Citations

Abstract Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort ( n = 5136) and transcriptomic data from a prospective cohort ( n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS‐M). We further assessed whether the taxonomy could guide management in patients receiving disease‐modifying therapies. Except for the “ TP53 ‐complex” subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS‐M and the taxonomy capture disease biology, other non‐genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML‐like, bi TET2 , SF3B1 , and TP53 ‐complex subgroups. Further integration of multi‐omics data may refine MDS classification, improving clinical decision‐making and guiding the development of targeted therapies.

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

Abstract The Tau Global Conference 2025, hosted by the Alzheimer's Association, CurePSP, and the Rainwater Charitable Foundation, convened international experts from academia, industry, government, and philanthropy to explore advances and challenges in tauopathy research. The meeting highlighted progress across tau biology, including emerging models of tau regulation, degradation, and propagation; advances in biomarker development for the diagnosis and staging of tauopathies; and evolving therapeutic strategies targeting diverse aspects of tau pathophysiology. Discussions also emphasized the importance of cross‐sector collaboration, and global initiatives to address disparities in tau research. This report synthesizes key insights from the conference and underscores the critical role of interdisciplinary, biomarker‐driven, and globally inclusive approaches in accelerating the translation of tau research into effective clinical applications.

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.

Importance Currently, there are no standardized outcome domains or measures in clinical trials for facial aging. Heterogeneity in outcome domains and measurement instruments across clinical trials creates difficulty in directly comparing interventions, determining superior therapies, and developing high-quality meta-analyses. Objective To develop a core outcome set (COS) of essential domains to be reported in clinical trials evaluating the efficacy of interventions for facial aging. Evidence Review PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials, and CINAHL were searched from September 2005 to September 2015. An updated search of the same databases was performed from September 2015 to February 2026. Studies were included if (1) they were randomized clinical trial or controlled clinical trial in design, (2) they assessed the efficacy or safety of an intervention for facial aging, (3) they were published in English, and (4) they involved human participants. Complementary sources, including patient interviews, were used to capture further relevant outcomes. Two rounds of Delphi surveys, followed by consensus meetings, were used to identify outcome domains considered most important by both patient and physician stakeholders. Findings The final COS consists of 6 outcome domains: (1) overall convenience of treatment; (2) time to return to normal work and social activity; (3) overall assessment of focused area of treatment (at the point in time when treatment is expected to provide peak benefit); (4) duration of treatment effect; (5) severity of persistent local or systemic adverse events, including pigmentary change, skin texture change, delayed healing, scarring, and serious adverse events; and (6) patient satisfaction with treatment. Conclusions and Relevance The 6 outcome domains identified through a Delphi consensus are recommended for reporting in future facial aging trials to ensure that outcomes that matter most to patients and clinicians are measured and that results are comparable across interventions.

Abstract BACKGROUND Remote, smartphone‐based cognitive testing may improve access to cognitive assessments for Alzheimer's disease and related dementias. We evaluated the feasibility, reliability, and validity of unsupervised smartphone‐based cognitive tests in a registry‐based cohort. METHODS Adults without a record of cognitive impairment ( N = 1815; ages 18–92) were recruited from the University of California, San Francisco (UCSF) Brain Health Registry to complete three unsupervised smartphone cognitive testing sessions within 2 weeks. Reliability was assessed with correlations between sessions. Linear regression models tested associations of smartphone tasks with demographics, self‐ and informant‐rated cognitive concerns, and web‐based cognitive testing (Cogstate Brief Battery). RESULTS Adherence was high (82.2%) and usability favorable. Test–retest reliability was moderate to strong ( ρ ’s = 0.61–0.85, all p ’s < 0.001). Lower smartphone scores were associated with older age, lower education, cognitive concerns, and worse Cogstate performance. DISCUSSION Findings support the feasibility, reliability, and validity of remote digital assessments in adults without a record of cognitive impairment.