Citations

Healthcare organizations face persistent tension between efficiency and human connection. Digital systems often fragment attention and unintentionally distance staff from patients. At Mayo Clinic, we developed a conversational artificial intelligence (AI) agent embedded in Microsoft Teams to address this challenge. Connecting and Communicating (CoCo) provides staff with communication guidance, drawing on institutional resources aligned with the Mayo Model of Communication (MMOC). Built in Microsoft Copilot Studio and shaped by clinicians and operational stakeholders, CoCo was designed to be values-aligned and usable within existing workflows. CoCo is an active, user-initiated tool used primarily before or after challenging interactions to support preparation, reflection, and communication planning. Staff valued CoCo as a “just-in-time coach” that reinforced empathy while reducing stress. From August 2025 through February 2026, CoCo supported 1,903 conversation sessions, with early descriptive analytics suggesting favorable satisfaction and response-quality signals. We share insights from development and deployment, along with practical recommendations, to support other organizations considering bespoke, values-aligned AI tools to enhance patient and staff experience. We distill practice-based implementation lessons around governance, human factors, and change management, and offer practical recommendations to inform similar efforts elsewhere.

Recognizing that antioxidant systems are impaired in the airways of premature infants, we used human fetal airway cells to explore the impact of oxygen on iron regulation and iron-mediated cell death (ferroptosis). We find moderate hyperoxia impairs antioxidant systems that prevent ferroptosis and dysregulates iron metabolism, while severe hyperoxia has a negative effect on mechanisms driving the early onset of ferroptosis. Inhibitors of ferroptosis decrease iron and lipid peroxidation, demonstrating links between oxygen and iron regulation in developing airways.

Apply It! By reading this article, you will: • Understand how commonly used cardiorespiratory fitness prediction equations can misclassify fitness in both high-performance and clinical populations. • Learn how common prediction equations were developed and why the population of prediction equation studies matters when interpreting estimated cardiorespiratory fitness and percent-predicted values. • Recognize how to critically assess the suitability of a given cardiorespiratory fitness equation based on your client’s or patient’s characteristics.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.

Abstract Background Sleep disturbances are common in Alzheimer's disease and have been reported in its preclinical stage. A detailed characterization of sleep disruption across the Alzheimer’s disease continuum is required to clarify its role as a potential therapeutic target and to evaluate the utility of sleep-related biomarkers. Methods Sleep architecture was characterized through detailed spectral and temporal analysis of the slow-wave activity using polysomnography across the Alzheimer’s disease continuum . Participants included: cognitively unimpaired individuals without abnormal Alzheimer’s disease biomarkers (n = 30), cognitively unimpaired individuals with abnormal Alzheimer’s disease biomarkers (n = 45), and individuals with mild cognitive impairment or mild dementia due to Alzheimer’s disease (n = 41). Associations between slow-wave features and longitudinal cognitive performance were tested in cognitively unimpaired participants. Sex differences were also explored. Results A stepwise increased wake after sleep onset and latency to rapid eye movement sleep was observed, alongside reduced spectral power of slow-wave activity and a progressive flattening of slow-waves across the Alzheimer’s disease continuum in both sexes. In cognitively unimpaired individuals, higher slow-wave slope and greater peak-to-peak amplitude were associated with better cognitive trajectories over time. Conclusions Sleep macro- and microarchitecture are altered early and progressively deteriorate across the Alzheimer’s disease continuum . Slow-wave features relate to cognitive trajectories in preclinical Alzheimer’s disease, with exploratory analyses suggesting stronger associations in men. These findings support slow-wave morphology as a candidate non-invasive marker for future sleep-based intervention studies.

Abstract In 2024, the National Academies of Sciences, Engineering, and Medicine (NASEM) published a report entitled Advancing Research on Chronic Conditions in Women. A major purpose of this report was to summarize the current state of research into a series of 21 chronic conditions that affect women differently or that are more common in women. The American Society for Bone and Mineral Research (ASBMR) established a working group to evaluate the NASEM report’s implications for women’s musculoskeletal (MSK) health. This perspective summarizes key research gaps identified in the NASEM report, including the lack of sex-specific data, underrepresentation of women in clinical studies, and minimal integration of MSK health with other chronic conditions. The NASEM report underscores the need for more comprehensive study designs that consider hormonal, genetic, and social determinants of health across the lifespan. In addition to osteoporosis, in this perspective, the ASBMR working group highlights emerging but poorly understood evidence of detrimental effects on musculoskeletal biology in non-MSK, chronic diseases that were identified in the NASEM report to disproportionately affect women. To advance the field, we advocate for better diagnostics, increased investment, and stronger representation of women in research studies. Addressing these gaps is critical for improving prevention, care, and outcomes for chronic conditions that uniquely or disproportionately affect women.

Abstract Drug-resistant epilepsy affects tens of millions of people worldwide and is associated with considerable morbidity and mortality. Thalamic deep brain stimulation and cortical responsive neurostimulation are proven treatments for focal epilepsy. Both have been used to target a range of thalamic nuclei; yet, the roles of these thalamic nuclei in focal seizure generation remain incompletely understood. Thirteen patients with drug-resistant focal epilepsy undergoing intracranial EEG were consented to undergo investigation of thalamocortical networks. Sampled regions included cortical, mesial temporal, and thalamic brain regions. Visual and spectral analyses were performed to identify seizure onset patterns and correlate thalamic and cortical seizure activity. Thalamic ictal discharges were observed in 89% of seizures. Of these, 56% demonstrated synchronous thalamocortical activity with distinct patterns. These onset patterns included hypersynchronous spiking, low-voltage fast activity, ictal baseline shifts, and broadband suppression. Multiple thalamic nuclei were involved in ictal organization and propagation, with the specific nuclei depending on the cortical seizure network. The thalamus plays a crucial role in focal onset seizure generation and propagation, with distinct seizure onset patterns and nuclei involved. These findings support exploring a broader range of thalamic nuclei in epilepsy neurostimulation and have implications for seizure detection settings in intracranial sensing devices.

Background: Thoracic aortic aneurysm (TAA) remains a high-risk vascular condition despite major advances in imaging surveillance, operative repair, and endovascular therapy. Medical management still relies largely on blood pressure control and global cardiovascular risk reduction. Renin–angiotensin–aldosterone system (RAAS) inhibitors are frequently used in TAA, but contemporary data evaluating survival and cardiovascular outcomes in broad TAA populations are limited. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2 inhibitors) have established cardiometabolic benefits, yet their role in TAA has not been well defined. Methods: We performed a retrospective multicenter cohort study of adults with imaging-confirmed TAA diagnosed between 1 January 2018 and 1 January 2026 using a Mayo Clinic electronic data platform encompassing more than 15 million patient records. Primary exposures were documented use of RAAS inhibitors, GLP-1 RAs, and SGLT2 inhibitors, evaluated individually and in prespecified combination-therapy analyses. Propensity score matching was used to balance demographics, comorbidities, aortic procedural history, and concomitant cardiovascular medications. Primary outcomes were all-cause mortality and major adverse cardiovascular events (MACE) through 60 months. Results: The study included 162,126 patients with TAA. After matching, RAAS inhibitor use was associated with higher 60-month overall survival (88.3% vs. 85.5%; hazard ratio [HR], 0.79; 95% CI, 0.76–0.83; p < 0.001) and MACE-free survival (86.1% vs. 84.2%; HR, 0.87; 95% CI, 0.83–0.91; p < 0.001). GLP-1 RA therapy was associated with higher overall survival (97.5% vs. 92.5%; HR, 0.32; 95% CI, 0.27–0.38; p < 0.001) and MACE-free survival (93.2% vs. 89.3%; HR, 0.62; 95% CI, 0.56–0.70; p < 0.001). SGLT2 inhibitor therapy was similarly associated with higher overall survival (89.8% vs. 81.5%; HR, 0.51; 95% CI, 0.47–0.54; p < 0.001) and MACE-free survival (86.3% vs. 79.1%; HR, 0.62; 95% CI, 0.58–0.66; p < 0.001). Combination therapy with RAAS inhibitors plus either GLP-1 RAs or SGLT2 inhibitors was associated with incremental improvements in overall survival and MACE-free survival compared with GLP-1 RA or SGLT2 inhibitor monotherapy. Conclusions: In this large propensity-matched TAA cohort, RAAS inhibitors, GLP-1 RAs, and SGLT2 inhibitors were each associated with improved survival and fewer major cardiovascular events, with additional benefit observed for RAAS-based combination therapy. These findings support further prospective investigation of integrated cardiometabolic and vascular-targeted therapy in TAA, while underscoring that observational associations should not be interpreted as proof of aneurysm-specific disease modification.