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Background. Liver transplant recipients face high risks of cardiometabolic events after transplant, driven by posttransplant weight gain, diabetes, hypertension, as well as immunosuppression-related side effects. Glucagon-like peptide-1 receptor agonists (GLP1RAs) improve metabolic and cardiorenal outcomes in nontransplant populations, but their role in liver transplant recipients remains understudied. Methods. This retrospective cohort study used TriNetX data (January 2010–December 2023) to compare outcomes in liver transplant recipients prescribed GLP1RAs (semaglutide, dulaglutide, liraglutide) within 1-mo posttransplant (n = 546) versus nonusers (n = 37 153). Propensity score matching (1:1) balanced demographics, comorbidities, and medications (n = 541 per group). Outcomes included mortality, hospitalizations, cardiovascular/renal/respiratory events, and graft outcomes. Results. Over a mean follow-up 838.5 d (SD 291.9) in the GLP1RA cohort and 884.3 d (SD 313.7) in the non-GLP1RA group, GLP1RA use was associated with a 43% lower all-cause mortality (7.0% versus 12.9%; hazard ratio [HR], 0.566; 95% confidence interval [CI], 0.381-0.841) and 39% fewer hospitalizations (60.4% versus 74.5%; HR, 0.613; 95% CI, 0.530-0.710). Acute heart failure (HR, 0.386; 95% CI, 0.285-0.524), renal failure/dialysis (HR, 0.489; 95% CI, 0.413-0.579), and respiratory failure (HR, 0.484; 95% CI, 354-0.662) risks were significantly reduced. No differences were observed in graft failure/rejection, myocardial infarction, stroke, atrial fibrillation/flutter, ventricular tachycardia, or ischemic optic neuropathy. Conclusions. Early GLP1RA initiation in liver transplant recipients was associated with reduced mortality, hospitalizations, respiratory, and cardiorenal complications without compromising graft safety. These findings support GLP1RAs as a promising adjunct therapy, warranting prospective trials to confirm benefits in this high-risk population.

Purpose To use publicly available injured list (IL) data to 1) describe Major League Baseball injury trends from 2010 to 2024; 2) analyze injury types and body regions affected to report the highest impact injuries; and 3) report the financial implications of time lost due to injury over this 15‐year period. Methods IL data for every Major League Baseball player (pitchers and position players) from the 2010 to 2024 regular seasons were collected using publicly available sources, including player demographics, IL placements, body part injured, and length of each IL stay. Player salaries were compiled to determine the “financial loss” per injury. Results Across 15 seasons, 8078 players were placed on the IL, totaling 478,085 days. The mean IL placements/year was 634, and the annual number increased by an additional 27 injuries/year ( P < .001). Mean total IL days/season was 31,872, and this increased year‐over‐year ( P = .008). Shoulder injuries represented the most IL placements (n = 1555), followed by elbow (n = 1344) and upper leg (n = 769). Elbow injuries averaged the most days missed/IL placement (mean: 93, median: 74) and the greatest total days missed (129,610), followed by shoulder (95,189). Pitchers comprised 55% of IL placements and most commonly injured their shoulder or elbow. Infielders and outfielders accounted for 21% and 16% of IL placements, respectively. A total financial loss of $9,636,753,291 was accumulated over 15 seasons. Financial loss increased season‐to‐season (+$32,309,178/year, P = .002). Elbow injuries accounted for the highest financial loss at $1,932,048,168 ($1,379,049 lost/injury). In 2024, $2,220,432 was lost/elbow injury, $1,110,573/shoulder injury, $1,397,322/any injury, and $952,973,760 total for the season. Conclusions This study observed that the number of players, IL placements, days spent on the IL, and financial loss due to injury increased steadily each year. Pitchers were the most frequently injured players with shoulder and elbow injuries being most common. Over the 15‐season study period, there was a total financial loss of nearly $9.7 billion due to salary paid to injured players, with elbow injuries contributing the highest loss at just under $2 billion. Clinical Relevance Injury trends and financial data from Major League Baseball highlight the growing burden of shoulder and elbow injuries, particularly in pitchers. These findings underscore the need for orthopaedic surgeons to focus on prevention, early intervention, and optimized surgical management of high‐impact injuries to reduce long‐term morbidity and associated financial costs at the elite level.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

ABSTRACT Recent advances in the diagnosis and management of reflux disease were the central focus of the inaugural Gatherings in Esophagology (GiE), which convened experts across gastroenterology, surgery, otolaryngology, pulmonology, and basic research. The sessions highlighted innovations in reflux monitoring—including high‐resolution manometry, wireless pH monitoring, and novel salivary biomarkers—while critically evaluating their diagnostic accuracy and clinical utility. Presentations explored the limitations of traditional proton‐pump inhibitor therapy, the emergence of potassium‐competitive acid blockers as a new class of acid suppressants, and the evolving role of adjunctive treatments such as mucosal protectants, reflux reducers, and neuromodulators for refractory symptoms. The discourse extended to advanced interventional procedures, including transoral incisionless fundoplication, magnetic sphincter augmentation, and the RefluxStop device, with discussion of patient selection, efficacy, and complication management. Discussants emphasized the pathophysiology and management of extraesophageal manifestations of reflux, the interplay between reflux and pulmonary disease, and the diagnostic challenges in pediatric populations. The meeting also addressed the integration of behavioral therapies, the role of the microbiome, and the application of artificial intelligence in reflux diagnostics. Collectively, these insights underscore a shift toward precision medicine in reflux disease, emphasizing individualized diagnostic strategies and tailored therapeutic approaches to improve patient outcomes.

Abstract Advances in biomarker technology, digital cognitive assessments, and amyloid‐targeting therapies have redefined the opportunities for accurate and early diagnosis and care of Alzheimer's disease (AD). These advances also create new possibilities for intervention before the onset of cognitive impairment. This paradigm shift has increased the focus on Stages 1 and 2 of AD, in which individuals are cognitively unimpaired but exhibit biological evidence of disease. While early identification of AD offers an opportunity to intervene early to delay progression and preserve quality of life, it also presents complex challenges related to communicating diagnostic results to patients and their families, contextualizing the cost effectiveness of early diagnosis and treatment, and implementation of and equitable access to treatments. Recent, successfully enrolled, preclinical AD trials highlight the complex strategies required to identify asymptomatic, biomarker‐positive individuals on a large scale, and demonstrate critical knowledge gaps in inclusion, follow‐up, and long‐term outcome measurement. The Spring 2025 Alzheimer's Association Research Roundtable (AARR) meeting brought together academics, clinicians, industry, and regulatory leaders to exchange perspectives on current challenges, key learnings, and potential strategies for identifying and treating individuals in very early stages of AD, effectively and safely. This paper presents key takeaways from the Spring 2025 AARR meeting. Highlights New criteria for Alzheimer's disease (AD) enable early identification of AD pathology. Blood tests and digital cognitive assessment tools may facilitate early and personalized care. Plasma biomarkers may be scalable in clinical settings but need confirmation or follow‐up assessments in those individuals with equivocal results. Early therapy shows best results in people with lower initial amyloid and tau burden.

CTNNB1- mutated hepatocellular carcinomas are characterized by a distinctive morphology and activation of the Wnt pathway. AXIN1 also plays a key role in the Wnt pathway, but the morphology of AXIN1-mutated tumors has not been examined. In addition, there are ongoing questions on the ability of AXIN1 mutations to activate the Wnt pathway in hepatocellular carcinoma. AXIN1 mutated tumors (N=18) were studied, along with control groups: CTNNB1 (N=17), APC (6), or “Other” genes in the Wnt pathway (5). Wnt pathway activation was studied by immunostains for beta-catenin and glutamine synthetase. Findings were supplemented by gene expression analysis using TCGA data. On histologic examination, the classic morphology associated with beta-catenin mutations was found in all 4 groups: 8/18 AXIN1 (44%), 10/17 CTNNB1 (59%), 4/6 APC (67%), and 1/5 Other (20%). By immunohistochemistry, Wnt pathway activation was found in 11/18 AXIN1 (61%), 15/17 CTTNB1 (88%), 6/6 APC (100%), and 5/5 (100%) of Other. In AXIN1- mutated tumors, the Wnt pathway was weakly activated. Glutamine synthetase stains also highlighted a new “progressed pattern” associated with distinct subnodules of staining. Tertiary lymphoid structures were uncommon except for cases with CTTNNB1 mutations plus additional mutations in the Wnt pathway. In summary, the classic morphology associated with CTNNB1 mutations is found in hepatocellular carcinomas with mutations in AXIN1 , APC , and other Wnt genes. AXIN1 mutated tumors have Wnt activation that is detectable but at lower levels than CTNNB1 mutated tumors. As tumors progress, their level of Wnt activation can change.

ABSTRACT Background Sunlight has profound impacts on physical and mental health, beyond vision, including effects on circadian rhythms, alertness, mood, and sleep. A family history of any mood disorders is strongly associated with psychiatric disorders including bipolar disorder. The purpose of this study was to evaluate the association between a family history of any mood disorder in patients with bipolar I disorder and solar insolation at varied international onset locations. Methods Data for this analysis were available from 5842 patients with a diagnosis of bipolar I disorder obtained at 83 collection sites in both hemispheres. This included 4752 patients from 71 collection sites in the northern hemisphere and 1090 patients from 12 collection sites in the southern hemisphere. Patient data variables were obtained from records or interviews. Solar insolation data were obtained from The National Aeronautics and Space Administration (NASA) Power database for each onset location, and the ratio of the mean monthly minimum/mean monthly maximum solar insolation was calculated. Typically, the ratio is largest near the equator (little yearly change in solar insolation) and smallest near the poles (large yearly change in solar insolation). Results A significant relationship was found between a family history of any mood disorder, the ratio of the mean monthly minimum/mean monthly maximum solar insolation, and gender. The odds of a family history of mood disorder increased as patient location nears the poles and decreased near the equator. Female gender also increased the odds of having a family history of a mood disorder. Conclusions This study highlighted the association between family history, solar insolation, and gender in international patients with bipolar I disorder. Given the profound effects of sunlight on human health, the family of patients with bipolar disorder who live in the same location with the same solar insolation, and especially females, may be at increased risk for a mood disorder.

ABSTRACT Background Primary lateral sclerosis (PLS) is an ultrarare upper motor neuron syndrome with a prognosis unique from classical ALS. The study of PLS is complicated by its rarity and the difficulty distinguishing PLS from ALS. We present data from a 1‐year prospective follow‐up study on PLS and efforts to distinguish it from ALS. Methods Seventy‐six PLS participants enrolled in this prospective natural history study. EMG studies, blood neurofilament light chain levels (NfLs), and demographic characteristics were obtained at baseline. At 1‐year follow‐up, repeat EMG studies were conducted to determine which participants fulfilled criteria for ALS. Baseline characteristics were then compared to determine features that predict reclassification. Results Seventy participants completed 1‐year follow‐up. Five of the 70 were reclassified to ALS (7.1%). Those reclassified had higher trends in baseline blood NfL levels (91.4 vs. 34.0 pg/mL, p = 0.13) and shorter symptom duration (39 vs. 69 months in the PLS group, p = 0.15). Reclassification was noted in both probable and definite PLS participants. All cases with a symptomatic duration of less than 2 years retained the PLS phenotype (5 of 5). NfL levels over 90 pg/mL predicted reclassification with 94% specificity and 60% sensitivity. No other features predicted reclassification to ALS. Conclusions In our population, reclassification of PLS to ALS occurred at a low frequency at 1 year follow‐up (7.1%). Baseline NfL was the strongest predictor in differentiating UMN dominant ALS from PLS at 1‐year follow‐up. Based on our data, we propose EMG and NfL criteria for enrollment in future PLS trials.

Abstract Objectives Traditional laboratory regulatory frameworks provide robust guidance for conventional clinical testing but lack requirements for data documentation for artificial intelligence and machine learning (AI/ML) solutions. This gap creates significant risks since AI solutions directly inherit patterns, biases, and limitations from their training data. Here, we describe the development and implementation of a comprehensive data documentation checklist with accompanying data lineage for use within an AI lifecycle framework for clinical laboratories. Methods Building on the previously established Clinical AI Readiness Evaluator (CARE) framework, we developed a comprehensive data checklist and data lineage methodology through a multiphase process, including (1) comprehensive review of existing AI/ML data documentation frameworks, (2) focused meetings with 3 institutional AI operations teams, and (3) 3 rounds of iterative refinement by our multidisciplinary team. The checklist’s effectiveness was then assessed using 3 diverse AI/ML projects moving through the CARE framework. Results The CARE Data Checklist and Data Lineage provide a structured approach to documenting critical aspects of datasets used in AI/ML projects, including data composition, demographics, collection methods, labeling processes, usage constraints, maintenance requirements, and a data readiness assessment. The checklist addresses unique data-centric challenges of AI/ML applications, facilitating transparency, reproducibility, and regulatory compliance. Conclusions The CARE Data Checklist and Data Lineage serve as both a technical guide and a communication tool bridging gaps between technical and clinical stakeholders. By working on these documents early in the AI lifecycle, laboratories can anticipate and address data-related challenges, ultimately saving time, optimizing resources, and improving the reliability of AI-augmented laboratory solutions.