{"id":29,"date":"2021-03-03T15:17:46","date_gmt":"2021-03-03T15:17:46","guid":{"rendered":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/?p=29"},"modified":"2021-03-03T15:17:46","modified_gmt":"2021-03-03T15:17:46","slug":"tackling-antimicrobial-resistance-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/2021\/03\/03\/tackling-antimicrobial-resistance-with-artificial-intelligence\/","title":{"rendered":"Tackling Antimicrobial Resistance with Artificial Intelligence"},"content":{"rendered":"<p>Researchers from the Centre for Antimicrobial Optimisation (CAMO) and the Centre for Bio-Inspired Technology at Imperial College London (ICL) have developed a new data-driven method to increase multiplexing capabilities of widely used PCR instrumentation.<\/p>\n<p>In two studies published last month, the team at ICL, demonstrated the method using single-molecule real-time PCR. This increases the throughput of molecular diagnostic platforms and reduces the cost of tests, without any changes to instrument hardware, by virtue of smarter data analytics.<\/p>\n<hr \/>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"241\" class=\"size-medium wp-image-30 aligncenter\" src=\"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/files\/2020\/10\/Artificial-Intelligence-DNA-300x241.png\" alt=\"Artificial Intelligence DNA\" \/><\/p>\n<p><em><span style=\"color: #808000\">&#8220;The<\/span><\/em><em><span style=\"color: #808000\">re is plenty of room to maximise the value of existing data using sophisticated machine learning methods.&#8221; <\/span><\/em><\/p>\n<p style=\"text-align: right\"><em><strong>-Dr Jesus Rodriguez-Manzano<\/strong><\/em><\/p>\n<p style=\"text-align: right\"><em>CAMO Chief Scientist<br \/>\n<\/em><\/p>\n<hr \/>\n<p>In the first <a href=\"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.analchem.0c02253\">study<\/a>, the team explored ways to enhance multiplexing capabilities by training machine learning models using the kinetic information in DNA\/RNA amplification curves. As a proof-of-concept study, this was shown using a 3-plex assay targeting common carbapenemase genes (KPC, NDM and VIM). In the second <a href=\"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.analchem.0c03298\">study<\/a>, the group incorporated thermodynamic information via melting curves, to enhance the method to high-level multiplexing applications, such as detecting nine variants of mobilised colistin resistance (<em>mcr-1<\/em> to <em>mcr-9<\/em>).<\/p>\n<p>For more information, read media coverage of the new methods from <a href=\"https:\/\/www.genomeweb.com\/pcr\/uk-team-enhances-real-time-pcr-multiplexing-using-data-analytics\">GenomeWeb<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>written by:<\/p>\n<p>Jesus Rodriguez-Manzano<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers from the Centre for Antimicrobial Optimisation (CAMO) and the Centre for Bio-Inspired Technology at Imperial College London (ICL) have developed a new data-driven method to increase multiplexing capabilities of widely used PCR instrumentation. In two studies published last month, the team at ICL, demonstrated the method using single-molecule real-time PCR. This increases the throughput [&hellip;]<\/p>\n","protected":false},"author":1509,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[306593,306585,306602,48173],"tags":[306585,272455,306604,306603],"class_list":["post-29","post","type-post","status-publish","format-standard","hentry","category-artifical-intelligence","category-camo","category-diagnostics","category-uncategorised","tag-camo","tag-machine-learning","tag-molecular-diagnostics","tag-multiplexing"],"_links":{"self":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/posts\/29","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/users\/1509"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/comments?post=29"}],"version-history":[{"count":13,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/posts\/29\/revisions"}],"predecessor-version":[{"id":62,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/posts\/29\/revisions\/62"}],"wp:attachment":[{"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/media?parent=29"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/categories?post=29"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs-staging.imperial.ac.uk\/antimicrobial-optimisation\/wp-json\/wp\/v2\/tags?post=29"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}