{"id":257052,"date":"2025-07-28T09:46:17","date_gmt":"2025-07-28T09:46:17","guid":{"rendered":"https:\/\/project.uniurb.it\/vitality\/?p=257052"},"modified":"2025-07-28T14:29:19","modified_gmt":"2025-07-28T14:29:19","slug":"machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders","status":"publish","type":"post","link":"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/","title":{"rendered":"Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;2px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;50px|||||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size=&#8221;16px&#8221; custom_margin=&#8221;||60px||false|false&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p><span style=\"font-weight: 400;\">Dott.ssa Sara Montagna<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Abstract<\/h3>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Fungal infections represent a serious global health threat. The new emerging pathogens and the spread of different forms of resistance are now hardly challenging the tools available in therapy and diagnostics. With the commonly used diagnoses, fungal identification is often slow and inaccurate, and, on the other hand, some drugs currently used as treatments are significantly affected by the decrease in susceptibility. Herein, the antifungal arsenal is critically summarized. Besides describing the old approaches and their mechanisms, advantages, and limitations, the focus is dedicated to innovative strategies which are designed, identified, and developed to take advantage of the discrepancies between fungal and host cells. Relevant pathways and their role in survival and virulence are discussed as their suitability as sources of antifungal targets. In a similar way, molecules with antifungal activity are reported as potential agents\/precursors of the next generation of antimycotics.Particular attention was devoted to biotechnological entities, to their novelty and reliability, to drug repurposing and restoration, and to combinatorial applications yielding significant improvements in efficacy.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||0px|||&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4>Keyword<\/h4>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Fungi, Antifungal drugs, Antifungal treatment, Antifungal diagnostics,Resistance, Antifungal target<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||0px|||&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4>Link<\/h4>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; link_option_url_new_window=&#8221;on&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p><a href=\"https:\/\/ora.uniurb.it\/item\/preview.htm?uuid=e6dc77e9-728b-407e-bca9-2903319ce6bf\" target=\"_blank\" rel=\"noopener\">https:\/\/ora.uniurb.it\/item\/preview.htm?uuid=e6dc77e9-728b-407e-bca9-2903319ce6bf<\/a><\/p>\n<p><a href=\"https:\/\/hdl.handle.net\/11576\/2725851\" target=\"_blank\" rel=\"noopener\" title=\"Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders\">https:\/\/hdl.handle.net\/11576\/2725851<\/a>\u00a0<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dott.ssa Sara MontagnaAbstractFungal infections represent a serious global health threat. The new emerging pathogens and the spread of different forms of resistance are now hardly challenging the tools available in therapy and diagnostics. With the commonly used diagnoses, fungal identification is often slow and inaccurate, and, on the other hand, some drugs currently used as [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[19],"tags":[],"class_list":["post-257052","post","type-post","status-publish","format-standard","hentry","category-wp1"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders - Vitality<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders - Vitality\" \/>\n<meta property=\"og:description\" content=\"Dott.ssa Sara MontagnaAbstractFungal infections represent a serious global health threat. The new emerging pathogens and the spread of different forms of resistance are now hardly challenging the tools available in therapy and diagnostics. With the commonly used diagnoses, fungal identification is often slow and inaccurate, and, on the other hand, some drugs currently used as [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\" \/>\n<meta property=\"og:site_name\" content=\"Vitality\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-28T09:46:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-28T14:29:19+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Scritto da\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo di lettura stimato\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/project.uniurb.it\/vitality\/#\/schema\/person\/15b1057b2f252eccd39e050ca6322397\"},\"headline\":\"Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders\",\"datePublished\":\"2025-07-28T09:46:17+00:00\",\"dateModified\":\"2025-07-28T14:29:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\"},\"wordCount\":402,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/project.uniurb.it\/vitality\/#organization\"},\"articleSection\":[\"WP1\"],\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\",\"url\":\"https:\/\/project.uniurb.it\/vitality\/machine-learning-enabled-prediction-of-metabolite-response-in-genetic-disorders\/\",\"name\":\"Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders - 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