Citations

Abstract Alternaria spp. are dematiaceous ascomycetes that can cause opportunistic infections in immunocompromised hosts. They are challenging to identify and treat. However, optimal antifungal therapy remains undefined. We retrospectively reviewed antifungal susceptibilities (AST) of all Alternaria spp. isolates from adults at a tertiary care center between 2011 and 2025. We evaluated culture sources, minimum inhibitory concentration (MIC) and minimal effective concentration (MEC) distributions, and MIC₅₀/MIC₉₀ and MEC50/MEC90 values. Clinical characteristics, treatment measures, and outcomes of patients with clinically significant Alternaria spp. infections were described. Forty-one patients had Alternaria spp. isolated, most commonly from skin (n = 13), bone/joint (n = 5), nail (n = 3), or cornea (n = 3). Posaconazole and itraconazole demonstrated low MICs (MIC₅₀/MIC₉₀: 0.06/0.5 and 0.25/0.5 µg/mL, respectively). Voriconazole and isavuconazole showed higher MIC₅₀/MIC₉₀ (1/2 and 2/4 µg/mL). Caspofungin (0.125/0.5 µg/mL) and amphotericin B (0.25/1 µg/mL) had low MICs and MECs.s. Alternaria spp. represented clinically significant infection in 27 cases (66%); 26% were solid organ transplant recipients and 15% had hematologic malignancies. Other risk factors included preceding trauma (22%) and autoimmune disease (15%). Posaconazole was the most frequently used empiric agent (6, 32%). Following AST results, 14 patients (52%) received oral antifungal therapy; posaconazole was used in all 14. In addition, 7 patients (26%) received combination therapy with IV amphotericin B. Adjunctive surgery occurred in 14 cases (52%). Five patients (19%) died before completing therapy; Alternaria infections occurred predominantly in immunocompromised hosts and were frequently managed with posaconazole and surgery. Interpretive criteria for Alternaria spp. are needed to guide empiric and definitive therapy.

Health care organizations are rapidly adopting artificial intelligence (AI)-enabled tools, yet nurse leaders often lack a clear framework to evaluate, govern, and safely scale these technologies. This article presents a practical, system-level AI governance model grounded in risk mitigation, interdisciplinary partnership, and nursing leadership decision rights commonly enacted through nursing professional governance structures. We outline core components of an effective governance structure, including clinical usefulness, safety and reliability, fairness and inclusiveness, transparency and explainability, privacy and security, and organizational accountability. Real-world examples illustrate how nurse executives can guide responsible AI adoption to reduce cognitive load, improve documentation efficiency, and enhance patient outcomes. The framework equips nursing leaders with actionable strategies and practical workflow checklist to lead AI transformation in complex health care environments.

Background New antiamyloid therapies (AAT) for treating patients with early Alzheimer disease (AD) have become available in the past 3 years. Although AAT can be beneficial in slowing progression of AD, they also come with potentially serious side effects, particularly for patients requiring anticoagulation or thrombolysis for cardiovascular conditions. Given that most patients with AD are older and likely to have cardiovascular risk factors, there is urgent need for additional guidance for clinicians about potential risks and benefits associated with AAT for patients with cardiovascular comorbidities, best practices for management of these patients, and special considerations with respect to emergency evaluation and treatment of patients on AAT who develop acute neurological symptoms. Methods The American Heart Association convened 75 multidisciplinary experts for the Amyloid‐Related Imaging Abnormalities Summit: Cardiovascular and Stroke Considerations for β‐Amyloid Immunotherapy in AD in July 2025. These experts were divided into 8 work groups to evaluate the current evidence related to AAT usage in patients with cardiovascular risk factors and conditions and develop consensus‐based guidance for clinicians. Results This statement outlines the outcomes of the Summit, including considerations for prescribers, primary care professionals, emergency personnel, and health care system stakeholders. These considerations are intended to improve identification of patients as appropriate candidates for AAT, management of patients with co‐occurring AD and cardiovascular conditions, and their emergency evaluation and treatment. The statement also includes guidance for shared decision‐making, implementation of system changes for improving dementia care, and recommendations for future research and data collection to add to the body of evidence for treating patients with concomitant AD and cardiovascular conditions. Conclusions Building on lessons learned from the establishment of stroke systems of care, the consensus‐based considerations outlined in this document can serve as a foundation for AD systems of care aimed at ensuring optimal use of these ground‐breaking therapies.

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy but are associated with immune-related adverse events, including ICI–associated acute kidney injury (ICI-AKI). ICI-AKI presents diagnostic and management challenges and can influence decisions regarding immunosuppression and ICI rechallenge, with important implications for both kidney and cancer outcomes. An international, multidisciplinary panel convened at the 34th Acute Disease Quality Initiative (ADQI) consensus conference in September 2024. Systematic literature searches of PubMed were conducted to identify studies on the epidemiology, mechanisms, diagnosis, management, rechallenge, and outcomes of ICI-AKI. The meeting followed the established ADQI process and used a modified Delphi method to achieve consensus. Evidence was reviewed and appraised by workgroups, and consensus statements were developed through structured discussion and voting. Observational data suggest that AKI occurs in up to 20% of patients receiving ICIs, with ICI-AKI accounting for approximately 2%–5% of cases. Acute tubulointerstitial nephritis is the most common lesion, observed in 80%–90% of biopsies, although glomerular diseases are increasingly recognized. No clinical features reliably distinguish ICI-AKI from other causes of AKI, and kidney biopsy remains the diagnostic gold standard. Emerging urinary, circulating, and imaging biomarkers show promise but are not yet ready for routine clinical use. Early glucocorticoid initiation (within 3 days of ICI-AKI diagnosis versus >3 days after diagnosis) is associated with higher rates of kidney recovery, although optimal dosing and duration remain uncertain. Recurrent ICI-AKI occurs in fewer than 20% of patients undergoing ICI rechallenge. Management of high-risk populations, including kidney transplant recipients and patients with autoimmune disease or advanced CKD, requires individualized, multidisciplinary decision-making. ICI-AKI is an important and potentially reversible complication of cancer immunotherapy; these consensus statements provide a framework for diagnosis and management, support cautious ICI rechallenge in selected patients, and identify priorities for future research.

ABSTRACT Purpose Despite 5‐year survival rates of over 90% among children and adolescents/young adults (CAYAs) with classic Hodgkin lymphoma (cHL), 15%–20% relapse after frontline therapy. Prior analysis of frontline Children's Oncology Group (COG) clinical trials demonstrated that, despite similar rates of relapse, non‐Hispanic Black (NHB) and Hispanic (vs. non‐Hispanic White [NHW]) patients experienced higher post‐relapse mortality. It is unknown whether post‐relapse disparities persist when second‐line treatment is delivered in a cooperative group trial setting. We examined overall survival (OS) by race and ethnicity in CAYAs enrolled in COG trials for relapsed/refractory (r/r) cHL. Methods A pooled analysis of individual‐level data from CAYAs (≤ 29 years) receiving therapy for r/r cHL on COG clinical trials (2001–2016) was conducted. The Kaplan–Meier method estimated 3‐year OS by racial and ethnic groups. Cox regression models examined associations of race and ethnicity and OS, adjusted for age, insurance, first versus ≥2 relapse, and time from initial diagnosis to relapse trial enrollment. Results Among 175 CAYAs treated on COG trials for r/r cHL (5.7% Asian or Pacific Islander, 14.9% Hispanic, 14.3% NHB, 61.7% NHW, 3.4% other), at median follow‐up of 4.9 years, 3‐year OS was 82.1% (95% confidence interval [CI], 75.3%–87.1%) and did not differ by race and ethnicity ( p = 0.36). In multivariable analyses, shorter time from diagnosis to relapse trial enrollment ( p = 0.01) and ≥2 relapses (vs. first, p = 0.004) conferred worse OS, with no significant effect of race and ethnicity ( p = 0.43). Conclusion Post‐relapse survival did not differ by race and ethnicity among CAYAs enrolled in COG trials for r/r cHL, suggesting access to clinical trials may mitigate OS disparities.

Abstract INTRODUCTION Although cerebrospinal fluid (CSF) biomarkers reflect neurodegeneration in Alzheimer's disease (AD), it remains unclear whether these biomarkers track neurodegeneration in early‐onset Alzheimer's disease (EOAD). METHODS In 80 EOAD patients, we examined correlations between eight CSF biomarker levels and cortical thickness decreases within the EOAD cortical atrophy signature. Significant correlations were then entered into a multiple regression analysis. We also examined contributions of EOAD atrophy and CSF biomarkers to cognitive impairment. RESULTS The levels of four CSF biomarkers correlated to EOAD signature atrophy. Stepwise regression analyses revealed that CSF neurofilament light chain (NfL) levels best predicted EOAD signature atrophy. Multiple regression showed EOAD signature atrophy combined with CSF NfL levels explained more variance in cognitive impairment than either factor alone. DISCUSSION Within EOAD patients, CSF NfL levels relate to the magnitude of cortical atrophy, and a combination of EOAD signature atrophy and CSF NfL levels most robustly predict cognitive impairment.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.