Masketair

COVID, measles, meningitis… can we skip to the part where we clean the air we breathe?

Context: Our team has studied how long numerous microbes remain viable in the air. In these studies, we found that the alkalinity of exhaled aerosol drove the decay of SARS-CoV-2. Consequently, elevated levels of CO2 results in SC2 remaining viable in the air for a longer period.

My take on the whole "AI cures cancer in dog in Australia". It's a very interesting story, but perhaps not for the reasons that are being noted. In 2007, Freeman Dyson published an essay in The New York Review of Books called “Our Biotech Future.” It contains one of the most memorable predictions about the future of biology I’ve ever read. “I predict that the domestication of biotechnology will dominate our lives during the next fifty years at least as much as the domestication of computers has dominated our lives during the previous fifty years.” Dyson believed biology would eventually follow the trajectory of computing. At first, powerful tools live inside large institutions - universities, government labs, major companies. Over time those tools get cheaper, easier to use, and more widely distributed. Eventually individuals start doing things that once required entire organizations. “Biotechnology will become small and domesticated rather than big and centralized.” He even imagined genome design becoming something almost artistic: “Designing genomes will be a personal thing, a new art form as creative as painting or sculpture.” Dyson's words rang in my mind as I read the "AI cures dog cancer" story. Much of the coverage framed this as an example of AI discovering new science. But that’s not really the interesting part of the story. The scientific pipeline involved here is actually well known. It closely mirrors the workflow used in personalized neoantigen vaccine research that has been under active development for years. The steps are fairly standard: sequence the tumor, identify somatic mutations, predict which mutated peptides might be recognized by the immune system, encode those sequences in an mRNA construct, and deliver them to stimulate an immune response. The biological targets themselves were almost certainly not new discoveries (I have been unable to find out what they are, but mutations in targets like KIT which are common might be involved). Partly therein lies the rub, since the hardest part of drug discovery, whether in humans or dogs, is target validation, the lack of which leads to lack of efficacy - the #1 reason for drug failure. In neoantigen vaccines, the proteins involved are usually ordinary cellular proteins that happen to contain tumor-specific mutations. AlphaFold which was used to map the mutations on to specific protein structures is now a standard part of drug discovery pipelines. The challenge is identifying which mutated peptides might plausibly trigger immunity. What is interesting though is how the pipeline was assembled. Normally, this type of workflow spans multiple domains - genomics, bioinformatics, immunology, and translational medicine - and in institutional settings those pieces are distributed across specialized teams, document sources and legal and technical barriers. Navigating the literature, selecting computational tools, interpreting sequencing results, and designing a candidate mRNA construct is typically a collaborative process. In this case, AI appears to have helped compress that process, pulling together data and tools from different sources. Instead of requiring multiple experts, a motivated individual was able to assemble the workflow with AI acting as a kind of guide through the technical landscape. I’ve seen something similar in my own work while building lead-optimization pipelines in drug discovery. The underlying science hasn’t changed, but the friction involved in assembling the workflow can drop dramatically. Tasks that once required stitching together multiple tools, papers, and areas of expertise can now often be executed much faster with AI helping navigate the terrain; and by faster I mean roughly 100x. That kind of workflow compression is powerful, to say the least. When the cost of navigating technical knowledge drops, more people can realistically assemble sophisticated research pipelines. This story is a great example of what naively seems like a boring quantitative acceleration of the research process. In that sense, therefore, the real novelty here is not the biology but the combination of three things: a non-specialist orchestrating a complex biomedical pipeline, AI acting as a navigational layer across multiple technical domains, and the resulting decentralization of capabilities that were once confined to institutional research environments. But I think the story also points to something deeper, which is a challenge to modern regulatory environments. Modern biomedical innovation does not operate solely according to what is scientifically possible. It is structured by regulatory frameworks - clinical trials, safety oversight, institutional review boards, and regulatory agencies. Those systems exist for important reasons, but they also assume that the development of therapies occurs primarily within large, regulated organizations. When individuals begin assembling pieces of these pipelines outside those institutions, the relationship between technological capability and regulatory oversight starts to shift. The dog in this story sits outside the human regulatory framework. That fact alone made the experiment possible. In other words, the story is not just about technological capability; it is also about how certain forms of experimentation can occur when they bypass the regulatory pathways that normally govern biomedical innovation. One is reminded of another Australian, Barry Marshall, who received a Nobel for demonstrating through self-experimentation that ulcers are caused by bacteria. This raises an interesting question: what happens when the tools for assembling sophisticated biological workflows become widely accessible while the regulatory structures governing them remain institution-centric? That tension may ultimately be the most important implication of this moment. Regulatory frameworks will need to adapt to this kind of citizen science. Seen in this light, the story about the AI-assisted vaccine is less about a breakthrough in cancer therapy and more about a glimpse of the early stages of something Dyson anticipated nearly two decades ago: the domestication of biotechnology. If AI continues to reduce the cognitive overhead required to navigate biological knowledge and assemble complex pipelines, the boundary between professional research and motivated individuals may begin to blur. That shift will require careful thinking about safety, governance, and responsibility. But it also carries an exciting possibility. Dyson imagined a world in which biological design might eventually become something like a creative craft practiced not only by institutions but also by curious individuals experimenting at smaller scales. For a long time that vision felt distant. Now, it feels like we may be seeing the first hints of it.

Indoor air quality is one of the biggest invisible health factors in modern life. But until recently, we had almost no way to see it. Now we’re starting to map it. Thread ↓

Reality: Yvonne has lived with it for 5 years. Long COVID is not just stress or anxiety. See her story: bit.ly/3O6Ji3X #LongCOVID #HealthInformationIntegrity #healthsecurity

🚨URGENT: @NYCMayorsOffice rejected our petition to light NYC teal for #LongCOVIDAwarenessDay. We need the @NYCCouncil to lead! 🗣️Write to your Council Member NOW to demand a motion for March 15 recognition. Don’t let millions remain invisible. 🔗 #heading=h.p1it9s70srri" target="_blank" rel="nofollow noopener">docs.google.com/document/d/1wp…

WHN’s Kids’ Zone Magazine is a free, downloadable issue packed with kid-friendly stories, art, science, and COVID-conscious lifestyle ideas made for families who are still choosing care. Download the latest issue for free + explore past editions anytime: whn.global/kidszone/ #PublicHealth #COVID #COVID19 #COVIDConscious #LongCOVIDAwareness

To not wear a mask. My whole life, I've never seen anything close to it. People would rather get sick than stand out. In the world we live in now, being immune to peer pressure is an extremely high value trait. It could save your life or someone else's.

¿Quieres ayudarnos a ampliar el acceso a información precisa y de calidad sobre salud pública en tu idioma? Los equipos de idiomas de la WHN traducen, recopilan y difunden recomendaciones de salud pública basadas en la ciencia para comunidades alrededor del mundo. (...)

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1/ If I hear one more public official call indoor air filters a "band-aid," I'm going to explode! It's so logical, yet so readily dismissed. Filtering indoor air should be as standard as filtering water. Full stop. 🧵

🆕Initiativen, Treffen & Informationen an einem Ort Gesundheit und Wissenschaft zusammen denken: Diese Übersicht sammelt Initiativen, Treffen und Infos rund um saubere Luft und Infektionsschutz im deutschsprachigen Raum. Ergänzungen sind sehr willkommen! whn.global/gemeinsam-hand…

Das RKI beschreibt Long COVID als fortwährendes Public-Health-Thema — mit Hinweisen auf infektionsassoziierte Organschäden, chronische Erkrankungen und noch nicht vollständig quantifizierbare längerfristige Folgen für Bevölkerung und Versorgungssystem. rki.de/DE/Aktuelles/P…

Want to help expand access to accurate public health information in your language? WHN Language Teams translate, connect, and amplify science-based guidance in communities around the world. To join: Sign up for WHN Slack: whn.global/whn-slack-work… Visit the #Languages channel Introduce yourself and share your language(s) Let’s make reliable health information accessible to everyone. #PublicHealth

@jlerollblues @michael_hoerger @DylanFresco No, we decided for those states to apportion Nationwide by state population for your model.

@ZurNull @michael_hoerger @DylanFresco Thank, masketair. Is that same data in the files I downloaded in the zip package?

Breaking: The CDC reports SARS-CoV-2 levels *quadrupled* in Minnesota in the past week. Using harmonized wastewater estimates, we project 1 in 12 people in Minnesota are actively infectious. This is equivalent to a 59% chance of exposure in a room of 10 people. You will note that many wastewater sites are not reporting. We may see the estimated levels retroactively corrected downward slightly. However, enough data are coming in that it does not fall in the CDC classification for "limited data." Four states (ND, NH, AR, and MS) have limited/no data, and many more states have <4 sites reporting. Overall, the data quality is not bad, perhaps a "B." These are the best real-time estimates we have, and they suggest people should #maskup, test, isolate when ill, quarantine for exposures, and get boosted today to reduce the risk of getting sick 2 weeks from now. These are among the highest statewide levels anywhere in the U.S. in a long time.

@jlerollblues @michael_hoerger @DylanFresco MN has strong coverage, but: We currently only display states that pass a calibration quality check against our Biobot-based infections series. MN’s wastewater–infections correlation in that window is ~0.576, just below our 0.6 cutoff, so that's why it’s excluded.

@michael_hoerger @DylanFresco Your so right. @ZurNull we no long er include Minnesota in our Whn reports after a long run. Any ideas?

Bei einigen Wettbewerben der Winterspiele in Italien gehören COVID-Schutzmaßnahmen weiterhin dazu – schlicht weil luftgetragene Infektionen nach wie vor im Umlauf sind. Wenn der eigene Körper die Lebensgrundlage ist, ist Vorbeugung Teil der Leistung.

The California Department of Public Health advises attendees to mask at the Olympics.