Citations

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.

Background. The adoption of technical variant grafts has expanded the donor pool for pediatric liver transplantation, but it also poses a risk of portal vein thrombosis (PVT). This study evaluated risk and prognostic factors of PVT in children undergoing liver transplantation with technical variant grafts. Methods. A total of 3137 pediatric patients from 19 centers were included: 470 received split/reduced deceased donor grafts and 2667 received living donor grafts. Multivariable logistic regression was used to identify risk factors, whereas time-dependent Cox models were used to assess the impact on survival. Results. PVT occurred in 102 patients (3.3%) and was associated with increased mortality (adjusted hazard ratio, 2.86; 95% confidence interval [CI], 1.13-7.25; P = 0.027) and graft loss (adjusted hazard ratio, 9.97; 95% CI, 4.90-20.3; P < 0.001). Risk factors included younger age, lower weight, higher Pediatric End-Stage Liver Disease score, longer cold ischemia and operation times, higher graft-to-recipient weight ratio, increased red blood cell transfusions, and biliary atresia. After adjusting for center, lower weight (odds ratio, 0.84 per kg; 95% CI, 0.71-1.00; P = 0.047) and biliary atresia (odds ratio, 2.34; 95% CI, 1.10-5.00; P = 0.027) remained predictors. Five-year patient (88.4% versus 93.1%) and graft survival (82.7% versus 96.3%) were significantly inferior in patients with PVT. Conclusions. Low-weight children with biliary atresia constituted a high-risk group, highlighting the necessity for early detection and management.

ABSTRACT Background While guidelines state that patients with NCCN favorable intermediate risk (FIR) prostate cancer (PCa) may consider active surveillance (AS), there is a relative lack of longer term follow up data informing AS inclusion criteria of patients with Grade Group (GG) 2 prostate cancer in the MRI era. Here we evaluate patient outcomes from a MRI‐screened AS‐eligible cohort of FIR GG2 PCa receiving radical prostatectomy (RP). Methods Patients with prostate MRI, NCCN FIR GG2 PCa (PSA < 10 ng/mL, ≤ cT2a, < 50% core positivity), and no prior treatments who underwent RP from 2015 to 2023 were included from a prospectively maintained prostatectomy registry. Primary outcomes were adverse pathologic features (APF) at RP (GG3‐5, ≥ pT3a, or pN1) and biochemical recurrence (BCR). Logistic regression and Cox regression identified factors associated with APF at RP and time to BCR, respectively. Results Of 522 patients, 110 (21%) had APF at RP and 45 (9%) developed BCR with median follow up 2.4 years (IQR 1.3–2.8). On multivariable logistic regression, % Gleason pattern 4 (%GP4, %GP4 > 25% OR 2.14, 95% CI 1.28–3.58, p = 0.004) was independently associated with APF. On multivariable Cox regression for time to BCR, PSAD (HR 1.06, 95% CI 1.04–1.09, p < 0.001) and positive surgical margins (HR 1.06, 95% CI 1.09–3.78, p = 0.03) were independently associated with BCR. PIRADS, % core positivity, and specimen involvement were associated with neither APF nor BCR. Conclusions % Gleason pattern 4 > 25% and PSAD are the primary factors associated with adverse outcomes and should be considered for patients with FIR GG2 PCa considering active surveillance. PIRADS and measures of biopsy cancer volume were not associated with APF or BCR. These results may inform patient selection for active surveillance among patients with FIR GG2 PCa.

ABSTRACT Germline variation shapes cancer risk, yet its influence on the evolutionary dynamics of established tumors remains poorly understood. In multiple myeloma, subclonal diversification drives disease progression and treatment failure, but the heritable factors that modulate this process are unknown. Here, we show that germline variation is associated with tumor evolutionary features, implicating inherited regulation in subclonal expansion. Integrating germline variation with tumor evolutionary parameters identifies variants associated with evolutionary features, with signals enriched in regulatory regions, consistent with a transcriptional basis. We further identify TBKBP1 as a key locus linking germline variation to tumor evolution and clinical outcome. Germline variation at this locus is associated with TBKBP1 expression and subclonal expansion, and TBKBP1 expression correlates with adverse prognosis, consistent across independent cohorts. Functional analyses demonstrate that TBKBP1 promotes proliferation and activates MYC, mTORC1 and non-canonical NF-κB signaling pathway. Together, these findings establish germline regulatory variation as a determinant of tumor evolutionary dynamics and identify TBKBP1 as a mediator linking inherited variation to subclonal expansion and disease progression in multiple myeloma.

Summary Human spaceflight has historically been led by government agencies, but the emergence of commercial organizations is enabling broader participation and new research opportunities. In this study, we present a comprehensive molecular characterization of early human responses to spaceflight, leveraging multi-omics data across the first three weeks of two commercial missions. Biospecimens from six individuals, four from the Axiom 2 mission (10 days) and two from Axiom 3 (21 days), were analyzed using single-cell and bulk RNA sequencing, alongside proteomic profiling. Individual and integrative analyses of these datasets reveal systemic changes in cell types, transcripts, and proteins related to immune regulation, osteoclast differentiation, NF-κB signaling, and blood homeostasis pathways. Importantly, several of the detected pathways align with physiological patterns observed in longer-duration missions. This work establishes a foundational resource for understanding early adaptation to spaceflight at the cellular and molecular levels, providing insights to reduce future space-travel health risks. Highlights First integrated multi-omics analysis using single-cell, bulk RNA sequencing and proteomic profiling of early human spaceflight responses across two commercial missions (Ax-2 and Ax-3). Distinct PBMC clustering was observed across Ax-2 and Ax-3, and post-flight samples. It showed fewer monocytes, dendritic cells, and megakaryocytes with increased naïve CD4⁺ and cytotoxic T cells. Multi-omics integration identifies shared biological signatures, including osteoclast differentiation, metabolic stress, and coagulation changes. These findings lay the foundation for developing countermeasures to protect immune, skeletal, and vascular health during spaceflight.