Citations

ABSTRACT TAR DNA‐binding protein 43 (TDP‐43) inclusions are defining pathological features of frontotemporal lobar degeneration (FTLD) but are also often observed in Alzheimer's disease ( AD ) and primary age‐related tauopathy (PART). TDP‐43 in AD is either associated with cognitive impairment or a protective‐life prolonging impact, and yet the localization, cellular and fragment characteristics of TDP‐43 need to be determined. We investigated the relationships between TDP‐43 volumetric inclusion burden in low likelihood AD (lAD) and definite PART by immunostaining against phosphorylated TDP‐43 (pTDP‐43), TDP‐43 C terminal (TDP‐C) and TDP‐43 N‐terminal (TDP‐N) fragments combined with 3D confocal imaging taken from eight regions: amygdala (basolateral [amygdala‐BL] and centromedial amygdala [amygdala‐CM]), the hippocampus (Cornu Ammonis [CA]‐1, CA2/3, CA4, dentate gyrus [DG] and subiculum [SUB]) and entorhinal cortex (ERC) and artificial intelligence (AI)–based segmentation via object recognition, reconstruction and quantification. We found amygdala‐CM in lAD and PART to have the overall greatest burden of pTDP‐43 whereas TDP‐N burden in amygdala‐BL of PART cases was greater than other TDP‐43 fragments. There was no difference in TDP‐43 burden in hippocampal subfields in PART. However, CA2/3 region showed greater pTDP‐43 burden while TDP‐N stood out in DG and SUB. Multiple comparisons among the groups revealed that TDP‐C was the only fragment showing differences among PART and lAD in CA2/3, DG and SUB regions. Overall, unbiased AI‐based volumetric burden analysis pipeline demonstrated unique fragment aggregation patterns in the neurodegenerative processes of PART and AD .

Healthcare Systems Science (HSS) is an important part of medical education because it helps physicians understand how healthcare systems work and how to improve them. However, it is not commonly included in graduate medical training. This manuscript describes the creation and use of a radiation oncology (RO) HSS curriculum that was built with medical school faculty and adapted to the needs of residents in our residency program. The curriculum included sixteen sessions over ten months and covered six domains: person centered care, population centered care, team-based care, high value care, leadership, and health policy economics and technology. Each session included a short lecture and a case discussion on topics such as financial toxicity, telemedicine, workflow improvement, burnout, health equity, and billing. The curriculum was used successfully with regular participation from residents and faculty, showing that it can fit into an existing residency schedule. Residents completed pre- and post-intervention surveys assessing understanding, consideration, confidence, and satisfaction related to HSS domains using 5-point Likert-scale items. Among residents with paired survey data, scores for understanding of HSS, incorporation of HSS into care planning, confidence applying HSS principles, and satisfaction with HSS integration all improved following curriculum completion. This experience shows that adding HSS to RO residency training is practical and useful, and it may also be a helpful model for other residency programs.

Abstract Background Adults with congenital heart disease (ACHD) represent a rapidly expanding population with evolving mortality patterns. Despite improved survival, excess mortality persists. Objective To evaluate the incidence, causes, and predictors of mortality in a contemporary ACHD cohort. Methods We performed a retrospective cohort study of adults (≥18 years) first evaluated at Mayo Clinic from 2002–2023. Baseline clinical, imaging, and electrocardiographic data were analyzed. Vital status was determined using institutional records and the Accurint national mortality database. Kaplan-Meier analysis and Cox proportional hazard models were used to evaluate mortality and identify independent predictors of mortality Results A total of 7,678 ACHD patients were included, with median age of 36.8 years and median follow-up of 11.4 years. During 78,768 patient-years of follow-up, 1,116 patients died (median age at death 57.2 years), corresponding to an annual mortality rate of 1.4%. The cumulative rate of all-cause mortality at 5, 10, 15, and 20 years was 6.9%, 12.0%, 19.0%, and 26.2%, respectively. When stratified by CHD complexity, the annual death rate in patients with severe CHD (2.4%/year) was twice that of patients with moderate or mild CHD (both 1.2%/year). Older age and ACHD subtypes, specifically, cyanotic heart disease (HR 3.9, 95% CI 2.9–5.3) and Fontan physiology (HR 3.2, 95% CI 2.3–4.4), were strongly associated with increased mortality. Additional independent predictors included male sex, ventricular dysfunction, advanced NYHA class, prior heart failure hospitalization, hypertension, smoking, coronary artery disease, renal dysfunction, and abnormal hemoglobin levels. Cardiovascular causes accounted for 57.7% of deaths with known etiology, predominantly heart failure (48.9%) and sudden cardiac death (21.9%), while non-cardiovascular causes were driven mainly by infection and malignancy. Conclusions In this large contemporary ACHD cohort, mortality was driven by ventricular dysfunction, heart failure, and systemic end-organ involvement in addition to the underlying congenital anatomy. Both cardiovascular and non-cardiovascular causes contributed significantly to mortality. These findings underscore the need for comprehensive multidisciplinary ACHD care focused on early recognition of cardiac functional decline, management of acquired comorbidities, and end-organ dysfunction.

Abstract Artificial intelligence is spreading through public health agencies and healthcare systems faster than workforces are trained to govern it. We describe RHAMI (Responsible Health AI Readiness and Maturity Index), the first human-centered automated composite index for health organizations to score AI readiness and maturity as one measurable system of science, economics and ethics, and the physician certification-to-Master’s pathway built to teach what it measures—both designed jointly by frontline public health professionals, clinicians, ethicists, engineers, leaders, and policy-makers in low- and middle-income countries and high-income countries.

ABSTRACT Background Radiotherapy is a cornerstone of treatment for pediatric central nervous system (CNS) tumors, but dose selection remains uniform despite interpatient variability in tumor radiosensitivity. This is consequential in children, for whom toxicity can have lifelong effects. Genomic-adjusted radiation dose (GARD) integrates tumor genomics with delivered radiation dose to quantify the biological effect of radiotherapy and has been validated across adult malignancies. Its relevance in pediatric CNS tumors is unknown. Methods We performed a cohort study using gene expression and data from 246 pediatric patients with high-grade glioma, medulloblastoma, or ependymoma from the Children’s Brain Tumor Network. GARD was calculated using a sequencing-adapted radiosensitivity index integrated with radiation dose through the linear–quadratic model. Associations of GARD and physical radiation dose with event-free and overall survival were evaluated using Cox proportional hazards models stratified by tumor type and anatomic location. Patients who did not receive radiotherapy comprised a negative control cohort (sham-GARD). Results Among patients receiving radiotherapy, physical dose was uniform, whereas GARD showed substantial interpatient variability in biological effect. Higher GARD was associated with improved event-free survival (hazard ratio [HR] 0·90, 95% CI 0·83–0·97; p=0·004) and overall survival (HR 0·90, 95% CI 0·83–0·99; p=0·018). Physical dose was not associated with either endpoint. Among patients who did not receive radiotherapy, sham-GARD was not associated with outcomes, supporting GARD as a treatment-specific predictor rather than a prognostic biomarker. Conclusions In pediatric CNS tumors, the biological effect of radiotherapy quantified by GARD was associated with outcomes, whereas physical dose was not. These findings challenge uniform radiotherapy dosing and support genomically informed dose individualization. Prospective evaluation of GARD-guided radiotherapy is warranted to optimize tumor control while minimizing long-term toxicity in children.

Abstract The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by “expansion” of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A “no-zone” hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care.