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NIH Grant Opportunities

 

Notice of Special Interest (NOSI): Administrative Supplements to Add or Expand Research Focused on Maternal Mortality (NOT-OD-20-104)

Application Receipt/Submission Date: June 22, 2020, by 5:00 PM local time of applicant organization.
Primary Contact: Nahida Chakhtoura (nahida.chakhtoura@nih.gov)

The goal of this NOSI is to provide investigators with relevant active NIH-supported grants from participating institutes the opportunity to address the scientific priorities that will lay the foundation for the Implementing  a Maternal health and Pregnancy Outcomes Vision for Everyone (IMPROVE), which is in development.


Notice of Special Interest (NOSI): Availability of Administrative Supplements to Advance Safe and Effective Therapeutics in Pregnant and Lactating Women and Children (NOT-HD-20-003)  

Application Receipt/Submission Date: June 1, 2020, by 5:00 PM local time of applicant organization.
Primary Contact: Dr. Aaron Pawlyk (pawlykac@mail.nih.gov)

The goal of this NOSI is to provide seed funds to support current and prospective investigators to generate preliminary data, collect samples, or develop robust models or analytic tools for future novel research of the OPPTB's program priorities (OPPTB Priorities). PLEASE NOTE: This NOSI applies to NICHD awardees and investigators who are currently funded by other participating NIH ICs if interested in expanding or moving into obstetric or pediatric pharmacology and therapeutics research. 


Genomic Predictors of Pregnancy Loss (R01 Clinical Trial Not Allowed) (RFA-HD-21-005)

Application Receipt/Submission Date: July 30, 2020, by 5:00 PM local time of applicant organization.
Primary Contact: Dr. Lisa Halvorson (lisa.halvorson@nih.gov)

The purpose of this Funding Opportunity Announcement (FOA) is to encourage research incorporating emerging genomic technologies to identify pathogenic variants that predict risk for embryonic/fetal loss in subsequent pregnancies beyond standard karyotype approaches.
Priority will be given to centers that propose highly innovative approaches for data integration, statistical analyses of genomic data, and understanding the impact of fetal and parental variation on the regulation and function of genes, developmental pathways, and gene networks. The incorporation of artificial intelligence/machine learning approaches is strongly encouraged.

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