Time Capsules in the Language of Science

In elementary school, I spent an art class layering greens. Jungle green fabric with a subtle coordinating stripe, delicate spring green tissue paper so translucent you could see through it, pieces of chunky emerald felt cut into the shape of leaves, sparkling seafoam and teal glitter, all of it pressed and glued onto a piece of tagboard in what my teacher called a monochromatic collage. All shades of one color. Mono: one. Chroma: color. I didn’t think about the word at the time because I was too busy glittering.

That same week, I took a science quiz on monocots and dicots and got the answer wrong. I couldn’t remember which plants had one seed leaf and which had two. When I got the test back and saw my error, it clicked. Of course a monocot has one seed leaf. I’d been using mono in art class while missing it on a science quiz down the hall. The root moved between rooms, but I hadn’t learned yet that it could.

That stuck with me. Although my formal education is in English and linguistics, my mind naturally sought out scientific connections. Greek and Latin roots were the place where those two interests didn’t have to compete. They had the precision of formulas, a combinatorial logic where twenty roots could unlock hundreds of words, and they worked on both sides of the hallway.

I write about science now, and the roots have remained my constant companion. A few weeks ago I was researching an organism called Rapaza viridis for a blog post on endosymbiosis. I’d never heard of it, but since viridis is Latin for green (the same root that gives us “verdant”), I already guessed this creature had something to do with photosynthesis. It does. R. viridis is a single-celled predator that steals chloroplasts from the algae it hunts and uses them to photosynthesize.

Learning more about the organism, I came across the term “transient chimerism.” Also new to me, but also immediately legible. The trans in “transient” is the same as in “transparent” and “transport”: ‘across,’ ‘through.’ Something passing through. Chimera: the Greek monster stitched together from a lion, a goat, and a serpent. Put together: a temporary state of being made from parts of more than one organism. The roots came through for me once again.

This keeps happening. I’ll hit a term I’ve never seen, and its pieces already feel familiar. I could spend this entire blog walking through terms and showing you their roots, but definitions only stay interesting for so long (and my family already compares me to the father in My Big Fat Greek Wedding). What I’ve become more interested in is something the roots do beyond defining. The roots that end up in a name tend to carry more than a definition. They carry an interpretation, an argument about the thing itself.

For years, I wondered about apoptosis. I knew apo meant ‘away from.’ I could see ptosis maybe shared something with “asymptote,” but I couldn’t figure out what cell death had to do with calculus. In 1972, John Kerr, an Australian pathologist, had been studying a form of cell death that looked nothing like anything he’d seen before. For most of pathology’s history, the only cell death anyone studied was the kind that showed up when something had gone wrong: a cell damaged by injury or infection or toxins, dying violently. That kind of death had a name: necrosis, from the Greek nekros (‘corpse’). The cell swells, ruptures, spills its contents, and triggers inflammation. Studying liver tissue, Kerr noticed a second kind of death happening quietly alongside it, in cells that hadn’t been damaged at all. The cell death he was watching was the opposite. The cell shrank, and its contents condensed. It broke apart into tidy packages that neighboring cells quietly absorbed. No mess. The body had planned this.

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New Approach Methodologies: Guidelines for Biological Ground Truths

FDA Regulation changes: The Problem is still Validation

In March 2026, the FDA published draft guidance that fundamentally changed how NAMs (New Approach Methodologies) are evaluated in drug development. If you’re designing NAMs, whether that be spheroids, organoids, or organs-on-a-chip, your model now must meet specific validation criteria set by regulators. This draft document, titled ‘General Considerations for the Use of New Approach Methodologies in Drug Development’ provides requirements on the use of NAMs, including in vitro, in silico and in chemico methods (FDA & CDER, 2026). This guidance is a big shift from aspirational recommendations towards clearer regulatory recommendations.

It comes on the coattails of consistent feedback and challenges the market has seen over the past few years as they attempt to transition and optimize away from animal models. Mainly being:

“How can I be sure the data I get from this non-animal model is reliable, trustworthy and relevant?”

This regulatory mandate from the FDA requires researchers to use models and assays that meet four validation criteria:

  • Context of use
  • Human biological relevance
  • Technical characterization
  • Fit-for-purpose

This guidance initially applies to antibody development, biologics, and will eventually be relevant for small molecules. The momentum in the market is sound and indicates that there is a real need for assays that meet these new requirements.

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Mitigating Disease by Testing Wastewater at Large Events

The FIFA World Cup has drawn fans from dozens of countries around the world to the United States, making it one of the largest international sporting gatherings in history. With thousands of people gathering together, it’s important for scientists and public health professionals to track contagious diseases. These pathogens spread easily, which is why early detection matters. 

Testing everyone who comes into an event is impractical, and if you wait too long, the disease may have already spread. How then do scientists preemptively use wastewater to check for disease spread without testing every individual?  

How do scientists test for wastewater? 

Wastewater surveillance, or wastewater-based epidemiology (WBE), is a rapidly growing field that has recently proved effective in tracking the spread of diseases in communities around the world. WBE refers to the process of analyzing the wastewater output from a population to detect the presence of certain compounds or pathogens. Though its use became widespread during the pandemic, it continues to show utility in monitoring other infectious diseases as well, including polio, influenza and monkeypox, among others.  

Samples collected from wastewater treatment plants provide a wealth of information, but the output from individual buildings can sometimes offer more specificity as to where exactly a pathogen is circulating. Occasionally, viruses that go undetected in samples from a treatment plant are still found on a micro level in sewage from facilities like hospitals or schools. 

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The Calcium-Platelet Connection in Cancer Metastasis 

Most cancer-related deaths from solid tumors aren’t caused by the primary tumor itself, but are the result of metastasis. Metastasis is the process by which cancer cells break away from the primary tumor, travel through the bloodstream and establish new tumors in distant tissues. This is a complex, multi-step process and cancer researchers have spent decades trying to understand and disrupt the metastatic cascade. What is known: platelets and calcium play important roles in metastasis. What is unknown: How do these two factors interact?  

Here, we explore a recent study published in Scientific Reports by researchers at George Washington University1. They investigate how calcium levels influence platelet-cancer cell interactions and what happens when both factors converge.  

red blood cells,activated platelet and white blood cells microscopic photos
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AI Identifies the Target. Someone Still Has to Validate It.

Did you see the movie where Spider-Man files his taxes? Or the one where Wonder Woman sits on hold with her insurance company while her pasta water boils over? Or where Captain America finds blight on his tomato plants and drives to the county extension office where he spends fifty minutes with a seventy-four-year-old master gardener named Marlys then leaves with a handwritten note covering his soil composition, his watering schedule, and what Marlys calls “the mulching situation”?

No. Because the ordinary day-to-day doesn’t stand a chance next to the saving of the world.

We spend most of our lives in the ordinary. Not because we’re failing to reach the extraordinary, but because the ordinary is what holds everything together while we get there. It’s not the backdrop but the foundation. It’s what the story depends on, whether or not it gets any credit.

Drug Discovery Has a Storytelling Problem

Drug discovery runs almost entirely on ordinary days, punctuated by the moments that make the news: a new target gets identified, a compound shows promise, a trial produces results. Those moments get the headlines, press releases and keynote slots. What doesn’t get the same attention is the years of work behind those moments: the assays, the failed experiments, the redesigns, the slow accumulation of evidence that either holds up or doesn’t. That work has always been the majority of drug discovery.

Some of the most important work in drug discovery ends in a result nobody publishes, but a dead end isn’t a failure of the program. It’s the program working. The researcher who rules something out has learned something true. That knowledge travels forward even when it doesn’t make the headline because it can redirect the next hypothesis, narrow the next experiment or just quietly move things along. That work moves research forward without anyone announcing it.

The Shiniest Thing in the Room

Artificial intelligence is drug discovery’s latest extraordinary announcement, and the fanfare is legitimate. Most of the druggable proteome has never been touched. Of approximately 4,500 human proteins considered druggable, all approved drugs to date work through only 716 distinct targets. Drug hunters knew there was more biology to address but lacked a way to find and prioritize candidates at scale. AI is changing that. By scanning genetic evidence, biological networks and scientific literature at a scale no human team can match, AI is surfacing targets that were previously out of reach and ranking them by the strength of the evidence behind them.

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What Do Floods Leave Behind? Research on Waterborne Pathogens in Soil after Flooding

Agricultural soils in floodplain areas face contamination from waterborne pathogens during flooding events, yet characterization of these microbial communities remains limited. Furtak and Marzec-Grządziel investigated potentially pathogenic microorganisms in cultivated soils from the Vistula River valley in Poland, comparing soil samples collected before and during simulated flooding conditions.

The Vistula River. Flooding events can deposit waterborne pathogens into adjacent agricultural areas.
Aerial view of the Vistula River in Poland at sunset in spring
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How Mass Spectrometry Is Reshaping Animal Health Research


Livestock, sustainable and herd of cattle on a farm in the countryside for eco friendly environment. Agriculture, animals and cows for meat, dairy or beef trade production industry in a grass field

Veterinary medicine increasingly demands the same analytical rigor applied in human clinical research. Whether researchers are characterizing the protein composition of an equine regenerative therapy, detecting drug residues in livestock or profiling pathogen virulence factors in production animals, the questions are complex, and the tools must be equal to them.

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Another Crack in the RAS Code: The Measurement Story Behind Daraxonrasib

In late May 2026, a clinical trial result landed in the New England Journal of Medicine and immediately rewrote what oncologists believed was possible for patients with metastatic pancreatic cancer. Before the paper was published, people in the field were already calling it “transformative.” The data, when it came, agreed. In a disease where most second-line treatments offer months at best, a drug called daraxonrasib nearly doubled how long patients lived compared to those who received chemotherapy.¹

RAS proteins, which regulate cell growth and are mutated in more than a third of all human cancers,² had spent forty years resisting every attempt to drug them. The protein’s surface offered no obvious foothold for a small molecule. Once the word “undruggable” attached itself to the RAS protein family, most of the field moved on to more cooperative targets.

Some researchers stayed. And Promega stayed committed to the question that never goes away: does this new compound work inside a living cell? When the next chapter of the RAS story arrived, the tools were ready. Daraxonrasib is one culmination of a much longer story, one that matters for every researcher pursuing a target the field has written off.

The First Answer

To understand what daraxonrasib represents, it helps to see how an earlier chapter of the RAS story faced the same fundamental measurement challenge.

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What Happens When You Delay an Ebola Vaccine Booster by 18 Months?

Ebola Virus Disease (EBOD) remains one of the most severe viral infections, with case fatality rates reaching 40% during the 2013-2016 West African outbreak that claimed over 11,000 lives (1). At this scale, durable protection isn’t optional.

Ebola Virus under the microscope

If you’ve followed vaccine development, you’ve probably noticed something counterintuitive. Shorter intervals between doses are not always better. SARS-CoV-2 mRNA vaccine studies have shown that extended intervals between doses enhance neutralizing antibody responses against multiple variants (5). Now, new research published in Nature Immunology suggests the same may be true for Ebola (1).

The findings challenge assumptions about how vaccine boosters should be timed and reveal something important about how our immune systems respond when given the space to do what they do best.

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The Hidden Costs of “Good Enough” Extraction

The translational oncology landscape has changed dramatically in the past two decades, and with it, the demands on the laboratories doing this work. Today’s translational oncology workflows require DNA and RNA from the same FFPE tissue section, cell-free DNA from large plasma volumes, and nucleic acids from heterogeneous batches of sample types processed in a single run. The analyte diversity has increased dramatically, and at the same time, the downstream assays interrogating those samples have grown more sensitive. The operational pressures have grown alongside the scientific ones. Labs are processing more samples than ever, but not with proportionally more staff. Same-day extraction to analysis is increasingly the expectation, not the exception. All of that change and complexity lands at the extraction step first.

Scientist holding up an FFPE tissue sample. Such samples are vital for nucleic acid extraction required for retroactive studies.

Extraction has long been treated as the step before the experiment, the part you complete before the real work begins. However, as these pressures on the translational laboratory grow, overlooking potential issues with extraction could be disastrous, particularly for labs working with limited, irreplaceable samples, because pre-analytical variability at the extraction step propagates through every downstream process. When extraction is overlooked, information in a sample can be lost and with it the insight into the biological question your downstream assay is asking.

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