BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Silvina Ponce Dawson (Universidad de Buenos Aires) DTSTART:20210617T200000Z DTEND:20210617T210000Z DTSTAMP:20260423T035925Z UID:SGFM/23 DESCRIPTION:Title: Ne sted pooling for the efficient detection of infected samples\nby Silvi na Ponce Dawson (Universidad de Buenos Aires) as part of Seminario de Geom etría y Física - Matemática UCN-USP\n\n\nAbstract\nThe rapid spread of the SARS-CoV-2 pandemic confronted us with the need of designing large-sca le\, cost-effective testing programs. To this end\, running the test on po oled samples can be a solution if the detection method (in the case of SAR S-CoV-2\, the method to detect the viral RNA) is sensitive enough. Cost-ef fective strategies\, on the other hand\, require the definition of efficie nt deconvolution and re-testing procedures for the identifcation of the ca rrier. In this talk I will describe an efficient nested pooling strategy t hat we have devised in collaboration with Ines Armendariz\, Pablo Ferrari\ , Daniel Fraiman and Mario Martinez and discuss its practical implementati on using droplet digital PCR (ddPCR)\, which was shown to be 10-100 times more sensitive than the current gold standard for viral RNA detection\, RT -qPCR. The latter part of the work was also done with Hugo Menzella. Among other aspects\, I will discuss how the RNA quantification that ddPCR prov ides at the end of the test and the nested nature of the strategy can be c ombined to perform self-consistency tests for a better identification of i nfected individuals and of unviable samples. A user-friendly interface is available at pooling.df.uba.ar for those who want to define their best poo ling strategy under different constraints.\n LOCATION:https://researchseminars.org/talk/SGFM/23/ END:VEVENT END:VCALENDAR