Redheads make up just 1–2% of the global population, and as a result, they’ve been historically underrepresented in medical research. This leads to real gaps in care, from anesthesia dosage anomalies to heightened pain sensitivity and increased cancer risk. There is no centralized research effort focused on this genetically distinct group.
As a longtime web3 "legal engineer," one of the things I began to notice while working with DeSci founders was that many of them, like me, had red hair - certainly at a higher rate than the 1-2% of the population we represent.
People with red hair carry genetic mutations that result in a lifetime of medical, cultural and social “otherness.” It is known that we feel pain, heat and cold differently, and that we need more anesthesia. At the same time, we are often disproportionately impactful in the communities in which we live.
We know a lot of stories about redheads. We don't know much about what we need to live long, healthy lives that may differ from the 98% of the population.
Vivian Flame, a BioAgent, addresses this by automating the discovery, curation, and sharing of scientific knowledge specifically relevant to redheads, then packaging it for distribution to third parties. Without AI, the scale and nuance of this work, which spans dermatology, pharmacogenomics, neuroscience, and pain science, would be prohibitively labor-intensive.
Vivian's two main jobs: and the problems they solve
Collect, digest, and share relevant data about MC1R research, promoting new hypotheses. This solves the problem of "actionable insight" that is currently lacking. Humans who cannot effectively do this work.
Manage and distribute research project data via NFTs.
This solves the problems of data reusability, merchantability, privacy and compliance in a global, transparent marketplace. TradSci data solutions do not.
These processes are enhanced and extended by knowledge graphs.
SPF STUDY
The starting point for Ginger Science is sunscreen. Redheads do not produce much eumelanin, which protects against cancer-causing UV radiation, i.e. sun. Our coppery pheomelanin is less effective. We have higher risk and incidence of skin cancer - I’ve had a few frozen off my proboscis myself. Redheads need sunscreen. But the sun protections available to redheads around the world vary.
In the United States, the FDA has not approved a new sunscreen since 1999. The FDA regulates sunscreens as drugs, not cosmetics. Since then, modern filters that are more effective and pleasant to use have been developed in Europe and Asia. Filters like Tinosorb, Uvinol and Mexoryl, cannot be sold in the United States as sunscreen, although they slip into the supply chain in cosmetic preparations. While it is possible to acquire some of these filters via gray market suppliers, it is difficult to verify the authenticity of the product. Counterfeits abound. The old-style compounds available in the USA like Avobenzone, Homosalate and Octinoxate are often unpleasant to use, have an offensive odor, sting the eyes, and potentially cause endocrine disruption. What is a redhead to do?
Global Sun Study: a perfect application of DeSci technology, to solve this problem for redheads. We will collect user data and scientific information in knowledge graphs that bring Ginger Science and MC1R research into relevant contexts. We will use this information to both improve our access to high-quality UV filters and to develop knowledge about the effects of those filters on humans with MC1R variation. We will remarket the data to OEM and reward redheads for participating using ZKP to protect their identities.
The first challenge was scope. I am building two full business function lifecycles during this hackathon. I spent several days of my first sprint assembling various tools, analyzing integrations and creating smart contracts. I was almost overwhelmed by the number of tools I needed. ElizaOS + the BioAgent Plugin repo saved me a ton of time so I could finish.
The second challenge was that I am not a knowledge graph expert, but I know they are critical for future growth and network effects. During this hackathon I have learned enough about how to handle knowledge graphs that I can extend the "scientific papers" agent to include knowledge graphs we produce around the data we produce, not just academic papers. For production, we will create an MVP knowledge graph about global sunscreen compounds.
The third challenge was wiring up the various parts of the application so they work together across multiple contexts and platforms. The BioAgent is critical to orchestrate the complexity of the Ginger Science workflow.
Fourth challenge was I have never built any type of AI project before, and I saved it for the last sprint of the hackathon, focusing on web3 and content development early on. The rapid changes of the ElizaOS project were boggling. The two Eliza starter projects I combined use different node versions. I had not used Cursor.ai before, so the learning curve of that app was not worth it. I had a lot of irksome debugging sessions with ChatGPT.
Last challenge was Solana. I developed the smart contracts on Base using Scaffold-Eth-2. I have never developed on Solana before. I wanted to get the Solana ElizaOS plugin-nft-generation working. I was able to get the BioAgent to call the plugin to mint the NFT, but I need another day of learning about Solana and the metadata handling to make it work on chain.
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