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.”
Some GEN editors were in sunny San Diego covering the hottest research, trends, and products from the American Association for Cancer Research meeting. We kick things off with news from America’s Finest City, particularly around the growing role of AI in oncology. Then we dive into two new research studies. In the first, scientists used CRISPR to identify genes in primary CD4+ T cells that promote or restrict HIV infection. The second study described engineered implantable liver constructs that could eventually serve as a stopgap for patients waiting for donor transplants. Finally, the acquisitions keep coming as Eli Lilly scoops up CAR T cell therapy developer Kelonia for $7B. Also, Revolution Medicines has shared some impressive data from a Phase III trial of its pancreatic cancer drug.
Listed below are links to the GEN stories referenced in this episode of Touching Base:
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.
We’re in a new era of AI-driven scams
When ChatGPT was released in late 2022, it showed how easily generative AI could create human-like text. This quickly caught the eye of cybercriminals, who began using LLMs to compose malicious emails. Since then, they’ve adopted AI for everything from turbocharged phishing and hyperrealistic deepfakes to automated vulnerability scans.
Many organizations are now struggling to cope with the sheer volume of cyberattacks. AI is making them faster, cheaper, and easier to carry out, a problem set to worsen as more cybercriminals adopt these tools—and their capabilities improve. Read the full story on how AI is reshaping cybercrime.
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.
Healthcare AI is here. We don’t know if it actually helps patients.
Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer—here’s why.
—Jessica Hamzelou
The story is from The Checkup, our weekly newsletter that gives you the latest from the worlds of health and biotech. Sign up to receive it in your inbox every Thursday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 DeepSeek has unveiled its long-awaited new AI model The Chinese company has just launched preview versions of DeepSeek-V4. (CNN) +It says V4 is the most powerful open-source platform. (Bloomberg $) + And rivals top closed-source models from OpenAI and DeepMind. (SCMP) + The model is adapted for Huawei chip technology. (Reuters $)
2 More countries are curbing children’s social media access Norway is set to enforce the latest ban. (Reuters $) + The Philippines could follow soon. (Bloomberg $) + Americans are pushing to get AI out of schools. (The New Yorker)
3 The US has accused China of mass AI theft as tensions rise A White House memo claims Chinese firms are exploiting American models. (BBC) + Beijing calls the accusations “slander.” (Ars Technica)
4 OpenAI set itself apart from Anthropic by widely releasing its new model It’s releasing GPT-5.5 to all ChatGPT users, despite cybersecurity concerns. (NYT $) + OpenAI says the new model is better at coding and more efficient. (The Verge)
5 Meta is cutting 10% of jobs to offset AI spending Roughly 8,000 layoffs are set to be announced on May 20. (QZ) + Anti-AI protests are growing. (MIT Technology Review)
6 Palantir is facing a backlash from employees Thanks to its work with ICE and the Trump administration. (Wired $) + Surveillance tech is reshaping the fight for privacy. (MIT Technology Review)
7 The era of free access to advanced AI is coming to an end AI labs are under mounting pressure to start turning profits. (The Verge)
8 Elon Musk’s feud with Sam Altman is heading to court The case has already revealed several unflattering secrets. (WP $)
9 A new movement is encouraging people to ditch their smartphones for a month “Month Offline” is like a Dry January for smartphones. (The Atlantic)
10 Spotify has revealed its most-streamed music of the last 20 years Featuring Taylor Swift, Bad Bunny, and The Weeknd. (Gizmodo)
Quote of the day
“We want a childhood where children get to be children. Play, friendships, and everyday life must not be taken over by algorithms and screens.”
—Norwegian Prime Minister Jonas Gahr Store announces age restrictions for social media.
One More Thing
NASA/JPL-CALTECH VIA WIKIMEDIA COMMONS; CRAFT NASA/JPL-CALTECH/SWRI/MSSS; IMAGE PROCESSING: KEVIN M. GILL
The search for extraterrestrial life is targeting Jupiter’s icy moon Europa
As astronomers have discovered more about Europa over the past few decades, Jupiter’s fourth-largest moon has excited planetary scientists interested in the geophysics of alien worlds.
All that water and energy—and hints of elements essential for building organic molecules —point to an extraordinary possibility. In the depths of its ocean, or perhaps crowded in subsurface lakes or below icy surface vents, Jupiter’s big, bright moon could host life.
Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients?
We don’t yet have a good answer.
That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto, argue in a paper published in the journal Nature Medicine this week.
Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies, they have also begun rapidly deploying them.
The problem is that many providers aren’t rigorously assessing how well they actually work.
Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients, then transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers.
A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. Early studies support these anecdotes and suggest that the tools can reduce clinician burnout.
That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.”
The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient.
But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest X-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately: What will this mean for those patients?
The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers.
Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says.
In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias.
The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens.
“I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.”
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Background: The aging and caregiving population is becoming increasingly diverse in the United States, leading to a growing need for culturally adapted interventions to address the unique needs of underrepresented groups, such as Asian Americans. However, interventions targeting Asian Americans and exploring cultural adaptation strategies remain limited in dementia caregiving research. Objective: This study aimed to describe the cultural adaptation process of an evidence-based intervention for Chinese and Korean American dementia caregivers, called the New York University Caregiver Intervention–Enhanced Support. Methods: We conducted a deductive content analysis and categorized our adaptation strategies into 5 elements: content, context, relationship fidelity and core elements, engagement, and cultural competence. Timing and types of responses to each adaptation strategy were also observed. Two authors conducted the initial analysis, and additional team members finalized the synthesis through discussion. The Template for Intervention Description and Replication (TIDieR) checklist was used to guide the methodological rigor. Results: Twenty-four major adaptations were identified and categorized. For content, we translated materials, used culturally relevant terms, incorporated ethnic-specific surveys and resources, created social media support groups on platforms widely used by the targeted population, and extended the time allocated to complete the 6 counseling sessions. Context adaptation included expanding the range of individuals eligible for family counseling sessions to include fictive kin, using online and social media apps for communication, cultural matching and training of staff, and partnerships with relevant community organizations. Relationship fidelity and core elements involved consulting with community experts, conducting focus group interviews with caregivers, having regular meetings with the developer of the original intervention and an experienced New York University Caregiver Intervention–Enhanced Support clinician as well as experts in Chinese and Korean culture, and continuing regular counseling supervision. To enhance engagement, we provided clear explanations of the study procedure, which emphasized the benefits in participants’ native languages and matched participants with social workers who shared the same cultural backgrounds. We also used a step-by-step contact approach and prolonged communication, explained staff roles to build rapport, and offered participant compensation. Finally, cultural competence was reflected in tailoring counseling techniques with respect for cultural beliefs, the use of euphemistic language for taboo subjects, and culturally appropriate refreshments to show respect and build interpersonal relationships. Conclusions: We systematically adjusted a counseling-based intervention, an approach less familiar among Asian Americans, to fit the cultural characteristics of the target population. A contribution of this study is using an integrated, theory-driven approach that combines 2 cultural adaptation frameworks while also capturing real-time adaptations informed by external feedback and self-reflection. This work provides a practical model for adapting evidence-based interventions to serve Chinese and Korean American dementia caregivers and may inform future adaptations for other East Asian populations. Trial Registration: ClinicalTrial.gov NCT05461495; https://clinicaltrials.gov/study/NCT05461495
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/e08f825f332b5b67120fb0f3ebf4f04c" />
WASHINGTON — President Trump heralded a drug pricing agreement with Regeneron on Thursday, closing the last of 17 deals initially sought by the White House last year.
Regeneron, as part of the private deal, will reduce prices on drugs to Medicaid, provide cholesterol medicine Praluent on TrumpRx for $225, and invest $27 billion in drug development in the United States.
On the same day, Regeneron also announced Food and Drug Administration approval of Otarmeni, the first gene therapy to be greenlit under the agency’s new National Priority Voucher program. In early trials, the drug provided modest hearing gains for people with a rare type of hearing loss, though its development has received pushback from parts of the Deaf community. Regeneron plans to offer the drug at no cost to American patients.
A genetic analysis of a large U.S. clinical trial suggests that vitamin D supplementation may reduce the risk of progression from prediabetes to type 2 diabetes, but only for those people who harbor specific variants of the vitamin D receptor gene. The study, led by researchers at Tufts University and published in JAMA Network Open, found that daily high-dose vitamin D lowered diabetes risk by 19% in participants with certain genotypes, opening the possibility of using vitamin D as a diabetes prevention strategy.
The new findings build on data from the Vitamin D and Type 2 Diabetes (D2d) clinical trial, a multi-site randomized study that enrolled more than 2,000 U.S. adults with prediabetes. Study participants were assigned to receive either 4,000 IU of vitamin D3 daily or a placebo. The subjects were then followed for a median of 2.5 years to assess progression to diabetes. The original trial did not show a statistically significant reduction in diabetes risk across all participants.
“But the D2d results raised an important question: Could vitamin D still benefit some people?” said lead author Bess Dawson-Hughes, MD, a senior scientist at the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University. “Diabetes has so many serious complications that develop slowly over years. If we can delay the time period that an individual will spend living with diabetes, we can stop some of those harmful side effects or lessen their severity.”
In their follow-on research, the Tufts noted that subsequent analysis of the D2d trial data showed that outcomes varied based on achieved blood levels of vitamin D in participants. The new study also found a genetic link to those who had improved outcomes.
To explore the role genetics might play, the investigators conducted a post hoc analysis of 2,098 D2d participants who consented to genetic testing. They focused on three common polymorphisms in the vitamin D receptor (VDR) gene: ApaI, BsmI, and FokI. The researchers first examined how vitamin D levels correlated with diabetes risk across genotypes, then evaluated how genetic variants influenced response to supplementation.
The data showed that the ApaI polymorphism is a key determinant of response. Participants with the AA genotype, which was about 30% of the cohort, did not experience a reduction in diabetes risk with vitamin D supplementation. By comparison, those with the AC or CC genotypes, the remaining 70% of participants, showed a 19% lower risk of developing diabetes when treated with vitamin D compared with placebo.
The biological basis for this effect is linked to the role the VDR gene plays in pancreatic β cells, where it influences insulin secretion and glucose regulation. Variations in the receptor may alter how effectively vitamin D exerts these effects, explaining why some individuals benefit from supplementation while others do not.
Earlier research has suggested there is a connection between vitamin D and diabetes risk. In earlier analyses of the D2d trial, participants who maintained higher blood levels of vitamin D experienced substantial reductions in diabetes incidence. These findings were supported by meta-analyses and observational studies, including research from the UK Biobank, which found that genetic variation in VDR could modify its activity.
“We hypothesized that VDR gene variants modify the association between achieved intratrial 25-hydroxyvitamin D (25(OH)D) level and diabetes risk and may modify the effect of vitamin D3 supplementation on the risk of developing diabetes,” the researchers wrote. 25(OH)D is the main form of vitamin D circulating in the blood.
The current study broadens knowledge on the role vitamin D can play in diabetes prevention by identifying the specific polymorphisms at play. The overlap between ApaI and BsmI variants provides further evidence of the role of VDR genetics, although the researchers noted that ApaI alone may be sufficient to identify likely responders.
“This genetic association analysis of the D2d study suggests that genetic variation in the VDR, specifically the ApaI polymorphism, is associated with diabetes risk at higher intratrial 25(OH)D levels and is associated with response to 4000 IU/d of vitamin D3 supplementation among adults with prediabetes,” the researchers wrote.
The implications for clinical care include the potential use of genetic testing to guide preventive treatment. A single test for the ApaI polymorphism could help identify patients with prediabetes who are most likely to benefit from higher-dose vitamin D supplementation.
While the results have established a link between variations in the VDR gene and diabetes development, the research noted that the study was not designed to assess the mechanisms underlying the genetic effects. Further, its sample size limited subgroup analyses by race and ethnicity.
“Our findings suggest we may eventually be able to identify which patients with prediabetes are most likely to benefit from additional vitamin D supplementation,” Dawson-Hughes said. “In principle, this could involve a single, relatively inexpensive genetic test.”
Next steps in this line of research include replicating the findings in independent cohorts and conducting prospective trials designed to test genotype-guided supplementation strategies.
Heart disease and cancer are the leading causes of death in the United States, but it is rare that cancer makes its way to the heart.
It’s an observation that clinicians have been grateful for, though largely unable to explain. But in a paper published Thursday in Science, researchers propose one potential explanation: The constant pressure that the organ is under from beating thousands of times a day and pushing gallons of blood creates an environment that is hostile to cancers. The study, which was conducted in mice, is preliminary, but outside experts said it points to potential new approaches for cancer treatments.
The days of “Just Say No,” it seems, are long gone.
Over the weekend, President Trump signed an executive order to increase the availability of certain psychedelics as treatments for mental health conditions, ordering that $50 million be spent, and that the Food and Drug Administration fast-track reviews to usher in their approval. At one point, the president joked to the motley assembly of administration officials, a former Navy SEAL, and the podcaster Joe Rogan: “Can I have some, please?”
On Wednesday, the Trump administration announced it had downgraded medical marijuana from the highest tier of controlled substances, and was pushing the Drug Enforcement Administration to do the same for recreational marijuana.
The president’s lenient tack on some mind-altering drugs ushers in a new world of right-wing drug policy. While the administration has emphasized hardline, militaristic tactics when it comes to fentanyl, its recent actions on “softer” drugs could represent a new era not just for Republican politics but also for American drug policy writ large.
“With this imminent move, we are now confronted with the most pro-drug administration in our history,” Kevin Sabet, the CEO of the anti-legalization advocacy group Smart Approaches to Marijuana, said in a statement. “Policy is now being dictated by marijuana CEOs, psychedelics investors, and podcasters in active addiction — it is a travesty and injustice to the American people of unprecedented proportions. The marijuana industry is the new Big Tobacco, and President Trump is welcoming them to the homes of families across this country with open arms.”
![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)
