Nine Diagnostics Joins U.S.-Dutch Consortium for Women’s Health Diagnostics
Nine Diagnostics joined a U.S.-Dutch consortium to co-produce a white paper on advancing diagnostic tools and treatments for gynecological cancers, serving as the molecular diagnostics partner with its AI-enabled multiomics nanosensor platform. The collaboration, organized by the Netherlands Innovation Network in Boston with support from the Dutch Ministry of Economic Affairs, united CC Diagnostics, Predica Diagnostics BV, ViciniVax B.V., and SHE Health Clinics with Nine Diagnostics.
Nine Diagnostics Selected for Merck Digital Sciences Studio Cohort 3
Nine Diagnostics was accepted into Merck Digital Sciences Studio Cohort 3, a 10-month digital accelerator providing direct investment, strategic mentorship, and industry connections to early-stage biomedical startups advancing AI and machine learning for drug discovery and development. Backed by Merck, Northpond Ventures, McKesson Ventures, and Microsoft for Startups, this selection marks Nine Diagnostics’ first major direct investment and accelerates development of its AI-enabled nanosensor platform.
Nine Diagnostics Wins Novo Nordisk Golden Ticket for LabCentral Residency
Nine Diagnostics was selected as one of two recipients of the 2024 Novo Nordisk Golden Ticket, a competitive award run jointly by Novo Nordisk’s Bio Innovation Hub and LabCentral recognizing early-stage life sciences companies advancing innovative approaches to healthcare challenges. The award provides recipients with lab space access, shared scientific infrastructure, and a direct connection to Novo Nordisk’s innovation team for scientific and drug development support.
Mijin Kim Named a 2023 STAT Wunderkind for Nanosensor Cancer Detection Research
Mijin Kim, PhD, a postdoctoral researcher in the Heller Lab at Memorial Sloan Kettering Cancer Center, was named a 2023 STAT Wunderkind, an annual honor recognizing 28 early-career researchers across North America whose work promises to reshape the life sciences. Her research applied carbon nanotube nanosensors and machine learning to detect ovarian cancer from blood, achieving 87% sensitivity and 98% specificity across 269 serum samples in a study published in Nature Biomedical Engineering.