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AI-CURA Automates Genetic Variant Classification

A new AI framework can classify hundreds of genetic variants as accurately as a human expert in a fraction of the time, research suggests.

Combining AI-assisted CURAtor (AI-CURA) with the latest large language models (LLMs) could streamline the diagnosis of rare genetic diseases.

The workflow system, described in Science Translational Medicine, performed as well as clinical experts in classifying 150 variants, while adhering to complex expert guidelines.

It was also able to categorize 150 variants with conflicting classifications.

“This study pushes the boundaries of fully automated variant interpretation,” commented senior journal editor Catherine Charneski, PhD, from the University of Bath.

Whole genome sequencing (WGM) has proven pivotal in ending the prolonged diagnostic odyssey of many patients with rare genetic disorders.

To manage the huge number of variants identified through WGS, attempts have been made by expert associations and working groups to establish guidelines and recommendations.

These now form a widely adopted classification system that categorizes variant-associated evidence into distinct rule-based categories.

But while some rules can be readily automated, most evidence still needs manual interpretation of the literature. This requires variant curators to possess broad knowledge across various aspects of molecular biology and genetics, as well as a deep understanding of expert recommendations to accurately interpret and score variants.

Wei Ma, PhD, and colleagues from the Hong Kong Genome Project (HKGP) therefore developed AI-CURA, a fully automated framework for variant classification that integrates LLMs to handle both literature-independent and literature-dependent evidence.

The tool integrates the assessment of evidence for non–literature-based criteria, which can be automated using standard bioinformatic tools, with a separate LLM-supported assessment of literature-based evidence.

Two state-of-the-art LLMs—DeepSeek-R1 and o3-mini-high—were tested for their ability to summarize literature-derived evidence relevant to variant classification.

The team found that the open-source DeepSeek-R1 outperformed o3-minihigh and had high sensitivity and 100% specificity in interpreting rules from the American College of Medical Genetics and Genomics (ACMG) that require understanding literature-based evidence.

They then tested it using 150 variants curated by ClinGen experts, with 150 expert-curated variants and 150 variants with conflicting classifications from the Clinical Genome Resource.

The open-source LLM DeepSeek-R1 showed high concordance with ClinGen experts in establishing a final diagnosis.

“In this study, DeepSeek-R1 demonstrated high accuracy (89.3 to 100%) in determining the application of seven literature-dependent ACMG rules,” the authors reported.

They added: “Our use of LLMs substantially streamlined the variant analysis and interpretation process. LLMs can finish summarizing the literature evidence in minutes.

“In comparison, curators in the HKGP typically spend around four hours per patient on WGS curation, with most of this time dedicated to reviewing literature.”

The post AI-CURA Automates Genetic Variant Classification appeared first on Inside Precision Medicine.

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Social Determinants of Health Improve Disease Risk Prediction Beyond Genetics Alone

A study by researchers at the Icahn School of Medicine at Mount Sinai has found that social determinants of health—including environmental conditions, health behaviors, access to resources, and social well-being—can contribute as much as or more than genetic risk in predicting several common diseases. The research, published in The American Journal of Human Genetics, showed that incorporating social, behavioral, and environmental information into disease-risk models improved prediction when incorporated with genetic information for conditions including asthma, chronic kidney disease, coronary heart disease, high cholesterol, breast cancer, and prostate cancer.

“Genes are an important part of the equation, but they do not determine destiny,” said senior author Samira Asgari, PhD, an assistant professor of genetics and genomic sciences at Mount Sinai. “We found that the circumstances of people’s lives—their environments, behaviors, and social experiences—can contribute as much as genetics to predicting disease risk. To truly understand health, we have to look at the whole person, not just their DNA.”

According to the researchers, complex diseases arise through the interaction of genetic predisposition with environmental, behavioral, and social influences, yet these factors are often studied in isolation. Existing genetic models often rely on polygenic risk scores, while epidemiological approaches focus on lifestyle, environmental exposures, or social factors independently. The researchers sought to bridge that gap by integrating both types of data into a single risk prediction framework.

To conduct the study, the team analyzed data from 413,457 participants in the All of Us Research Program, a nationwide research effort in the U.S. supported by the National Institutes of Health. The team combined genetic information, electronic health records, and survey responses, with more than 100 environmental, behavioral, and social variables were evaluated, to create a broad picture of the different factors that may influence health.

Rather than selecting a limited number of known social risk factors in advance for their survey, the researchers used a statistical technique called multiple correspondence analysis, or MCA. The approach converted more than 100 categorical social, environmental, and behavioral measures into low-dimensional representations that helped identify patterns of non-genetic risk.

The choice to use MCA distinguished the study from many previous approaches. Past methods often have depended on selecting a small set of established risk factors or using statistical procedures that prioritize only the strongest predictors. By contrast, MCA identifies patterns across many correlated variables simultaneously, allowing researchers to examine broader social and environmental variables without assuming beforehand which factors have the most influence on health.

The analysis found known contributors to disease risk such as economic status and smoking, but also identified factors that receive less attention in published studies, including loneliness and spirituality. First author Abhijith Biji, a PhD candidate at Icahn School of Medicine, said the data showing associations involving loneliness was particularly notable.

“Some risk factors, such as smoking, have been studied extensively for decades,” Biji said. “What is especially intriguing is that we also observed associations involving factors like loneliness. Understanding how these experiences may become biologically embedded could open new avenues for research and ultimately improve our understanding of disease.”

When the researchers incorporated the MCA-derived measures into prediction models alongside demographic information and polygenic risk scores, predictive performance improved across all six diseases studied. For four of the six diseases, the gains from the MCA-based measures exceeded those attributable to polygenic risk scores.

The findings also suggested that genetic and non-genetic influences generally act independently rather than modifying one another. The researchers found little evidence for broad gene-environment interactions. Instead, inherited genetic risk and social, behavioral, and environmental context appeared to contribute additively to disease risk.

“This additive relationship suggests that interventions targeting social and behavioral factors can reduce disease risk regardless of genetic background, offering hope for broadly applicable public-health strategies,” the researchers wrote.

The researchers noted that the study does not establish causation. Because many survey responses were collected at a single point in time and some exposures may have occurred after disease onset, the findings should be considered as contributions to disease-risk prediction rather than proof that specific factors cause disease.

Building on this work, the team next will seek to integrate social determinants of health with additional biological measures and look mechanisms that may directly connect social experiences to disease. The investigators will also bring in longitudinal data, harmonize survey instruments across cohorts, and integrating other data types to better understand how environmental, behavioral, and social factors influence disease development and interact with biological processes.

“Our goal is to build a more complete understanding of health and disease,” Asgari said. “By combining genetics with social and environmental context, we can move toward risk models that better reflect the realities of people’s lives and help advance more personalized approaches to health.”

The post Social Determinants of Health Improve Disease Risk Prediction Beyond Genetics Alone appeared first on Inside Precision Medicine.

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Judge temporarily blocks subpoenas in criminal probe of transgender care at New York hospitals

NEW YORK — A judge temporarily blocked federal prosecutors in Texas from getting access to the medical records of transgender patients treated at New York hospitals on Wednesday, saying they were part of an improper government effort to “demonize and eradicate an entire population of transgender” people.

Judge Katherine Polk Failla ruled a day after hearing oral arguments in Manhattan, calling the government’s pursuit of the most sensitive medical records of a “uniquely vulnerable group” of patients treated over a six-year period to be “most egregious” and unconstitutional.

Read the rest…

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Catheter-Based OCT Imaging Shows Promise for Noninvasive Endometrial Cancer Diagnosis

A research team at Washington University in St. Louis has developed a catheter-based optical imaging method that could be used as an “optical biopsy” for detecting endometrial cancer and its precancerous lesions. The approach, described in the journal npj Imaging, uses three-dimensional optical coherence tomography (OCT) imaging combined with a machine learning algorithm which examines and analyzes the entire endometrial cavity to identify tissue changes associated with endometrial intraepithelial neoplasia (EIN) and endometrial cancer.

“Current endometrial biopsy practice has an estimated false-negative rate of about 10% (approximately 90% sensitivity), largely due to sampling limitations and interpretive variability,” said senior investigator Quing Zhu, PhD, a professor of engineering at Washington University. “With our three-dimensional OCT imaging system combined with machine learning, we can image the entire endometrial cavity in two to three seconds and may have a potential to achieve higher sensitivity than random biopsy sampling.”

Endometrial cancer is the most common gynecologic malignancy in the United States, with estimated 69,000 cases projected to be diagnosed in 2025. As with most cancers, early detection has a significant impact on treatment outcomes with five-year survival rates between 80% and 90% when it is diagnosed at stage I.

Existing diagnostic tools have limitations that can impact early and accurate diagnosis. For instance, transvaginal ultrasound is ineffective for early EC, while endometrial biopsy has a 10% false-negative rate due to sampling and interpretive variability.” Although hysteroscopy allows direct visualization of the uterine cavity, it does not provide information about subsurface tissue architecture.

In an interview with Inside Precision Medicine, Zhu said the most widely used diagnostic approaches can miss cancers or depend heavily on operator skill. She noted that the low resolution of transvaginal ultrasound limits detection of early disease, while operative hysteroscopy requires cervical dilation and carries procedural risks. Endometrial biopsy, she added, can miss cancers that occupy less than half of the endometrial cavity surface.

The new approach developed by Zhu and team uses OCT, a light-based imaging technology that creates high-resolution cross-sectional images of tissue. This imaging method uses low-coherence interferometry to measure the echo time delay and intensity of backscattered light, producing real-time images of tissue microstructure with micrometer-scale resolution with tissues penetration depths of approximately one to two millimeters.

To create a method to comprehensively image the endometrium the WashU team developed a custom 3.1-millimeter catheter. Zhu said that the catheter rotates within the endometrial cavity at roughly 600 revolutions per minute while being pulled back automatically at a constant speed. Depending on uterine size, a 3- to 5-centimeter segment of the cavity can be imaged in approximately two to three minutes. The resulting volumetric scans provide three-dimensional views of tissue structure and optical properties throughout the cavity. The team then applied computational analysis to identify functional, structural, and radiomic features based on OCT intensity and scattering images.

To test this OCT/machine learning approach, the researchers evaluated the technology on 57 freshly excised hysterectomy specimens representing a range of conditions, including normal endometrium, benign abnormalities, EIN, and endometrial cancer. OCT identified 34 specimens that contained either high-risk precancerous lesions or early-stage cancers.

The OCT images revealed differences among normal endometrium, benign endometrium, high-risk precancerous lesions, and cancers at different stages. This new method attained an exploratory sensitivity of 94% and specificity of 87%. A cross-validated logistic regression classifier produced sensitivity of 91% and specificity of 83%.

“These findings support catheter-based 3D OCT as a promising noninvasive optical biopsy approach to improve detection of endometrial cancer,” the researchers wrote in the abstract.

The work builds on earlier investigations of OCT in endometrial disease. Previous research had shown that OCT could distinguish endometrial pathologies, but in those studies the imaging was slow or limited to two-dimensional analysis. “This study is the first to combine catheter-based 3D OCT imaging with functional, structural and radiomic feature analysis to assess the endometrial cavity,” the researchers wrote.

Researchers believe the technology could improve patient care by reducing dependence on repeated tissue biopsies. In the introduction, they wrote that “a real-time, noninvasive, high-resolution modality for subsurface imaging could improve diagnostic accuracy, reduce unnecessary biopsies, and support fertility-sparing management.” Such a tool could be particularly useful for women undergoing serial monitoring while receiving hormone-based treatment.

The investigators describe the method as an optical biopsy because it provides diagnostic information without requiring removal of tissue. “Unlike traditional tissue biopsy, it does not require painful physical tissue samples,” Zhu told Inside Precision Medicine.

The technology is still in an early stage of development. Zhu said future development will require a forward-viewing catheter to improve imaging of the uterine fundus and developing methods for faster data acquisition.

Zhu is now looking to secure funding and begin studies in patients to establish in vivo feasibility and to eventually move the technology into clinical trials.

The post Catheter-Based OCT Imaging Shows Promise for Noninvasive Endometrial Cancer Diagnosis appeared first on Inside Precision Medicine.

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Labcorp Launches Expanded Test for Severe Chemotherapy Side Effects

In step with the trend toward more selective use of chemotherapy, Labcorp has launched an expanded version of its DPYD Genotype test, which helps identify cancer patients at increased risk for severe side effects from fluoropyrimidine-based drugs. The test is now the only offering, from a national laboratory provider, that detects all Tier 1 and Tier 2 DPYD variant alleles recommended to be tested for by the Association for Molecular Pathology.

The DPYD gene encodes the enzyme DPD, which metabolizes more than 80% of 5-FU. Patients with reduced or absent DPD activity can experience serious, potentially life-threatening side effects, including diarrhea, neutropenia, and neurotoxicity when given fluoropyrimidines 5-FU or capecitabine.

Such pharmacogenomic (PGx) testing is used to help identify patients who are at greater risk for adverse drug reactions from certain treatments based on their genetic makeup. Once a chemotherapy regimen is recommended, PGx testing can help guide treatment decisions and reduce the risk of toxicity. DPYD testing is one of the most well-established examples of PGx. 

“Pharmacogenomic testing is typically incorporated early in the treatment process, once a chemotherapy plan has been established, to give clinicians information about a patient’s inherited ability to metabolize certain medications or respond to them,” Annette Taylor, PhD, MS, told Inside Precision Medicine. She is associate vice president, strategic director, pharmacogenomics, Labcorp.

Fluoropyrimidines are one of the most widely used chemotherapy agents for colorectal, pancreatic, gastrointestinal, breast, and head and neck cancers. However, up to 9% of cancer patients carry DPYD variants that can negatively affect their ability to break down such drugs. That variant contributes to an estimated 1,300 deaths in the U.S. each year. By identifying the full range of Tier 1 and Tier 2 DPYD variants, the new test helps reduce the risk that vulnerable patients will receive the treatment.

 “Advances in pharmacogenomics are reshaping cancer care,” said Marcia Eisenberg, PhD, chief scientific officer at Labcorp. “Our expanded DPYD test identifies patients at risk for severe toxicity before treatment begins, supporting safer, more personalized care.”

The U.S. Food and Drug Administration (FDA) recently updated its product labeling for 5-FU and capecitabine, which includes a Boxed Warning about the risk of severe adverse reactions or death in patients with complete DPD deficiency. The agency also advises testing for DPYD variants before treatment with 5-FU or capecitabine unless immediate treatment is necessary and recommends avoiding use of these drugs in patients with certain homozygous or compound heterozygous DPYD variants associated with complete DPD deficiency. 

In addition, recent updates to National Comprehensive Cancer Network (NCCN) guidelines for colon cancer and other relevant indications reference these Boxed Warnings and the recommendation for DPYD testing. Further, Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines recommend adjusting or avoiding treatment based on a patient’s DPYD metabolizer status as determined by DPYD testing.

“There are other pharmacogenomic tests available beyond DPYD testing that can provide clinically actionable information for certain therapies and treatment settings. Common tests include UGT1A1 genotyping for irinotecan and TPMT/NUDT15 testing for thiopurines,” Taylor said.

Other tests offered by Labcorp include the UGT1A1 Irinotecan Toxicity test, which helps guide chemotherapy with irinotecan, commonly used for metastatic colon and rectal cancer.  Labcorp also offers the TPMT and NUDT15 Genotyping test, useful for optimizing therapy with thiopurine drugs (azathioprine, mercaptopurine, and thioguanine). 

The post Labcorp Launches Expanded Test for Severe Chemotherapy Side Effects appeared first on Inside Precision Medicine.

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Self-Renewing Blood Progenitors Could Expand the Reach of Cancer Cell Therapy

A team of researchers at the University of Southern California has developed a method to expand a key population of blood-forming progenitor cells in the laboratory while preserving their identity and function, overcoming a longstanding barrier in hematology and opening new possibilities for cancer immunotherapy.

The study, published in Cell, describes how investigators generated large numbers of granulocyte-monocyte progenitors (GMPs)—immune precursor cells that give rise to macrophages, monocytes, and neutrophils—using a culture system that enables these cells to self-renew in vitro. The work not only challenges conventional assumptions about hematopoietic progenitor biology but also provides a potentially scalable platform for engineering immune cells designed to attack cancer.

“This is the first time we can pick single progenitor cells and expand them in large quantities without differentiation,” said senior author Qi-Long Ying, PhD, professor of stem cell biology at USC. “They retain the original identity.”

The achievement addresses a problem that has frustrated researchers for decades. Although hematopoietic stem cells and their descendants have been extensively studied, scientists have struggled to maintain specific blood-forming progenitor populations in culture over long periods without the cells differentiating into mature immune cells.

Ying said the project grew out of his laboratory’s experience working with embryonic stem cells, which can be maintained indefinitely in culture. He reasoned that if embryonic stem cells could be expanded long term, similar approaches might eventually be developed for stem and progenitor cells found in bone marrow.

After years of experimentation, the researchers established culture conditions that selectively support GMPs, a progenitor population responsible for generating several innate immune cell types involved in recognizing and destroying abnormal cells.

Challenging a longstanding paradigm

According to co-author Daniel McKim, PhD, one of the most surprising findings was not simply the ability to expand GMPs but the demonstration that these progenitor cells could undergo extensive self-renewal in vitro.

“The prevailing theory has been that hematopoietic progenitors are short-lived intermediate cells that are incapable of self-renewal,” McKim said. “One of the distinctions between hematopoietic stem cells and progenitors is the belief that these cells are not able to self-renew. What we found is that under the right conditions, they can.”

The researchers emphasize that the self-renewal phenomenon occurs in culture. Once transplanted back into animals, the GMPs behave like normal progenitor cells, producing downstream immune populations before eventually becoming depleted.

Still, the ability to generate vast numbers of GMPs in vitro represents a significant technical advance. The investigators report expansion levels approaching eight orders of magnitude while maintaining the cells’ progenitor characteristics.

Building better cell therapies

Beyond the basic biology, the researchers see major implications for cancer immunotherapy.

Current cellular immunotherapies are dominated by CAR T-cell approaches, which have transformed treatment for several blood cancers but have shown more limited success against solid tumors. Investigators have long been interested in developing therapies based on macrophages and other innate immune cells because those cells naturally infiltrate tumors and can reshape the tumor microenvironment.

However, translating those concepts into viable therapies has proven difficult. Mature macrophages and monocytes are challenging to genetically engineer, difficult to manufacture at scale, and often fail to persist after infusion.

The newly expanded GMPs may provide a solution. Because the progenitor cells can be generated in large numbers and genetically modified before transplantation, they offer a renewable source of tumor-fighting immune cells.

“In our body these cells are very rare,” Ying said. “The mature cells cannot grow, and it is very challenging to genetically modify them. Now we have progenitor cells that can be expanded long-term in large quantities, and we can easily genetically modify them. That makes everything possible.”

The team engineered both mouse and human GMPs with chimeric antigen receptors (CARs) and evaluated them in mouse models. Unlike mature macrophages, which often become trapped in organs such as the lungs and liver after infusion, the progenitor cells distributed broadly throughout the body and engrafted within the bone marrow.

Once established, the cells generated populations of macrophages and monocytes capable of infiltrating tumors.

McKim noted that this approach may overcome several limitations that have hindered macrophage-based immunotherapies. “One of the big issues has been that it’s hard to engineer these cells, and when you put them back into the body they don’t get where they need to go,” he said. “The progenitors solve both problems. They’re easy to engineer, and they expand after transplantation.”

Implications for solid tumors

The researchers believe progenitor-derived innate immune therapies may offer advantages in solid tumors, where CAR T-cell approaches have struggled.

Tumors often create highly suppressive microenvironments that limit T-cell activity. Macrophages and related innate immune cells, by contrast, naturally migrate into tumors and can help stimulate broader immune responses.

“Monocytes and macrophages love going into tumors,” McKim said. “They can kill tumor cells themselves, but they can also help generate a natural antitumor immune response by the host.” That capability could prove particularly important in cancers that evade treatment by losing specific target antigens, a common mechanism of resistance to CAR T-cell therapy.

Although the work remains preclinical, the investigators believe the platform could eventually support a wide range of immune-engineering applications beyond cancer.

 

The post Self-Renewing Blood Progenitors Could Expand the Reach of Cancer Cell Therapy appeared first on Inside Precision Medicine.

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Medra Launches Reasoning Layer for Drug Discovery Robotics

As AI infrastructure for drug discovery continues to proliferate with reasoning workflows capable of generating hypotheses, candidate molecules, and experimental plans, Medra CEO Michelle Lee, PhD, argues that physical AI is the solution to addressing the next bottleneck: experimental validation at scale. 

“Building foundation models in biology that can predict and cure disease will take thousands of years of data generation,” Lee explained in an interview with GEN Edge. “The more I looked at the field, the more I realized that this data problem is actually a robotics problem.”   

In a new collaboration with the Defense Advanced Research Projects Agency (DARPA), Medra has launched AI Experimentalist, the scientific reasoning layer of its robotics platform. The system translates high-level research goals expressed in natural language into executable workflows that span the entire experimental cycle, from literature review, wet-lab execution, data analysis, and protocol refinement. 

In a blog post, Medra presents an example where scientists prompt to “build an Epidermal Growth Factor Receptor (EGFR) blocking antibody assay cascade.” AI Experimentalist can propose small optimizations in execution, including testing linear DNA templates in parallel, optimizing expression conditions, and feeding results immediately into the next run, for compounding time savings from days to hours. 

Partners can access AI Experimentalist through physical AI labs deployed on site at customer facilities or operated remotely through Medra’s flagship science laboratory, Medra Lab 001 (ML001), which unveiled in April and touts running experiments 24/7. Medra describes the 38,000 square foot facility as the largest autonomous lab in the United States. 

Artisanal nature 

In contrast to industrial automation, which has been powerful for repeatable tasks, such as combinatorial chemistry and screening, physical AI equips the same hardware with sensors to enable intelligent decision-making. 

While many robotics players in biology are focused on the manufacturing step, Medra has the ambitious goal of accelerating end-to-end drug discovery campaigns. 

“The artisanal nature of science is actually what makes certain experiments work and others fail,” said Lee. She noted that seemingly subtle variables, such as the angle of a pipette or the precise timing of mixing reagents, can have an outsized impact on experimental outcomes.  

Medra is currently working with partners across academia, biopharma, and government to run and develop assays across a wide array of applications, including antibody discovery, protein engineering, gene editing, and cell biology. 

Looking ahead, Lee says the bottleneck is not robotic capability, but integration and deployment. AI Experimentalist addresses this challenge through a multi-agent architecture and model-agnostic harness that allows Medra to incorporate new biological AI models and scientific agents. Among them are NVIDIA Nemotron models for protocol editing and optimization and the newly launched NVIDIA BioNeMo Agent Toolkit. 

“The flexibility of physical AI will be incredibly key in making scientific discovery truly autonomous,” asserts Lee. 

The post Medra Launches Reasoning Layer for Drug Discovery Robotics appeared first on GEN – Genetic Engineering and Biotechnology News.

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Spotlight on RNA Therapeutics

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Image of Drew Weissman, MD, PhD

Drew Weissman, MD, PhD

Professor in Vaccine Research
Penn Medicine

Panelist

Image of Drew Weissman, MD, PhD

Drew Weissman, MD, PhD

Drew Weissman, MD, PhD, is a world-renowned physician and Roberts Family Professor in Vaccine Research at Penn Medicine. He is best known for his contributions to RNA biology and the development of COVID-19 RNA vaccines. Weissman and Katalin Karikó, PhD, were jointly awarded the 2023 Nobel Prize in Medicine for their discoveries that enabled the modified mRNA technology used in Pfizer-BioNTech and Moderna’s vaccines to prevent COVID-19. More than 15 years ago, Weissman and Karikó found a way to modify mRNA and developed a delivery technique to package the mRNA in lipid nanoparticles. The COVID-19 RNA vaccine received FDA approval in August 2021.

Weissman is one of the academic leaders of the NSF AIRFoundry, an effort to leverage AI to improve, accelerate, and scale the design, manufacture, and delivery of RNA, which officially opened in April 2026. Weissman’s lab is currently working on a pan-coronavirus vaccine, a universal flu vaccine, and a vaccine to prevent herpes. They are working with Penn colleagues to develop cancer therapeutics with mRNA technology. And they are developing a SARS-CoV-2 mRNA vaccine with Chulalongkorn University in Thailand to help residents of Thailand and other Asian countries access lifesaving vaccines.

Before joining Penn in 1997, Weissman was a fellow at the National Institutes of Health studying HIV in the lab of Anthony Fauci, MD. Weissman received his bachelor’s degree and master’s degree from Brandeis University. He earned his MD and PhD from Boston University and completed his residency at Beth Israel Hospital.



Image of Zachary Ives, PhD

Zachary Ives, PhD

Professor of Computer and Information Science
University of Pennsylvania

Panelist

Image of Zachary Ives, PhD

Zachary Ives, PhD

Zachary Ives, PhD, is the department chair and Adani President’s Distinguished Professor of Computer and Information Science at the University of Pennsylvania. Zack’s research interests include data integration and sharing, data provenance and trustworthiness, and machine learning systems. He is a recipient of the National Science Foundation (NSF) CAREER award, and an alumnus of the DARPA Computer Science Study Panel and Information Science and Technology advisory panel. He has also been awarded the Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching and an IEEE Technical Committee on Data Engineering Education Award, and he is a fellow of the ACM.

 

Zack is one of the academic leaders of the U.S. NSF Artificial Intelligence-driven RNA BioFoundry (NSF AIRFoundry), an $18-million effort to leverage AI to improve, accelerate, and scale the design, manufacture, and delivery of RNA. The center officially opened in April 2026.

Zack studied computer science at Sonoma State University and holds a PhD in computer science from the University of Washington. He joined the faculty of Penn in 2003. He is a co-author of the textbook Principles of Data Integration. He has been an associate editor for the Proceedings of the VLDB Endowment and The VLDB Journal.



Image of Silvi Rouskin, PhD

Silvi Rouskin, PhD

Asst. Professor of Microbiologyy
Harvard Medical School

Panelist

Image of Silvi Rouskin, PhD

Silvi Rouskin, PhD

Born in Bulgaria, Silvi Rouskin, PhD, is an assistant professor of microbiology at Harvard Medical School. She is the winner of the 2021 Vilcek Prize for Creative Promise in Biomedical Science. Following a six-year spell at the Whitehead Institute, where she was the Andria and Paul Heafy Whitehead Fellow, Silvi joined the faculty of Harvard Medical School in 2021.

Silvi’s Harvard lab studies alternative RNA structures and the myriad roles they have in both viral and human biology. In particular, the lab studies how RNA folding informs alternative splicing and how misfolding can lead to disease. The lab developed DMS-MaPseq (dimethyl sulfate mutational profiling with sequencing) and DREEM (Detection-of-RNA-folding-Ensembles-using-Expectation-Maximization) algorithm to distinguish multiple RNA conformations formed by the same underlying sequence in vivo at single nucleotide resolution.

Silvi immigrated to the United States as a teenager to pursue a career in science. She holds a degree in physics from Florida Institute of Technology and a PhD in biochemistry and molecular biology from the University of California, San Francisco. Her interest in RNA began while working as a staff research associate in the lab of Joseph DeRisi, PhD, at UCSF, where she began developing techniques for the detection of viruses associated with human disease.



Broadcast Date: 

  • Time: 

In anticipation of RNA Day (on August 1), GEN invites you to join our exciting Spotlight virtual event on RNA Therapeutics on Wednesday, July 29.

We are living in a “post-genomic” world where RNA is no longer just a messenger but a programmable drug and molecular therapeutic. From the global impact of mRNA vaccines to advances in RNA editing and the potential of circular RNA, the field of RNA therapeutics is truly taking off. RNA is rapidly becoming a universal software for precision medicine.

Over 2.5 hours, this GENSpotlight on RNA Therapeutics brings you three interlinked sessions that feature outstanding researchers exploring various aspects of RNA biology and therapeutics, including:

  • A keynote panel including two founding members of the AIRFoundry (Artificial Intelligence-driven RNA BioFoundry) at the University of Pennsylvania—Zachary Ives, PhD, and Nobel laureate Drew Weissman, MD, PhD

  • A talk from Silvi Rouskin, PhD, a leading microbiologist at Harvard Medical School, presenting new research on alternative RNA structures and their relevance in health and disease

  • Presentations from our two sponsors, 4basebio and Aldevron

  • Registration to our Spotlight on RNA Therapeutics is entirely free. We look forward to celebrating RNA Day with you (a few days early).

Produced with support from:

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The Download: introducing the Engineering issue

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.

Introducing: the Engineering issue

We can’t fix everything, but we can be ambitious. We can take on the challenge of making the world better through human ingenuity. That’s what the new Engineering issue of MIT Technology Review is all about. 

Sometimes the challenges we face are giant, like tunneling beneath the seafloor. Some exist at the nanoscale, as with a new ASML machine powering the future of chipmaking. Others represent problems at a planetary scale and in truly unknown territory, like replicating a volcano’s mechanism to cool the Earth on purpose.

These incredible engineering stories show we can come together to get to work and, when the smoke clears, find we’ve made real progress. Subscribe now to read all of them—and more—in the full print issue.

Stripe, Anthropic, and OpenAI are backing an effort to stop respiratory infections

The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles.

Now, the payment company Stripe is funding a new $500-million nonprofit aiming to prevent both the common cold and the flu. Its eventual goal is to get rid of respiratory viruses altogether.

Anthropic, OpenAI, and Bill Gates have also backed the venture, which will investigate whether modern technologies can counter the common cold and the flu. Dive into the nonprofit’s plans.

—Antonio Regalado

MIT Technology Review Narrated: inside the hunt for the most dangerous asteroid ever

As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed. 

This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever discovered —all under the tightest of timelines and with the highest of stakes.

—Robin George Andrews

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 China has taken the US’s crown for the world’s fastest supercomputer 
Shenzhen’s LineShine overtook California’s El Capitan. (Axios)
+ China had not had a machine at the top of the list since 2017. (NYT $)
+ But the supercomputer race isn’t geared for AI work. (Reuters $)

2 Mythos reportedly found flaws in classified US government systems
A US official said Anthropic’s model identified certain vulnerabilities. (AP News)
+ The model has now been suspended over US security concerns. (BBC)
+ The NSA has lost access to Anthropic’s tools in fallout. (Engadget)
+ The feud raises new questions about AI safety. (MIT Technology Review) 

3 A US pilot reported seeing Iranian drones swarm in “jellyfish” formation
Which would represent an alarming advance in Iranian drone capabilities. (CNN)
+ The US is heading toward a drone-filled future. (MIT Technology Review)

4 Mark Zuckerberg directed Meta to create a prediction markets app
It will be similar to Polymarket and Kalshi. (NYT $)
+ But won’t let users wager real money. (The Verge) 
+ Another new app, Meta Photos, will create media with AI. (Reuters $)

5 SpaceX’s “Starfall” just launched a secretive test flight
The orbital delivery spacecraft blasted off for the first time yesterday. (Axios)
+ It could also support space manufacturing. (New Scientist $)

6 Alibaba has sued the US for being linked to the Chinese military
It wants to be removed from a Pentagon blacklist. (Reuters $)

7 Nvidia’s banned AI chips have doubled in price on China’s black market
The DGX B300 now costs more than $1.1 million. (Financial Times $)

8 Tesla claims a driver “manually overrode self-driving” in a deadly crash
It said the accelerator was pressed “all the way to 100%.” (The Verge $)

9 The US science retreat has created an opportunity for Europe
But questions about funding and innovation remain. (Nature)
+ Trump has dealt many blows to US science. (MIT Technology Review)

10 Meta’s new smart glasses ditch Ray-Bans for Kylie Jenner 
Meta logos and Jenner designs have replaced the Ray-Ban branding. (Wired $)

Quote of the day

“It’s blasphemy against AI if ‌you say it’s a bubble.”

—SoftBank founder and CEO Masayoshi Son tells shareholders that the AI boom is still in its early stages, Reuters reports.

One More Thing

ERIK CARTER


Video games are dividing South Korea

They say StarCraft was the game that changed everything. When the science fiction strategy game arrived in South Korea in 1998, it wasn’t just a hit—it was an awakening.

Out of 11 million copies sold worldwide, 4.5 million were in the country. The game was so popular that it triggered another boom: “PC bangs,” pay-as-you-go gaming cafés.

StarCraft and PC bangs spoke to a generation of young South Koreans boxed in by economic anxiety and rising academic pressures. But they also sparked arguments about game addiction. They’ve led to feuds between government departments—and a national debate over policy.

Read the full story.

—Max S. Kim

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ This archive lovingly documents the beautiful design of over 1,700 obsolete objects.
+ Classic TV theme tunes like Hey Arnold! Have been revived in a musician’s marvellous samples.
+ Marvel at the mind-boggling geometry of nature and see how bees perfectly construct honeycombs.
+ Hear the ominous, deeply atmospheric tones of a custom string instrument built inside a plastic drainage pipe.

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California Still Golden Despite Job Losses: Industry Group

SAN DIEGO—California’s three life sciences clusters all lost jobs last year, yet the industry remains a major engine of innovation and economic growth, according to a report released by the state’s largest life sciences organization to coincide with the Biotechnology Industry Organization (BIO) International Convention being held here.

BIOCOM California quantified the economic impact of the Golden State’s life sciences industry as generating $394 billion in economic output in 2025—a figure that goes beyond the direct impact of the 406,505 people employed by life sciences employers across the state. The impact figure includes indirect impact (activity generated through suppliers, vendors, and subcontractors supporting the industry) and induced impact (the household spending generated by workers employed in both life-sci organizations and supporting industries).

When indirect and induced impact are accounted for, the life sciences sustain 1,079,365 jobs statewide, the report stated.

However, all three of the state’s top-tier life-sci clusters—the San Francisco Bay Area, San Diego, and the Los Angeles/Orange County region—saw decreases in employment within the industry last year, according to the report.

San Francisco ranks second in the latest edition of GEN’s nationally-quoted A-List of Top 10 U.S. Biopharma Clusters, unchanged from a year ago, while San Diego slid one position to sixth, and LA/Orange County slipped one notch to eighth.

“Continued biotech winter”

“California, like the other states in the country, are still showing the effects of the pandemic and the recovery from that, because it was such a large run up of investment and hiring and building of new space, followed by a pretty significant drop off in 2022, 23,” Tim Scott BIOCOM California’s president and CEO, explained in an interview with GEN conducted at the organization’s booth within the convention’s exhibition floor.

“And then we have the continued biotech winter that’s been caused mostly through the instability at the federal level in terms of policy,” Scott added.

He cited NIH funding cuts, the delay in re-authorizing the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) seed funding programs, tariffs, and the “most favorite nation” drug pricing framework championed by the Trump administration as a vehicle for lowering drug prices: “All of these things have led the industry and the investors in the industry to pause.”

Most of the life-sci job decline was concentrated in the Bay Area and San Diego regions, which together accounted for 88% of job losses.

The San Francisco Bay Area saw its life-sci workforce slide 2.7% from 2024 to 137,779 jobs last year, driven mainly by decreasing employment in scientific/research tools, biotechnology, and biopharmaceuticals. The San Diego region finished 2025 with 61,866 jobs, a 2.55% decline from the previous year, due primarily to the loss of jobs in the R&D in physical, engineering, and life sciences and electromedical and electrotherapeutic apparatus manufacturing sectors.

Greater Los Angeles, which BIOCOM California defines as Los Angeles, San Bernardino, and Ventura counties, saw its employment base shrink 0.5% year-over-year, to 143,153 last year, with the largest employment decrease coming in drug wholesaler positions. Orange County’s life-sci workforce also dipped by 0.5%, sliding to 57,213 jobs, driven by cuts in scientific/research tools and medical devices and equipment employment—though Orange County also saw increases in biotechnology and research and testing jobs.

“Significant driver”

“In spite of the slight decrease in growth this year, we’re still at about $400 billion in economic output for California in the life sciences. That’s the second largest industry in California,” Scott said. “It’s still a significant driver of economic activity and of innovation.”

Another driver of innovation, NIH funding, stayed flat last year compared to 2024 at $5.23 billion for all of California. But the number of NIH awards statewide fell 8.5% from 9,384 in 2024 to 8,587 in 2025.

Life science manufacturing jobs fell by 2.1% last year to 143,572 jobs, though they still accounted for more than one-third (35.3%) of all of the industry jobs in the state. Across 31 life science industry sub-sectors, 23 recorded job losses, with the largest declines in medical laboratories and R&D within the physical, engineering, and life sciences job category.

But the state’s life-sci manufacturing segment is eventually expected to grow as drug developers either strive to meet growing demand, reshore their production in the United States to avoid tariffs, or both. Gilead Sciences began construction in September 2025 of a new 180,000 square-foot development and manufacturing facility, part of a companywide $32 billion U.S. investment strategy. Two months later, Novartis opened a 10,000-square-foot radioligand therapy (RLT) manufacturing facility for cancer treatments in Carlsbad, CA, the pharma giant’s third U.S.-based RLT site.

While biopharmas and contract manufacturers have announced hundreds of billions of dollars in new projects, projects announced for California remain mostly under construction, so hiring levels have not yet risen to account for the new manufacturing activity, Scott said.

Potential challenges loom

Two more potential challenges loom for California life science companies—one from Washington, the other from Sacramento.

Scott said BIOCOM California is paying attention to federal efforts aimed at further scrutinizing activity between U.S. and Chinese biopharmas.

Earlier this month, Reps. John Moolenaar (R-MI), chairman of the Select Committee on China, and Congresswoman Debbie Dingell (D-MI), introduced the Biotech Investment National Security Act (BINSA). BINSA would amend the Comprehensive Outbound Investment National Security (COINS) Act, enacted last year, by adding pharmaceutical and biological product development to the list of sectors subject to screening of investments by the U.S. government.

The measure would subject U.S. pharmaceutical licensing deals, joint ventures, and equity investments with Chinese covered foreign persons to U.S. Treasury Department review, as well as explicitly cover licensing deals involving technology and intellectual property. BINSA also requires the Secretary of War (formerly Defense) to assess within 60 days whether U.S. capital investment in Chinese biotechnology negatively affects national security and military readiness.

“We’re trying to find the balance between protecting American interests with regard to intellectual property and also competing with China. And we’re balancing that with cooperating with China,” Scott said. “You can imagine a politician in Washington, D.C., wants to really protect our interests. A biotech entrepreneur in California wants to go anywhere in the world to find resources to be able to move their drug toward the clinic.”

In Sacramento, Gov. Gavin Newsom, who leaves office at year’s end when his second term expires, has proposed permanently limiting the amount of business tax credits that a corporation can claim each year. Starting in 2027, corporate taxpayers would be allowed to claim a maximum of either $5 million or 50% of their pre-credit tax liability, whichever is greater. The limit would not affect taxpayers with less than $5 million in credits.

According to California’s Legislative Analyst’s Office (LAO), recent tax collection data shows that fewer than 100 corporate taxpayers in California would be affected. LAO has estimated that the proposal would raise $850 million in 2026–27, since the cap would only apply to part of the fiscal year, and $1.7 billion to $1.8 billion annually between 2027–28 and 2029–30.

However, the R&D credit likely accounts for most of the proposal’s fiscal effect, according to the LAO, since the R&D credit accounts for the overwhelming majority of business credit usage and carry-forward balances. And the roughly 100 affected businesses include many of the largest biopharma giants, Scott said.

“That is a really big tool for engaging pharma and encouraging investment in California. Without the R&D tax credit, companies are less likely to want to invest in California,” Scott asserted. “The R&D tax credit has had a direct effect on driving the growth of the biotech industry in California.”

The post California Still Golden Despite Job Losses: Industry Group appeared first on GEN – Genetic Engineering and Biotechnology News.