PLA (polylactic acid) “plant-based” plastics are now everywhere (e.g., compostable cups/lids/containers; lots of campus adoption at Harvard/MIT; brands like Notpla/NoTree-style alternatives get lumped into the same mental bucket by users).

Urgent question: When PLA contacts hot water/coffee, is it actually less risky than conventional plastics (PE/PP/PET), or can it be worse in terms of micro/nanoplastic shedding, additive leaching, and bioreactivity?

I’m specifically trying to separate:

• the polymer backbone (PLA chemistry) vs
• processing + additives (plasticizers, colorants, stabilizers, slip agents, nucleating agents, coatings, etc.)

1) Hot-liquid contact: what leaches from PLA vs PE/PP/PET?

Questions:

• Under realistic coffee conditions (80–100°C; minutes to hours; acidic coffee; fats/creamer), what’s the best evidence on:
  • soluble leachates (monomers/oligomers, lactide, additives)
  • micro/nanoplastic particle shedding (MNPs)
• Are there comparative studies that do matched conditions across PLA vs PE/PP/PET (same geometry, same stirring/abrasion, same analytics)?

2) “Biocompatible” in medicine vs safe for chronic ingestion

PLA is used for resorbable sutures/implants, but that’s not the same exposure:

• localized vs oral ingestion
• controlled medical-grade formulation vs consumer packaging
• known degradation pathways vs unknown additive mix

Question: how transferable is “medical PLA is biocompatible” to “PLA cups are safe for hot drinks”?

3) Bioclearance: PLA fragments vs PE/PP fragments

PLA hydrolyzes to lactic acid eventually—but what about intermediate-sized fragments?

Questions:

• Are PLA micro/nanoparticles more likely to hydrolyze/clear than PE/PP/PET once inside tissues?
• Does PLA behave more like a biodegradable polymer in vivo, or do small particles persist long enough to cause the same “frustrated clearance” problems proposed for other plastics?

4) Additives + coloring: is PLA processing “as artificial” as petro-plastics?

Marketing often implies “plant-based = natural.”

Questions:

• In real consumer PLA cups/containers, how big is the risk contribution from:
  • dyes/pigments
  • PFAS or fluorinated coatings (if used)
  • mineral fillers
  • plasticizers / stabilizers
  • printing inks / adhesives / multilayer laminates
• Are compostable products more likely to include specific additives to hit stiffness/heat tolerance targets?

5) Do PLA products shed more MNPs than PE/PP/PET?

I’ve seen claims/papers suggesting some PLA items shed more MNPs than conventional plastics under heat.

Questions:

• What’s the best evidence here?
• Is higher shedding driven by lower mechanical robustness, crystallinity, or hydrolysis at elevated temperature?

6) Cellular uptake + lysosome “clogging” risk: PLA vs PE/PP vs plant fibers

Key practical question:

• If PLA sheds more particles, are those particles less likely (or more likely) to:
  • cross gut barriers
  • enter cells
  • accumulate in lysosomes / impair autophagy
  • persist in macrophages

How does this compare to:

• natural microparticles (wood/cellulose/cotton/plant fibers) that humans have long been exposed to
• PE/PP/PET MNPs (more hydrophobic, slower degradation)

Are there good head-to-head in vitro / in vivo studies of particle fate and lysosomal stress across these classes?

7) High-leverage outcome: what should “people who care” do now?

Since PLA is already the “ethical choice” default in many institutions, a clear answer is high leverage.

If the evidence is uncertain, what’s the least-regret recommendation for hot beverages?

• paper + aqueous barrier?
• stainless/ceramic default?
• truly fiber-based (molded pulp) with verified coatings?
• avoid PLA for hot liquids specifically?

If you have strong citations (good analytics, realistic exposures, and not wildly confounded protocols), please drop them. I’m especially interested in rigorous comparisons and any evidence about tissue persistence vs biodegradation in vivo.

There’s a tension I keep circling around in longevity strategy:

• Earlier intervention often seems more effective (analogous to early-stage cancer being more treatable; or “younger systems” being more plastic/responsive).
• But replacement/implantation carries its own risks (surgical trauma, infection, thrombosis, immunology, chronic inflammation, device/tissue failure, long-term cancer risk, etc.).

I’m trying to reason about timing—especially under the possibility of sudden/discontinuous progress in tissue engineering (e.g., scalable immunocompatible organs, better immune cloaking, improved vascularization/innervation, safer conditioning regimens).

A related motivating stat that gets cited in this area: a minority of people show strongly accelerated “biological age” in a specific organ, and a small fraction show multi-organ acceleration (numbers like “~20% single-organ agers; ~1–2% multi-organ agers” get mentioned—if you know the best source/caveats, please share).

Core question

At what age / risk level does elective tissue or organ replacement become net-positive?

And more specifically:

1) Decision threshold framing

If we model this as:

• baseline hazard of organ failure / mortality without replacement,
• hazard introduced by surgery + perioperative inflammation,
• long-term hazard of the replacement itself (immunosuppression, cancer, chronic rejection, fibrosis, clot risk, arrhythmia, etc.),
• and the benefit of “resetting” an organ’s aging clock,

what does the tipping point look like as a function of age?

Is there a generic answer like “this becomes relevant around midlife (40–60) only if perioperative mortality < X and long-term complication rate < Y”, or does it collapse into organ-specific cases (kidney vs liver vs heart vs thymus vs cartilage vs skin vs vasculature)?

2) ‘Young people are more responsive’ vs ‘don’t cut open healthy 25-year-olds’

If some people have one organ aging much faster than others:

• Should we treat that like a targeted risk factor (replace/repair the outlier organ earlier),
• or like a biomarker that suggests systemic aging risk (so replacement won’t fix the upstream driver)?

What would you need to see to justify intervention in someone “too young” by conventional standards?

3) Which tissues/organs are first candidates for elective replacement?

Candidates that seem plausibly earlier than “whole heart replacement”:

• immune system components (e.g., thymus/hematopoietic system)
• cartilage/meniscus / joint surfaces
• lens/retina-support tissue
• vascular grafting / targeted vessel replacement
• liver partial replacement / hepatocyte repopulation

Which of these have a realistic near-term risk profile that could make a mid-age elective intervention rational?

4) Interaction with aging biology (inflammation, senescence, immune tone)

How much does the older host environment degrade the replacement (e.g., systemic inflammaging, fibrosis propensity, senescent cell burden)?

Is there a “prep regimen” that seems necessary (senolytics, immune modulation, microbiome, anti-fibrotics) before replacement makes sense?

5) Discontinuous progress scenario planning

Suppose we suddenly get:

• cheap, vascularized, innervated organs
• low-rejection immune compatibility (or immune cloaking)
• low perioperative risk

Would the optimal strategy shift toward much earlier replacement, maybe even pre-symptomatic, especially for identified “organ agers”?

Or do you expect replacement to remain mostly late-stage because systemic aging dominates outcomes?

6) What would convince you?

What dataset/study design would settle this?

• large biobank linking organ-specific biological age (multi-omics) to outcomes
• longitudinal imaging + functional tests
• RCTs of replacement vs best medical therapy in mid-age high-risk “organ agers”
• animal work showing lifespan/healthspan extension from organ swapping with controlled immunology

I’m looking for (a) best citations, (b) strong opinions on the right framing, and (c) “first practical wedge” ideas where elective replacement might become relevant sooner than most people think.

Voytek et al. (2015) popularized a now-replicated aging signature in EEG/MEG spectra: the aperiodic 1/f-like component tends to flatten with age (exponent becomes less negative), with additional age effects on oscillatory peaks.

I’m trying to understand what makes the exponent/slope effect so robust and what it actually means biologically, longitudinally, and intervention-wise.

1) Why is the exponent change with age so robust?

Broadband/SNR: fitting the aperiodic component uses many frequencies vs a narrow peak. Convergent mechanisms: many micro-level degradations may push spectra toward similar tilt changes (synaptic/dendritic filtering, time constants, neuromodulators/arousal, microcircuit E/I balance, conduction dispersion).

Question: Are there good mechanistic models that explain why so many different biological changes collapse onto a similar exponent trajectory?

2) Does “more aperiodic noise” imply “too much high-frequency vs low-frequency activity”?

A flatter 1/f mathematically implies relatively more higher-frequency power vs low-frequency power.

Question: What is the cleanest interpretation of this in terms of neural population activity (vs artifacts, vigilance, EMG, referencing differences, etc.)?

3) Is this mainly an inhibition story (E/I) or something else?

A common hypothesis is that flatter spectra reflect reduced inhibitory control / altered E:I.

Questions:

• Is there strong evidence that age-related exponent flattening reflects inhibition weakening more than excitation (or changes in inhibitory synchrony) rather than changes in synaptic kinetics / dendritic filtering / neuromodulatory tone?
• Are there datasets linking exponent to GABA measures, interneuron markers, or pharmacology in a way that is specific and replicable?

4) Longitudinal universality and heterogeneity

Cross-sectional results are strong; true within-person longitudinal work is harder.

Questions:

• Is exponent flattening longitudinally true in essentially everyone, or are there stable “resisters” with slower slope change?
• If resisters exist, what predicts it best (sleep, vascular risk, fitness, cognitive engagement, meds, inflammation)?

5) Clinical trajectories: epilepsy, ADHD, autism

Questions:

• Is the age-related exponent change accelerated, slowed, or qualitatively different in epilepsy / ADHD / autism once you control for meds, sleep, and artifacts?
• Any convincing longitudinal work here?

6) Structure + fluid spaces: ventricles, parenchyma fraction, PVS

Questions:

• Do exponent changes correlate with ventricular enlargement, parenchyma fraction, or enlarged perivascular spaces (PVS)?
• If yes: are these correlations region-specific (frontal vs posterior) or global?

7) Topography + tissue: frontal > occipital? grey vs white matter?

Questions:

• Are exponent changes typically larger in frontal/association cortex vs occipital/sensory?
• Do they track more with grey-matter decline (synapses/dendrites) or white-matter microstructure (myelin integrity / conduction dispersion)?
  • Note: gross WM volume decline often looks later than young-adult onset of exponent flattening.

8) State, interventions, and recovery after disturbances

Questions:

• Are exponent responses to interventions (e.g., modafinil, cholinergics), expertise engagement (experts doing “their thing” vs not), learning, gaming (e.g., Starcraft), meditation, calorie restriction, etc. directionally consistent across ages?
• If not consistent, what is consistent?
• Is there a reproducible “return to baseline” dynamic after perturbations like sleep deprivation or sustained cognitive activity?

9) What would convince you?

If you were designing the decisive study, would you prioritize:

• large-N longitudinal EEG with careful vigilance control + spectral parameterization,
• paired EEG + MRI (atrophy, myelin metrics, PVS quantification),
• pharmacology and/or MRS (GABA/glutamate),
• or invasive ECoG / animal validation?

Would love pointers to the best papers/datasets and any strong opinions on what’s real vs confounded here.

I want to float a community-building idea: what if José Luis Ricón became a kind of icon/mascot/protagonist of longevity — a “second Bryan Johnson”, but maybe at ~1/4 the intensity/measurement budget?

Rationale (social, not scientific):

• He’s a well-known, very funny presence in adjacent communities (e.g., Manifold Markets),
• he seems to have unusually high betweenness centrality between longevity and many other fields,
• and a charismatic, open-minded public protagonist might help the longevity space feel more human, more playful, and more cross-community.

Discussion questions

1. Is this a good idea at all?

• What could go right (community engagement, clearer public narrative, recruiting talent)?
• What could go wrong (parasocial pressure, privacy harms, unwanted attention, turning science into celebrity)?

1. Consent + governance:

• What would an ethical framework look like (explicit consent, right to stop, data ownership, what’s public vs private)?
• How do we avoid putting social pressure on a person to become “the mascot”?

1. What should the “1/4 Bryan Johnson” measurement bundle be? If someone voluntarily opted in, what are the highest-ROI measurements that are:

• scientifically meaningful
• not too invasive
• affordable
• interpretable to a broad audience

Examples (just brainstorming):

• basic labs (lipids, HbA1c, inflammation markers)
• wearables (sleep, HRV)
• body composition
• periodic fitness tests
• maybe a lightweight cognitive battery

1. Funding model:

• crowdfunding? sponsorship? grants?
• how to avoid conflicts of interest (supplement shilling)?

1. Narrative style: If the goal is good science and good public engagement, what tone works?

• honest about uncertainty
• focus on learning, not optimization theater
• comedy allowed (maybe required)

If José is ever actually interested, I think the first step would be: ask privately, get explicit opt-in, then co-design a measurement plan that prioritizes autonomy and avoids turning a human into a content object. Curious what this forum thinks.

I want to understand a classic aging pattern: prefrontal cortex (PFC) grey matter often shows earlier/stronger decline than primary sensory/occipital regions (which are sometimes described as relatively preserved).

Questions

1. Why PFC first? What are the leading mechanistic explanations for earlier PFC structural decline?

• higher metabolic demand / firing rates / synaptic turnover?
• different myelination / late maturation (developmental timing → vulnerability)?
• vulnerability to vascular supply differences?
• stress/glucocorticoid sensitivity?
• differences in neuromodulatory inputs (DA/NE) and their oxidative burden?

1. Excitatory vs inhibitory balance: Is PFC decline related to different E/I ratios or inhibitory interneuron vulnerabilities with age?

• Does inhibitory function decline first, causing network instability and downstream structural changes?
• Are there known cell-type-specific losses (PV, SST interneurons) that are region-specific?

1. Granular vs agranular PFC: Is decline different between granular PFC (with a prominent layer 4) vs agranular/dysgranular areas?

• Any evidence that particular PFC subregions/lobules are more spared?

1. Laminar patterns (layers 1–6): Do we know if decline is layer-specific?

• Does it show up in layer 4 of PFC earlier than layer 4 of other cortices?
• Are superficial layers (2/3) vs deep layers (5/6) affected differently?
• Are dendritic arbor changes vs soma loss vs synapse loss separable by layer?

1. Damage vs ‘downscaling’: Is PFC shrinkage primarily:

• irreversible damage (cell death, axon loss, demyelination), or
• adaptive/energetic downscaling (lower anabolic:catabolic ratio, synaptic pruning, reduced dendritic complexity)?

1. Use-it-or-lose-it vs rate-of-living: Which framing matches the evidence better?

• “Use it or lose it” (activity maintains structure)
• “Use it more and it gets damaged” (wear-and-tear / oxidative burden)
• Some non-linear optimum: intermittent / fractal reinforcement schedules (work-rest cycles, meditation) that maximize maintenance while minimizing damage

1. Interventions: What interventions have the strongest evidence for slowing PFC structural/functional decline?

• cognitive training
• aerobic exercise
• sleep
• stress reduction/meditation
• neuromodulation

If you have citations to laminar MRI/histology, single-cell aging atlases, or primate aging work that directly addresses these subquestions, I’d love pointers.

I want to understand the concept of “membrane editing” in lipid homeostasis and whether long-lived species might have unusually robust membrane-editing machinery.

I’m thinking of work in the spirit of papers like “Membrane editing with proximity labeling reveals regulators of lipid homeostasis” (title paraphrased), where proximity labeling is used to identify proteins that shape membrane composition and lipid handling in specific compartments.

Questions

1. What counts as membrane editing? What are the main protein classes that actively ‘edit’ membranes in vivo? For example:

• lipid remodeling enzymes (acyltransferases, phospholipases)
• desaturases/elongases
• lipid flippases/scramblases
• cholesterol handling/transport proteins
• ER–mitochondria contact site machinery (lipid transfer)
• lysosomal/autophagic membrane turnover pathways

1. Which compartments matter most for aging? Is the key action in:

• mitochondria (cardiolipin remodeling)
• ER (lipid synthesis + quality control)
• plasma membrane (raft composition, mechanotransduction)
• nuclear envelope

1. Comparative longevity: Do we have evidence that membrane-editing systems are more robust or differently regulated in long-lived organisms like:

• bowhead whales
• naked mole-rats (NMRs)
• long-lived birds (cockatoos)
• supercentenarians

What would we look for?

• slower drift in membrane lipidome with age
• better resistance to peroxidation
• faster turnover/remodeling of damaged lipids

1. Adduct prevention/removal during formation: Some lipid damage proceeds through reactive intermediates (lipid peroxides, aldehydes like 4-HNE/MDA) that form adducts on proteins and lipids.

• Are there enzymes/proteins that can effectively ‘edit out’ or neutralize these intermediates during the process of forming, before they become long-lived crosslinks/adducts?
• Is the main defense enzymatic detox (e.g., aldehyde dehydrogenases, glutathione conjugation) vs membrane remodeling (replace damaged lipids) vs proteostasis (remove adducted proteins)?

1. Methods/datasets:

• Which proximity labeling approaches are best for mapping membrane-editing regulators (APEX, BioID/TurboID) in specific compartments?
• Are there aging time-course lipidomics + proximity labeling datasets?

If you have key reviews, gene lists, or comparative datasets, please share. I’m especially interested in concrete hypotheses like: “long-lived species have unusually strong cardiolipin remodeling / aldehyde detox / lipid turnover that prevents adduct accumulation.”

Most of the micro/nanoparticle conversation focuses on synthetic micro/nanoplastics (MNPs). But humans and animals have always been exposed to huge amounts of natural particulates, especially cellulose-based fibers (wood, cotton, plant debris), plus other biogenic particles.

So I want to ask: what are the baseline tissue burdens of natural fibers, and how do they compare to MNPs?

Questions

1. Rates / concentrations: Do we have measurements of cellulose/wood/cotton microparticles in:

• human lung / blood / placenta / liver
• human brain (if any)
• herbivores/plant eaters (deer, ruminants) as high-exposure cohorts

What are the typical concentrations and size distributions?

1. Relative to MNPs: When studies measure microplastics in tissue, do they also quantify natural fibers in the same samples?

• If yes, what are the ratios (natural fibers : synthetic polymers)?
• If not, is that a major interpretability gap (i.e., we’re missing the baseline for “particles in tissue”)?

1. Bioaccumulation and brain:

• Do cellulose/cotton/wood fibers bioaccumulate in brains the way some MNP reports claim plastics do?
• If they don’t, why not? (biodegradability, enzymatic breakdown, immune clearance, different surface chemistry?)

1. Mechanism differences: Even if natural fibers enter tissues, do they cause less harm because they:

• biodegrade / hydrolyze
• don’t fragment into persistent nanoscale particles with membrane affinity
• don’t carry the same additives / hydrophobic pollutants
• have different protein corona / immune interactions

1. Do natural fibers ‘decompose into nanoplastics’? (Probably not literally plastics, but do they produce nanoscale cellulose fragments that can enter cells/nuclei?)

• Do cellulose nanofibers enter cells or nuclei? Under what conditions?
• Do they integrate into membranes or alter membrane mechanics like some hypothesize for nanoplastics?

1. Occupational disease contrast: We know cotton workers can get lung disease (e.g., byssinosis) at high exposures.

• Why don’t we see an equally clear occupational syndrome for plastic-handling workers?
• Is it under-diagnosed, or are dose/particle properties fundamentally different?

What I’m looking for

• Studies that explicitly measure both natural fibers and synthetic polymers in the same tissues
• Methods that can reliably distinguish cellulose vs polymer particles (and control contamination)
• Any comparative “high plant-fiber exposure” animal data (deer/ruminants)

If the right answer is “we don’t know because the methods are too inconsistent,” that itself is important — but I’d love pointers to best-practice measurement protocols.

I want to discuss a high-stakes question around micro/nanoplastics (MNPs) in brains and the idea of a looming plastic debt / threshold problem.

Context: Matt Campen and colleagues have discussed striking claims like ~0.5% of the brain (mass fraction) being microplastic and exponential increases over time (e.g., ~1.5× every ~8 years, depending on the specific analysis). Many people criticize aspects of the measurement pipeline (e.g., pyrolysis-based methods, contamination, polymer identification, etc.), but Campen has argued the trendlines persist, including for non-PE/PP components. Meanwhile, the quality of MNP studies overall is often inconsistent and contested.

Regardless of the exact headline number, the key meta-point is: environmental microplastic levels have been increasing rapidly (possibly exponentially) for decades.

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Hypothesis (plastic debt framing)

If environmental MNP levels are increasing roughly exponentially, then human tissue loads may also be increasing over time, potentially with lag + saturation, but still with a nonlinear trajectory.

This leads to a scary dynamic: we might not get a clear early warning signal (in population-level cognition/neurology) until tissue loads cross some functional threshold, after which harm becomes obvious and hard to reverse.

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Questions

1) Do environmental exponentials imply human exponentials?

• What is the best evidence for time trends in human MNP loads (blood, placenta, lung, brain)?
• Are there plausible reasons tissue loads would not track the environment (homeostatic clearance, behavioral changes, filtration, regulatory changes), or would track it with strong damping?

2) Measurement controversies: what can we trust?

• What are the strongest critiques of pyrolysis-based quantification, and what controls address them?
• Which methods are currently most credible for tissue MNPs (µFTIR, Raman, pyrolysis-GC/MS, microscopy + spectroscopy, etc.)?
• How should studies report uncertainty and contamination controls to make trend claims believable?

3) Thresholds and early warning

If the “threshold” story is right, how do we estimate the threshold before we see clear population-level IQ/neuroplasticity changes?

Possible approach: use natural experiments / high-variance sentinel species and human subpopulations.

4) Seabirds as sentinel cohorts

There’s substantial variation in MNP loads across seabird species and ecologies.

• Can we use seabirds to estimate dose–response relationships for neurobehavior, reproduction, immune function, or other performance metrics?
• Which seabird datasets are best and how do they measure MNP loads?

5) Human heterogeneity as signal

In humans, MNP loads may differ by:

• geography (SE Asia, coastal vs inland)
• diet/seafood exposure
• occupation/airborne exposure
• health status (e.g., dementia, BBB integrity, membrane integrity)

Do existing datasets already have enough variance to test associations with cognition/neurodegeneration biomarkers?

6) ‘Most human value’ framing

If neuroplasticity / cognition are downstream of subtle membrane/BBB/inflammation effects, then MNPs could threaten a large fraction of what we care about.

• What biomarkers should we track now (BBB permeability, neuroinflammation, lipid peroxidation markers, white matter integrity, etc.) alongside MNP load?

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What I’m looking for

• The best primary papers and critiques on brain MNP measurements (including Campen et al.)
• Time series or cohort data that can test exponential trends
• Sentinel-species work that could help estimate functional thresholds
• A rigorous framework for “plastic debt” analogous to climate debt: delayed harm, nonlinear thresholds, and irreversibility

If you think the exponential trend is an artifact, I’m equally interested — but I’d like the strongest argument and what evidence would falsify the threshold concern.

I want to tackle a socially important question that seems to polarize quickly:

Is glyphosate/Roundup really as low-risk as many mainstream summaries (including LLM answers) imply, or as problematic as some MAHA advocates claim?

This post has two parts: (1) a hypothesis about why the debate is so sticky, and (2) a request for the best evidence on relative risks (including non-human species).

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Hypothesis (about the perception gap)

A big driver of public disagreement is that glyphosate’s harms (if present) are hard to visualize, compared to a generic “Pesticide X” that represents agents with obvious, acute, visible biological impacts.

• For “Pesticide X”, people can point to clear phenotypes (acute toxicity, obvious developmental disruption, immediate die-offs).
• For glyphosate, the claim of low risk often depends on dose, exposure context, formulation, and endpoints that are subtle (chronic effects, microbiome, endocrine, carcinogenicity debates, ecological indirect effects).

So the challenge is: how do you communicate a “lack of risk” (or low relative risk) when the evidence is largely statistical, conditional, and endpoint-specific?

I’d love an infographic concept that contrasts “Pesticide X” (obvious harms) vs glyphosate (risk assessment that depends on exposure + endpoints), to show why the conversation gets derailed.

References / context links:

• Rapamycin.news thread: https://www.rapamycin.news/t/trump-admin-epa-approved-4-new-fluorinated-pesticides/21395/22
• Claude artifact: https://claude.ai/public/artifacts/55f87999-0bf3-4012-8b71-b30589fef673
• Aristotle thread: https://aristotle.science/share/thread/thr_dWsyuT833FsWRnBnUp7A5Fbe

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Evidence questions (what I’m actually trying to learn)

1) Relative risk (compared to other herbicides/pesticides)

On the surface, glyphosate seems less scary than many other herbicides. But is that true when you compare:

• real-world exposure levels
• common formulations (Roundup vs pure glyphosate)
• acute vs chronic endpoints
• ecological indirect effects

What are the best comparative risk sources (EPA/EFSA/IARC, systematic reviews, meta-analyses) that explicitly compare glyphosate to other widely used herbicides?

2) What do MAHA advocates actually claim — and which claims are strongest?

For example, some advocates (e.g. Thomas Massie is often mentioned in these discussions) raise concerns about safety.

• Which specific endpoints are they most concerned about (cancer, endocrine, microbiome, developmental, etc.)?
• Which of those have the most evidence vs weakest evidence?

3) Non-human impacts (my bias: protect the shrimp)

A big missing piece in many summaries is non-human species.

• What are the best studies on glyphosate / Roundup impacts on crustaceans (shrimp, lobsters), aquatic invertebrates, and marine food webs?
• Are there known impacts on long-lived species (e.g., bowhead whales) that would be mediated indirectly (prey base, habitat, endocrine disruptors)?

I care about: lethality, reproduction, development, molting, neurobehavior, immune function.

4) ‘Safe for stochastic parrots + stochastic cockatoos’?

For birds: what do we know about glyphosate exposure via diet/habitat and effects on reproduction, development, and microbiome?

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What I’m asking the community for

• Best comparative evidence (not just “glyphosate is safe/unsafe”)
• Clear separation of glyphosate active ingredient vs formulation adjuvants
• Human health vs ecosystem impacts
• Anything quantitative: effect sizes, exposure ranges, benchmark doses

If you disagree with the premise, I’m also interested in that: maybe the key story is simply regulatory capture vs activist misinformation. I’m trying to sort signal from noise.

I’m trying to understand the neurotoxicity / accelerated-aging risk landscape for prescription stimulants (amphetamine mixed salts / Adderall; methylphenidate / Ritalin), specifically around oxidative stress, membrane lipid peroxidation, and potential dopaminergic terminal damage.

I’m not looking for personal medical advice — more a map of the best evidence and how to reason about dose/spacing and biomarkers.

Questions

1) Dose + spacing: what’s the ‘ideal’ schedule to minimize damage?

• How does risk scale with daily dosing vs every-other-day vs intermittent use?
• Is damage approximately linear in cumulative exposure, or are there threshold / non-linear effects (e.g., recovery dynamics, sensitization, depletion of antioxidant capacity)?
• Any models framing this as a recovery-rate / resilience problem (acute vs chronic stress)?

2) Circuit specificity

If there is oxidative/peroxidative damage, is it expected to differ between:

• VTA / basal ganglia (striatal terminals)
• PFC
• other regions (hippocampus, etc.)

Mechanism hypotheses: dopamine autoxidation, mitochondrial load, excitotoxic coupling, differences in antioxidant enzymes, iron, membrane composition, etc.

3) Translational dose: rhesus macaques vs rats vs humans

There are primate studies discussed as showing scary nerve terminal damage at some dosing regimes (and that primate-relevant doses can be lower than rodent scaling would suggest).

• What are the best-cited rhesus macaque findings here?
• What is the minimum human-relevant dose/exposure that plausibly overlaps with those regimes?
• How should one convert dose across species (mg/kg, allometric scaling, brain exposure / Cmax / AUC)?

4) Oxidative stress countermeasures

What interventions have evidence (preclinical or human) for reducing stimulant-associated oxidative stress / lipid peroxidation? For example:

• antioxidants (which ones, and why those?)
• selegiline (MAO-B inhibition; potential reduction of DA oxidative metabolites?)
• plasmalogens / membrane remodeling
• mitochondrially targeted antioxidants
• lifestyle: sleep timing, exercise, nutrition

Key question: how to reduce damaging oxidation without blunting beneficial adaptive redox signaling (NRF2/KEAP1) or altering therapeutic effect?

5) Biomarkers and readouts

• Should HVA (homovanillic acid) be measured as a readout of dopamine turnover/oxidative load?
• Do Adderall/Ritalin reliably increase HVA in CSF/plasma/urine? Under what conditions?
• What other biomarkers are more informative for membrane damage/peroxidation (F2-isoprostanes, 4-HNE adducts, MDA, oxysterols, etc.)?

6) Combining stimulants

• What is known about taking Adderall and Ritalin together (even if uncommon clinically)?
• Would combined use plausibly be additive/synergistic for oxidative load, or does one saturate the same pathway?

If you have key reviews, primate papers, or a good mechanistic framework (especially with quantitative scaling and biomarkers), please share. I’m especially interested in evidence that distinguishes therapeutic dosing from neurotoxic regimes and whether spacing meaningfully changes long-term risk.

I’m trying to reconcile a puzzle about omega-3 PUFAs and longevity/metabolic health.

On one hand:

• Omega-3s (EPA/DHA) often correlate with better health outcomes and sometimes longevity signals in humans, and there are plausible mechanisms (anti-inflammatory lipid mediators, insulin sensitivity improvements, etc.).
• Some data suggests people with higher PUFA relative to SFA/MUFA ratios have better outcomes.

On the other hand:

• Incorporating highly unsaturated fatty acids into membranes should increase susceptibility to lipid peroxidation and oxidative damage.
• Comparative longevity work (e.g., often associated with Gustavo Barja’s arguments) suggests long-lived animals tend to have more saturated/less peroxidizable membrane composition.

Questions

1. Core reconciliation: How can omega-3s be beneficial for longevity/healthspan while being more peroxidation-prone? What are the dominant mechanisms that could outweigh the oxidation risk?

   • improved insulin sensitivity and metabolic homeostasis?
   • anti-inflammatory signaling (resolvins/protectins/maresins)?
   • membrane fluidity effects that improve receptor/transport function?
   • effects on gene regulation (PPARs, etc.)?

2. Are resolvins/pro-resolving mediators ‘that strong’?

• Is the key benefit mediated by a relatively small fraction of omega-3s converted into specialized pro-resolving mediators (SPMs)?
• What are the rate-limiting steps and how variable is SPM production across individuals?

1. Oxidized fish oil: Why doesn’t oxidized fish oil consistently show strong negative effects in the way we might expect?

• Is it simply that real-world oxidation levels are too low?
• Are oxidized lipids rapidly detoxified/cleared?
• Are some oxidation products signaling molecules with mixed effects?

1. Where do omega-3s go in the body? Given that typical supplementation is a small fraction of total fatty acid flux:

• What fraction ends up in membrane phospholipids vs triglyceride stores vs being β-oxidized?
• How fast is turnover in different tissues (RBCs, liver, adipose, brain)?
• Do omega-3s preferentially remodel certain membrane domains (mitochondria, ER, lipid rafts)?

1. Species differences / Barja-style membrane peroxidizability:

• If long-lived animals trend toward lower membrane peroxidizability, how do we interpret human data where higher PUFA ratios correlate with longevity?
• Is this a confounding issue (diet quality, socioeconomic factors, overall metabolic state)?
• Or do humans benefit because we’re usually in an inflammatory/insulin-resistant regime where omega-3s provide large corrective effects?

1. Broader PUFA vs omega-3 specifically: Some longevity correlations in humans are with overall PUFA:SFA/MUFA ratios, not just omega-3 intake.

• Which PUFA species are most predictive (LA/AA vs EPA/DHA)?
• Is the signal really omega-3-specific or a marker of broader lipid remodeling?

If you have good reviews or key studies (human cohorts + mechanistic lipidomics; comparative biology work on membrane composition; oxidation/peroxidation measurements), I’d love pointers.

I’m trying to understand an apparent pattern in the longevity/healthspan literature:

• Many generic dietary antioxidants (vitamin C/E, etc.) show weak/inconsistent longevity benefits in humans and often disappointing results in model organisms.
• Yet some compounds (often cited: astaxanthin, melatonin, and especially mitochondrially targeted antioxidants like MitoQ/SkQ/SS peptides depending on context) sometimes look more promising.

Questions

1. Why don’t most antioxidants work? What are the best mechanistic explanations?

• ROS are signaling molecules (hormesis) and indiscriminate scavenging disrupts adaptive stress responses?
• Wrong compartment: antioxidants don’t reach the relevant ROS microdomains?
• Wrong species: they don’t neutralize the dominant reactive species in vivo?
• Kinetics/stoichiometry: scavenging rates too slow vs endogenous systems (SOD/catalase/GPx)?
• Redox cycling / pro-oxidant effects?

1. Why might astaxanthin / melatonin be different? Is it mainly:

• membrane localization (lipophilicity / bilayer partitioning)?
• effects beyond direct scavenging (gene expression, circadian signaling, mitochondrial dynamics)?
• metabolite activity?

1. Why do mitochondrially targeted antioxidants work better (when they do)?

• Is mitochondrial ROS a key driver of damage (mtDNA, cardiolipin peroxidation, Fe–S cluster disruption)?
• Is it about local concentration in the inner membrane / matrix vs cytosol?
• Are they acting more like redox buffers than simple scavengers?

1. Reductive stress: People say antioxidants can induce “reductive stress.”

• Does this really happen in vivo at typical doses?
• What biomarkers best demonstrate reductive stress (NADH/NAD+, GSH/GSSG, protein S-glutathionylation patterns, etc.)?
• Is reductive stress a plausible reason for failed trials?

1. Avoiding blunting NRF2/KEAP1 signaling: If ROS are needed to trigger NRF2-mediated adaptive programs, what properties let an antioxidant reduce damaging ROS while preserving useful redox signaling?

• spatial targeting (mitochondria, membranes, specific organelles)
• mild uncoupling / electron transport modulation
• reaction selectivity (specific radicals)
• conditional activation (prodrugs)

1. Chemical motifs / design principles: What are the “special” chemical motifs that help an antioxidant target the right areas without causing global redox flattening? Examples I’m thinking about:

• TPP+ targeting groups (MitoQ-style)
• amphipathic peptides (SS peptides)
• carotenoid-like membrane-spanning structures (astaxanthin)
• indoleamine properties (melatonin)

I’m looking for a mechanistic framework + key references (especially comparative studies showing why some antioxidants preserve adaptive stress responses while others suppress them).

I’m confused by an empirical pattern I keep hearing: that upregulating proteasome β7 (often referred to as PSMB4 / β7, part of the 20S core) is unusually pro-longevity compared to upregulating other proteasome subunits.

Intuitively, the proteasome is a large multi-subunit complex with strict stoichiometry (20S core + 19S regulatory particle), so I’d expect that increasing a single subunit would either:

• do nothing (if assembly is limited by other subunits), or
• cause imbalance/misassembly, unless other subunits are co-induced.

But reports suggest β7 upregulation can still meaningfully improve proteostasis/longevity.

Questions

1. Assembly bottleneck hypothesis: Is β7 rate-limiting for 20S/26S assembly in some contexts (so increasing it increases the number of assembled proteasomes even without matching increases in all subunits)?

2. Stoichiometry compensation: If β7 is increased, how is stoichiometry maintained?

   • post-transcriptional buffering?
   • selective degradation of excess subunits?
   • chaperone-mediated assembly that ‘uses up’ existing pools?

3. Regulatory effects: Could β7 upregulation be acting as a proxy for a broader program (e.g., activating proteostasis pathways, NRF2/FOXO/HSF1, proteasome assembly chaperones), rather than acting purely as a structural subunit increase?

4. Complex specificity: Is the effect tied to:

   • 20S core abundance vs 26S
   • immunoproteasome vs constitutive proteasome
   • changes in gate opening / substrate entry
   • reduced aggregation via improved turnover of specific damaged proteins

5. Downstream phenotypes: What are the key downstream readouts that improve when β7 is upregulated?

   • polyubiquitinated protein load
   • stress resistance
   • lifespan/healthspan
   • tissue specificity (neurons vs muscle vs gut, etc.)

If anyone has the best mechanistic papers (worm/fly/mouse or human cell data) explaining why β7 is special, I’d love pointers. Also open to the possibility this is an artifact of how overexpression experiments are done / how subunits are measured.

I want to ask about the role of macromolecular crowding in long-lived neurons and how it might contribute to (or protect against) aging phenotypes.

A general background point (quoting a summary statement + refs):

Cells are highly crowded, with macromolecular concentrations estimated to be between ~80 and 400 mg/mL (Cayley et al., 1991; Zimmerman and Trach, 1991). Macromolecular crowding retards diffusion and influences protein volume and association equilibria (Dix and Verkman, 2008; Ellis, 2001; Zhou et al., 2008), including condensate formation in vitro and in vivo (Delarue et al., 2018; Woodruff et al., 2017). These effects are caused by steric exclusion and weak chemical interactions (Gnutt and Ebbinghaus, 2016; Rivas and Minton, 2016; Sarkar et al., 2013), and depend on concentration/size/shape and are larger when crowders are smaller than the reacting molecule (Marenduzzo et al., 2006; Rivas and Minton, 2016). For example, increased ribosomes slow diffusion of 20–40 nm particles but not average-sized proteins.

Questions

1. Neurons as extreme longevity cells: How does crowding evolve over decades in neurons (soma vs axon vs dendrites vs synapses)? Is there evidence for systematic increases/decreases in effective crowding with age?

2. Stiffer proteins / aggregates / crosslinks: How should we think about crowding contributions from:

• stiffer proteins / cytoskeletal assemblies
• amyloid-like aggregates
• protein–protein and protein–DNA crosslinks
• glycation/AGE crosslinks

Do these change the “crowding regime” in a way that preferentially impairs transport/localization in neurons?

1. Diffusion vs active transport: What failure modes are expected when crowding increases?

• slowed diffusion of larger complexes
• impaired phase separation / aberrant condensates
• altered reaction equilibria
• cytoskeletal transport congestion (“traffic jams”)

1. Condensates & RNA granules: Are there known links between age-related crowding changes and:

• stress granules
• RNP granules
• nuclear speckles
• synaptic condensates that might explain proteostasis/splicing/local translation issues in aging neurons?

1. Comparative angle: Do long-lived species (e.g., naked mole-rat, bats, bowhead whales) show evidence of better maintenance of crowding homeostasis (proteostasis, aggregate clearance, crosslink prevention) in neurons?

2. Measurement: What are the best ways to measure crowding in neurons across age?

• FCS/FRAP
• viscosity probes / molecular rotors
• tracer diffusion at multiple length scales
• ribosome density proxies

If you have recommended reviews or key experiments (especially neuron-specific aging), please share. I’m particularly interested in whether crowding changes are primary drivers vs downstream consequences of other aging hallmarks (proteostasis collapse, cytoskeletal disorganization, nuclear transport deficits).

I want to raise a question/mini-review topic about perivascular spaces (PVS) in autism and how to interpret them.

There are imaging findings where some autistic children show enlarged PVS that, at least superficially, resemble PVS features more commonly discussed in older adults or vascular/white-matter disease contexts. But it’s not clear whether enlarged PVS in young autistic people implies the same pathophysiology as enlarged/dilated PVS in aging.

Core question

When we see enlarged PVS in autism, to what extent does it correlate with (or predict) the same downstream outcomes as “damage-associated” PVS changes in older brains — versus being a relatively benign anatomical variant or a different mechanism entirely?

Sub-questions / angles

1) Correlation with aging-like ‘damage’ signatures

• In older adults, PVS enlargement is often discussed alongside small vessel disease, white matter hyperintensities, BBB dysfunction, inflammation, etc.
• Do autistic individuals with enlarged PVS show the same correlates (WMH burden, microbleeds, perfusion changes, cortical thinning, cognitive decline trajectories), or is the correlation structure different?

2) Sleep and glymphatic mechanisms

• PVS are often tied (at least conceptually) to glymphatic clearance and sleep-dependent waste removal.
• Many autistic people have sleep disturbances.
• How much of the PVS signal in autism is plausibly related to chronic sleep disruption / altered sleep architecture?
• Are there studies relating PVS metrics to sleep measures (actigraphy, polysomnography) in autism specifically?

3) “Non-damage” vs “damage” PVS enlargement

Hypothesis framing:

• There may be PVS enlargement as a baseline trait (developmental/anatomical) that is not necessarily pathological (“non-damage”).
• There may also be PVS enlargement as an acquired marker of impaired clearance, vascular dysfunction, or inflammation (“damage”).

So:

• Can we distinguish these two regimes using imaging features (regional distribution, shape descriptors, co-occurring diffusion/vascular markers)?
• Do autistic children with enlarged PVS tend to normalize over time, remain stable, or progress?

4) Forecasting / longitudinal interpretation

If enlarged PVS is present early:

• Does it forecast increased risk of later-life small vessel disease phenotypes?
• Or does it forecast an increased reservoir/volume that could be neutral/beneficial for clearance?
• More generally: what does early PVS enlargement foretell about accumulation of PVS-related non-damage vs PVS-related damage over decades?

What I’m looking for

• Canonical autism + PVS imaging papers (especially pediatric MRI studies)
• Any longitudinal cohorts tracking PVS in autism
• Mechanistic models tying sleep/glymphatic function to PVS measures in neurodevelopment
• Methods: best quantitative PVS metrics (count, volume fraction, size distribution, regional maps) and how to avoid confounds (scanner differences, motion, segmentation bias)

If you have citations or data pointers, please share. I’m especially interested in studies that explicitly try to disentangle developmental trait vs pathology marker interpretations of enlarged PVS.

I’m looking for comparative evolution evidence around longevity: do we have published dN/dS (ω) analyses for long-lived organisms — especially:

• bowhead whale
• naked mole-rat (NMR)
• Greenland shark
• cockatoos / other long-lived birds

Questions

1. Are there datasets/papers that provide gene-level dN/dS (or branch-site tests) specifically highlighting longevity-associated genes/pathways in these species?

2. Do any regions of the genome show noticeably higher ω (positive selection / relaxed constraint) in long-lived lineages, particularly in what we might call:

• “control” / regulatory components
• amplification / signaling
• repair / maintenance pathways
• fine-tuning components (e.g., modulators rather than core essential machinery)

1. If there are known longevity gene sets (DNA repair, proteostasis, mTOR/IGF, ECM/mechanotransduction, etc.), do those sets show a systematic shift in ω in long-lived species compared to short-lived relatives?

2. Methodology: what’s the best practice here to avoid artifacts?

• ortholog mapping
• alignment quality
• accounting for effective population size and life history
• distinguishing positive selection vs relaxed constraint

If you have references (bowhead/NMR genome papers, Greenland shark comparative genomics, avian longevity genomics) or existing tables of ω estimates, please share. Even pointers to the right supplementary tables are great.

I’m interested in the idea that cell size increases / cell size control changes with age (thinking of work associated with the Skotheim lab and the broader ‘cell size + senescence + dilution’ literature).

Questions

1. With aging, why might the cytosolic-to-nuclear volume ratio change?

   • growth without division?
   • senescence-associated hypertrophy?
   • nuclear envelope/lamina constraints?

2. How do size and geometry changes affect a cell’s ability to get mRNAs and proteins where they need to go?

   • nuclear import/export capacity vs demand
   • diffusion times and crowding
   • cytoskeletal transport limits
   • local translation vs long-range transport

3. Which processes are most likely to fail first when cells get too large? (e.g., transcriptional scaling, proteostasis, nucleocytoplasmic transport, organelle distribution)

4. Is this phenomenon different in very long neurons (with long axons) compared to other cell types?

   • Do neurons experience analogous ‘size’ problems as a function of axonal length/volume rather than soma size?
   • Are there known age-related shifts in nucleocytoplasmic transport or mRNA localization that are neuron-specific?

5. What are the best datasets/assays to quantify this across age?

   • imaging-based size + nuclear size metrics
   • single-cell RNA/protein scaling
   • nucleocytoplasmic transport reporters

Looking for key papers, especially any that connect size control to aging phenotypes and to transport/localization failures.

I keep seeing ENCODE cCREs (candidate cis-Regulatory Elements) used as a key annotation layer, and I want to understand why they’re such a big deal and how to use them well.

Questions

1. What’s the core value proposition of ENCODE cCREs?

   • Are they essentially a standardized consensus of promoter/enhancer-like regions from chromatin accessibility + histone marks?
   • What evidence supports that they generalize across tissues/cell types (or do they not)?

2. How should we interpret cCRE categories (promoter-like vs enhancer-like vs CTCF-bound) when thinking about gene regulation?

3. In the UCSC genome browser, we often see layered tracks like H3K27ac alongside other histone modifications (H3K4me1, H3K4me3, H3K27me3, H3K9me3, etc.).

   • Why is H3K27ac often emphasized over other marks in practice?
   • What are common failure modes of over-interpreting H3K27ac alone?
   • What combination of marks is most diagnostic for “active enhancer” vs “poised” vs “repressed”?

4. For aging research specifically: if we talk about “entropy/erosion” of H3K27ac landscapes with age, should we expect similar erosion in other marks? Or could H3K27ac change while others remain stable?

If you have a favorite short primer or a canonical ENCODE/cCRE paper that explains the construction + intended use, please share.

I’m trying to understand whether aging-associated erosion / entropy increase in chromatin landscapes is mark-specific.

A lot of discussion focuses on H3K27ac (active enhancers/promoters), but: do other acetylation marks behave differently? And what about methylation marks?

Questions

1. For histone acetylation marks other than H3K27ac (e.g., H3K9ac, H4K16ac, H3K14ac, H3K18ac, H3K27ac vs H3K27me3 interplay), do we see different aging trajectories in:

   • peak sharpness vs broadening
   • signal-to-noise
   • cell-to-cell heterogeneity
   • enhancer vs promoter behavior

2. For histone methylation marks (e.g., H3K4me3, H3K36me3, H3K27me3, H3K9me3), which ones show the strongest ‘entropy’ signatures with age? Are any comparatively stable?

3. Comparative longevity: Are there datasets that let us compare these trajectories across:

   • bowhead whales
   • naked mole-rats (NMRs)
   • human supercentenarians (or exceptional longevity cohorts)
   • cockatoos (or other long-lived birds)

4. If long-lived species differ, what’s the best mechanistic interpretation?

   • better maintenance of chromatin writer/eraser balance?
   • differences in DNA damage/repair coupling to chromatin marks?
   • better cell-type composition stability?

5. Metrics: what are good quantitative definitions of “entropy increase” here?

   • peak width distributions
   • KL divergence vs young reference
   • per-cell variability in single-cell CUT&Tag/CUT&RUN

Looking for key references and datasets (bulk ChIP-seq, CUT&Tag, single-cell chromatin profiling) that explicitly compare multiple marks across age and ideally across species.

I want to propose / ask about a framing for longevity biology:

Longevity motifs may live in protein–protein interaction (PPI) networks, not in individual genes — and they may often involve interactions between genes that are not usually associated with each other (i.e., not just high-clustering/cohesive modules).

Questions

1. Does this framing resonate with what people see in comparative longevity (NMRs, bowheads, bats) or human healthspan genetics?

2. If longevity-relevant network patterns are not just “tight clusters” (high clustering coefficient / obvious modules), what graph motifs should we look for instead?

   • cross-module bridges / edge rewiring
   • ‘weak ties’ that stabilize global dynamics
   • long-range regulatory couplings
   • motifs that increase controllability / robustness

3. Methodologically: how would you test this?

   • Compare motif enrichment across species in ortholog-mapped PPI networks?
   • Use differential coexpression / differential network analysis across age?
   • Identify edges whose presence/weight predicts longevity phenotypes?

4. What would be the best operational definition of a “longevity motif” in a PPI network (and how to avoid artifacts from incomplete interactomes)?

If anyone has pointers to papers using network rewiring / cross-module interaction patterns to explain longevity, I’d love references.

I’m wondering whether stiffness-sensing / mechanotransduction pathways are a central lever for naked mole-rat (NMR) longevity and cancer resistance.

In particular, thinking about:

• YAP/TAZ (Hippo pathway output)
• integrins / focal adhesion components (FAK/PTK2, talin, vinculin, paxillin)
• actomyosin tension (RhoA/ROCK, myosin II)
• nuclear mechanosensing (lamins, LINC complex)
• ECM composition/remodeling (collagens, LOX crosslinking, MMPs)

Questions

1. Do NMR tissues show distinctive regulation of YAP/TAZ activity with age (localization, phosphorylation state, target gene programs)?

2. Is there evidence that NMR longevity depends on maintaining mechanotransduction fidelity (vs just having ‘stiffer’ or ‘softer’ tissue)?

3. How does this interact with known NMR traits (e.g., high-molecular-weight hyaluronan, cancer resistance, hypoxia tolerance)?

4. Are there comparative datasets (NMR vs mouse) that measure mechanotransduction readouts across age (transcriptome + chromatin + imaging)?

5. If you had to pick mechanosensing genes to prioritize as candidate longevity regulators in NMRs, what would be the top picks and why?

Pointers to key papers/reviews welcome — especially anything that explicitly ties YAP/TAZ/mechanics to longevity phenotypes rather than just development or fibrosis.

I’m trying to understand the dynamical systems explanation for why acute stress vs chronic stress can have dramatically different outcomes for an organism’s homeostasis and long-term resilience.

I’ve seen related ideas discussed in Peter Fedichev / Gero.ai work (recovery rates, resilience, critical slowing down), and I’d like a more rigorous framing using dynamical systems / PDE intuition.

What I’m asking

1. In dynamical systems terms, how do we formalize the difference between acute and chronic stress?

   • Acute stress as a transient forcing term?
   • Chronic stress as a parameter shift (changing the vector field) or sustained forcing?

2. How do concepts like:

   • limit cycles / attractors / basins of attraction
   • critical slowing down / loss of recovery rate
   • bifurcations and tipping points
   • noise-driven transitions explain why repeated/sustained stress can push the system into qualitatively worse regimes (frailty, dysregulation) compared to an equivalent “dose” delivered acutely?

3. PDE intuition: what does it mean to talk about “decay coefficients” in PDEs in this context?

   • Is the idea that homeostatic regulation behaves like a dissipative system where eigenmodes decay, and aging/chronic stress reduces the dissipation (slower decay of perturbations)?
   • Are there toy PDE/ODE models (reaction–diffusion, Ornstein–Uhlenbeck, linear response kernels) that capture this well?

4. Long-term robustness: how do we model an organism that must remain resiliently adaptive to many possible perturbations over long time horizons?

   • tradeoffs between fast adaptation vs stability
   • maintaining diversity of compensatory pathways vs specialization

What I’m looking for

• A clean mathematical story (even with toy models)
• How Fedichev/Gero’s “resilience / recovery rate” maps onto eigenvalues / return times
• Any references that connect chronic stress to drift in control parameters, reduced damping, or basin shrinkage

If you have recommended papers/books/notes, or your own favored toy model, I’d love to see it.

I’m trying to ask a more regulatory / upstream question about aging resilience:

Across multiple modalities, aging looks like loss of tight regulation / increased entropy (Vmem heterogeneity, mechanotransduction drift, H3K27ac peak erosion, splicing noise, cryptic splice sites, alternative isoforms that ‘shouldn’t’ exist).

The question

What are the most upstream regulators — genes and/or regulatory marks — that control a cell’s sensitivity/resilience to these aging-associated breakdowns?

Specifically I’m interested in upstream factors/marks that influence one or more of:

• Maintaining membrane potential (Vmem) with age (low drift / low heterogeneity)
• Mechanotransduction fidelity with age
• Maintaining H3K27ac distribution (reducing increases in ‘entropy’/peak erosion)
• Maintaining transcript integrity (reducing transcriptomic noise)
• Maintaining splicing fidelity (reducing cryptic splice sites / aberrant isoforms)

And I’m explicitly asking about:

• genes (master regulators, chromatin remodelers, splicing/APA regulators, signaling hubs)
• histone acetylation marks (beyond H3K27ac if relevant)
• epitranscriptome marks (e.g., m6A, pseudouridine, etc.)
• polyadenylation/APA-related marks (CPSF/CSTF machinery, 3’ end processing, 3’UTR usage shifts)

What I’m looking for

1. If you had to nominate the top few upstream levers that are causal (not just correlates), what would they be and why?

2. Any datasets that jointly measure these layers (ATAC/ChIP + RNA isoforms + maybe electrophysiology or mechanotransduction readouts) across age?

3. Evidence from perturbations (CRISPR, small molecules, partial reprogramming) that show restoring upstream regulators reduces downstream entropy increases while maintaining differentiation.

4. How to operationalize “upstream” here: network inference? causal mediation? time-ordering in longitudinal data?

Citations or concrete starting points (specific factors, pathways, or reviews) welcome.

I’m trying to map the gene-level (and metabolite/pathway) defenses against protein damage from glucose-driven glycation and subsequent oxidative/peroxidative chemistry.

In particular I want to enumerate the main steps and ask: what genes/metabolites intervene at each step?

Damage sequence (please correct/refine)

1. Glucose + protein amines → Schiff base (early glycation)
2. Amadori rearrangement (more stable ketoamine adduct)
3. Reactive carbonyl species formation (e.g., methylglyoxal/glyoxal/3-deoxyglucosone) and Michael-type adducts on proteins
4. Progression to AGEs (crosslinks, advanced glycoxidation products)
5. Coupled oxidative damage / lipid peroxidation byproducts (e.g., 4-HNE, MDA) that also form adducts

Questions

A) Which genes/proteins are the core protectors/repair systems for protein glycation/oxidative damage?

• detox of reactive dicarbonyls
• deglycation enzymes (if any)
• proteostasis pathways that remove damaged proteins
• antioxidant / redox systems that prevent downstream oxidation

B) Specifically, what are the key genes for the earliest (first 3) steps?

• Is there true enzymatic reversal/repair of early glycation and Amadori products?
• Which systems primarily prevent progression to dicarbonyl stress and Michael adduct formation?

C) Can we produce a step-by-step map like:

• Step 1: inhibitors, competitive substrates, glucose handling
• Step 2: anything that blocks Amadori stabilization
• Step 3: methylglyoxal detox / dicarbonyl scavengers
• Step 4: AGE breakers vs clearance
• Step 5: aldehyde detox / lipid peroxidation adduct handling

D) For each step, what are the best-known metabolites / interventions? (e.g., glutathione system, carnosine, aminoguanidine, metformin effects on MG, NRF2 activation, etc.)

What I’m looking for

• A gene list (human + orthologs) organized by step
• Reviews that cover “dicarbonyl stress”, deglycation, glyoxalase system, AGE clearance
• Any evidence about which components matter most for longevity/healthspan

If you can point to a canonical pathway diagram or a great review, that’s perfect.

I’m curious whether tools from topological data analysis / network topology (Robert Ghrist, Chad Giusti, simplicial complexes, persistent homology) can say something useful about longevity, especially in long-lived mammals like naked mole-rats (NMRs).

In particular, thinking about PPN network motifs (I’m using “PPN” loosely here: higher-order network motifs beyond pairwise edges — cliques, cavities, mesoscale structure).

Questions

1. Candidate motifs: What kinds of simplicial-complex structures (cliques, higher-order cycles/cavities, rich-club-like higher-order motifs) would we expect to matter for longevity-related phenotypes?

   • robustness / redundancy
   • fault tolerance under node loss
   • modularity vs integration tradeoffs
   • controllability / homeostatic regulation

2. Persistent homology as a biomarker: Is there evidence that persistent homology features (e.g., long-lived H1/H2 classes; barcode summaries) are higher or more stable in:

   • NMR vs mouse/rat networks (brain connectomes? gene regulatory networks? protein interaction networks?)
   • human groups with better maintenance of function with age (education/cognitive reserve), even if they don’t ‘age slower’ biologically?

3. Interpretation: If we saw stronger persistence (or different Betti number trajectories) in long-lived organisms, what mechanistic story could connect that to aging?

   • better homeostatic attractors?
   • better multi-scale buffering / degeneracy?
   • fewer brittle bottlenecks?

4. What data should we use? What’s the best concrete substrate to test this?

   • single-cell gene regulatory networks across age
   • coexpression networks
   • electrophysiology / functional connectomics
   • protein–protein interaction graphs

5. Methods: Any recommended pipelines for building simplicial complexes from biological networks (choice of filtration, noise handling) that avoid obvious confounds (sample size, coverage differences across species)?

If you have references where Giusti/Ghrist-style simplicial methods were used on aging/longevity, or even on comparative neurobiology across species, please share. Also happy to hear reasons this is a dead end / not well-posed.

SRSF1 is a major splicing factor (SR protein) and splicing/isoform regulation seems to degrade with age. So naively I’d expect SRSF1 (or cis-regulatory elements controlling it — cCREs, CpG methylation sites, eQTLs/sQTLs) to show up as a strong signal in human longevity genetics/epigenetics.

But it doesn’t seem to be a ‘top longevity gene’ in the usual places (GWAS hits, strong age-associated CpGs, etc.).

Questions

1. Is that impression correct? Are there actually strong human signals implicating SRSF1 regulation in longevity/healthspan that I’m missing (GWAS, TWAS, colocalization, methylation clocks, sQTLs)?

2. If SRSF1 isn’t a top hit, why might that be? Possible explanations I’m considering:

• Constraint / essentiality: too essential; variants are deleterious and selected against
• Pleiotropy / cancer risk: splicing factors are often oncogenic when dysregulated, so ‘good for longevity’ variants might not exist or are rare
• Trans effects dominate: aging-related splicing noise might be mostly downstream of global cellular state (stress, chromatin, inflammation) rather than SRSF1 cis regulation
• Tissue specificity / cell-type composition: signals get washed out in bulk tissues
• Measurement artifacts: standard assays capture other splicing regulators better than SRSF1

1. Are there other splicing regulators whose cCREs/CpGs do show up robustly in longevity/aging datasets, and if so which ones and why them?

If you have citations (GWAS/TWAS/clock papers) or pointers to datasets to check, please drop them.

I’m trying to compare a bunch of aging-associated phenomena that look like increases in entropy / loss of sharply peaked organization (i.e., distributions broaden, states become more heterogeneous/noisy):

• H3K27ac: erosion of sharply peaked enhancer/promoter acetylation landscapes
• Protein localization: spatial mislocalization / less precise compartmentalization
• Splicing isoforms: isoform distributions broaden; more aberrant splicing / intron retention
• Long-transcript vs short-transcript ratios: transcript length/isoform-length distributions shift/broaden
• Transcriptomic noise: increased cell-to-cell variance in expression
• DNA methylation: CpG site “spread” / drift (DMEs vs VMEs; broader variance patterns)
• Cell polarity: loss of apical–basal polarity, planar polarity, etc.

Questions

1. Ordering / earliest onset: In real datasets, where do these “entropy increases” show up first?

   • Are there known time-orderings in a single tissue (e.g., methylation drift → chromatin acetylation changes → splicing noise → localization/polarity)?
   • Do we have any longitudinal / time-course single-cell multi-omics that can support causal ordering?

2. Most vs least reversible: For each category above, how reversible is the entropy increase with:

   • partial reprogramming (OSKM/OSK variants),
   • cell rejuvenation interventions while maintaining differentiation,
   • transient expression vs full iPSC reset?

3. What’s the ‘hardest’ entropy? Which of these entropy increases tends to be least reversible (or requires loss of identity) and why?

   • Is polarity/protein localization entropy largely downstream of cytoskeletal/ECM remodeling (hard to reset)?
   • Is methylation drift easier/harder than chromatin acetylation to restore?
   • Is splicing noise largely reversible if you restore splicing factor expression/stoichiometry?

4. Metrics: What are the best quantitative measures people use for “erosion of peaked values” / entropy in each domain?

   • entropy / KL divergence of peak distributions (H3K27ac)
   • variance inflation / dispersion metrics (scRNA-seq noise)
   • isoform entropy (splicing)
   • methylation variance (DMEs vs VMEs)
   • polarity index / spatial autocorrelation metrics

What I’m looking for

• Reviews or key papers that explicitly frame aging as entropy increase / loss of spatial & regulatory precision
• Studies that compare multiple modalities in the same samples (best case: single-cell multi-omics + imaging)
• Evidence about what OSK/OSKM or other interventions can/can’t reverse without dedifferentiation

If you have a favorite dataset, a well-known ordering result, or a strong argument for which modality is most ‘primary’ vs most downstream, I’d love pointers.

I’m trying to unpack a claim I’ve heard attributed to Leo Pio López: that with aging there are increased levels of Vmem heterogeneity and/or degradation of bioelectric spatial organization.

I’d like to understand mechanistically how this could happen and what the best evidence is.

Questions

1. Mechanism: What cellular/biophysical changes plausibly drive increased Vmem heterogeneity with age?

   • ion channel expression drift?
   • gap junction coupling changes?
   • membrane composition / leak conductance changes?
   • mitochondrial/ATP constraints on pumps (Na/K ATPase)?
   • inflammatory signaling affecting electrophysiology?

2. Geometry + mechanics hypothesis (my speculation): How much of this could be due to increased stiffness and reduced ability of cells to precisely target membrane curvature / tortuosity because the cytoskeleton (actin, microtubules, vimentin) becomes disorganized or ‘less goal-directed’?

   • Is there evidence that cytoskeletal remodeling with age causes spatially noisier localization of channels/transporters?
   • Any known links between vimentin dynamics and membrane potential patterning?

3. Developmental vs post-developmental “goal-directedness”: If bioelectric patterning is tightly regulated during development, what maintains it later? Does maintenance deteriorate simply because there’s less selection pressure / fewer active feedback loops post-development?

What I’m looking for

• Key review papers or primary experiments measuring Vmem heterogeneity across age
• Model systems where this is quantified (neurons, epithelia, planaria, zebrafish, etc.)
• Any mechanistic work tying cytoskeletal stiffness/disorganization to bioelectric pattern stability

If anyone has citations, notes from talks, or a pointer to where Leo Pio López discusses this in detail (paper, preprint, slide deck, podcast), that would help a lot.

I’m trying to track down which genes are most associated with changes in the ratio of long-transcript to small/short-transcript mRNAs with age — and whether the drivers differ between bowhead whales, naked mole-rats (NMRs), and human supercentenarians.

What I mean by the phenotype (clarify/adjust if wrong):

• With aging, some tissues show transcriptome-wide shifts toward shorter isoforms (often via alternative polyadenylation / 3’ UTR shortening) and/or changes in splicing (intron retention, exon skipping), which can change the effective distribution of transcript lengths.
• The measurable outcome I’m imagining is something like:
  • long-isoform / short-isoform abundance ratio per gene (or aggregated genome-wide),
  • or a length-weighted expression metric that can be compared across ages/species.

Background hypotheses (very tentative):

• If this is real and robust, I’d expect strongest associations in:
  • RNA processing / splicing regulators (e.g., SR proteins, hnRNPs),
  • polyadenylation/cleavage factors (APA machinery),
  • transcription elongation / Pol II processivity factors,
  • RNA decay / NMD regulators.
• The comparative longevity angle: bowheads/NMRs might maintain “youthful” isoform-length distributions longer, or show different regulatory loci (orthologs) stabilizing long-isoform usage.

What I’m asking the community:

1. What datasets / papers directly quantify age-related changes in transcript length distributions (isoform length, 3’UTR length, long vs short isoform ratios) in:
   • bowhead whale,
   • naked mole-rat,
   • supercentenarians (or extreme longevity cohorts)?
2. For those datasets: which genes (or pathways) come out as the top correlates/associations with the long/short ratio change?
3. Are there known cross-species conserved regulators of APA/splicing changes with age that we should check first?

Operationalizing suggestions welcome:

• Best metric to compute (per-gene isoform ratio vs global length index)?
• Best method to avoid platform artifacts (bulk RNA-seq vs 3’-tag seq vs long-read)?
• How to do fair cross-species comparisons (ortholog mapping, tissue matching, cell-type composition)?

If you’ve got specific gene candidates with citations (or a pointer to a table/supplement), that’d be ideal. Otherwise even “start here” references are helpful.