Citations

Abstract Patients with multiple myeloma have an elevated risk of severe complications from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) infection. To demonstrate proof of concept for a pandemic preparedness and response network, the American Society of Hematology Research Collaborative Multiple Myeloma Program developed a prospective, decentralized study within a site-based provider network. In the COVID-19 outcomes in myeloma and the impact of vaccines (COSMIC) study, patients provided consent, permitted electronic health record data collection, completed patient-reported outcomes at baseline, day 30, and day 180 through an electronic study portal, and were compensated for participation. Clinicians at participating sites facilitated study recruitment and shared best practices monthly to accelerate study execution. The COSMIC study rapidly consented 201 patients from 8 participating sites over 4 months (22 January 2024 through 17 May 2024). Consented participants were 30 to 93 years old, with a median age of 67; 46% were female, 69% were non-Hispanic White, 17% were non-Hispanic Black, and 8% were Hispanic/Latino. Patient-reported data completion was 97% at baseline, 81% at 1 month, and 79% at 6 months. At baseline, 55% of patients reported a history of COVID-19 infection and had received a median of 4 SARS-CoV-2 vaccines. For all patients, there were an additional 18 COVID-19 infections and 57 vaccines received during the 6-month observation period. There were no significant differences in health-related quality of life over time or by history of COVID-19 infection. COSMIC demonstrates the feasibility of an innovative evidence generation strategy to drive rapid accrual of patients to address an evolving area of need. This trial was registered at www.clinicaltrials.gov as #NCT05831787.

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.

Abstract Background: Amyloidosis is a complex disease with a multitude of sequalae, particularly with the variants that cause cardiac amyloidosis. There is minimal literature on the types of procedures these patients undergo and the associated postprocedural outcomes. This gap of knowledge makes it difficult for anesthesia providers to risk stratify amyloidosis patients. Methods: This is a single-center, retrospective chart review study performed at a tertiary care medical center that serves as a referral center for patients with amyloidosis. Patients with a known diagnosis of transthyretin (ATTR) or light chain (AL) amyloidosis who underwent procedures requiring anesthesia between January 1, 2018 and October 24, 2024 were included in the study. The primary outcome was 30-day all-cause mortality. Secondary outcomes included other postoperative complications and analysis of types of procedures requiring anesthesia. Results: During the study period, 160 patients underwent a total of 466 procedures requiring anesthesia. Overall, 30-day mortality rate for all encounters was 1.7% (2.7% AL vs 0.5% ATTR, OR 5.71). Endoscopy was the most common procedure, accounting for 26.6% of all procedures. At the patient level, several procedures were more common in ATTR patients including TEE/Cardioversions (21.7% vs 6.6%, p=0.008), electrophysiology (36.2% vs 9.9%, p<0.001), and neurosurgery (13.0% vs 3.3%, p=0.031). Conclusions: In conclusion, patients with amyloid variants that cause cardiac amyloidosis carry substantial postoperative risk when undergoing procedures, with a trend toward increased mortality in patients with AL compared to ATTR. Patients with ATTR are more likely to require neurosurgery and treatment of arrythmias than patients with AL. Additional studies are needed to determine specific diagnostic parameters to help optimize these patients in the future. Trial Registration : Not applicable. Not a clinical trial

ABSTRACT Interactions between clinical pharmacists and industry partners continue to evolve as health care increases in complexity and technology assumes a more dominant role. To address the corresponding ethical challenges, the 2025 Industry Relations Committee of the American College of Clinical Pharmacy (ACCP) was charged with updating the 2008 ACCP position statement, Pharmacists and Industry: Guidelines for Ethical Interactions . This update expands its perspective, reflecting the broader and more influential roles pharmacists play across the health care ecosystem. All clinical pharmacist–industry interactions should be grounded in improving patient care. Through this lens, six recommendations are provided: (1) Pharmacists should not engage in industry partnerships or collaborations that might unduly influence objectivity, independence, or fairness in clinical and professional judgments; (2) Pharmacists should disclose and mitigate financial, consulting, or other relationships that constitute potential conflicts of interest and should not participate in activities where an existing conflict cannot be resolved; (3) Pharmacists who participate in industry‐sponsored research should follow accepted ethical, regulatory, and scientific standards; (4) Pharmacists providing continuing education programs or developing drug information materials should maintain control of content; ensure their content is fair, balanced, and free from bias; and disclose and mitigate any conflicts; (5) Formal instruction on ethical interactions with industry professionals should begin early and continue through all stages of pharmacist education, training, and professional development; (6) Pharmacists should maintain the confidentiality and privacy of patients and other health care professionals when working with industry partners. Questions to guide personal industry interactions are provided. This paper does not detail every emerging area (e.g., artificial intelligence, social media), but recommendations are applicable wherever clinical pharmacists leverage their professional expertise. By maintaining a critical, discerning, and ethical approach to industry collaborations, clinical pharmacists can uphold professional obligations while contributing to the advancement of evidence‐based practice, ultimately prioritizing patient welfare.

BACKGROUND We constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting. METHODS PRSmix + , a multi-trait PRS framework, is used to train a composite PRS (PRS multi ) in COPDGene non-Hispanic White participants ( n = 6,647). Associations of PRS multi with COPD status (GOLD 2–4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American ( n = 2,466), ECLIPSE ( n = 1,858), Mass General Brigham Biobank ( n = 15,152), and All of Us ( n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRS multi and were validated with proteomic data in COPDGene ( n = 5,173) and UK Biobank ( n = 5,012). RESULTS PRSmix + selected 7 traits for PRS multi . In multivariable models, PRS multi was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28–1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11–0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS multi outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION COPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Abstract Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

Abstract The human X chromosome accounts for ∼5% of the genome. Despite its size, the role of X-chromosomal variants in human diseases is not well understood, mainly due to its distinct inheritance pattern and the frequent omission of sex chromosomes from genome-wide association studies. This study used whole-genome sequencing data from 888 multiple system atrophy (MSA) cases and 7128 controls to perform an X-chromosome-wide common variant association study. Our analyses revealed two sex-differential risk loci: one located at Xq28, associated with increased risk for MSA in females [index variant: rs4898389, odds ratio (OR) = 1.69, 95% confidence interval (CI) = 1.40–2.03, P-value = 2.43 × 10⁻⁸], and another at Xp22.2 within the TBL1X gene, identified in males (index variant: rs6638956, OR = 1.48, 95%CI = 1.26–1.73, P-value = 9.92 × 10⁻⁷). In the Xq28 locus, colocalization analyses pointed to FAM3A and PLXNA3 as genes of interest. These findings emphasize the vital role of sex-differential genetic factors in the pathogenesis of MSA.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Vaginal Cancer outline the recommended diagnostic workup, staging considerations, and treatment options for this rare malignancy. Because vaginal cancer is uncommon and shares many biologic and clinical characteristics with cervical cancer, several management recommendations, particularly systemic therapy, are extrapolated from evidence and practices established for cervical cancer. This guideline excerpt summarizes key components of management, including diagnostic evaluation and workup, principles of staging, pathology considerations, radiation therapy principles, and primary treatment recommendations for both early-stage and advanced disease. It also details approaches to manage relapses, including locoregional recurrence and distant metastases, and provides an in-depth overview of systemic therapy recommendations for vaginal cancer.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.