Citations

Background While delivery of supplemental oxygen is a life-saving therapy, exposure to high oxygen, called hyperoxia, leads to increased intensive care unit mortality. Hyperoxia induces oxidant-mediated acute lung injury (ALI) and pulmonary cell death, called hyperoxic ALI (HALI). Elucidating molecular mechanisms in HALI could identify therapeutic targets in ALI. Methods In the current study, we examined in vivo effects of HALI on Beclin-1 (BCN1), which regulates autophagy, and modulation of BCN1 by epidermal growth factor receptor (EGFR). Effects of HALI on BCN1 and autophagy were examined in mice with genetically decreased EGFR (EGFR Wa5/+ ). Wildtype (WT) and EGFR Wa5/+ mice were exposed to 100% oxygen for 24–72 hours along with normoxia controls (eight groups; n=4–6/group), and analysis of pulmonary BCN1 and autophagy was completed. Results In WT, HALI led to increased BCN1 (59% increased total BCN1/β-Actin; p<0.01) in lung and alveolar epithelium (484% increased H-score; p<0.001). HALI led to decreased microtubule-associated protein 1B-light chain (LC3B)-II/-I ratios (43% decrease; p<0.05) and increased p62 (93% increase; p<0.05), suggesting reduced autophagic flux. In human alveolar type-II cells derived from induced pluripotent stem cells (AT2s iPSC ), HALI caused increased LDH release (130% increase; p<0.0001) and decreased LC3B-II/-I ratios (32% decrease; p<0.05). We previously showed that EGFR Wa5/+ mice are protected in HALI. In the current study, EGFR Wa5/+ mice showed increased pulmonary BCN1 (122% increase; p<0.05), reduced phosphorylated-(p-)/total BCN1 (47% decrease; p<0.05) and decreased LC3B-II/-I ratios (70% decrease; p<0.01) in HALI compared with WT. In addition, wortmannin (1 mg/kg), which decreases BCN1-mediated autophagy, increased mortality in HALI compared with vehicle control (n=10/group; 18% decreased hours survived; p<0.001). Conclusions These data delineate a novel cell death pathway in HALI involving BCN1 and EGFR with therapeutic potential.

INTRODUCTION: A Model for End-Stage Liver Disease-lactate (MELD-LA) score is an independent predictor of inpatient mortality and organ failure validated in more than 8,600 patients across diverse settings. However, its utility in sepsis, particularly when combined with lactate clearance (LA clearance), has not been fully evaluated. In this study, we examined the dynamic role of MELD-LA with 24-hour LA clearance as a predictor of 30-day mortality in critically ill patients with cirrhosis and sepsis. METHODS: We analyzed cirrhosis patients with sepsis admitted to the intensive care unit (ICU) in a large US healthcare system (20 hospitals) from 2014 to 2022, examining MELD-LA and LA clearance within 24 hours as predictors of 30-day mortality. We externally validated our findings in the Asian Pacific Association for the Study of the Liver-acute-on-chronic liver failure Research Consortium, a multinational Asian cohort (31 centers) from 2009 to 2019. RESULTS: A total of 3,879 cirrhosis patients with sepsis were admitted to the ICU (43.2% female, 20.5% Hispanic, 12.4% on dialysis; mean initial LA 4.5 mmol/L, MELD 20.8, MELD-LA 17.6). Survivors showed a median lactate change of −1.04 (35% clearance) compared with −0.4 (12%) for nonsurvivors. LA clearance within 24 hours was influenced by the presenting MELD-LA. In the North American cohort, ≥35% LA clearance was associated with improved 30-day survival (odds ratio 0.41, 95% CI 0.31–0.53; P < 0.001). Similarly, in the Asian Pacific Association for the Study of the Liver cohort (n = 1,259), ≥35% clearance was linked to a 60% reduction in mortality (odds ratio 0.42, 95% CI 0.28–0.62; P < 0.001). When stratified by LA clearance, patients with LA clearance of ≥35% had a higher percentage of early administration of resuscitative measures than those with LA clearance of < 10% especially regarding early antibiotic administration (82.5% vs 67.6%; P value <0.001). DISCUSSION: Early reduction in initial LA by one-third predicts 30-day mortality and may indicate treatment response in cirrhosis patients with sepsis in the ICU. Combining MELD-LA and LA clearance early enables better risk stratification and informs management.

INTRODUCTION: To perform a proof-of-concept study to evaluate the performance of individual and combined anorectal manometry and balloon expulsion testing (BET) parameters for diagnosing dyssynergic defecation (DD) using machine learning (ML). METHODS: A single gastrointestinal motility specialist assessed (2008–2018) 307 patients. A ML model was constructed on anorectal manometry/BET data. BET was weight-based in 235 patients and time-based in 72 patients. Missing data were imputed. Data were split 75% train/25% test with preprocessing performed. Five common ML models were trained, and performance was compared using receiver operating characteristic (ROC) area under the curve and accuracy based on 100 bootstrapped samples. The most accurate models were fine-tuned and evaluated on the test set. RESULTS: The median age was 40 years, body mass index 22.2 kg/m 2 , and 81% were women. Two-hundred and 22 (72.3%) patients had a clinical diagnosis of DD. Compared with patients without clinical diagnosis of DD, patients with DD had significantly lower rectoanal pressure gradient (RAG) (median −34.7 [IQR −57.1, −13.0] vs −24.6 [IQR −44.5, −2.4] mm Hg, P = 0.009) and more abnormal BET (59.5% vs 11.8%, P < 0.001). Four features were retained in the optimized model as predictors of DD: abnormal BET, greater resting anal pressure (RAP), and more negative RAG. The optimal parameter was abnormal BET ( P = 0.003), but combinations of 2 manometry results (RAP and RAG) yielded positive predictive values (PPVs) >80%. The optimized logistic regression model had an area under the curve of 0.878, with a sensitivity of 75.0% and specificity of 81.8% at a probability threshold of 0.704. DISCUSSION: Abnormal BET, or a combination of 2 manometric parameters (RAP and RAG), provides >80% positive predictive value for diagnosing DD in patients with chronic constipation.

Abstract Objective Recent studies suggest a unique disease profile in elderly patients with primary aldosteronism (PA), who have a high prevalence of comorbidities and low clinical cure rate post-adrenalectomy. This study aims to investigate histopathology and genetic characteristics of PA in late adulthood. Methods Formalin-fixed, paraffin-embedded adrenal tissue sections from 114 patients with PA who underwent unilateral adrenalectomy at age 60 or older were used. Aldosterone synthase (CYP11B2) immunohistochemistry (IHC) was performed for histopathologic analysis. Somatic DNA variants were identified in aldosterone-producing tissue following CYP11B2 IHC-guided sequencing. Results Histopathologic analysis revealed that 56 adrenals contained an aldosterone-producing nodule (APN) (49%), 44 contained an aldosterone-producing adenoma (APA) (39%), 7 contained multiple APN (6%), and 7 contained only non-functioning adenomas [NFA, of which 6 had at least one aldosterone-producing micronodule (APM) in the adjacent tissue] (6%). NFAs were also identified alongside APN/APA in 24 cases. An APN was the most common histologic subtype in men (51/88, 58%), while APA was predominant in women (18/26, 69%). Of 67 APNs with successful sequencing, the majority had variants in CACNA1D (40/67, 60%). KCNJ5 variants were the most common alteration in APA (14/44, 32%). Conclusions Older individuals with lateralized PA exhibit distinct genetic and histologic causes of disease with sex differences in mutation prevalence and adrenal histopathology. In APN, the most frequently altered gene is CACNA1D, while KCNJ5 variants are most common in APA. Unlike young patients with PA, the presence of NFA is a frequent observation in late adulthood individuals with PA.

Abstract Objectives To develop and validate an agent-based Large Language Model (LLM) system for extracting structured data from breast cancer synoptic pathology reports and assess the performance gap between synthetic and real-world validation. Materials and Methods We developed a modular artificial intelligence (AI) agent-based framework employing sequential specialized LLMs for parsing pathology reports and extracting structured data. We normalized College of American Pathologists (CAP) cancer protocols into 8 sections, 86 subsections, and 229 discrete fields. Seven leading LLMs (gemini-2.5-pro, llama3.3-70b, phi4-14b, deepseek-r1 14B/70B, gemma3-27b, gemini-2.0-flash-lite) were validated using dual evaluation: synthetic validation (864 controlled test cases) and real-world ground truth (6651 annotated fields from 90 pathology reports). Results Synthetic validation demonstrated strong performance (accuracy: 93.8%-99.0%). Real-world evaluation revealed field extraction recall ranging from 61.8% to 87.7%, demonstrating a substantial “reality gap” with performance drops of 11-32 percentage points. The gemini-2.5-pro model achieved the highest real-world recall (87.7%). Model size did not predict performance: the 14B-parameter deepseek-r1 (77.6%) outperformed its 70B-parameter counterpart (70.4%). Discussion The substantial performance degradation from synthetic to real-world data underscores the complexity of authentic clinical documentation. Smaller models can achieve competitive or superior recall, reducing computational costs. With even the best models missing 12%-38% of annotated fields, mandatory human verification is essential for clinical deployment. Conclusion While LLM-based extraction systems show promise for pathology data extraction, synthetic validation alone provides false confidence. Rigorous real-world ground truth evaluation with expert annotation is essential before clinical deployment. These systems are best positioned as screening tools with mandatory human oversight rather than autonomous decision-making systems.

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care.

ABSTRACT Melasma is a chronic hyperpigmentation disorder that disproportionately affects women and individuals with darker skin types, leading to a significant psychosocial burden. This meta‐analysis aims to evaluate the safety and efficacy of the 755‐nm picosecond alexandrite laser (PSAL) compared with conventional therapies in the management of melasma. PubMed, Ovid Medline, Embase, Cochrane Library, and Google Scholar were searched from inception to April 10, 2025, in accordance with PRISMA guidelines. Randomised controlled trials (RCTs) assessing PSAL versus control comparators in adults with melasma and reporting Melasma Area and Severity Index (MASI) or Modified MASI outcomes were included. The review was registered prospectively on PROSPERO (CRD420251022381). Risk of bias was assessed using ROB2. Pooled effect sizes were calculated using a random‐effects model. Five RCTs ( n = 139 patients) were included. Change in MASI favoured triple combination cream (TCC) over PSAL therapy (MD = 1.82, 95% CI: 1.11, 2.52, I 2 = 0%). Post‐inflammatory hyperpigmentation was more common in the PSAL group compared to topical creams (OR = 6.86, 95% CI: 1.47, 32.07, I 2 = 0%) but had no difference with the Q ‐switched lasers (OR = 4.09, 95% CI: 0.62, 26.97, I 2 = 0%). There was no incidence of hypopigmentation or infection reported. PSAL appears inferior to the TCC in reducing MASI scores. Overall certainty is low due to small, heterogenous trials. Low rates of irreversible adverse events with PSAL may support trials in refractory cases, but long‐term RCTs with larger sample sizes are required to evaluate durability and recurrence outcomes.

ABSTRACT Background Cardiac amyloidosis (CA) is frequently complicated by atrial fibrillation (AF), yet outcomes after left atrial appendage occlusion (LAAO) in this population remain poorly defined. Methods We conducted a retrospective TriNetX study of adults with AF undergoing LAAO, comparing patients with and without CA after 1:1 propensity matching. Outcomes were assessed using Kaplan–Meier analyses and Cox regression. Results Among 532 matched pairs, mortality and major adverse cardiovascular events were similar between groups, whereas major bleeding was higher in CA (HR 1.90). Conclusions LAAO yields comparable ischemic outcomes in CA, though bleeding risk is increased.