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Purpose: Subretinal drusenoid deposits (SDDs) are a distinct entity in age-related macular degeneration (AMD) and associated with photoreceptor impairment during progression. Their early impact on photoreceptors remains incompletely understood. This study examined photoreceptor reflectivity during the phase when SDDs were not clinically detectable on optical coherence tomography (OCT) using adaptive optics scanning laser ophthalmoscopy (AOSLO).

Design: Longitudinal observational study.

Participants: Patients with intermediate AMD.

Methods: Six eyes of four patients with intermediate AMD and predominantly SDDs underwent multimodal imaging 3-4 times over 3.5 years. Individual SDDs were graded using an OCT-based 3-stage system at each time point. Cross-sectional retinal structure and photoreceptor reflectivity at the location where the new SDDs developed during follow-up were evaluated using OCT and AOSLO.

Main outcome measures: Photoreceptor reflectivity change prior to and during SDD development.

Results: Forty-eight retinal locations where new dot-type SDDs developed during follow-up were identified. AOSLO revealed reduced photoreceptor reflectivity in these regions before OCT demonstrated clinically evident deposits (stage ≥ 1) between the ellipsoid zone and the retinal pigment epithelium at the corresponding sites. The mean time to development of stage 1, stage 2, and stage 3 SDDs was 11.78 ± 5.01, 17.40 ± 6.08, and 18.72 ± 4.08 months, respectively.

Conclusions: High-resolution adaptive optics confocal imaging enables detection of photoreceptor optical property alterations at a stage when SDDs are not yet evident on OCT. This finding underscores the exceptional sensitivity of photoreceptors to minimal structural or functional perturbations during SDD formation and defines an early window for potential intervention.

Relapsed and/or refractory disease remains the leading cause of death in AML, highlighting the need for broadly applicable, high-sensitivity approaches to MRD detection. We developed AML-CAPP-Seq ( Ca ncer P ersonalized P rofiling by Deep Seq uencing), a personalized hybrid-capture assay that tracks both canonical AML drivers and patient-specific variants identified by whole-exome sequencing. In 56 patients with longitudinal plasma and matched peripheral blood and bone marrow samples, AML-CAPP-Seq enabled universal MRD assessment and resolution of clonal dynamics using a median of 30.5 variants per patient. Plasma ctDNA outperformed cellular compartments for MRD detection and more strongly predicted relapse-free (HR 17.8, p<0.0001) and overall survival (HR 17.0, p<0.0001) than standard-of-care MRD methods. Among 29 allogeneic transplant recipients, peri-transplant ctDNA-MRD dynamics markedly improved relapse risk stratification (HR 36.0, p=0.0009). Together, these results establish personalized ctDNA profiling as a minimally invasive, highly sensitive, and generalizable platform for enhanced clinical MRD detection and clonal surveillance in AML.

Significance statement: We present a personalized blood test for acute myeloid leukemia that tracks patient-specific circulating tumor DNA, enabling sensitive, universal, noninvasive detection of residual disease. It outperforms standard-of-care marrow and cell-based methods for predicting relapse and survival, including after transplant, reveals clonal dynamics, and supports individualized disease monitoring and risk-adapted treatment.

Why some surgical participants experience pain that extends beyond the original site of injury while others do not remains poorly understood. Both pain intensity and widespread pain contribute to recovery and quality of life, yet their psychosocial correlates are often examined separately. Using data from two large pre-surgical cohorts-participants preparing for knee replacement or thoracic surgery-we examined associations between sociodemographic and psychosocial factors, pain intensity at surgical and non-surgical sites, and widespread chronic pain. Across cohorts and outcomes, fatigue showed the strongest and most consistent associations with pain intensity and widespread pain, independent of other measured factors. Fatigue also occupied a central position in statistical association networks and accounted for substantial shared variance among multiple psychosocial variables, including sleep disturbance, depression, stress, and socioeconomic disadvantage. Pain at non-surgical sites was strongly associated with widespread pain and frequently accounted for observed associations between surgical-site pain and widespread pain. Together, these findings highlight robust patterns of association linking fatigue, pain intensity, and widespread pain in pre-surgical populations.

One sentence summary: Fatigue is the strongest and most consistent factor linked to how pain intensifies and spreads before surgery.

Detecting low variant allele fraction (VAF) mosaic variants without matching controls remains a major challenge in genomics, limited by technical noise, lack of benchmarks, and computational scalability. We present the DRAGEN mosaic caller, a hardware-accelerated approach identifying variants down to ∼1-2% VAF with low false-positive rates and hour-scale runtimes for mosaic SNV/indel detection from bulk sequencing. To support evaluation, we introduce a genome-wide low-VAF benchmark for variants between 1-10% VAF. Application to blood, sperm, and brain tissues revealed patterns, including mosaic hotspots and mutational signatures. The first analysis of HG002 blood showed that many "mosaic" variants defined from HG002 cell lines are likely culture-derived and not in vivo mutations. Importantly, DRAGEN also enables personalized assembly pangenome references to improve alignment and mosaic variant detection in complex regions. Together, this development makes routine low-VAF discovery feasible, opening new opportunities to study mosaic mutations in healthy and disease individuals.

Introduction: Mortality and smoking rates vary over time across the US. The Cancer Intervention and Surveillance Modeling Network-Lung Working Group (CISNET-LWG) has developed a smoking history generator to describe the effects smoking on health. This work further refines these parameters and quantifies effects on life expectancy.

Methods: Data from the National Health Interview Survey (NHIS) and the Tobacco Use Supplement to the Current Population Survey (TUS-CPS) were used to estimate smoking history parameters for each state. The age-period-cohort was used in most cases, but an age-cohort mode was used for cessation probabilities. Population mortality data were used to estimate mortality rates for all causes, lung cancer, and non-lung cancer. These were partitioned by smoking status.

Results: California and Kentucky are states with more or less aggressive tobacco control. The difference between population cohort life expectancy and life expectancy of never smoker was greater for males than for females, and it was greater in Kentucky than California because of higher smoking rates. These differences decreased with time. Similar result are shown for each state.

Conclusions: Variation in smoking parameters and mortality trends vary considerably among states. These show variation in exposure to tobacco smoking and their effects on life expectancy. The Southeast region tends to have greater differences from never smokers because of higher smoking rates. However, there are also other factors affecting mortality rates.

Purpose: To create a publicly available, interactive data platform to visualize various data measures reflecting Colorado and its residents to support research and outreach efforts, specifically focusing on cancer burden and disparities throughout the state. This platform, named ECCO (Exploring Cancer in Colorado), aims to integrate diverse public data sources into a unified, user-friendly interface, accessible to researchers, community members, and outreach programs alike.

Methods: A multi-disciplinary team developed ECCO, leveraging public data sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment. The platform's architecture employs a three-tiered web application model, utilizing a PostgreSQL database, a backend API built with FastAPI, and a Vue 3 frontend with an Open Layers map. Data is organized geographically at the county and/or census tract levels, categorized into measure categories (e.g., socio-demographics, cancer risk factors), and further filterable by demographic characteristics. An automated Extract-Transform-Load (ETL) data pipeline ensures regular updates of the data.

Results: The platform visualizes data such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates. Additionally, ECCO incorporates location-specific data for cancer care facilities, health services, environmental exposures, and political boundaries. To date, ECCO has had 1.1K unique visitors and over 19K pageviews according to Google Analytics.

Conclusion: The ECCO platform provides a valuable tool for understanding and addressing cancer disparities in Colorado. By integrating diverse data sources and offering interactive visualization, ECCO enhances the ability of researchers, community members, and outreach programs to identify populations at risk, inform interventions, and support research priorities.

Availability: The application and code are available at https://coe-ecco.org/ and https://github.com/colorado-cancer-center/ecco .

Content summary: Key Objective: This work sought to develop ECCO (Exploring Cancer in Colorado), an interactive, easily-accessible data platform designed to visualize and understand diverse cancer-related data measures reflective of Colorado and its residents.Knowledge generated: ECCO integrates public data from sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment, visualizing measures such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates at both county and census tract levels. The platform also incorporates location-specific data on cancer care facilities, health services, environmental exposures, and political boundaries.