Kristina Pleitt is a senior staff scientist at Thermo Fisher Scientific with 15 years of experience in biopharmaceutical process development and manufacturing. Her expertise includes downstream process development, process scale-up, integrated continuous processing, and clinical and commercial production, with a particular focus on advancing intensified downstream strategies that improve process efficiency, performance, and manufacturability.
Broadcast Date:
Time:
Analytical developers and downstream scientists are under constant pressure to improve process efficiency while maintaining product quality for monoclonal antibodies (mAbs). Yet, many platform purification processes still leave untapped opportunities for optimization, cost reduction, and simplification.
In this webinar, our speaker will explore a series of practical, chromatography-focused strategies designed to enhance mAb purification performance while reducing cost of goods (COGs). Using real-world examples and process scenarios, attendees will gain insight into:
Identifying opportunities to simplify traditional mAb purification workflows
Improving Protein A performance and lifetime to drive cost savings
Enabling efficient two-step downstream processes through better impurity management
Addressing difficult HCP and aggregate clearance challenges with advanced polishing strategies
A live Q&A session will follow the presentation, offering you a chance to engage directly with our expert and discuss how these approaches can be applied to your own processes.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Three reasons why DeepSeek’s new model matters
On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that handles large amounts of text more efficiently.
While the model remains open source, its performance matches leading closed-source rivals from Anthropic, OpenAI, and Google. It is also DeepSeek’s first release optimized Huawei’s Ascend chips—a key test of China’s dependence on Nvidia.
AI systems have already gained impressive mastery over the digital world, but the physical world remains humanity’s domain. As it turns out, building an AI that composes novels or code apps is far easier than developing one to fold laundry or navigate city streets. To bridge this gap, many researchers believe you need something called a world model.
Subscribers can watch an exclusive roundtable unveiling the technologies and trends on the list, with analysis from MIT Technology Review’s AI reporter Grace Huckins and executive editors Amy Nordrum and Niall Firth.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 China has blocked Meta’s $2 billion acquisition of AI startup Manus Regulators cited national security grounds. (WSJ $) + Beijing called the deal a “conspiratorial” attempt to hollow out its tech base. (FT $) + The country is tightening its grip on AI firms that try to leave. (TechCrunch) + The decision escalates China’s AI rivalry with the US. (Bloomberg $) + But there will be no winners in their competition. (MIT Technology Review)
2 Google is investing up to $40 billion in Anthropic In a deal valuing the AI firm at $350 billion. (CNBC) + The funding will support the firm’s growing computing needs. (TechCrunch) + Anthropic and OpenAI are fighting for compute capacity. (Axios)
3 President Trump just fired the entire National Science Board The NSF has played a crucial role in developing technology. (The Verge) + The move heightens fears over political interference in US science. (Nature)
4 Conspiracy theories about the Washington shooting are proliferating online Over 300,000 posts appeared on X using the keyword “staged.” (NYT $) + The theories are also swirling on Bluesky and Instagram. (Wired)
5 The AI compute crunch is starting to hit the broader economy. It’s affecting jobs, gadgets, and electricity prices. (404 Media) + The AI compute explosion is the tech story of our time. (MIT Technology Review)
6 Elon Musk says a new banking tool brings X close to a “super app” He’s pledged to launch the tool this month. (Bloomberg)
7 AI optimism is surging across Asia while US sentiment cools The divide could shape where adoption happens fastest. (Rest of World)
8 Apple is tying its new CEO’s ascent to its first foldable iPhone It wants to build the buzz around John Ternus. (Gizmodo)
9 Twelve firms are developing the Golden Dome’s space-based interceptors They’ve won contracts worth up to $3.2 billion. (Ars Technica)
10 NASA has shared promising results from Artemis II The spacecraft and rocket fared well. (Engadget)
Quote of the day
“Getting out the truth and establishing facts and reliable information takes time. But our audiences really don’t have that kind of patience.”
—Amanda Crawford, associate professor at the University of Connecticut, tells the NYT why conspiracy theories are gaining traction online.
One More Thing
MIRIAM MARTINCIC
Welcome to Kenya’s Great Carbon Valley: a bold new gamble to fight climate change
Kenya’s Great Rift Valley is home to five geothermal power stations, which harness clouds of steam to generate about a quarter of the country’s electricity. But some of the energy escapes into the atmosphere, while even more remains underground for lack of demand. That’s what brought Octavia Carbon here.
Last year, the startup began harnessing some of that excess energy to remove CO2 from the air. The company says the method is efficient, affordable, and—crucially—scalable. But the project also faces fierce opposition.
An oral medicine for hair loss successfully spurred hair growth in a late-stage trial, startup Veradermics announced Monday.
Veradermics assessed the pill in two ways: by how many hairs grew within a square centimeter of the scalp, on average, and by how satisfied participants were with the results. Over the course of six months, men who took the drug, known as VDPHL01, either once or twice daily had between 30 and 33 more hairs per square centimeter of scalp. Men in the placebo group grew approximately seven additional hairs.
Between 79% and 86% of men taking VDPHL01 said they saw improvement, along with between 72% and 84% of the clinical trial investigators — results that pleased Reid Waldman, a dermatologist turned Veradermics’ chief executive.
That’s right. If you have treatment-resistant depression, this could be the regular morning routine in your future. The hat would activate a blueberry-sized device implanted in your skull that sends a pulse of electricity into your brain.
This is Jacob Robinson’s vision — and it got closer to reality on Friday after the Food and Drug Administration approved a request from Robinson’s startup, Motif Neurotech, to start an initial feasibility trial to test the efficacy of their device in treating depression that hasn’t responded to other treatments. Scientists have been zapping brains to alleviate depression for decades through a method called transcranial magnetic stimulation, or TMS. Motif wants to do the same thing, but with a twist.
Data from a new study in Nature Neuroscience shows that the brain may be more mechanically connected to the body than previously appreciated. Using mice and computational simulations of fluid motion, the team identified a possible biological mechanism that helps explain why exercise benefits brain health. Specifically, they found that abdominal contractions compress blood vessels that are connected to the spinal cord and brain, which helps the organ move gently within the skull. This movement facilitates the flow of cerebrospinal fluid over the brain, potentially washing away neural waste and preventing the development of neurodegenerative disorders.
The work, which is described in a paper titled “Brain motion is driven by mechanical coupling with the abdomen,” builds on past studies exploring how sleep and neuron loss influence how and when cerebrospinal fluid flushes the brain, according to Patrick Drew, PhD, a professor of engineering science and mechanics, neurosurgery, biology, and biomedical engineering at Penn State University. Drew is the corresponding author on the study.
“Our research explains how just moving around might serve as an important physiological mechanism promoting brain health,” said Drew. The contraction of abdominal muscles to push blood from the abdomen into the spinal cord acts “just like in a hydraulic system” that puts pressure on the vertebral venous plexus, a network of veins that connect the abdominal cavity to the spinal cavity which causes the brain to move. Computational simulations show “that this gentle brain movement will drive fluid flow in and around the brain” removing harmful waste.
To view this mechanism in moving mice, the scientists used two-photon microscopy, which allows for high-definition imaging of living tissue, and microcomputed tomography, which supports high-resolution three-dimensional examination of whole organs. They observed the brains shifting in the moments before the mouse moved and right after their abdominal muscles tightened, anticipating further movement.
To ensure that the abdominal contractions were the reason for the observed shift rather than other movements, the scientists applied gentle and controlled pressure to the abdomens of anesthetized mice. They observed that the mice’s brains moved in response. “Importantly, the brain began moving back to its baseline position immediately upon relief of the abdominal pressure,” Drew said, suggesting “that abdominal pressure can rapidly and significantly alter the position of the brain within the skull.”
The next step was digging deeper into the fluid’s movement in the brain as well as assessing if the brain’s movement could induce fluid flow. For this task, members of the team developed various techniques to capture this information including conducting imaging experiments of living mice and generating computational simulations of fluid motion.
“Modeling fluid flow in and around the brain offers unique challenges because there are simultaneous, independent movements, as well as time-dependent, coupled movements,” explained Francesco Costanzo, PhD, a professor of engineering science and mechanics, biomedical engineering, mechanical engineering, and mathematics, who led the computational modeling aspects of the project. “Accounting for all of them requires accounting for the special physics that happens every time a fluid particle crosses one of the many membranes in the brain. So, we simplified it” using the analogy of a sponge for the brain. By simplifying it in this way, Costanzo explained, the team could model how fluid flows through a structure with varied spaces.
Sticking with the analogy, “we also thought of it as a dirty sponge—how do you clean a dirty sponge?” Costanzo continued. “You run it under a tap and squeeze it out. In our simulations, we were able to get a sense of how the brain moving from an abdominal contraction can help induce fluid flow over the brain to help clear waste products.”
Further studies are necessary to understand how this mechanism works in human bodies particularly how it cycle cerebrospinal fluid around the brain, and helps to protect against neurodegenerative disease. “This kind of motion is so small. It’s what’s generated when you walk or just contract your abdominal muscles, which you do when you engage in any physical behavior. It could make such a difference for your brain health,” Drew said. Overall, “our research shows that a little bit of motion is good, and it could be another reason why exercise is good for our brain health.”
Paris-based One Biosciences, an Institut Curie-backed startup, plans to set up, staff, and equip a high-complexity lab and computational analytics operation in Albany, NY, as its first U.S. location.
Empire State Development is supporting this expansion with up to $525,000 in performance-based Excelsior Jobs Program tax credits in exchange for the company’s job commitments, which anticipate 42 life science jobs and $18 million in investments over the next five years.
Officials at One Biosciences say the company will bring its proprietary technology to the first-of-its-kind hub in Albany to address the unmet clinical and scientific needs to characterize the tumor ecosystem by means of a single-cell profiling approach.
“We are excited to accelerate support of our pharma, biotech, and academic collaborators through our AI-driven single-cell technologies, which will ultimately benefit physicians and their patients,” added Vincent Miller, MD, executive chairman, One Biosciences. “The local Albany life sciences ecosystem gives us access to a community of like-minded researchers and physicians committed to leveraging technology to improve health and is an ideal location from where to serve the U.S. globally.”
“Life science research and development is vital to creating the treatments that help people heal, survive and live longer,” said New York governor Kathy Hochul. “Through our targeted efforts, we are working to ensure that cutting edge companies like One Biosciences not only grow here, but that the next generation of medical breakthroughs happen in New York State.”
A study headed by researchers at City of Hope and the University of California, Berkeley has found that physical and mechanical properties of normal human mammary epithelial cells can offer a “functional readout” of biological age and breast cancer susceptibility.
The team created a novel, high-throughput microfluidic platform that can assess women’s breast cancer risk at the cellular level. The mechano-node-pore sensing (mechano-NPS) platform, which the researchers claim is the first of its kind, squeezes individual breast epithelial cells, creating a taxing environment to measure how they deform, recover, and behave under stress.
Using the platform the researchers uncovered an unexpected insight, which is that breast cells appear to have a “mechanical age” separate from a person’s chronological age, demonstrated by how the cells physically respond to stress. For their study the team developed a machine learning classifier, MechanoAge, to estimate chronological age based on the mechanical phenotypes, and a biological age-based risk index, Mechano-RISQ.
“We learned that the older the mechanical age, as determined by how cells respond to being squeezed through our microfluidic device, the higher the risk for breast cancer,” explained Lydia Sohn, PhD, the Almy C. Maynard and Agnes Offield Maynard Chair in Mechanical Engineering at UC Berkeley. The researchers suggest that, as more than 90% of women lack a known genetic predisposition to or a family history of breast cancer, their innovative approach could fill a critical gap in risk assessment and save countless lives.
Sohn is co-senior author of the team’s published paper in eBioMedicine, titled “MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells,” in which they concluded, “Age-related biomechanical changes may represent a fundamental hallmark of cellular function, with distinct mechanical phenotypes underlying critical processes in aging, cancer, and potentially other diseases. Recognizing and utilizing these biomechanical markers could greatly enhance early detection, refine risk stratification, and improve targeted intervention strategies.”
Breast cancer is one of the most frequently diagnosed cancers worldwide and a leading cause of cancer-related mortality among women, the authors noted, and “… has long been the subject of efforts to improve risk stratification and early-detection strategies.”
About 6% of women who develop breast cancer carry known genetic mutations. But for women outside this group, risk is estimated indirectly based on population models or measurements like breast density. These approaches can both overestimate and underestimate women’s individual breast cancer risk, leading to over-screening, under-screening, unnecessary worry or missed warning signs. And despite significant progress in screening technologies and therapeutic interventions, accurately determining which individuals—particularly among those considered average risk—are most likely to develop breast cancer remains what the team calls “one of the most persistent challenges in oncology and public health.”
For these “ostensibly average-risk individuals,” the team added, “it remains difficult to identify those with latent risk that stems from cellular, molecular, and biophysical alterations that current models are not designed to capture.”
Researchers Mark LaBarge of City of Hope (right) and Lydia Sohn (left) UC Berkeley [City of Hope and UC Berkeley]
Currently, there is no non-genetic test available that can identify women at higher risk for breast cancer. A downside to screening mammograms is that they can catch cancer only once it has begun to grow. Co-senior author, Mark LaBarge, PhD, a professor in the Department of Population Sciences at City of Hope, said “For women with a known genetic risk factor for breast cancer, there are things you can do like follow a higher-risk screening protocol. For everybody else, you’re left wondering, ‘Am I at high risk?’”
Emerging evidence links cellular aging and biophysical alterations with cancer susceptibility. For their reported study the researchers used the mechano-NPS platform to profile primary human mammary epithelial cells (HMECs) from women of different ages and risk backgrounds. They also developed a machine learning algorithm that identifies and measures cells that show signs of accelerated aging, quantifying an individual breast cancer risk score.
In this type of mechano-node-pore sensing, an electrical current is measured across a liquid-filled channel, much like how current is measured across a wire. As cells pass through, they disrupt the current, generating measurements about the cells’ size and shape. By making parts of the channel very narrow, researchers squeeze cells, then measure how long it takes each cell to recover its normal shape.
Machine-learning algorithms developed by the researchers were then used to detect differences in cells from older and younger women. The researchers found that the physical properties of breast cells changed with age; cells from older women were stiffer and took longer to bounce back after being squeezed.
Then came a surprising finding: a subset of younger women had cells that behaved like they came from older women. These cells came from women with genetic mutations that put them at high risk of breast cancer. Researchers then refined the algorithm to assign a risk score based on all the mechanical and physical properties measured in the cells. This algorithm successfully identified women with known genetic risks. Next the team used it to compare cells from healthy women, women who had family history of breast cancer and cells taken from the healthy breast of women with breast cancer in the other breast. “Normal epithelial cells from women with germline mutations, strong family history of cancer, or contralateral breast cancer exhibit mechanically aged phenotypes despite normal histology,” the investigators stated. “Together with prior molecular and epigenetic studies, these findings support a model in which accelerated biological aging of mammary epithelia may underpin breast cancer susceptibility across genetic and non-genetic risk groups.”
Using the MechanoAge platform, researchers shifted the scientific lens to the cellular level, calculating risk by looking for physical changes in individual cells. “Mechanical phenotyping captures an integrative cellular state that reflects underlying molecular networks rather than single biomarkers,” the team noted. “Mechano-RISQ offers a proof of principle approach for identifying individuals at elevated risk of breast cancer, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of mammary epithelial cell aging.”
“With accuracy, we were able to figure out which women were at high risk of breast cancer and which women didn’t seem to be,” LaBarge said. “By translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors—not just risk estimates, but evidence drawn directly from their own cells.” In their paper the scientists further stated, “This approach could enable earlier, individualized risk stratification, particularly for women who lack identifiable high-risk mutations yet harbor susceptible tissue states.”
Importantly, the AI platform uses simple electronics that would be easy and affordable to replicate on a large scale. “Our team isn’t the first to measure the mechanical properties of cells; however, other approaches require advanced imaging technology that’s expensive, cumbersome and has limited availability,” said Sohn. “In contrast, MechanoAge uses computer chips that are simpler than an Apple Watch and ‘Radio Shack parts’ that are cheap and easy to assemble, potentially making the device highly scalable.”
While engineers study the aging of materials such as metals, concrete and polymers, this is the first time that mechanical age has been quantified in biological cells. The finding that cells have a “mechanical age” separate from the individual’s chronological age would not have been possible without MechanoAge.
This work grew out of more than 12 years of collaboration between the two labs, combining engineering innovation with cancer and aging biology. The long-term partnership enabled discoveries that neither group could have reached alone. “It’s a true collaboration. We’ve learned a lot from each other,” Sohn said. “In my view, this is what happens when you have a real collaboration that develops over a long time,” LaBarge added. “This result is not what we imagined at the beginning.”
A cellular-resolution molecular map details how Down syndrome alters human brain development before birth. The study analyzed more than 100,000 nuclei from human prenatal neocortex samples collected across 26 pre-genotyped donors during gestational weeks 13 to 23—the only window during which all the cortical neurons a person will carry for their entire life are generated. The findings suggest that Down syndrome disrupts the developmental sequence of that process, creating shifts that may help explain later differences in cognition, learning, and sensory processing.
“There’s a new level of detail here that had never existed before,” said Luis de la Torre-Ubieta, PhD, an assistant professor of psychiatry and biobehavioral sciences at UCLA and a member of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research. “For the first time, we can really try to understand systematically what’s going on in the developing brain of individuals with Down syndrome.”
“No one had looked at the developing human brain in Down syndrome directly using single-cell genomics,” he continued.
The Down syndrome research field has historically focused on two areas: the adult brain and the disorder’s connection to neurodegeneration. What remained largely unexamined, despite clear indicators that Down syndrome is a developmental condition, was how the condition shapes the developing brain itself.
The development of the prenatal neocortex typically follows a tightly orchestrated sequence. Progenitor cells must first divide repeatedly to expand their own pool, building up a sufficient base for all future neurons. Only then do they begin differentiating into neurons, starting with deep-layer cell types and progressing toward upper-layer cells in a carefully timed order.
The study found that progenitor cells appear to rush prematurely into neuron production, depleting their own pool and skewing the balance of neuron types generated. Specifically, the researchers observed a relative increase in upper-layer intratelencephalic neurons and a reduction in deep-layer corticothalamic neurons.
Those two cell populations play fundamentally different roles: CT neurons project outward from the cortex—connecting to brain structures and to the spinal cord to govern sensation and movement; IT neurons wire within the cortex, connecting the two hemispheres and contributing to information processing. This finding offers a new hypothesis for how early developmental changes might contribute to the cognitive profile of the condition.
The finding also offers a new answer to a longstanding question in the field: Why do people with Down syndrome tend to have smaller brains? Earlier theories centered on elevated rates of cell death. The current study found less evidence of widespread neuronal death and instead points to the depletion of the progenitor pool.
The study employed paired single-nucleus multiomics to reconstruct not just a snapshot of which cells are present, but the regulatory programs that guide cell fate—and how those programs are disrupted in Down syndrome. Systems-level approaches also led them to uncover alterations in cell metabolism and changes in how the vasculature interacts with the developing nervous system, both of which could speed up neuron production.
The study’s significance extends beyond Down syndrome. The researchers specifically tested for overlap between the molecular disruptions they identified and the genetic risk signatures associated with other neurodevelopmental and neuropsychiatric conditions, including autism, epilepsy, and developmental delay. They found substantial convergence, particularly in the gene-regulatory networks governing the specification of IT versus CT neurons.
“Down syndrome could be a model to understand intellectual disability and neuropsychiatric disorders more broadly,” de la Torre-Ubieta said. “Also to uncover the shared biology underlying these conditions—because the mechanisms are often still unknown.”
The publication coincides with a companion paper from researchers at the University of Wisconsin-Madison, appearing in the same issue of Science. While the UCLA study focuses on the prenatal period, the Wisconsin team examined the postnatal brain, studying Down syndrome between approximately one and five years of age.
Together, the two papers provide a continuous molecular view of Down syndrome brain development from mid-gestation through infancy—a resource that did not previously exist and that the researchers expect will serve as a reference for their field for years to come.
While the researchers are careful to emphasize that the findings do not point to a near-term clinical application, the study provides the clearest picture yet of the cellular and molecular events that distinguish the Down syndrome brain during development, and a framework for identifying future therapeutic targets.
![Researchers Mark LaBarge of City of Hope (right) and Lydia Sohn (left) UC Berkeley [City of Hope and UC Berkeley]](https://www.genengnews.com/wp-content/uploads/2026/04/Low-Res_Sohn-LaBarge1-300x169.jpg)