Traumatic brain injury (TBI) represents a major medical, social and economic concern worldwide due to significant mortality – especially among younger populations – and long-term disabilities. Various pathological brain lesions (e.g., intracerebral bleedings, necrotic-ischemic lesions, tissue avulsion) are produced by impacting mechanical forces. Among these, diffuse axonal injury (DAI) is one of the most significant brain lesions typically associated with trauma. However, DAI is not necessarily linked with TBI exposure. Therefore, the term “traumatic axonal injury (TAI)” is commonly used.

Non-steroidal anti-inflammatory drugs (NSAIDs) have long been used to treat a variety of inflammatory conditions.  Many of these conditions, such as cancer or arthritis, require long term use of the NSAIDs due to the chronic nature of the disease.  However, the NSAIDs in current use have toxicities associated with their long-term use that hinder their use for these chronic conditions.    

Since its emergence in 2019, COVID-19 infected over 600 million people and over 6 million people have died from the disease. COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Neutralizing antibodies have been developed to bind to the receptor binding domain (RBD) on the spike (S) protein. Blocking the interaction of the RBD and the ACE2 receptor, is critical in neutralizing the virus. However, the S2 subunit, is also critical for viral infection and entry into human cells.

Developing computer systems that can recognize and locate image features associated with disease is a challenge for developing fully-automated and high precision computer assisted diagnostics. Joint learning of language tasks in association with vision tasks (association of image features with text annotation) adds an additional level of challenge.  Furthermore, scaling-up approaches from small to large datasets presents additional issues, particularly related to medical images.

Epidermal growth factor receptor variant III (EGFRvIII) is a variant of EGFR that is an excellent target for immunotherapy because of its expression in cancer cells and not in normal cells. 

RNA interference (RNAi) is a biological response to double-stranded RNA that regulates expression of protein-coding genes and is a natural mechanism for gene silencing. Delivery of short, interfering RNA (siRNA) leads to RNAi of the targeted genes. 

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. Recent studies using fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (RNAseq) to elucidate tissue composition and cell-cell interactions in the TME led to improved biomarkers of patient response and new treatment opportunities. However, the use of FACS is limited to simultaneously measuring the expression of a few protein markers, whereas the use of single-cell RNAseq has been limited due to cost and scarcity of fresh tumor biopsies.

Castrate-resistant prostate cancer (CRPC) is characterized by androgen-independent cancer cells that have adapted to the depletion of hormones and continue to grow. Abnormal androgen receptor signaling is known to drive advanced castrate-resistant prostate cancer.

The National Cancer Institute seeks parties interested in collaborative research to co-develop a method to generate RNA molecules suitable for nanoparticle and biomedical applications.

Mutation of amino acid 61of the neuroblastoma rat sarcoma viral oncogene homologue (NRAS) is a known driver of oncogenesis in melanoma. Glutamine (Q) to lysine (K) mutation at this position of NRAS is prevalent in approximately 10% of all melanoma cases and associated with aggressive tumors and low patient survival. Therefore, Q61K mutated NRAS is an important candidate for targeted therapies, including cellular immunotherapy.