Damage ◽  
Analysis Model ◽  
Small Bowel Mucosa

AbstractPotential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children.

scholarly journals The impact of morphological changes in small bowel mucosa on iron metabolism in children with celiac disease

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Leonid Klimov ◽  
Marina Stoyan ◽  
Victoria Kuryaninova ◽  
Vyacheslav Kashnikov ◽  
Valentina Botasheva ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Small Bowel ◽  
Iron Metabolism ◽  
Morphological Changes ◽  
Small Bowel Mucosa ◽  
The Impact ◽  
Bowel Mucosa

Celiac disease antibody levels reflect duodenal mucosal damage but not clinical symptoms

2021 ◽  
pp. 1-6
Author(s):  
Aki J. Käräjämäki ◽  
Juha Taavela ◽  
Christian Nielsen ◽  
Mårten Lönnqvist ◽  
Marcus Svartbäck ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Clinical Symptoms ◽  
Mucosal Damage ◽  
Disease Antibody ◽  
Antibody Levels

scholarly journals Serum Cytokines Profile in Treated Celiac Disease Compared with Non-celiac Gluten Sensitivity and Control: a Marker for Differentiation

2018 ◽  
Vol 27 (3) ◽  
pp. 241-247 ◽  
Author(s):  
Fatemeh Heydari ◽  
Mohammad Rostami-Nejad ◽  
Ali Moheb-Alian ◽  
Mostafa Haji Mollahoseini ◽  
Kamran Rostami ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Inflammatory Cytokines ◽  
Clinical Symptoms ◽  
Elevated Serum ◽  
Serum Levels ◽  
Mucosal Damage ◽  
Gluten Sensitivity ◽  
Ifn Γ ◽  
The Mean ◽  
And Control

Background & Aims: There is increasing evidence regarding elevated serum levels of inflammatory cytokines in patients with celiac disease (CD), but little is known about their levels in patients with non-celiac gluten sensitivity (NCGS). The aim of this study was to evaluate the serum levels of inflammatory cytokines in Iranian patients with CD and NCGS and to compare them with those of healthy individuals. Methods: A total of 110 treated CD, 15 with NCGS, and 46 healthy subjects were enrolled during 2016. Serum levels of IL-1, IL-6, IL-8, IL-15 and IFN-γ were measured using ELISA, and compared between groups. The correlation of the severity of mucosal damage and clinical symptoms with serum levels of cytokines was also assessed. Results: The mean serum levels of IFN-γ (p = 0.04) and IL-6 (p = 0.007) were significantly different between the patients in the CD and control groups, and IL-8 was significantly higher in the CD group compared with patients in the NCGS group (p = 0.04). Statistically significant correlations were observed between the serum levels of IFN-γ and abortion (p = 0.01), IL-1 and weight loss (p = 0.043) and infertility (p = 0.0001) in CD patients, and between IFN-γ and abortion (p = 0.01) and infertility (p = 0.01) in the NCGS patients. Moreover, no significant relationship was observed between the severity of mucosal damage and the serum level of the studied cytokines. Conclusions: Inflammatory cytokines are implicated in the pathogenesis of CD, and their serum levels might help to identify a diagnostic marker to differentiate CD from NCGS. However, further studies with a larger sample size are recommended.

scholarly journals Non-Celiac Gluten Sensitivity: A Challenging Diagnosis in Children with Abdominal Pain

2018 ◽  
Vol 73 (Suppl. 4) ◽  
pp. 39-46 ◽  
Author(s):  
Frank M. Ruemmele
Keyword(s):  
Abdominal Pain ◽  
Celiac Disease ◽  
Clinical Symptoms ◽  
Gluten Sensitivity ◽  
Gluten Free ◽  
Clear Cut ◽  
Clinical Presentations ◽  
New Findings ◽  
Three Phase ◽  
Gi Symptoms

Several disorders related to the ingestion of gluten are well recognized despite overlapping clinical presentations: celiac disease, an autoimmune enteropathy triggered by gluten ingestions in susceptible individuals, allergy to wheat, and more recently non-celiac gluten sensitivity (NCGS). While celiac disease and wheat allergy are well-known disorders with a clear-cut diagnosis based on clinical tests and biological parameters, NCGS is a more difficult diagnosis, especially in children with functional gastrointestinal (GI) complaints. NCGS is considered a syndrome of intestinal but also extraintestinal symptoms occurring within hours, but sometimes even after several days of gluten ingestion. In children, the leading symptoms of NCGS are abdominal pain and diarrhea, while extraintestinal symptoms are rare, in contrast to adult patients. No precise diagnostic test nor specific biomarkers exist, except a rather cumbersome three-phase gluten-exposure, gluten-free diet, followed by a blinded placebo-controlled gluten challenge with crossover to provoke symptoms elicited by gluten in a reproducible manner that disappear on gluten-free alimentation. Recent data indicate that the peptide part of wheat proteins is not necessarily the sole trigger of clinical symptoms. Mono- or oligosaccharides, such as fructan and other constituents of wheat, were able to provoke GI symptoms in clinical trials. These new findings indicate that the term gluten sensitivity is probably too restrictive. The incidence of NCGS was reported in the range of 1–10% in the general population and to increase steadily; however, most data are based on patients’ self-reported gluten intolerance or avoidance without a medically confirmed diagnosis. Treatment consists of gluten avoidance for at least several weeks or months. Patients with NCGS require regular reassessment for gluten tolerance allowing with time the reintroduction of increasing amounts of gluten.

Precision medicine-based machine learning analyses to explore optimal exercise therapies for individuals with knee osteoarthritis

2021 ◽  
Vol 29 ◽  
pp. S397-S398
Author(s):  
S. Kim ◽  
M.R. Kosorok ◽  
L. Arbeeva ◽  
T. Schwartz ◽  
Y.M. Golightly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knee Osteoarthritis ◽  
Precision Medicine

scholarly journals Gliadin Sequestration as a Novel Therapy for Celiac Disease: A Prospective Application for Polyphenols

2021 ◽  
Vol 22 (2) ◽  
pp. 595
Author(s):  
Charlene B. Van Buiten ◽  
Ryan J. Elias
Keyword(s):  
Immune Response ◽  
Celiac Disease ◽  
Gastrointestinal Symptoms ◽  
Autoimmune Disorder ◽  
Treatment Method ◽  
Mucosal Damage ◽  
Protective Effects ◽  
Novel Therapy ◽  
Gluten Proteins ◽  
Prospective Application

Celiac disease is an autoimmune disorder characterized by a heightened immune response to gluten proteins in the diet, leading to gastrointestinal symptoms and mucosal damage localized to the small intestine. Despite its prevalence, the only treatment currently available for celiac disease is complete avoidance of gluten proteins in the diet. Ongoing clinical trials have focused on targeting the immune response or gluten proteins through methods such as immunosuppression, enhanced protein degradation and protein sequestration. Recent studies suggest that polyphenols may elicit protective effects within the celiac disease milieu by disrupting the enzymatic hydrolysis of gluten proteins, sequestering gluten proteins from recognition by critical receptors in pathogenesis and exerting anti-inflammatory effects on the system as a whole. This review highlights mechanisms by which polyphenols can protect against celiac disease, takes a critical look at recent works and outlines future applications for this potential treatment method.

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Distributed Data ◽  
X Ray ◽  
Uniform Distributions ◽  
Privacy Laws ◽  
Data Owner ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

scholarly journals Deletion of FaeG alleviated Enterotoxigenic Escherichia coli F4ac-induced apoptosis in the intestine

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pengpeng Xia ◽  
Yunping Wu ◽  
Siqi Lian ◽  
Guomei Quan ◽  
Yiting Wang ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Clinical Symptoms ◽  
Mucosal Damage ◽  
Enterotoxigenic Escherichia Coli ◽  
Intestinal Epithelial ◽  
Antibody Microarray ◽  
E Coli ◽  
Fimbrial Subunit ◽  
Induced Apoptosis ◽  
Weaning Piglets

AbstractEnterotoxigenic Escherichia coli (ETEC) F4ac is a major constraint to the development of the pig industry, which is causing newborn and post-weaning piglets diarrhea. Previous studies proved that FaeG is the major fimbrial subunit of F4ac E. coli and efficient for bacterial adherence and receptor recognition. Here we show that the faeG deletion attenuates both the clinical symptoms of F4ac infection and the F4ac-induced intestinal mucosal damage in piglets. Antibody microarray analysis and the detection of mRNA expression using porcine neonatal jejunal IPEC-J2 cells also determined that the absence of FaeG subunit alleviated the F4ac promoted apoptosis in the intestinal epithelial cells. Thus, targeted depletion of FaeG is still beneficial for the prevention or treatment of F4ac infection.

scholarly journals Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

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