There’s something special about the APOE2 variant of the APOE (apolipoprotein E) gene: People who carry it tend to live longer, and they have a lower risk of developing Alzheimer’s disease.
Scientists are still trying to figure out why, and now, they have a new lead.
A team led by researchers at the Buck Institute for Research on Aging in the US set out to answer that question using human stem cell-derived neurons and mouse studies.
The emergence of effective immunotherapies has revolutionized therapies for many types of cancer. However, current immunotherapy has limited efficacy in certain patient populations and displays therapeutic resistance after a period of treatment. To address these challenges, a growing number of immunotherapy drugs have been investigated in clinical and preclinical applications. The diverse functionality of peptides has made them attractive as a therapeutic modality, and the global market for peptide-based therapeutics is witnessing significant growth. Peptides can act as immunotherapeutic agents for the treatment of many malignant cancers. However, a systematic understanding of the interactions between different peptides and the host’s immune system remains unclear.
When researchers at Yale gave a shelved cancer drug to old mice whose brains were already riddled with Alzheimer’s-like damage, the animals started remembering again. Synapses that had gone quiet flickered back to life. Proteins that mark healthy brain connections climbed toward normal levels. And when the drug was taken away, the cognitive gains stuck.
The drug is saracatinib, originally developed by AstraZeneca under the code name AZD0530 to treat solid tumors. It never panned out for cancer. But a team led by Stephen Bhatt and Christopher van Dyck at Yale School of Medicine recognized that its molecular target, an enzyme called Fyn kinase, plays a central role in how amyloid-beta destroys synapses in Alzheimer’s disease. Their work, published across several peer-reviewed studies between 2015 and 2020, has made saracatinib one of the more closely watched examples of drug repurposing in neuroscience. As of mid-2026, the compound’s preclinical results remain striking, but its clinical story is more complicated.
The heart’s constant beating may actively suppress tumor growth in cardiac tissues, a new Science study reports. This is because cellular pathways in these tissues alter gene regulation in cancer cells to keep them from proliferating.
The findings shed light on the role of mechanical forces in protecting the heart from cancer and may pave the way to new cancer therapies based on mechanical stimulation.
The heart rarely develops cancer, and, at the same time, it lacks regenerative capacity, as cardiomyocytes stop proliferating after birth. This suggests that mechanisms limiting cardiac regeneration may also protect against cancer. In this work, we investigated the role of mechanical load and used in vivo cancer models and ex vivo engineered heart tissues to show that mechanical load reduces cancer cell proliferation in the myocardium. Spatial transcriptomics of human cardiac metastases revealed decreased histone methylation and chromatin compaction. These changes affect chromatin accessibility at proliferation-related loci, with Nesprin-2 identified as a key mechanosensor. Our results uncover how mechanical forces protect the heart from cancer and suggest potential strategies for cancer therapy based on mechanical stimulation.
What if every scientific paper you read was just the “highlight reel” of a much longer, messier, and more complicated movie? You see the breakthrough, but you never see the hundreds of hours of footage showing what didn’t work.
Ultimately, the ARA marks a shift toward a future where “The Last Human-Written Paper” isn’t the end of science, but the beginning of a much deeper, machine-readable conversation.
However, this shift toward radical transparency comes with its own set of hurdles. While ARAs make AI agents more efficient, the study found a “prior-run box” effect where seeing a human’s past failures actually limited an AI’s ability to think outside the box and find creative new solutions. There is also a significant cultural and technical gap to bridge: the system relies on researchers being willing to expose their “messy” unfinished work, and even with better data, the jump in actual experiment reproduction was relatively modest. Furthermore, the reliance on “compilers” to translate old papers into this new format risks baking in errors or “hallucinations” if the original source was vague, proving that while machine-readable data is powerful, it isn’t a magic fix for the inherent complexities of scientific discovery.
We systematically evaluated the medical reasoning abilities of an LLM across six diverse experiments, comparing the model with hundreds of expert physicians. Overall, the model outperformed physicians across experiments, including in cases utilizing real and unstructured clinical data taken directly from the health record in an emergency department. These diagnostic touchpoints mirror the high-stakes decisions taken in emergency medicine departments, where nurses and clinicians make time-sensitive choices with limited information. Our results showed that humans, GPT-4o, and o1 all improved their diagnostic abilities as more information was available; o1 outperformed humans at multiple touchpoints, with the widest gap at initial ER triage, where there is the least information available.
The rapid pace of improvement in LLMs has substantial implications for the science and practice of clinical medicine. Although applying AI to assist with clinical decision support is sometimes viewed as a high-risk endeavor (22, 23), greater use of these tools might serve to mitigate the human and financial costs of diagnostic error, delay, and lack of access (24, 25). Our findings suggest the urgent need for prospective trials to evaluate these technologies in real-world patient care settings and for health care systems to prepare for investments for computing infrastructure and design for clinician-AI interaction that can facilitate the safe integration of AI tools into patient-care workflows. This includes the development of robust monitoring frameworks to oversee the broader implementation of AI clinical decision support systems (22), monitoring not just final diagnostic accuracy but other metrics crucial for successful deployment, including safety, efficiency, and cost.
We emphasize that our study addresses only text-based performance for both humans and machines; clinical medicine is multifaceted and awash with nontext inputs, including auditory (such as the patient’s level of distress) and visual information (for example, interpretation of medical imaging studies) that clinicians routinely use. Existing studies suggest that current foundation models are more limited in reasoning over nontext inputs (26, 27); future work is needed to assess how humans and machines may effectively collaborate (28) in use of nontext signals. This requires new benchmarks, trials, and technological solutions to more faithfully measure clinical encounters. Existing investment in increasingly pervasive ambient AI scribes and other passive monitoring technologies holds promise to serve as the basis for such investigations.
Metastatic cancer remains a significant global health challenge, contributing to the majority of cancer-related mortality due to late detection, therapeutic resistance and the complexity of disseminated disease. Recent advances in artificial intelligence (AI) and augmented reality (AR) are transforming the landscape of metastatic cancer detection and management. AI-driven tools, including radiomics, deep learning models, and predictive analytics, enhance early identification of metastatic lesions, improve diagnostic accuracy, and support personalized treatment strategies by integrating multimodal clinical, imaging and molecular data. At the same time, AR technologies are increasingly applied in image-guided surgery, real-time tumor visualization and patient education, enabling more precise interventions and improved clinical decision-making.