‘Real sense of grief': Healthcare professional reflects on loss of neurosurgeon, family killed in NY plane crash
Dr. Michael Groff was widely known throughout the northeast for his impact in neuroscience. It was a private plane which crashed just before noon on Saturday in Copake, New York right by the Massachusetts state line.
Rochester neurosurgeon among 6 killed in NY plane crash
Also on board, Dr. Groff's wife, urogynecologist Dr. Joy Saini, who was well established in the Boston area, and two of their three children, Karenna Groff, a former MIT soccer player named in 2022 NCAA Woman of the Year, and Jared Groff, a 2022 graduate of Swarthmore College who worked as a paralegal along with their respective significant others.
Dr. Groff was the Executive Medical Officer of Rochester Regional Health's neuroscience service line just started in the role July last year.
Executive Vice President & Chief Medical Officer for Rochester Regional Dr. Robert Mayo describes Dr. Groff as a well-trained and very accomplished surgeon, previously serving as faculty at Harvard Medical School, and in a role of Women's Neurosurgery Spine Division & Fellowship Training program.
'He told me why he chose to come here. He was excited by the vision that Rochester Regional Health has for the opportunity to build a service line across the large footprint of the health system in ranging from Batavia to Rochester Clifton Springs, New York, and up to the north country in Saint Lawrence so he was excited about seeing the region of the size grow and scope and scale related to neurosurgery and neurosciences,' Dr. Mayo said. 'There's a real sense of grief and loss or very heartbroken by this. Our collective sympathies and condolences are extended to his family and extended family, and we have had some communication with them by phone and email.'
Funeral arrangements are being made, and internally, Rochester Regional Health leadership have been conducting meetings and direct one on ones with faculty and staff impacted by this tragedy.
Copyright 2025 Nexstar Media, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
2 days ago
- Yahoo
Recursion Pharmaceuticals (RXRX) Expands AI Drug Discovery Pipeline with New Clinical Candid
We recently published . Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) is one of the best healthcare stocks. Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) is a clinical-stage biotechnology company integrating artificial intelligence (AI), machine learning, and high-throughput biology to accelerate drug discovery, with a focus on oncology and rare diseases. Its proprietary Recursion OS platform streamlines therapeutic identification and development. In July–August 2025, the company acquired full rights to REV102, an oral ENPP1 inhibitor from Rallybio, aimed at treating hypophosphatasia (HPP), a rare genetic disorder with no approved oral disease-modifying therapies. This acquisition expands Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX)'s rare disease pipeline and gives it full control over a promising preclinical program. At the same time, the business streamlined its portfolio by discontinuing or pausing several programs, narrowing its focus to six active candidates, four in oncology and two in rare diseases. Its innovative AI-driven platform and pipeline have also drawn attention from investors seeking the best healthcare stocks in biotech. The corporation continues to expand its AI-driven capabilities, most notably through a collaboration with MIT to advance the Boltz-2 AI model on its BioHive-2 platform. This initiative aims to accelerate molecular discovery and synthesis, potentially shortening the path from discovery to clinical trials. The rollout of Recursion OS 2.0 and new partnerships with biopharma and data science firms further enhance its discovery and development capabilities. Copyright: dolgachov / 123RF Stock Photo Upcoming milestones in 2025 include multiple clinical trial initiations and data readouts, such as REC-617, a CDK7 inhibitor in oncology, and REC-4881 for familial adenomatous polyposis. Following its late 2024 merger with Exscientia, Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX)'s oncology pipeline has broadened, with several candidates expected to advance into human studies. While we acknowledge the potential of RXRX as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: The Best and Worst Dow Stocks for the Next 12 Months and 10 Unstoppable Stocks That Could Double Your Money. Disclosure: None. Sign in to access your portfolio


CNET
4 days ago
- CNET
Generative AI Gave MIT Scientists a New Tool to Fight Antibiotic-Resistant Bacteria
Antibiotic-resistant bacteria are dangerous because they already "know" what most antibiotics look like. Scientists at MIT have found a way to create something new: using generative AI to design two antibiotic compounds from scratch that can kill drug-resistant gonorrhea and MRSA in lab dishes and mice. Antibiotic resistance is one of the world's biggest public health threats yet new antibiotics have been scarce for decades. Traditional drug discovery methods rely on screening known chemical libraries -- a slow process with a limited pool of existing molecules to test. In contrast, MIT's AI system generated more than 36 million theoretical compounds, many with chemical structures never seen before, and zeroed in on two standouts. Both are unlike any antibiotic currently in use, offering a glimpse at how AI can move beyond speeding up research to imagine medicines that might have been impossible to find otherwise. "We wanted to get rid of anything that would look like an existing antibiotic, to help address the antimicrobial resistance crisis in a fundamentally different way," said Aarti Krishnan, MIT postdoc and one of the study's lead authors. "By venturing into underexplored areas of chemical space, our goal was to uncover novel mechanisms of action." Read also: Do You Really Learn When You Use AI? What MIT Researchers Found How the science was pulled off The MIT team bypassed the limitations of screening existing chemical libraries by asking AI to invent molecules from scratch, generating more than 36 million theoretical compounds, which were then narrowed down to a few to be tested against drug-resistant superbugs. This involved two AI-driven strategies: Fragment-based design: The AI began with a chemical fragment (labeled F1) that showed promise against gonorrhea. It produced millions of derivatives, ultimately refining a shortlist of about 1,000 candidates. Of the 80 chosen by researchers, NG1 emerged as a standout compound that successfully treated drug-resistant gonorrhea in cell cultures and a mouse. Unconstrained generation: The team let the AI roam freely, designing molecules on its own, aiming at MRSA. This produced more than 29 million candidates, which were filtered down to 90 compounds for synthesis. Twenty-two were produced, six performed well in lab tests and one in particular, DN1, proved able to eliminate MRSA skin infections in mice. NG1 and DN1 are structurally distinct from any currently known antibiotics and appear to destroy bacteria by disrupting their cell membranes. NG1 specifically targets LptA, a previously untapped bacterial protein involved in constructing the outer cell membrane. What's next for antibiotic research Phare Bio, a nonprofit in the Antibiotics-AI Project, is refining NG1 and DN1 to improve their drug properties, while researchers expand the AI platform to target other tough pathogens like Mycobacterium tuberculosis (the causative agent of tuberculosis) and Pseudomonas aeruginosa (a group of bacteria that often causes infections in health-care settings). The study, first published in the journal Cell, signals a hopeful turn in the global struggle against superbugs. However, these findings are early-stage. Initial tests and lab results are encouraging, but human safety and efficacy must be established through rigorous lab refinement and clinical trials, a process that could span several years. This effort builds on MIT's previous breakthroughs in AI-guided antibiotic development, including halicin, discovered in 2020 via deep learning, and abaucin, discovered in 2023 via a machine-learning algorithm. Read more: AI Essentials: 29 Ways to Make Gen AI Work for You, According to Our Experts
Yahoo
5 days ago
- Yahoo
AI designs antibiotics for gonorrhoea and MRSA superbugs
Artificial intelligence has invented two new potential antibiotics that could kill drug-resistant gonorrhoea and MRSA, researchers have revealed. The drugs were designed atom-by-atom by the AI and killed the superbugs in laboratory and animal tests. The two compounds still need years of refinement and clinical trials before they could be prescribed. But the Massachusetts Institute of Technology (MIT) team behind it say AI could start a "second golden age" in antibiotic discovery. Antibiotics kill bacteria, but infections that resist treatment are now causing more than a million deaths a year. Overusing antibiotics has helped bacteria evolve to dodge the drugs' effects, and there has been a shortage of new antibiotics for decades. Researchers have previously used AI to trawl through thousands of known chemicals in an attempt to identify ones with potential to become new antibiotics. New superbug-killing antibiotic discovered using AI Now, the MIT team have gone one step further by using generative AI to design antibiotics in the first place for the sexually transmitted infection gonorrhoea and for potentially-deadly MRSA (methicillin-resistant Staphylococcus aureus). Their study, published in the journal Cell, interrogated 36 million compounds including those that either do not exist or have not yet been discovered. Scientists trained the AI by giving it the chemical structure of known compounds alongside data on whether they slow the growth of different species of bacteria. The AI then learns how bacteria are affected by different molecular structures, built of atoms such as carbon, oxygen, hydrogen and nitrogen. Two approaches were then tried to design new antibiotics with AI. The first identified a promising starting point by searching through a library of millions of chemical fragments, eight to 19 atoms in size, and built from there. The second gave the AI free reign from the start. The design process also weeded out anything that looked too similar to current antibiotics. It also tried to ensure they were inventing medicines rather than soap and to filter out anything predicted to be toxic to humans. Scientists used AI to create antibiotics for gonorrhoea and MRSA, a type of bacteria that lives harmlessly on the skin but can cause a serious infection if it enters the body. Once manufactured, the leading designs were tested on bacteria in the lab and on infected mice, resulting in two new potential drugs. "We're excited because we show that generative AI can be used to design completely new antibiotics," Prof James Collins, from MIT, tells the BBC. "AI can enable us to come up with molecules, cheaply and quickly and in this way, expand our arsenal, and really give us a leg up in the battle of our wits against the genes of superbugs." However, they are not ready for clinical trials and the drugs will require refinement – estimated to take another one to two year's work – before the long process of testing them in people could begin. I found a bacteria-eating virus in my loo - could it save your life? Dr Andrew Edwards, from the Fleming Initiative and Imperial College London, said the work was "very significant" with "enormous potential" because it "demonstrates a novel approach to identifying new antibiotics". But he added: "While AI promises to dramatically improve drug discovery and development, we still need to do the hard yards when it comes to testing safety and efficacy." That can be a long and expensive process with no guarantee that the experimental medicines will be prescribed to patients at the end. Some are calling for AI drug discovery more broadly to improve. Prof Collins says "we need better models" that move beyond how well the drugs perform in the laboratory to ones that are a better predictor of their effectiveness in the body. There is also an issue with how challenging the AI-designs are to manufacture. Of the top 80 gonorrhoea treatments designed in theory, only two were synthesised to create medicines. Prof Chris Dowson, at the University of Warwick, said the study was "cool" and showed AI was a "significant step forward as a tool for antibiotic discovery to mitigate against the emergence of resistance". However, he explains, there is also an economic problem factoring into drug-resistant infections - "how do you make drugs that have no commercial value?" If a new antibiotic was invented, then ideally you would use it as little as possible to preserve its effectiveness, making it hard for anyone to turn a profit.