Vitalist bay was a phenomenal experience last year! If you’re at all interested in aging this is the event you need to go to!

Big takeaway from looking through all the events: longevity, AI, and women's health are hot🔥. ARPA-H, the US govt's new $1.5bil/year granting agency, is on the ground at JPM looking for innovators shaping biotech's future. Lots of exciting events, see you all there! --Aevitas

Here's our guide to JPM week. Hope to see some of you at these events :)

One year down, and what a journey its been! @AevitasHouse has been an incredible community of researchers and builders solving urgent challenges in human health. For 2026, we're expanding our support of projects in aging, bioengineering, and systems biology. Especially if you're early stage or new to SF, we'll help you find the right people and supporters for your project. If you've been looking for a hardcore, technical biotech community to call home, apply for our 2026 cohort!

I’ll be one of the mentors for the workshop! And ofc excited for the debate 😁. Come hear some of my hot takes next week 🔥

You can see some of our analysis below! Current clinical tools struggle to identify patient-specific drivers of disease. The same chronic condition can have different biological causes across individuals. But if you have a healthy baseline of your biology, you can compare your before and after results to identify what's going wrong if you get sick. That's what motivated me to get my PMBCs sequenced, the first of several datapoints in my model of homeostasis. Really enjoyed highlighting the power of these research tools! 🔍 IMMUNE STATUS ASSESSMENT: • STEADY-STATE IMMUNITY: Low inflammatory cytokine expression suggests healthy, non-activated immune status • ACTIVE SURVEILLANCE: High chemokine expression indicates ongoing immune patrol and tissue surveillance • MEMORY RESPONSES: Strong antibody production suggests established immunological memory • CYTOTOXIC READINESS: NK and T cell activation markers indicate prepared anti-viral/anti-tumor responses • Robust memory T cell trafficking (RANTES dominance) T CELL COMPARTMENT ANALYSIS: • Balanced CD4 /CD8 T cell ratio (0.86:1) indicates healthy immune status • High proportion of memory T cells suggests prior antigen exposure • MAIT cells (6.5%) - important for mucosal immunity and pathogen response • Regulatory T cells (3.0%) - normal proportion for immune homeostasis B CELL MATURATION STATUS: • Balanced memory:naive B cell ratio (1.07:1) indicates active immune responses • Age-associated B cells present but low (1.0%) - normal for healthy adults • No detectable plasma cells suggests no active inflammatory response INNATE IMMUNE LANDSCAPE: • NK cells show CD16 dominance (8.8:1 ratio) - typical cytotoxic profile • Classical monocytes predominate (4.85:1) - normal circulating pattern • Low DC2 and pDC populations consistent with steady-state conditions OVERALL IMMUNE PROFILE: • T cell dominance (76.9%) typical of healthy PBMCs • No signs of immune activation or inflammatory states • Diverse immune cell repertoire with all major populations represented • Quality metrics suggest high-quality sample with minimal technical artifacts

I’ll be analyzing my own blood proteomics and single cell RNA data! A lot of these tools are available to researchers but haven’t made it to the clinic. We’re working on bridging that gap and unlocking new dimensions of personalized healthcare.

biologists continue to mistake single-cell technical sampling noise for meaningful cellular heterogeneity. the apparent sparsity of single-cell data is due largely to technical artifacts like transcript capture and sequencing depth--if there are hundreds of thousands of transcripts per cell but you sequence only 5-10k unique molecules, of course you get mostly zeroes in any given cell. pseudo-bulking makes this clear: when you aggregate these noisy single cell samples, the vast majority of the genome shows expression greater than zero this is because the entire genome is being pervasively (yet stochastically) transcribed across nearly all cell types, including terminally differentiated cells (with a few obvious exceptions, e.g. enucleated red blood cells, spermatozoa, etc). hence you should expect to find at least one copy of nearly every transcript if you sample a single cell deeply enough over time it is the *relative* or *ranked* differences in expression compared to this low-level baseline that dictate cellular identity and function yet this pervasive transcription extends to retrotransposons, heterochromatin, and other supposedly "silenced" regions of the genome--all of which are systematically discarded in most standard processing pipelines. stare at the raw .fastq for long enough and this becomes obvious thus, this pervasive genome-wide transcription has implications not only for how we train virtual cell models on single-cell RNA count data, but for our understanding of cellular biology as a whole: to truly plumb the depths of the cell, we must venture beyond the reference genome into transcriptional terra incognita, where we will encounter eldritch species of non-coding RNAs that may throw into question much of what we know about the biology of the cell

Love this screenshot - formal, rational systems are great because they are very precise - but their fundamental incompleteness pushes us into the irrational wilderness if we want to find truth & bring it back inside

Both axolotls and naked mole rats don’t undergo thymic involution, and both have negligible senescence with age…

Abundant non-invasive stem cells seems game changing 👀

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