Radically different
The AI market is full of big promises of grand transformation. Health care is a prime target for those promises, beset as it is by financial pressures, labor shortages, and the growing burden of caring for an aging population. AI developers are targeting functions that vary widely, from curing cancer and performing surgery to streamlining routine administrative tasks.
The opportunity is genuine, but execution can be difficult. Numerous software vendors have tried to “fix” health care challenges but failed because they misunderstood the environment. “Health care is very complex,” says Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, which supports the buildout and deployment of digital solutions for health care companies through data-based insights and expert validation. “Solution developers must have a deep focus on clinical and technical capabilities, and then align their solutions to the relevant business impacts. If they miss any dimension, the solution will not be adopted or drive value.”
AI applications for health care are proliferating rapidly. The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. More than half of these were approved in the past three years, with the earliest dating as far back as 1995. Non-radiological applications carry out tasks as diverse as tracking sleep apnea, analyzing heart rhythms, and planning orthopedic surgeries.
AI applications that do not count as medical devices— for example, those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows that are often conventionally managed by whiteboards and sticky notes. Such functions may well outstrip clinical uses in their impact on health systems. A recent survey of technology leaders found that 72% said their top priority for AI was reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity.
Any health care-related application can potentially impact patient care, whether directly or indirectly, and AI apps that are poorly designed or inadequately trained and validated can put patients at risk. Providers recognize that risk: In the same survey, 77% said immature AI tools are a significant barrier to adoption. Regulators and lawmakers are also keeping an eye on the risks as development and adoption burgeon, though the U.S. regulatory picture is still in flux, as a 2024 report to Congress on AI in health care observes.
To tackle some of the technical challenges, many health care providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of health care organizations intend to pursue partnerships with third-party vendors to develop customized generative AI solutions as a primary strategy as opposed to building them in-house or buying off-the-shelf products.
But health care-specific AI applications must also be tailored to the nuanced clinical needs of medical providers as well as the complex business and regulatory considerations of the wider sector. This is where developers can benefit from working with a partner with a deep understanding of the health care environment to tailor applications to what providers want and need most. Doing so helps to position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Background: The increasing reliance on patient portals for electronic health records has widened the digital health care access gap, particularly among low-income and Medicaid-insured populations. However, resources exist to assist low-income patients with portal enrollment; in obtaining a free smartphone; and, in New York, in obtaining low-cost internet. Automated bidirectional SMS text messaging offers a scalable and cost-effective strategy for identifying low-income patients’ digital health needs and eligibility for resources by using screening questions and providing tailored information on how to access available resources. Objective: This study aimed to increase portal access among low-income patients using automated bidirectional SMS text messaging and assess its feasibility and acceptability. Methods: This quality improvement initiative involved sending automated, bidirectional SMS text messages in English to 12,381 Medicaid-insured and/or low-income patients from a primary care practice. Messages assessed patients’ digital health needs and provided adaptive, personalized resources and assistance for enrolling in the patient portal and for accessing digital technology. We assessed response rates and follow-up portal enrollment rates. We surveyed participants regarding the acceptability, appropriateness, and usability of the SMS text messaging intervention, as well as their subsequent use of the patient portal. We performed descriptive statistics and a binomial probability test. Results: In total, 9.2% (1140/12,381) of patients responded to the SMS text messages, with 3.9% (481/12,381) opting out and 5.3% (659/12,381) actively engaging. Among respondents, 71.1% (469/659) completed the follow-up survey. Respondents were predominantly female (336/469, 71.6%), with ages ranging from 18 to 65 years or older. Most respondents rated the message’s clarity (420/469, 89.6%), its usefulness (400/469, 85.2%), and the demonstration of care by their health team (350/469, 74.6%) favorably. Concerns regarding privacy (61/469, 13%) and trustworthiness (71/469, 15%) were noted. Notably, 71% of initially unenrolled patients activated their patient portals after the intervention (=.007), exceeding the hypothesized expectations. Conclusions: Automated bidirectional SMS text messaging had mixed effects on promoting patient portal use among low-income patients. Response rates to SMS text messages were low when delivered from an unknown phone number. Among responders, most reported that these messages were useful and that they would recommend them to others. Research is needed to determine optimal strategies for introducing the program and vendor phone numbers to patients to improve engagement.
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The biotech industry has long operated on a simple premise: FDA-regulated, evidence-based science determines how medicines reach patients, not litigation. That premise was already tested in an earlier Texas case challenging mifepristone’s Food and Drug Administration approval — an unprecedented effort to unwind decades of scientific review through the courts. It is now, once again, under strain.
On Friday, the 5th Circuit Court of Appeals reinstated an in-person dispensing requirement for mifepristone, a medication that has been used safely by millions for more than two decades. The drug manufacturer, Danco, appealed to the Supreme Court within hours and on Monday morning, SCOTUS granted one-week stay halting the order. In other words, mifepristone is available again through the mail and at pharmacies — but it’s unclear for how long that will be true. And it signals that even well-established, FDA-approved medicines are vulnerable to judicial override of FDA regulatory decisions.
Research led by New York University suggests a marker of epigenetic aging could be linked to depression.
The team found that accelerated aging of a type of white blood cell called a monocyte was significantly associated with the psychological and cognitive expressions of depression in a group of women with and without HIV.
“Depression is not a one-size-fits-all disorder—it can look really different from person to person, which is why it’s so important to consider varied presentations and not just a clinical label,” said lead researcher Nicole Beaulieu Perez, PhD, assistant professor at NYU Rory Meyers College of Nursing, in a press statement.
“Our study reveals unique biological underpinnings of mental health that are often obscured by broad diagnostic categories.”
As reported in The Journals of Gerontology Series A, the researchers analyzed blood samples and depression scores from 440 women, 261 living with HIV and 179 without, from the Women’s Interagency HIV Study. They tested women with HIV as people with this disease and others affecting the immune system are at greater risk of depression than the general public.
The team looked at biological aging using two epigenetic clocks: a broad multi-tissue clock and a monocyte-specific clock that measures chemical modifications to DNA in these cells.
Depression was measured using the CES-D questionnaire, which separates physical, bodily expressions of depression such as fatigue, appetite loss, and agitation from psychological and cognitive expressions of the disorder such as hopelessness, anhedonia, and feelings of failure.
Accelerated monocyte aging was significantly associated with the psychological and cognitive expressions of depression and with anhedonia specifically, even after adjusting for HIV status, race, and ethnicity. The broader multi-tissue Horvath clock showed no association with any depression domain, suggesting it is the monocyte-specific aging signal, not generalized biological aging, that tracks with mood and cognitive symptoms.
Diagnosis of depression relies largely on self-reported symptoms and not a specific physiological test. The finding that monocyte aging maps onto cognitive and mood symptoms rather than physical ones is counterintuitive, since monocytes are inflammatory cells that one might expect to track physical, inflammation-driven complaints like fatigue.
The study is small and cross-sectional, so causality cannot yet be established, but if the claims of the study were validated it could help to personalize treatment for depression in the future.
“The dynamics of monocyte aging and depression warrant further study to clarify mechanistic links,” conclude the authors.
“Our findings bring us a step closer to this goal of precision mental health care, especially for high-risk populations, by providing a biological framework that could guide future diagnosis and treatment,” adds Beaulieu Perez.
The post Biomarker of Epigenetic Aging Could Signal Depression appeared first on Inside Precision Medicine.
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Two of the most powerful people in AI—Sam Altman and Elon Musk—began their face-off in court in Oakland, California, last week. Musk is suing OpenAI, alleging that the millions he spent to fund it around a decade ago were meant for a nonprofit, not a corporation, and that the company has reneged on that mission since.
The stakes are high—even a partial win for Musk could set OpenAI back as it reportedly plans to go public this year. But most of the attention comes from the spectacle of a feud on X now playing out in federal court. “Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light,” my colleague Michelle Kim wrote before it began. And the trial unfolds as the cultural backlash against AI swells; some of the signs held by protesters outside the courthouse suggest that to a significant number of people, whatever the outcome of Musk v. Altman, we all lose.
Most of us have had to observe the trial from afar, but Michelle, who also happens to be a lawyer, has been in court each day. I caught up with her to learn what’s unfolded thus far and what might come next.
Can you give us the overview of what this case is actually about? What exactly is being decided, and who is favored right now?
Elon Musk is arguing that Sam Altman and OpenAI president Greg Brockman have breached the company’s charitable trust by effectively converting OpenAI into a for-profit company. Musk alleges that is not what they promised him in the company’s early days. He has asked for several remedies, like a crazy amount of damages and removing Sam Altman. But the main remedy he wants is unwinding OpenAI’s restructuring. [In October 2025 OpenAI struck deals with the attorneys general of California and Delaware that would essentially allow its nonprofit portion to have less day-to-day control of OpenAI. It’s a compromise from what OpenAI originally proposed, but Musk still wants to stop it.]
OpenAI argues that Elon Musk actually agreed to have the company operate a for-profit arm, because he knew building AI is very expensive. So it’s about proving what Musk knew, what he didn’t know, and whether he really was deceived by Altman and Brockman.
There’s a big debate about when exactly Musk found out about this alleged misconduct. Musk founded OpenAI with Altman and Brockman in 2015, and he brought the suit in 2024. There’s a statute of limitations for charitable trust claims; you need to have brought a claim within three to four years after you find out about the alleged misconduct. So Musk tries to paint a picture that back in the day he was a little suspicious, but that it was really only in 2022 that he realized OpenAI was no longer committed to its original charitable mission, and that he had been scammed. It’s only the first week of trial, but I’m not sure Musk has proved this to the judge and jury.
What were some standout moments thus far?
At one point one of Elon Musk’s lawyers said, “We could all die as a result of AI.” I think a lot of the people in the room were really shaken by this comment, and the judge told Musk’s lawyer: You talk about all these safety risks that OpenAI has when building AI, but Musk is also creating a company that’s in the same exact space. She basically said, I’m sure there’s plenty of people who also don’t want to put the future of humanity in Elon Musk’s hands.
And then the lawyers just kept going on and on about the catastrophic risks of AI and whether Elon Musk or OpenAI was in the better position to steward AI safety. And the judge sort of snapped. She said very sternly that this trial was not about whether or not artificial intelligence has damaged humanity. And I thought that was a really striking standout moment of the trial that pointed at how even though it is technically just about whether Elon Musk was really deceived by OpenAI, it’s also become a huge discussion about AI safety and some of the practices that the labs are engaging in when building AI.
Can you give us a look behind the curtain at how getting into this trial works?
There are tons of reporters. This is a very high-profile suit, so I have to wake up around 4:30 a.m. and show up to the Oakland courthouse at 6 a.m. sharp to get in line. And on some days, even 6 a.m. doesn’t get you into the courtroom. There are lots of photographers in front of the courthouse, especially on days when you know Musk or Altman and Brockman are present. And there’s also some concerned citizens who want to watch the trial. I usually have to wait, like, two hours in line to get in to be one of the 30 people who claim the unreserved seats in the courtroom.
What has it felt like to see Elon Musk testify? How would you describe his demeanor?
He shows up in a crisp black suit. He can be this inflammatory person on X, but in the courtroom, he is calm, cool, collected, and looks very comfortable. He has been in a lot of lawsuits. He knows how to talk to the jury and how to present himself in front of them and the judge. He’s also cracking jokes with his lawyer and even the opposing party’s lawyer and the judge.
And he can be witty. There was this one moment when OpenAI’s lawyer was asking Musk a question and sort of fed him an answer. And Musk said “That’s not a leading question, that’s a leading answer.” The judge intervened and said, “You’re not a lawyer, Elon.” And then he was like, “Well, I did take Law 101.”
That said, he does get flustered and uncomfortable when OpenAI’s lawyer asks tough, piercing questions. Which he’s been doing.
What are the biggest things we’ve learned that weren’t clear in the earlier phases of this case?
On the fourth day of the trial, Musk admitted during cross-examination that xAI distills OpenAI’s models to train its own models, which was shocking. Musk followed up by saying that this is standard practice among all the labs now and that xAI wasn’t doing anything beyond what others were already doing. But a lot of the journalists started typing away at their laptops as soon as Musk made this comment.
I also learned that there’s just so much scheming among Big Tech executives. You know about it vaguely, but to hear firsthand accounts and read their emails and text messages is fascinating.
For example, there was a text message between Musk and Mark Zuckerberg of Meta, where they’re kind of teaming up to stop OpenAI’s restructuring. They’re even trying to make a bid to buy all the assets of OpenAI’s nonprofit. The level of scheming that goes on among these executives is mind-blowing.
What happens next?
OpenAI’s president, Greg Brockman, who was meticulously taking notes during some of Elon Musk’s testimony, is expected to testify next week. And Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. I’m expecting that to open the floodgates to this crazy discussion about who can be trusted to build AI.
A bunch of other high-profile people are expected to testify, like former OpenAI chief scientist Ilya Sutskever, former CTO Mira Murati, and Microsoft CEO Satya Nadella.
The trial is supposed to last around three weeks. The nine jurors will deliver an advisory verdict that guides the judge on how to decide Musk’s claims against OpenAI. The judge doesn’t have to listen to the jury and can decide however she wants. If she decides OpenAI is liable, then she’ll decide what sort of remedies are appropriate.
MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.
Maternal health is a known crisis in the U.S., where pregnant women and new mothers die at a rate several times higher than in comparable countries. In recent years, increased awareness of the problem has led to interventions at the federal and state level and a strengthening of surveillance and data collection. Even as sizable improvements continue to be elusive, the picture of how many new mothers are dying, and why, is becoming clearer.
A research letter published on Monday in JAMA Pediatrics argues fathers deserve similar attention. To bolster their assertion, the authors reported the results of a pilot study in Georgia of deaths among fathers of children born in a single year, which found nearly 800 deaths in the first five years of fatherhood.
Nvidia CEO, Jensen Huang, asserts that accelerated computing has a missing word: application acceleration. The “vertically integrated” and “horizontally open” chip maker is set on building the infrastructure that delivers AI into real world use.
“Accelerated computing is not a chip problem,” said Huang when he took the stage for his annual NVIDIA GTC keynote in San Jose in March. “The only way for us to accelerate applications and bring tremendous speed up and cost reduction is through domain specific acceleration.”
That mission has hit drug discovery, where approval timelines exceed a decade and clinical trial failure rates approach 90%.
A new wave of platforms from Amazon Web Services (AWS), OpenAI, and Anthropic have customized general-purpose assistants into AI-powered workflows for science research. The trend points to the growing role of cloud infrastructure and agentic AI in unifying fragmented tools, streamlining data management, and making domain expertise more accessible.
In April, AWS introduced Amazon Bio Discovery, a “lab-in-the-loop” workflow that combines access to more than 40 open-source and proprietary biological foundation models with AI agents that guide experimental design. The platform also integrates CRO partners, including Twist Bioscience, Ginkgo Bioworks, and A-Alpha Bio, for lab validation. The launch was announced at the AWS Life Sciences Symposium at the Javits Center in New York.
In collaboration with Memorial Sloan Kettering Cancer Center, Amazon Bio Discovery designed nanobodies with nanomolar affinities by generating nearly 300,000 candidates that were narrowed to the top 100,000 for wet lab testing in weeks, a noticeable reduction from the up to one year timeline typical of traditional methods.
Dan Sheeran, vice president and general manager, healthcare and life science at AWS, explains that while biological AI models have driven breakthroughs in areas, like protein design, their reliance on coding expertise and complex compute infrastructure remains a significant barrier to broader accessibility.
“Choosing the right model for a given task is itself a significant challenge. Computational biologists, the specialists who bridge AI and biology, are in short supply,” Sheeran told GEN Edge. “The result is a collaboration bottleneck, not because the science isn’t available, but because the tooling doesn’t support how these teams need to work together.”
David Younger, PhD, co-founder and CEO of A-Alpha Bio, adds that the partnership with AWS highlights a “fundamental gap” in AI-powered drug discovery, the lack of high-quality, experimental data at scale to evaluate protein design models. In silico candidates designed using Amazon Bio Discovery can be rapidly validated in the lab with A-Alpha’s AlphaSeq platform, which quantitatively measures protein-protein interactions by the hundreds to millions.
“The convergence of technology and life sciences isn’t just about faster compute or better algorithms,” Younger told GEN Edge. “It’s about connecting those advances to real-world, experimental observations.”
Amazon Bio Discovery is built on the same AWS infrastructure that is currently adopted by 19 of the top 20 global pharmaceutical companies. Each organization’s data is isolated within its application environment, and all proprietary data, models, and designs remain customer-owned.
Two days after Amazon Bio Discovery’s launch, OpenAI announced GPT-Rosalind, a specialized reasoning model that supports evidence synthesis, hypothesis generation, and experimental planning for research across biology, drug discovery, and translational medicine. The platform includes a freely accessible life sciences research plugin for Codex that connects to over 50 public multiomics databases, literature repositories, and computational biology tools.
The model is available through a trusted-access program for qualified enterprise customers in the U.S. Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific are among GPT‑Rosalind’s customers.
“Research organizations are actively looking for systems that are built for scientific workflows, not adapted from general-purpose models, and life sciences remains one of the most important areas where better tools could meaningfully accelerate progress,” wrote OpenAI in an email to GEN Edge when describing the motivation for building GPT-Rosalind.
Named after Rosalind Franklin, PhD, whose work was critical in the discovery of the DNA double helix, the model scored 0.751 on BixBench, a benchmark that evaluates large language model (LLM) performance in bioinformatics and computational biology tasks. The score was a modest lead ahead of GPT-5.4, xAI’s Grok 4.2, and Google’s Gemini 3.1 Pro.
On LABBench2, a benchmark spanning literature retrieval, database access, sequence manipulation, and protocol design, GPT-Rosalind outperformed GPT-5.4 on six out of 11 tasks. The largest improvement was shown on CloningQA, which requires end-to-end design of DNA constructs and enzyme reagents for molecular cloning workflows.
GPT-Rosalind is one step in OpenAI’s growing momentum across pharma and healthcare. In recent weeks, the company introduced ChatGPT for Clinicians to support clinical workflows, such as documentation and medical research, alongside partnerships with Novo Nordisk to enhance workforce AI readiness and improve manufacturing and supply chain efficiency, and Massive Bio to expand access to clinical trials.
Anthropic is forging its own path into life sciences, having recently drawn attention for acquiring Coefficient Bio, a roughly 10-person AI drug discovery start-up founded by former Genentech scientists, for $400 million.
The OpenAI competitor has also been building Claude for Life Sciences, the AI assistant specialized for researchers, clinical coordinators, and regulatory affairs managers, since last fall.
In an October blog post, Anthropic reported that the customized platform powered by Claude Sonnet 4.5 scored 0.83 in Protocol QA, a benchmark that tests the model’s understanding of laboratory protocols. The score outperformed the human baseline of 0.79 and Sonnet 4’s performance of 0.74. Claude for Life Sciences also incorporates several connectors to scientific platforms, including Benchling’s digital notebooks, PubMed literature, and 10x Genomics tools for single cell and spatial analysis.
“We want to give scientists the same experience as software engineers of having a brainstorming partner to work with and to delegate tasks,” said Eric Kauderer-Abrams, PhD, head of biology and life sciences at Anthropic, in a video accompanying the product launch.
In January, Anthropic expanded the platform to Claude for Healthcare, a complementary set of tools that allow healthcare providers, payers, and health tech companies and startups to use Claude for medical purposes through HIPAA-ready products.
When reflecting on these life science releases, Enke Bashllari, PhD, founder and managing director at Arkitekt Ventures, says the three are “playing different games.” OpenAI is selling the “sharpest reasoning engine” with limited access, while AWS is building infrastructure and lab integration. Anthropic is betting on breadth of workflow and making acquisitions to close the specialization gap.
“For startups, the question isn’t which platform wins. It’s which layer you build on,” wrote Bashllari on LinkedIn.
Chris Leiter, founder and general partner at Atria Ventures, believes the shift to bioconsumerism will be the “most significant period of disruption for life sciences in the modern era.”
“Medicine is the use case that justifies the entire buildout,” wrote Leiter on LinkedIn. “The public skepticism starts to erode when the output is a drug that reaches a patient five years early, or a diagnostic that catches a cancer no doctor would have seen.”
As models increasingly move beyond isolated predictions into complex reasoning across biological systems, the question is no longer whether to adopt, but how quickly the industry can adapt to a new scientific discovery paradigm.
Huang says it best, “we are now in the beginning of a new platform shift. The inference inflection has arrived.”
The post Big Tech Targets Drug Discovery with Wave of Life Science Platforms appeared first on GEN – Genetic Engineering and Biotechnology News.
Feuer brings more than two decades of clinical leadership to inaugural role
New York, NY – The Child Mind Institute, an independent nonprofit dedicated to transforming the lives of children affected by mental health disorders, today announced Vera Feuer, MD, as the organization’s first-ever Chief Clinical Officer. A seasoned specialist in pediatric and adolescent mental health, Dr. Feuer will oversee clinical strategy, including patient care in the New York City and San Francisco Bay Area offices, and help drive innovation across treatment and research.
Dr. Feuer most recently served as Vice President of Child and Adolescent Psychiatry at Northwell Health and is a professor of psychiatry, pediatrics, and emergency medicine at the Zucker School of Medicine at Hofstra/Northwell Health. A board-certified psychiatrist, she brings more than 20 years of clinical expertise and leadership in pediatric and adolescent mental health and crisis care to the Child Mind Institute. She has helped pioneer innovative standards of care in pediatric crisis behavioral health care and suicide prevention, and she has led the development of programs for the pediatric medical community and school district partnerships to expand mental health care access for students.
“I am thrilled to join the Child Mind Institute and beyond excited to bring my extensive experience of working with youth and families, creating access to care and innovative program development, and to work with this remarkable team,” Dr. Feuer says. “Together we will deepen access to high-quality, evidence-based care and develop programs that meet the needs of kids where they are.”
Dr. Feuer will provide strategic oversight to a multidisciplinary team of more than 70 clinicians who deliver over 70,000 patient appointments annually, ensuring continued excellence in delivering care, developing school-based programs, and establishing community partnerships while expanding access to high-quality mental health services. As a member of the Child Mind Institute’s executive leadership, Dr. Feuer will be instrumental in shaping the organization’s clinical vision, providing medical expertise to improve outcomes for children and families and guiding the integration of research through data-driven approaches that advance care and innovation.
“At a time when youth mental health needs are more urgent than ever, Dr. Feuer’s exceptional leadership brings crucial guidance to meet this moment,” says Harold S. Koplewicz, MD, founding president and medical director of the Child Mind Institute. “Our mission has always been to transform how families access and experience mental health care. With Dr. Feuer at the helm as our Chief Clinical Officer, we remain steadfast in our commitment to strengthening our clinical foundation, shaping the future of mental health and helping families nationwide.”
To learn more, visit childmind.org, and read Dr. Feuer’s full biography.
The Child Mind Institute is dedicated to transforming the lives of children and families struggling with mental health and learning disorders by giving them the help they need. We’ve become the leading independent nonprofit in children’s mental health by providing gold-standard, evidence-based care, delivering educational resources to millions of families each year, training educators in underserved communities, and developing tomorrow’s breakthrough treatments.
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For press questions, contact our press team at childmindinstitute@ssmandl.com or our media officer at mediaoffice@childmind.org.
The post The Child Mind Institute Names Dr. Vera Feuer as Chief Clinical Officer appeared first on Child Mind Institute.
The budding scientist had left India for the U.S. for her Ph.D., because as she saw it, no other country offered the same opportunities for researchers. Set to finish her doctorate this summer, she also had a postdoctoral fellowship lined up in America.
Now those plans have changed. New and intrusive burdens for renewing a component of her visa — which required her to make public her social media profiles for U.S. review while she was back in India — caused her to be away from the lab for two months during the crunch time of wrapping up her degree.