vittorio

schizophrenia isn't one disease, it's many, each with a unique brain structure signature

a massive study of 6,000 brains just overturned decades of assumptions
1/ 

for decades, psychiatry treated schizophrenia as a single disease with predictable neural patterns.
but a new study, pulling MRI data from 6,000+ patients across 22 countries, just made that idea obsolete

it’s not one illness. it’s a spectrum of biological chaos
2/

patients show massive variability in brain structure: cortical thickness, surface area, and subcortical volume fluctuate wildly.
but one feature is interestingly uniform across all cases:
the folding pattern of the right anterior cingulate cortex.
what does this mean?
3/ 

folding patterns in this region solidify in early childhood.
this suggests schizophrenia isn’t just a condition that emerges later, it’s baked into neural development from the start.
the brain’s blueprint was altered before symptoms even began.
4/

this kills the old model of schizophrenia as a disease of chemical imbalance alone.
it’s a neurodevelopmental condition with structural hardwiring issues.
the variability explains why symptoms differ so much: paranoia, hallucinations, cognitive impairment.
no two cases are the same.
5/

but here’s the good news:
if the disorder is fundamentally structural, psychiatric drugs targeting chemicals are treating the wrong thing.
the whole field needs a reset.
6/

another new meta-analysis (Mancini et al., 2025) confirms anticholinergic medications worsen cognitive decline in schizophrenia.
psych drugs aren’t just failing, some are actively damaging cognition.
this has massive implications.
7/ 

anticholinergic burden directly correlates with worsening memory, attention, and executive function.
patients on high doses perform 2 standard deviations below the general population in cognitive tasks.
we're drugging them into functional collapse.
8/

even worse:
the study found tapering off anticholinergics actually improved cognition.
this should be a breaking news.
we have millions of patients on drugs that make them worse, not better.
9/

so where does this leave us?
- schizophrenia is not one disorder, it’s a spectrum of structural abnormalities.
- most treatments target symptoms, not causes (obviously).
- some drugs may be accelerating cognitive decline instead of treating the disease.
10/

what’s next?
neuroplasticity-based treatments. precision psychiatry. structural neuroengineering.
psychiatry needs to stop throwing chemicals at a structural problem.
the real cure for schizophrenia won’t be found in pills, it’ll be rewiring the brain itself.
11/

ai-driven brain mapping, gene therapy, and neurostimulation are the future of psychiatry.
whoever cracks this first controls the next great leap in mental health.
how long until we start seeing real rewiring tech hit the market?
12/

https://doi.org/10.1176/appi.ajp.20240260

HOLY SHIT IT'S HAPPENING

AI can now write genomes from scratch.
Arc Institute an NVIDIA just published Evo-2, the largest AI model for biology, trained on 9.3 trillion DNA base pairs spanning the entire tree of life.

it doesn’t just analyze genomes. it creates them
1/ 

Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome-scale

Evo 2 is FULLY OPEN, including model parameters, training code, inference code, and
the OpenGenome2 dataset LMFAO

2/ 

think of it as a DNA-focused LLM. instead of text, it generates genomic sequences. it reads and interprets complex DNA, including noncoding regions usually considered jink, generates entire chromosomes and new genomes, and predicts disease-causing mutations, even those not understood

3/

this is biology hacking
AI is moving beyond describing biology to designing it. this allows for synthetic life engineered from scratch, programmable genomes optimized by AI, potential new gene therapies, and lays the groundwork for whole-cell simulation. 
biology is becoming a computational discipline

4/

it was trained on a dataset of 9.3 trillion base pairs from bacteria, archaea, eukaryotes, and bacteriophages
it processes up to 1 million base pairs in a single context window, covering entire chromosomes. it identifies evolutionary patterns previously unseen by humans

5/

Evo-2 has demonstrated practical generation abilities, creating synthetic yeast chromosomes, mitochondrial genomes, and minimal bacterial genomes. 
this is computational design in action.

6/ 

Evo-2 understands noncoding DNA, which regulates gene expression and is involved in many genetic diseases. 
it predicts the functional impact of mutations in these regions, achieving state-of-the-art performance on noncoding variant pathogenicity and BRCA1 variant classification. 
this could lead to advances in precision medicine

7/

Evo-2 uses stripedhyena 2, combining convolution and attention mechanisms, not transformers. 
it models DNA at multiple scales, capturing long-range interactions, and autonomously learns features like exon-intron boundaries and transcription factor binding sites without human guidance. 
it’s not just memorizing
it’s understanding biology.

8/

Evo-2 predicts whether mutations are harmful or benign without specific training on human disease data. 

WHAT

it outperforms specialized models on BRCA1 variants and handles noncoding mutations effectively, suggesting it has learned DNA’s fundamental principles

9/

Evo-2 generates DNA sequences that influence chromatin accessibility, controlling gene expression. 

it has embedded simple Morse code into epigenomic designs as a proof of concept, not a practical application. this shows potential for designing programmable gene circuits.

make me blonde. thank you

10/

Evo-2 is FULLY OPEN SOURCE, including model parameters, training data, and code. 
this will lead to massive widespread innovation in bioengineering, lowering barriers to genome design. 
it’s a revolution moment for the field.

the era of biotech is here

11/

The Arc Institute aims to model entire cells, moving beyond DNA to whole organisms. 

this could lead to AI creating new life forms and synthetic biology becoming AI-driven. 

the future involves programming life at increasing scales

12/

three years ago, AI focused on chatbots. 
now it generates genomes. soon, it will design complex biological systems. this is a new phase
humans are no longer just studying biology but rewriting its code. 

biology’s future is computational. are you prepared?

13/

https://arcinstitute.org/manuscripts/Evo2