Sebalf

The major hurdle of current gene therapies is delivery to the tissue where the defective gene product is causing damage. For instance lipid nanoparticles are only being used to deliver gene therapies to the liver, because if you inject them they just end up there and not much anywhere else. In this case they are using an virus called "adeno asociated virus 5" (AAV5), which does not naturally infect the brain AFAIK. The blood brain barrier (basically just extra impermeable blood vessels), as well as other immunological features in brain tissue, evolved specifically to keep the brain as unaffected as possible from anything bad going on in the body, seeing as any infection/poisoning of the brain is varying degrees of catastrophic and would easily kill you in the ancestral environment.

I don't know the details of why AAV5 in particular is their vector of choice in this case, but for whatever reason thats what they've gone with. AFAIK there are no viral or other vectors that consistently infect all brain tissue when injected/ingested, so maybe that's just the best option available. Anyways, it seems that in order to get it to the actual brain tissue that is damaged by the huntington protein (all of it? One particular area?), the best way is to inject it where it needs to go. If you could just pump it into the CSF that would perhaps make things a little bit more tolerable, seeing as you could then just do a spinal tap and inject it that way, but apparently that doesn't work. Or maybe a generalized AAV5 infection has more side effect then targeted injections. Just speculating here.

Vibe coder that reads hacker news chiming in here. I think that the true usefullness of LLMs for coding is often lost on the usual audience of this website, because most people here have extremely high standards of what they expect LLMs to accomplish.

But take people like me, I am an MD who was always into computers, but that just ended up going down a separate series of life decision, and could never find the time or energy to actually learn to code. When GPT-4 arrived, I started trying out using it for a medically-related coding hobby project, which eventually escalated into an ongoing PhD. Now, the fact is that this whole thing would just never have happened without LLMs. I would have never even thought of starting such a project, and if I did I wouldn't have had the time, and would have never made any progress even if I did. Vibe coding enabled me to do something entirely outside the scope of my previous capabilities. And the reality is that if I hadn't been able to do everything myself (down to the point of installing hardware managing the servers I am using), the project as a whole just wouldn't have happened.

The code I produce isn't going into production anywhere, it is only used for my particular purposes, it is not exposed to the web in any way, and so typical LLM issues like security etc. are a non-issue. And while my understanding of what my code is actually doing is pretty rudimentary (for instance, basic syntax conventions is something I just never bothered to learn), this doesn't really matter in practice. If it works it works.

Frankly, this take is so reductionistic that it's useless. You can substitute "mathematical functions" with "biochemistry" and apply the exact same argument to human beings.

What I'd like is for people to stop pretending we have any idea what the hidden layer of an LLM is actually doing. We do not know at all. Yes, words like "statistics" and "mathematical functions" can accurately describe the underlying architecture of LLMs, but the actual mechanism of knowledge processing is not understood at all. It is exactly analogous to how we understand quite a lot about how neurons function at the cellular level (but far from everything, seeing as how complicated and opaque nature tends to be), but that we have no idea whatsoever what exactly is happening when a human being is doing a cognitive task.

It is a fallacy to confuse the surface level understanding of how a transformer functions, to the unknown mechanisms that LLMs employ.