{"id":14599,"date":"2019-10-10T21:37:06","date_gmt":"2019-10-10T20:37:06","guid":{"rendered":"https:\/\/cri1149.mlcom-dev.net\/?post_type=publications&p=14599"},"modified":"2025-09-11T16:48:03","modified_gmt":"2025-09-11T14:48:03","slug":"colonic-microrna-profiles-identified-by-a-deep-learning-algorithm-that-predict-responses-to-therapy-of-patients-with-acute-severe-ulcerative-colitis","status":"publish","type":"publication","link":"https:\/\/cri1149.mlcom-dev.net\/en\/publication\/colonic-microrna-profiles-identified-by-a-deep-learning-algorithm-that-predict-responses-to-therapy-of-patients-with-acute-severe-ulcerative-colitis\/","title":{"rendered":"Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis."},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"class_list":["post-14599","publication","type-publication","status-publish","hentry"],"acf":{"numero_de_publication":"[:fr]Clin Gastroenterol Hepatol. 2019 Apr;17(5):905-913[:en]Clinical Gastroenterology and Hepatology[:]","date_de_publication":"20190401","numero_doi":"","equipe":[143],"auteurs-liste":[{"texte_libre":false,"auteur-lien":13123,"auteur-text":""},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Uzzan M"},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Laharie D"},{"texte_libre":false,"auteur-lien":13049,"auteur-text":""},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Denost Q"},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Daniel F"},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Belleannee G"},{"texte_libre":false,"auteur-lien":13043,"auteur-text":""},{"texte_libre":true,"auteur-lien":null,"auteur-text":"Wainrib G"},{"texte_libre":false,"auteur-lien":13133,"auteur-text":""},{"texte_libre":false,"auteur-lien":13131,"auteur-text":""},{"texte_libre":false,"auteur-lien":13160,"auteur-text":""}],"auteurs-manuel":"","liens_externes":null,"liens":[{"lien":"https:\/\/www.cghjournal.org\/article\/S1542-3565(18)30947-9\/fulltext"}],"paragraphe":"

Abstract<\/h3>\r\n
\r\n

BACKGROUND & AIMS:<\/h4>\r\n

Acute severe ulcerative colitis (ASUC) is a life-threatening condition managed with intravenous steroids followed by infliximab, cyclosporine, or colectomy (for patients with steroid resistance). There are no biomarkers to identify patients most likely to respond to therapy; ineffective medical treatment can delay colectomy and increase morbidity and mortality. We aimed to identify biomarkers of response to medical therapy for patients with ASUC.<\/p>\r\n

METHODS:<\/h4>\r\n

We performed a retrospective analysis of 47 patients with ASUC, well characterized for their responses to steroids, cyclosporine, or infliximab, therapy at 2 centers in France. Fixed colonic biopsies, collected before or within the first 3 days of treatment, were used for microarray analysis of microRNA expression profiles. Deep neural network-based classifiers were used to derive candidate biomarkers for discriminating responders from non-responders to each treatment and to predict which patients would require colectomy. Levels of identified microRNAs were then measured by quantitative PCR analysis in a validation cohort of 29 independent patients-the effectiveness of the classification algorithm was tested on this cohort.<\/p>\r\n

RESULTS:<\/h4>\r\n

A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (area under the curve, 0.91). We identified 3 algorithms, based on microRNA levels, that identified responders to infliximab vs non-responders (84% accuracy, AUC = 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC = 0.79).<\/p>\r\n

CONCLUSION:<\/h4>\r\n

We developed an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues.<\/p>\r\n<\/div>","paragraphe_en":"","documents":null},"yoast_head":"\nColonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis. - CRI<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis. - CRI\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cri1149.mlcom-dev.net\/publication\/colonic-microrna-profiles-identified-by-a-deep-learning-algorithm-that-predict-responses-to-therapy-of-patients-with-acute-severe-ulcerative-colitis\/\" \/>\n<meta property=\"og:site_name\" content=\"CRI\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/CRI-Centre-de-recherche-sur-linflammation-100116118884153\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-11T14:48:03+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/cri1149.mlcom-dev.net\/publication\/colonic-microrna-profiles-identified-by-a-deep-learning-algorithm-that-predict-responses-to-therapy-of-patients-with-acute-severe-ulcerative-colitis\/\",\"url\":\"https:\/\/cri1149.mlcom-dev.net\/publication\/colonic-microrna-profiles-identified-by-a-deep-learning-algorithm-that-predict-responses-to-therapy-of-patients-with-acute-severe-ulcerative-colitis\/\",\"name\":\"Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis. - 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