
LDL Receptor Appearance as a Diagnostic Early Detector
Detecting widespread Low Density Lipoprotein Receptor (LDLR) expression in microscopic slides of brain tumor samples.
GlioDAO is dedicated to funding the development of transformative and translational treatments for brain cancer. We prioritze funding specifically for Glioblastoma Multiforme (GBM), Anaplastic Astrocytoma (Grade III & IV), and Diffuse Midline Gliomas (DMG), but may expand to other rare forms of brain cancer.
Our goal is to fund high-risk, early-stage research to move promising studies past the Valley of Death. As a cure-motivated community, we are not constrained by shareholders or investors and are willing to invest in moonshots, translating projects to a point where private funding is more likely to be successful.
We prioritize funding research across four key categories, however, we remain open to other exceptional projects if they strongly align with our mission and potential for impact.
Once you submit a preliminary application, our Neuro-Oncology Working Group will offer feedback and help refine your proposal to meet high-impact research and industry standards. After revisions, your final application goes to a community vote, with funding decisions made in under a month. If not approved, you're encouraged to revise based on feedback and reapply.
An IP-NFT (Intellectual Property Non-Fungible Token) represents the rights to a research project, including agreements, licenses, and data, stored securely on the blockchain.
Legal agreements and IP are bundled into a digital contract and minted as an IP-NFT.
The IP-NFT can then be broken into smaller pieces called IP Tokens.
$GLIO holders can purchase these IP Tokens to help directly fund research and manage IP outcomes.
This system makes it easier to fund, track, and support breakthrough brain cancer research, while giving the community a say in the innovation it helps unlock.
Detecting widespread Low Density Lipoprotein Receptor (LDLR) expression in microscopic slides of brain tumor samples.
An AI-driven study to enhance GBM diagnosis by refining texture-based histopathological analysis across diverse datasets
A digital phenotyping study using passive data to assess social isolation in GBM patients and caregivers