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Polycystic kidney disease (PKD) arises from mutations in cilia-associated genes, such as PKD1 and PKD2 , expressed in renal epithelial cells, leading to progressive kidney dysfunction and end-stage kidney disease (ESKD). PKD patients exhibit significant heterogeneity in disease progression, largely due to genetic and environmental modifiers. Like patients, mouse models of PKD also exhibit significant heterogeneity with regards to the gene mutated, age of disease onset, and rate of disease progression. To elucidate the cellular and molecular consequences of these variables, we constructed an integrated single-cell RNA sequencing atlas across mouse models of PKD, mapping changes in cell type composition, gene expression, and intercellular signaling networks across the whole atlas and within individual models. Across models, single cell RNA sequencing (scRNAseq) data revealed increased Spp1 (osteopontin) expression and signaling from PKD-enriched clusters. Global deletion of Spp1 in Pkd1 RC/RC mice resulted in a modest reduction in cyst severity and improved kidney function. From these studies, we created a freely available, searchable website (https://bmblx.bmi.osumc.edu/scPKD/) that can be used to identify cross- and intra-model changes in gene expression, guiding researchers to new therapeutic targets for treating PKD.

The Pulmonary Embolism Reporting and Data System (PE-RADS) provides a standardized framework for reporting acute pulmonary embolism at CT and MR angiography to guide treatment considerations based on clot location and right heart imaging features.

ABSTRACT Clinical pharmacists are increasingly recognized as vital contributors to acute stroke care, yet their role remains inconsistently defined and underutilized across health care systems. This consensus statement explores the expanding presence of clinical pharmacists within interdisciplinary stroke teams, particularly in the management of both acute ischemic stroke ( AIS ) and hemorrhagic stroke. This consensus statement panel was composed of clinical pharmacists, emergency medicine physicians, and a vascular neurologist. The Grading of Recommendations, Assessment, Development, and Evaluation ( GRADE ) approach was used to assess the level of certainty and strength of recommendations. We focused on two questions regarding the clinical pharmacist's role in caring for adult patients presenting to the emergency department ( ED ) with AIS or hemorrhagic stroke. Despite the very low level of evidence, a consensus was reached for the following recommendations: (1) clinical pharmacist involvement in AIS care for adult patients presenting to the ED and (2) clinical pharmacist involvement in hemorrhagic stroke care for adult patients presenting to the ED . Based on the available literature, we believe optimizing the clinical pharmacist role is a critical factor in improving time‐sensitive treatment and equity in care delivery.

ABSTRACT As emergency department (ED) crowding and patient boarding reach critical levels nationwide, the role of the emergency medicine pharmacist (EMP) must be both defined and safeguarded. While EMPs are increasingly valued for their contributions to acute care, their responsibilities often unintentionally expand during periods of operational strain, particularly when admitted patients remain in the ED for prolonged durations. EMPs should ideally be resourced to remain focused on time‐sensitive, high‐risk medication management where their expertise is most impactful and not be used as default providers for all pharmacotherapy needs during ED boarding. This perspective outlines our position on the ideal scope of EMP practice during times of congestion, advocating for clearly defined role boundaries, strategic collaboration with other pharmacy team members, and system‐level support to prevent dilution of emergency care. EMPs must be empowered to prioritize critical ED workflows, support resuscitations, and lead initiatives in education, protocol development, and patient safety, without being overextended by responsibilities that can be absorbed by other team members.

RELA-related disease is a rare, autosomal-dominant condition presenting with immune dysregulation and immunodeficiency. RELA encodes the p65 subunit, a component of the canonical NFκB pathway. The underlying pathogenesis, at least in part, involves impaired NFκB activation, increased susceptibility to TNFα-induced apoptosis, and hyperinflammation. To date, only a modest number of cases have been documented worldwide, and the full clinical and functional spectrum of disease remains incompletely defined. This study represents a large global cohort identified with RELA variants, comprising 40 patients from 30 institutions, expanding our previous report of a single large kindred (1). The clinical spectrum includes arthritis, mucocutaneous ulcers, uveitis, dermatitis, irritable bowel disease, recurrent infections, lymphopenia, hypogammaglobulinemia, recurrent fevers, and susceptibility to neoplasia. The patients represent 36 kindreds with 28 unique RELA variants, of which 15 are missense, 7 nonsense, and 6 frameshift variants. Fifteen are in the Rel homology domain, 7 in the transcriptional activation domain, and 6 in the C-terminal region proximal to TAD (Fig. 1). Functional studies assessed the canonical NFκB pathway in 27% of the cohort (n = 11; healthy controls = 40). Patient peripheral blood mononuclear cells (PBMCs) were used to quantify total and phosphorylated p65 (ph-p65, Ser529) by flow cytometry after stimulation with PMA (100 ng/ml) and ionomycin (1 uM). In 8 patients, we assessed the kinetics of IκBα degradation, required for p65 phosphorylation and nuclear translocation of p65/p50. All patients tested had decreased total p65, while 64% had decreased ph-p65 with altered phosphorylation kinetics. Nearly 45% had impaired IκBα degradation. One patient (P4) demonstrated increased IκBα degradation and ph-p65, while a related proband (P1) had decreased ph-p65. Additionally, P4 did not have the same mucocutaneous clinical phenotype as P1 despite sharing the same RELA variant. This could reflect the effect of other genetic modifiers, epigenetic, environmental, or stochastic variations, which could lead to variable expressivity and/or incomplete penetrance. All patients appeared to have haploinsufficiency, as none had normal levels of p65. This study expands the clinical and genetic spectrum of RELA-related disease and provides mechanistic insights into the impact of these variants on p65 phosphorylation and downstream function, resulting in variable expressivity. Figure 1. RelA protein structure. Represents unique variant locations and categories, key domains, and phosphorylation sites. Source: UniProt. Adapted from (2). BioRender.

Background: Long COVID, the chronic sequela of SARS-CoV-2 infection, has affected millions of people worldwide. However, its pathogenesis and treatment remain unknown. To address this critical gap, the National Institutes of Health (NIH) developed the Researching COVID to Enhance Recovery (RECOVER) Initiative that included the Viral Persistence and Reactivation, and Immune Dysregulation (RECOVER-VITAL) study as one of the first of five NIH-sponsored, integrated platform protocols to support the rigorous and rapid investigation of potential interventions for Long COVID. Methods: Experts across academia, the NIH, and the community of patients and caregivers were brought together to design the RECOVER-VITAL protocol. We present the challenges and rationale underpinning critical components of this protocol and provide evidence for use of the platform for efficient execution of current and future Long COVID clinical trials. Facets of the RECOVER-VITAL protocol, including inclusion criteria, intervention groups, study procedures, investigation of biomarkers, and endpoints, were carefully crafted to advance the current and future science and operations of Long COVID clinical trials, particularly in the absence of existing data. The initial trial within the RECOVER-VITAL platform evaluated two durations of an antiviral drug, nirmatrelvir/ritonavir, to test the proposed etiology of viral persistence on the Long COVID symptom clusters of autonomic dysfunction, cognitive dysfunction, and exercise intolerance. The primary outcome measures were patient-reported, and secondary outcomes included performance-based measures for each of the symptom clusters of interest. Conclusions: The RECOVER-VITAL platform protocol has substantial implications for the design and conduct of future Long COVID clinical trials.

BACKGROUND: The implementation of data science concepts, skills, and tools in critical care research and practice faces multiple, complex barriers. METHODS: We developed an implementation science-based framework and method for measuring the adoption, implementation, and sustainment of data science concepts, skills, and tools in critical care—the Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM). Our IRLM specifies constructs for: 1) key determinants (i.e., barriers and facilitators) influencing the implementation of data science concepts, skills, and tools in critical care; 2) implementation strategies deployed by the SCCM Discovery DSC to address these determinants; 3) theorized mechanisms of action by which these strategies affect outcomes; and 4) upstream and downstream implementation outcomes influenced by implementation strategies. RESULTS AND CONCLUSIONS: We believe that our model can facilitate more rigorous measurement of theoretically grounded, empirically assessable factors driving implementation of data science concepts, skills, and tools in critical care.

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC > 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .