Citations

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Background and Aims: Immunotherapy has emerged as an effective treatment for advanced HCC. We aimed to investigate the real-world effectiveness of immunotherapy compared to lenvatinib in HCC. Approach and Results: From the TriNetX database, we used a target trial emulation framework and identified patients with HCC who received first-line treatment with immunotherapy (atezolizumab/bevacizumab or tremelimumab/durvalumab) or lenvatinib between or between August 2018 and December 2023. OS was compared using Kaplan-Meier analysis and Cox proportional hazards regression. After propensity score matching, 1203 patients were included in each group. Immunotherapy was associated with improved OS versus lenvatinib (median survival: 545 vs. 425 d; HR: 0.86, 95% CI: 0.76–0.97). Regarding treatment type, atezolizumab plus bevacizumab showed improved survival compared to lenvatinib (n=1070 in each group; HR: 0.87, 95% CI: 0.77–0.99), while the point estimate favored durvalumab plus tremelimumab versus lenvatinib (HR: 0.81, 95% CI: 0.59–1.12), though this difference was not statistically significant, likely due to small sample size. Regarding etiology, immunotherapy had improved OS compared to lenvatinib in viral hepatitis (n=510 in each group; HR: 0.74, 95% CI: 0.61–0.89) and alcohol-associated liver disease (n=190 in each group; HR: 0.65, 95% CI: 0.49–0.87), but not in metabolic dysfunction–associated steatotic liver diseases (n=156 in each group; HR: 0.96, 95% CI: 0.70–1.31). Conclusions: In this real-world analysis, immunotherapy was associated with improved OS compared to lenvatinib in advanced HCC, with consistent benefit across most subgroups. These findings support the use of immunotherapy as a first-line treatment for advanced HCC.

Key Points Activin A is implicated in profibrotic and prosenescent kidney injury and correlates with kidney injury markers in animals and humans. Follistatin, through activin A antagonism, reduces senescence burden, macrophage infiltration, and proinflammatory pathway activation in murine diabetic kidney disease. Follistatin and other antagonists of activin A signaling pathways may be promising, novel therapeutics for diabetic kidney disease. Background Circulating activin A, an inflammatory mediator implicated in profibrotic kidney injury and cellular senescence-induced adipose tissue dysfunction, is increased in human diabetic kidney disease (DKD) and directly correlates with kidney dysfunction. We tested the hypothesis that activin A increases kidney injury, senescent cell abundance, and macrophage infiltration in DKD and antagonism through follistatin (FS) therapy diminishes these effects. Methods An accelerated nephropathy type 2 diabetes (db/db) mouse model was generated by implantation of angiotensin II-loaded osmotic minipumps resulting in increased albuminuria and glomerular and tubular injury. Kidney repair effects of FS (5 µ g intraperitoneal; two doses) were assessed through markers of kidney injury, fibrosis, inflammation, cellular senescence, and macrophage infiltration. In vitro studies examined antiactivin effects of FS on high glucose-exposed human monocytes, renal fibroblasts, and renal tubule epithelial cells. Results Activin A antagonism with FS reduced senescence (p19), proinflammatory (including senescence-associated secretory phenotype), and profibrotic markers including activin A. FS improved kidney morphology, restored podocyte markers (nephrin and Wilms tumor-1), and reduced kidney injury biomarkers, albuminuria and kidney fibrosis. FS decreased kidney macrophage and leukocyte infiltration and absent in melanoma 2 inflammasome activation. FS seemed to suppress inflammation through the toll-like receptor-4/NF kappa-light-chain-enhancer of activated B cells pathway in vivo further supported in human macrophages in vitro . In addition, FS reduced hyperglycemia-induced renal fibroblast activation and renal tubule epithelial cell senescence in vitro . Conclusions Activin A is a mediator of kidney injury through macrophage-associated inflammation in murine DKD. FS acts through senomorphic activities which inhibit profibrotic, proinflammatory, and prosenescence signaling by activin A. Hence, antiactivin targeting may aid in the development of a promising, novel therapeutic for DKD.

Key Points Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation. Better performance was observed in most recently published studies and those with a low or unclear risk of bias. Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios. Background Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established. Methods PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model. Results Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I 2 >90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool. Conclusions Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.

Abstract Objectives To assess quantitative and qualitative image characteristics in phantoms and patients using a simultaneous ultra-high resolution (UHR) and multi-energy (ME) dual-source cardiac scan mode in a photon counting detector (PCD) CT scanner. Methods UHR-ME cardiac scans were performed to determine the spatial resolution and multi-energy performance. UHR reconstructions were completed to measure transverse and axial spatial resolution in terms of modulation transfer function (MTF) and slice sensitivity profile (SSP). Virtual monoenergetic images (VMIs), virtual non-contrast (VNC) images, and iodine concentration images were created using moderately sharp reconstruction kernels to assess ME performance. With IRB approval, coronary CT angiograms were acquired in 19 patients using the UHR-ME mode, from which iodine contrast-to-noise ratio (CNR) was measured. Various ME post-processing tasks were performed to show the utility of the spectral capabilities. Results VMIs showed a mean CT number error of 6.0%, while iodine accuracy was 0.71 mg/cc (root-mean-square-error). Spatial resolution matched well with a standard UHR mode. Patient images showed a trend of higher CNR at lower keV, while higher keV images showed decreased calcium blooming. ME techniques to separate iodine and calcium were used successfully. Conclusions The combination of UHR-ME cardiac mode retains UHR image quality while providing flexibility for ME post-processing. Advances in knowledge Cardiac UHR-ME PCD-CT scanning offers simultaneous UHR and ME imaging without loss of spatial or temporal resolution or quantitative accuracy.

AbstractA deeper understanding of pathophysiology, from the initiation of tumors of epithelial cells to the outcomes associated with it, is imperative to test potential interventions for these diseases. However, few models can accomplish this because of the complexity of origin cells and their transformation. We established an epithelial cell original tumor mouse model by conditional KrasG12D activation and tumor suppressor gene p53 deletion specifically in epithelial cells of the whole body using Cytokeratin 19 (CK19) promoter via Cre‐ERT mediation. Multiple tumors derived from epithelial cells developed in this novel model. This model can recapitulate the progress of intestinal, pancreatic, liver, lung, and subcutaneous tumors which derive from epithelial cells. This model is suitable for studying the pathophysiology of tumors derived from epithelial cells and may have the potential to test interventions.

BACKGROUND: Percutaneous tracheostomy (PT) is generally considered a safe procedure, but complications such as malpositioning, bleeding, and tracheal ring rupture remain concerns, particularly during initial needle insertion. This study investigates the feasibility, ease of use, and safety of a novel device and technique for bedside PT, named the Illuminated Tracheal Alignment Guide (iTAG). METHODS: An interventional pilot study evaluated the feasibility and safety of the iTAG device and method. The study was approved by our local institutional review board and a Food and Drug Administration waiver was granted for use of our device. Patients in a neurocritical care unit requiring tracheostomy were screened and consented for inclusion. Exclusion criteria included significant vascular overlap and specific ventilator settings. The iTAG method involves a laser light source and a needle guide with a hard stop, used in conjunction with standard PT equipment. Data on demographics, procedure details, and early complications were collected and compared with historical control data from patients who underwent standard tracheostomy (ST). RESULTS: From January 2023 to July 2024, 30 patients underwent PT using the iTAG device. The mean time from intubation to tracheostomy was 15.53 days, with a mean ICU length of stay of 31.14 days. The iTAG group experienced significantly fewer early complications compared with the historical ST control group, including reduced hemorrhage, and there were no instances of tracheal ring fracture, posterior wall injury, or pneumothorax. The iTAG method allowed for safe PT in all patients. CONCLUSIONS: The iTAG device enhances the safety and efficacy of PT by providing precise visualization and limiting needle penetration, reducing early complications. Its use expands patient candidacy for PT and offers a valuable tool for training less-experienced practitioners. Further research with larger cohorts and randomized controlled trials is needed to confirm these findings and establish the iTAG method as a standard of care for PT.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.