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Importance Patients undergoing cardiac surgery with cardiopulmonary bypass often require platelet transfusions for bleeding. Platelets are routinely stored at room temperature (20-24 °C) for up to 5 to 7 days; however, cold storage (1-6 °C) may allow for increased storage duration without loss of hemostatic function. Objective To determine the maximum cold-storage duration, up to 21 days, for which cold-stored platelets (CSPs) are noninferior or superior in hemostatic efficacy to room-temperature platelets (RTPs) when transfused in actively bleeding patients undergoing cardiac surgery with cardiopulmonary bypass. Design, Setting, and Participants A phase 3, multicenter, randomized, partially blinded, adaptive, noninferiority, storage duration–ranging trial conducted in pediatric and adult patients undergoing cardiac surgery with planned cardiopulmonary bypass at 27 sites in the US and Australia from December 2021 to March 2025. Statistical analysis was conducted from September 9 to October 3, 2025. Intervention Patients were randomized 2:1 to receive CSPs with a maximum of 21 days of storage vs RTPs with a maximum of 7 days of storage. Main Outcomes and Measures The primary outcome was a hemostatic efficacy score with values ranging from 1 to 5 and higher values indicating greater bleeding. The noninferiority margin was 1 point. The success criterion was met if at least 1 cold-storage duration of 7 days or more had a posterior probability of noninferiority of at least 97.5%. The secondary outcome was 24-hour chest tube output. Results Of the 1000 patients who underwent platelet transfusion, 989 were included in the primary analysis (650 CSPs and 339 RTPs). The cohort had a mean (SD) age of 42.1 (30.1) years and 67.8% were male. CSPs were noninferior to RTPs for the primary outcome, with a probability of greater than 99.9% for all cold-storage durations. Pooled across storage durations, CSPs had a mean difference from RTPs of 0.09 (95% credible interval, −0.06 to 0.23). Median (IQR) chest tube output at 24 hours (8.9 mL/kg [5.2-15.4] for CSPs vs 8.4 mL/kg [5.5-15.9] for RTPs; difference in medians, 0.4 [95% CI, −1.0 to 1.5]) was not statistically different between study groups. In a post hoc analysis, as all storage durations were noninferior, considering the CSPs as a single group, the hemostatic efficacy score was similar between both groups: mean (SD) of 3.08 (1.15) CSPs vs 2.99 (1.10) RTPs. There were no differences in venous or arterial thrombotic events, transfusion-associated adverse events, acute respiratory distress syndrome, kidney failure, septic shock, and mortality between the study groups, except for increased reexploration rates in the CSP group. Conclusions and Relevance For patients undergoing cardiac surgery, CSPs stored up to 21 days are noninferior to RTPs for the control of active surgical bleeding. Use of CSPs may allow for increased availability of platelets by reducing wastage and shortages and may allow for incorporation of platelets into inventory at locations where platelets cannot be maintained due to the 5- to 7-day shelf life of RTPs. Trial Registration ClinicalTrials.gov Identifier: NCT04834414

The NCCN Guidelines for Survivorship offer guidance for health care providers who care for survivors of adult-onset cancer. These guidelines include screening, evaluation, and treatment recommendations for common physical and psychosocial problems resulting from cancer and its treatment and provide a framework for care coordination. They also present guidance for helping cancer survivors to enhance their wellness and maintain a healthy lifestyle. This article summarizes the panel’s current recommendations and recent updates regarding anxiety, depression, distress, and trauma in cancer survivors.

Mesothelioma is a rare cancer that originates from the mesothelial surfaces of certain sites within the body. Pleural mesothelioma is the most common type and represents approximately 85% of mesotheliomas. The NCCN Guidelines for Mesothelioma: Pleural provide recommendations for evaluation and treatment in patients with pleural mesothelioma. The NCCN Guidelines will continue to be updated annually based on available clinical evidence and panel consensus.

Background General practitioners (GPs) have limited tools for chest pain patients to identify (non-)obstructive coronary artery disease (OCAD). This trial evaluates whether GP access to the coronary artery calcium score (CACS) improves diagnostic efficiency and cardiovascular risk management (CVRM). Methods COroNary Calcium scoring as fiRst-linE Test to dEtect and exclude coronary artery disease in GP patients with stable chest pain (CONCRETE) is a pragmatic, non-blinded implementation study that cluster-randomised 101 Dutch GP offices into CACS or standard of care (SOC). Patients aged ≥40 (men) or ≥45 (women) years, without known CAD, with possible cardiac or non-cardiac chest pain were included. The primary outcome was the proportional 2-year increase in CVRM registrations at GP office level. Patient-level secondary outcomes included cardiologist referral rate, OCAD diagnosis and CVRM enrolment. Results At GP office level, no significant difference in CVRM registration increase was seen for CACS versus SOC (46 CACS practices: +0.3%, 45 SOC practices: +0.2%, p=0.77). Per protocol analysis for patient-level outcomes included 583 patients (57.5% women, mean age 60.6±9.1 years) (CACS arm: 466; SOC arm: 117), recruited between January 2019 and October 2023. The CACS arm showed 47.1% relative reduction in cardiologist referrals compared with SOC (42.5% vs 80.3%; modelled difference 31.3% (95% CI 14.8% to 47.8%)). OCAD detection rates were not significantly different (CACS: 4.9, SOC: 7.7%, p=0.14). Patients with higher CACS had a higher probability of cardiologist referral (from 8.4% for CACS 0 to 94.2% for CACS ≥400, difference +85.7% (95% CI 79.4% to 91.9%)) and of OCAD diagnosis (0% for CACS 0 to 21.2% for CACS ≥400, OR 9.08 (95% CI 4.20 to 19.67)). Compared with SOC, more CACS patients were enrolled in CVRM (36.3% vs 17.0%, p<0.01). Conclusions In this pragmatic, primary-care implementation and proof-of-concept trial, CACS was an effective diagnostic test for stable chest pain patients with (very) low likelihood of OCAD. CACS reduced unnecessary cardiology referrals and enhanced individual preventive care, without demonstrable practice-level impact on CVRM registrations. Registration The CONCRETE study is registered under CCMO Register NL66821.042.18 and closed for enrolment.

ABSTRACT Background ACS‐NSQIP began capturing operative approach in 2022. With increased adoption of minimally invasive (MIS) techniques for malignancy, a contemporary review of benchmark outcomes for open, laparoscopic and robotic gastrectomy is warranted. Methods We performed a retrospective cohort study of adults in ACS‐NSQIP (2022–2024) undergoing partial or total gastrectomy (CPT 43631‐4 and 43620‐2) for cancer. Outcomes included operative time, length of stay (LOS), unplanned conversion‐to‐open, and 30‐day morbidity, mortality, and readmission. Multivariable logistic regression evaluated patient and operative factors associated with morbidity. Results Among 1,459 patients (median age 66, 60.4% male), 66.8% underwent partial gastrectomy and 33.2% total gastrectomy. Approaches were 61.2% open, 19.5% laparoscopic, and 19.3% robotic. Thirty‐day mortality (1.8%), major complications (18.2%), and readmissions (10.3%) did not differ by approach. Unplanned conversion‐to‐open occurred in 14.0% of MIS. Robotic had longer median operative time of +1.2 and +1.7 h than laparoscopic and open and median −1 and −2 days shorter LOS respectively ( p < 0.001). Hypoalbuminemia (OR 3.10), CKD ≥ 3 (OR 1.75), and anemia (OR 1.48) were independently associated with major complications ( p < 0.05). Conclusion Operative approach influences operative time and LOS but not short‐term morbidity and mortality. The newly available ACS‐NSQIP operative approach variable enables approach‐specific benchmarks for cancer‐related gastrectomy.

Financial toxicity is a significant but often overlooked consequence of cancer for adolescents and young adults (AYAs, 15–39 years old), who face limited savings, unstable employment, and insurance gaps that heighten financial stress. As part of a larger initiative to determine whether our cancer center can use an existing social drivers of health (SDOH) screening process to identify patients who may benefit from targeted supportive interventions, we sought to characterize financial strain, toxicity, and SDOH needs in AYAs treated in our cancer center and compare them to both younger and older cancer patients. This is a cross-sectional observational study at Mayo Clinic of patients with an oncology-related department visit between 8 September 2023–4 June 2024 (N = 81,731) who completed routine institutional electronic screenings for financial strain and SDOH. Survey items assessed financial strain, transportation barriers, and food insecurity; institutional billing data captured financial toxicity (unresolved balances, collection referrals, write-offs). AYAs (n = 5655) were more likely than non-AYAs to report medium/high financial strain, have unpaid debt, or have an unresolved balance, referral to a collection agency, or bad debt write-off, with nearly double the odds of medium/high financial strain compared to patients over 40 (OR 2.02, 95% CI 1.84–2.23, p < 0.001). Within AYAs, ages 26–32 bore the greatest burden: 24.2% in the medium/high-risk category, 9.4% with unresolved billing issues or collections, and 11.6% reporting food insecurity. These findings identify AYAs—particularly those 26–32—as a distinct high-risk group for financial toxicity and health-related social needs, underscoring the need for targeted interventions, tailored financial navigation, and policy efforts to reduce economic barriers to care. Future research should evaluate the effectiveness of these interventions in mitigating financial toxicity, addressing health-related social needs, and improving patient outcomes in this population.

Abstract The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by “expansion” of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A “no-zone” hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

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.

Abstract TAR DNA‐binding protein 43 (TDP‐43) inclusions are often associated with hyperphosphorylated tau, thus neurofibrillary tangles as the hallmark of Alzheimer's disease (AD) and primary age‐related tauopathy (PART). TDP‐43 in AD is associated with cognitive impairment, and while staging is known, the localization, cellular and inclusion characteristics of TDP‐43 are yet to be elucidated. We investigate relationships between TDP‐43 inclusions and the tangle maturation continuum in AD, PART, and co‐pathologies by multiplex immunostaining combined with artificial intelligence (AI)–based segmentation via object recognition, reconstruction, and quantification. We performed anti‐phosphorylated TDP‐43 immunofluorescence with phosphorylated tau labeling different stages and modifications of tangles (AT8, pS396, TauC3, MN423, GT38) in three controls, three cases with PART and TDP‐43 (PART‐TDP), five cases with high likelihood AD and TDP‐43 (AD‐TDP), and four cases of high likelihood AD with TDP‐43 and Lewy Body disease (AD‐TDP‐LBD). Confocal imaging was taken from eight regions: amygdala (amygdala‐BL and amygdala‐CM) and hippocampus (Cornu Ammonis (CA)‐1, CA2/3, CA4, dentate gyrus (DG), subiculum (SUB)), and entorhinal cortex (ERC) and quantified with AI segmentation to identify 3D spatial relations, thus the maturity of neurofibrillary tangle associated TDP‐43 (TAT) inclusions. TATs, which were either identified by pTDP‐43 and AT8 or pTDP‐43 and pS396 double positivity, were also investigated by Thioflavin S (ThioS) histochemistry. We found pS396 labeled mature TATs predominated in PART and AD in every region. Basolateral and centromedial amygdala displayed overall greatest number of pre‐TATs and mature TATs. Mature TATs were homogenously distributed among hippocampal subfields whereas CA4 and DG had the greatest mature TAT composition. ERC revealed closer numbers of pre‐TATs and mature TATs yet mature TATs predominated all groups. Unbiased AI‐based object identification, reconstruction, and TAT maturation analysis pipeline in conjunction with TDP‐43, tau, and ThioS multiplex immunostaining demonstrated unique aggregation and maturation patterns, highlighting region‐specific dynamics in the neurodegenerative processes of PART and AD.