Citations

Abstract Although the human genome encodes a vast repertoire of noncoding RNAs that regulate gene expression, the noncoding genome remains underexplored due to technical challenges. Specifically, during transcriptomic sequencing data alignment, the overlap between noncoding and coding loci can create ambiguous read alignments that are subsequently discarded from downstream analysis. For this reason, most of the noncoding genome is excluded from standard genomic annotations used for sequencing alignment. To address this challenge and enable concurrent profiling of the coding and noncoding transcriptome, we systematically integrated standard coding (GENCODE) and noncoding (LncBook) genome annotations, preserving coding gene annotations and removing overlapping noncoding regions. The resulting integrated genome annotation expanded the number of annotated noncoding genes from 40,785 to 138,296 while preserving all coding genes and reducing ambiguous read assignment. To evaluate the utility of our integrated genome annotation for uncovering novel, biologically relevant noncoding RNAs (ncRNAs), we realigned CD138-positive bulk RNA-seq (N = 942) and CD138-negative single-cell RNA-seq (N = 478) data from the MMRF CoMMpass study, generating a comprehensive coding-noncoding atlas of the myeloma bone marrow microenvironment with noncoding genes representing 51% of highly variable genes and displaying significant cell type specificity. Tumor expression profiling based on this integrated profiling identified 15 clusters, including two enriched for amp(1q21) or t(4;14) and associated with shorter progression-free survival (PFS). Differential expression and systematic filtering yielded 19 candidate high-risk ncRNAs, including previously uncharacterized ENSG00000310209 , which was associated with poor PFS (HR = 1.141, P = 0.0025), increased IRF4 activity, Wnt pathway activation, CCL5 signaling, and the accumulation of anergic-like CD8+ T cells. These findings establish integrated coding-noncoding analysis as a strategic approach for discovering functional ncRNAs from transcriptomic sequencing data.

Photodynamic priming (PDP), a fallout of photodynamic therapy, transiently modulates the tumor microenvironment (TME), enhances therapeutic susceptibility, and promotes immunogenic cell death through the release of damage-associated molecular patterns (DAMPs). Pancreatic ductal adenocarcinoma (PDAC) remains non-responsive to current therapies with a desmoplastic and immunosuppressive TME that limits drug delivery and blunts responses to immune checkpoint blockade. We investigated whether PDP could enhance anti-PD1 therapy responses in PDAC using patient-derived organoids (PDOs). PDOs were treated with Visudyne and red light (25, 75, and 100 J/cm²), followed by assessment of cytotoxicity, DAMP expression (HSP60, calreticulin, HMGB1), and transcriptomic changes. Monocyte-derived dendritic cells (mDCs) from healthy donors were cocultured with PDP-treated (25 J/cm 2 ) PDOs, then with matched naïve T cells. mDC and T cell activation markers were analyzed. Pembrolizumab (anti-PD1) was added to PDO-mDC-T cell cocultures to evaluate combined effects on PDO viability and T cell activation. PDP induced dose-dependent cytotoxicity and upregulated DAMPs. Gene profiling in PDOs showed increased IFN-γ , TNF-α , and CXCL12 , with reduced PD-L1 , TGF-β1 , and FOXP1 expression. mDCs exposed to PDP-treated PDOs upregulated CD40, CD86, and MHC-II, driving activation of CD4 + and CD8 + T cells, evidenced by elevated PD1 expression. Addition of pembrolizumab further decreased PDO viability and amplified effector cytokines ( IFN-γ , TNF-α , FASLG , granzyme B, IL-2 , IL-21 ). PDP was associated with modulation of the PDAC TME toward a more immunogenic phenotype by enhancing tumor immunogenicity, activating dendritic cells and T cells, and potentiating PD1 blockade. These findings provide mechanistic support for further preclinical and clinical evaluation of PDP combined with checkpoint inhibition in PDAC.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Background : Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality. Optimal treatment decisions are challenging, and existing treatment allocation frameworks may not fully reflect the evolving therapeutic landscape or contemporary clinical practices in North America. Methods : Using a modified Delphi process, a multidisciplinary panel of 20 North American experts in hepatology, medical oncology, surgery, radiology, and radiation oncology developed a consensus-based treatment framework for HCC, termed the BEACON-HCC system. The framework was informed by current evidence and expert opinion through iterative discussion and voting. In a pilot study using 29 real-world patient cases, we assessed concordance between external expert recommendations and BEACON-HCC recommendations and Barcelona Clinic Liver Cancer (BCLC) 2025 treatment recommendations. Results : The BEACON-HCC treatment allocation framework diverges from prior frameworks by incorporating nuanced clinical features such as degree of intrahepatic tumor burden and vascular invasion, and adverse tumor prognostic markers, to align treatment allocation with tumor biology and therapeutic potential. Key innovations include the incorporation of emerging modalities such as external beam radiation therapy (EBRT), transarterial radioembolization (TARE), and systemic-locoregional combination therapies. In the pilot exercise, expert treatment decisions showed 96.6% concordance with BEACON-HCC recommendations and 72.4% concordance with BCLC recommendations. Conclusions : BEACON-HCC is a consensus-based framework for treatment allocation that incorporates the expanding range of therapeutic options available to patients in the North American HCC population. Expert treatment decisions showed a high concordance with the BEACON-HCC system; further validation using empiric clinical data is planned through the HCC-Live Consortium.

Importance Pyoderma gangrenosum (PG) is a rare neutrophilic dermatosis characterized by sterile ulcerative skin lesions. Multiple comorbidities and clinical features have been associated with PG, but no standardized guidelines exist for classifying PG phenotypes. Objective To develop an expert-established classification framework for PG phenotypes to inform treatment guidelines and future research endeavors. Evidence Review In this modified Delphi consensus study that included 23 board-certified dermatologists and Medical Dermatology Society members with expertise in PG, panelists completed 5 rounds of anonymous, iterative online surveys that were administered from December 2023 through July 2025. Before the beginning of the consensus exercise, a literature review was performed by nonvoting researchers to summarize existing data on PG clinical associations and comorbidities. A PubMed search from inception to September 2023 identified observational studies, narrative reviews, systematic reviews, and meta-analyses describing PG clinical associations and comorbidities that were published in English. Experts indicated their agreement with proposed PG phenotypes and disease modifiers with a consensus threshold of 70% or greater. Anonymous comments and aggregated results were presented in each subsequent round. In the final round, a framework of PG phenotypes and disease modifiers was proposed for agreement. Findings Twenty-three board-certified dermatologists and Medical Dermatology Society members with expertise in PG completed 5 rounds of iterative surveys. Consensus was reached on the final set of PG phenotypes and disease modifiers, with 83% of experts in agreement. PG phenotypes were overall classified into 2 major groups: PG with autoinflammatory syndromes and nonsyndromic PG. Nonsyndromic PG included 4 phenotypes: inflammatory bowel disease–associated PG, PG in association with hematologic cancers and blood dyscrasias, drug-induced PG, and other (including idiopathic) PG. Disease modifiers of PG phenotype presentations included involvement of special sites (head/neck, genitals, or peristomal skin) and extracutaneous manifestations. Conclusions and Relevance This expert-established, descriptive framework provides a standardized classification system for distinct PG phenotypes and its modifiers. This nomenclature may inform upcoming clinical guidelines and allow for consistency in reporting epidemiological research and outcomes among patients with PG.

Abstract Objective Epilepsy duration is a modifiable risk factor in the outcome of definitive epilepsy surgery; however, an analogous effect in palliative procedures has not been shown. We reviewed the Pediatric Epilepsy Surgery Database data for an association between epilepsy duration and seizure reduction in palliative procedures. Methods Patients enrolled between January 2018 and April 2025 who underwent their first epilepsy surgery with palliative intent with 6 months of follow‐up were included. Procedures included neuromodulation, corpus callosotomy, hemispherotomy, lesionectomy, and lobectomy where surgical intent was not seizure freedom. Outcomes of seizure freedom, 90% seizure reduction, and 50% seizure reduction were considered at 6–12 months and >12 months from surgery. Duration from epilepsy onset to surgery was compared for patients above and below each outcome threshold at each time point. Logistic regression analysis for the association between epilepsy duration and seizure reduction adjusted for potential confounders including procedure type, etiology, and other clinical factors. Logistic regression analysis was performed on the overall cohort and subgroups of patients with each procedure. Results A total of 588 patients were included. Initial univariate analysis suggested that epilepsy duration at time of surgery was significantly associated with seizure freedom and 90% seizure reduction at both 6–12 months and >12 months. After adjusting for confounders, only seizure freedom at >12 months was significantly associated with duration of epilepsy. When individual procedures were considered, only lobectomy was sensitive to duration of epilepsy in multivariate analysis, with significant impacts on >50% and >90% seizure reduction at >12 months. Lesional epilepsy predicted seizure freedom at >12 months. Neuromodulation and corpus callosotomy were less likely to achieve seizure reduction than other procedures. Significance We did not find an association between early epilepsy surgery and seizure reduction. This reflects the heterogeneity of our population, including different types of surgical procedures and lesional and nonlesional epilepsies.

Abstract Background Complications such as intermittent pouchitis (IP) and chronic inflammatory disorders of the pouch (CP) are common after ileal pouch-anal anastomosis (IPAA), particularly among patients with both ulcerative colitis and primary sclerosing cholangitis (PSC). There are no standardized approaches for prophylaxis against these complications for patients with PSC undergoing IPAA. We compared outcomes among patients with PSC and IPAA who received antibiotic or immunosuppressive therapies to those who did not. Methods We designed a retrospective cohort study that included patients with ulcerative colitis and PSC from 7 tertiary care centers that underwent IPAA between 1994 and 2023. Patients were categorized on whether they received medications following IPAA that may provide primary prophylactic benefit, such as antibiotics, probiotics, immunomodulators, or biologic medications; and the proportion of patients developing IP and CP was evaluated. Results A total of 172 patients were included in the cohort, and 13 (7.6%) patients received antibiotics or immunosuppressant medications. Antibiotics and probiotics were the most common therapy (5 patients, 2.9%), followed by biologics, small-molecule inhibitors, immunomodulators, or mesalamine (all 1 patient, 0.6%). Compared to patients not receiving antibiotics or immunosuppression, patients receiving antibiotics or immunosuppression were significantly less likely to develop IP (69.2% vs 91.2%; P = .03; odds ratio 0.22; 95% CI, 0.06-0.80) and CP (23.1% vs 56.0%, P = 0.04; OR 0.24, 95% CI, 0.06-0.89). Conclusions In this retrospective cohort of patients with ulcerative colitis and PSC undergoing IPAA, usage of concomitant antibiotics or immunosuppression was low. However, use of concomitant antibiotics or immunosuppression may decrease the burden of inflammatory conditions of the pouch in these high-risk patients.

Scientific evidence regarding AKI and acute kidney care has advanced in recent years. However, a knowledge gap remains on the implementation of these evidence-based practices (EBPs) into clinical care. Implementation science (IS) is focused on ensuring that this knowledge is translated in an effective, efficient and sustainable fashion. Objectives: 1) Define the current status of IS; 2) Define a roadmap for accelerating IS with a focus on patient/care partner advocacy, digital tool application, social determinants of health, and resource-limited settings; and 3) Develop a robust and broad research agenda incorporating IS methodology into the programs. Evidence Review: ADQI XXXV was conducted through a modified Delphi process with virtual meetings preceding an in-person meeting. Five workgroups were determined a priori to focus on 1) IS definitions and methods applicable to AKI and acute kidney care; 2) IS literature in AKI and acute kidney care; 3) Innovations in IS to accelerate the adoption, adherence to, and sustainment of EBPs in AKI and acute kidney care; 4) IS methods in resource-limited settings; and 5) Recommendations to identify and evaluate EBPs that are ready for implementation or de-implementation. Prior to the in-person meeting, each workgroup met virtually to review the literature and develop framework questions to address their objectives. During the two-day in person meeting, through iterative discussions, questions and supporting statements were finalized. These questions and statements were agreed upon through voting to achieve consensus, defined as agreement of ≥80%. Conclusions and Relevance: We report a structured multidisciplinary consensus for defining the role of IS in AKI and acute kidney care. Future programs should address these consensus questions and apply these statements along with IS methodology in the translation of science into clinical practice and the implementation/de-implementation of EBPs in clinical care.

Urinary urea is a key biomarker for tracking protein oxidation and nitrogen balance. A persistent analytical limitation is that conventional total-nitrogen assays do not deconvolute urinary urea from urinary ammonia, and direct measurement still requires capital-intensive clinical analyzers. Here, we implement and independently validate two cost-effective methods that resolve urinary urea by pairing the presence and absence of urease: Method 1 is a urease-coupled potentiometric assay using an ammonium ion-selective electrode (ISE method); Method 2 is a microplate Berthelot–salicylate colorimetric assay employing a repurposed consumer-grade aquarium ammonia test kit (API®) (API method). Each method was validated against the Roche Cobas C311 clinical analyzer (ISE for urinary ammonia and API for urinary urea) in healthy adult volunteers (n = 10 each), and API–ISE were then compared head-to-head for urinary urea. ISE vs. Cobas yielded R2 = 0.967, slope 95% CI [0.867, 1.173], and Bland–Altman bias of −1.02 mM (95% LoA [−7.40, +5.36] mM). API vs. Cobas yielded R2 = 0.973, slope 95% CI [0.790, 1.039], and bias of −1.55 mM (95% LoA [−11.56, +8.46] mM). API–ISE urinary urea comparison confirmed mutual agreement (R2 = 0.9976, slope 95% CI [0.946, 1.024]; bias of +3.47 mM; 95% LoA [−14.60, +21.53] mM), with mean recoveries near 100% (API: 100.7 ± 2.9%; ISE: 100.6 ± 5.4%; 20–350 mM). Both methods agreed with the clinical analyzer, supporting their potential for decentralized, resource-limited testing; clinical and point-of-care validations are needed.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.