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Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

Background: Mu opioid receptors (MORs) in peripheral tissues mediate adverse effects of opioids that impair health-related quality of life (HRQoL) and may stimulate cancer progression via mitogenic signaling. Naloxegol, a peripherally acting MOR antagonist (PAMORA), is approved for opioid-induced constipation. Safety and efficacy of naloxegol have not been evaluated concurrently with systemic cancer therapy. Methods: We conducted a randomized, double-blind, placebo-controlled trial of naloxegol in patients with advanced lung adenocarcinoma starting first-line systemic therapy. Results: Only 50 patients were enrolled; the trial was terminated early due to slow accrual. Two of the three components of the feasibility primary endpoint were not met (accrual and PRO completion). At 6 months, FACT-L emotional well-being was better with naloxegol (p = 0.0113). There were trends towards better Trial Outcome Index (p = 0.0505) and physical well-being (p = 0.0628) with naloxegol. Bowel function favored naloxegol for constipation (p = 0.0223), rectal pain during defecation (p = 0.0075), and abdominal pain from constipation (p = 0.0113). Adverse event frequency and severity, PRO-CTCAE, urinary hesitancy, pain scores, and progression-free and overall survival were comparable between naloxegol and placebo. Conclusions: Naloxegol appears to be safe and tolerable, with a signal of improved HRQoL and previously unappreciated benefit for emotional well-being, without adverse clinical outcomes. Our findings should be confirmed in larger studies. ClinicalTrials.gov ID: NCT03087708.

Early allograft failure (EAF) after living donor liver transplantation (LDLT) remains a clinical challenge. Existing prediction models developed for deceased donor transplantation poorly apply to LDLT due to distinct surgical and physiological factors. This study identifies clinical determinants of EAF and develops an LDLT-specific prediction model. We conducted a multicenter retrospective cohort study from 17 high-volume LDLT centers (January 2016–December 2020) with external validation at a tertiary center in Saudi Arabia (January 2015–December 2022). The primary outcome was EAF (graft loss or patient death ≤90 d). Multivariable mixed-effects logistic regression identified preoperative/intraoperative risk factors. The EAGLE-LDLT model was constructed using postoperative laboratory values. Performance was compared against established models (EAD, MEAF, A2ALL) using ROC analysis and decision curve analysis. The development cohort included 2944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with a 5.5% EAF rate. External validation included 1020 recipients (median MELD 21, 6.7% EAF). Independent risk factors for EAF were MELD (OR 1.06, 95% CI 1.04–1.08), donor BMI (OR 1.05, 95% CI 1.00–1.10), portal vein thrombosis (OR 1.73, 95% CI 1.13–2.63), and hepaticojejunostomy (OR 1.58, 95% CI 1.06–2.36). The EAGLE-LDLT model incorporating peak ALT (>468 U/L), peak INR (>1.9), and bilirubin (>3.5 mg/dL) and INR (>1.3) at POD7, demonstrated superior discrimination (AUC=0.81) compared with MEAF (AUC=0.77, p =0.004), EAD (AUC=0.67, p <0.001), and A2ALL (AUC=0.65, p <0.001). EAGLE-LDLT achieved balanced sensitivity (75.0%) and specificity (73.7%), effectively stratifying patients into high-risk (15% of patients; 40.4% EAF incidence) and low-risk groups. Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at the highest risk for EAF postoperatively.

Background In pediatric patients presenting after blunt trauma, the incidence of clinically significant cervical spine injury (CSI) is approximately 1%. Although multidetector CT (MDCT) is widely accepted for cervical spine clearance in adults, its utility in children remains uncertain. The purpose of this study is to evaluate the sensitivity of MDCT in identifying clinically significant CSI in pediatric patients, stratified by age groups: adolescents (12 years to 17 years) and children (0 years to 11 years). Methods We designed a national, multicenter, prospective observational study to assess the diagnostic performance of MDCT in detecting clinically significant CSI in pediatric trauma patients. Data are being collected from participating centers on imaging findings, clinical outcomes, and the presence of confirmed CSIs after blunt trauma. The analysis plan includes stratification by age group and calculation of sensitivity, specificity, and predictive values of MDCT for CSI. Results This abstract outlines the background, methodology, and analytical framework of the study. Data collection and analysis are ongoing. This study represents the largest known cohort of pediatric patients undergoing cervical spine imaging after blunt trauma and is the first prospective study of its kind focused exclusively on this population. Conclusions This study will provide critical data to inform guidelines for pediatric cervical spine clearance after blunt trauma. By evaluating the sensitivity of MDCT in detecting clinically significant CSI, the findings aim to support evidence-based imaging strategies and improve the safety and efficiency of pediatric trauma care.

163 Background: Being physically inactive is an independent predictor of poor outcomes in patients with stage III colon cancer. Whether physical activity can mitigate the oncologic risk of postoperative MRD is unknown. Methods: We concluded a post hoc analysis of a phase III trial evaluating celecoxib vs. placebo and 3 vs. 6 months of adjuvant chemotherapy in patients with resected stage III colon cancer. Self-reported physical activity was assessed in standardized measures of metabolic equivalent hours per week (MET-h/wk) at 2 timepoints: 1) midway through and 2) 6 months after completing adjuvant chemotherapy, modeled as a time-weighted average. MRD was determined via a clinically validated, tumor-informed circulating tumor DNA (ctDNA) assay (Signatera, Natera, Inc.). Disease-free survival (DFS) and overall survival (OS) were estimated using flexible parametric survival models. Results: Among 763 patients (median follow-up 6.0 years), 24% were highly active (≥18 MET-h/wk) and 32% were inactive (<3 MET-h/wk); 18% were ctDNA positive. Adjusting for covariates, DFS improved in patients who were ctDNA negative and moderately active (Ref: inactive; 3-17.9 MET-h/wk; HR=0.62, 95% CI [0.40-0.97]) or highly active (HR = 0.53 [0.30-0.94]). No benefit was seen among patients who were ctDNA positive (Ref: inactive; moderately active: HR = 1.20 [0.75-1.92]; highly active: HR=0.83 [0.45-1.54]). The interaction was not significant (Pint=0.269). Physical activity did not impact OS regardless of ctDNA status. The interaction of physical activity and adjuvant celecoxib use is shown in Table. Conclusions: Among patients who were ctDNA negative, physical activity was associated with improved DFS, and may demonstrate increased survival benefit alongside adjuvant celecoxib. The additive effects of both interventions were inconclusive among those who were ctDNA positive. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org; Natera, Pfizer; NCT01150045. Disease-free survival by physical activity level and celecoxib use, stratified by ctDNA status. 3-yr event # at risk Adjusted HR (95% CI) P-value Pint ctDNA Negative 0.258 Inactive + placebo 23 182 Ref Active + placebo 17 132 0.74 (0.43-1.28) 0.283 Inactive + celecoxib 23 167 0.92 (0.57-1.48) 0.720 Active + celecoxib 10 146 0.40 (0.22-0.79) 0.007 ctDNA Positive 0.221 Inactive + placebo 26 33 Ref Active + placebo 15 20 0.96 (0.50-1.84) 0.903 Inactive + celecoxib 34 54 0.62 (0.37-1.05) 0.076 Active + celecoxib 14 29 0.34 (0.18-0.64) 0.001 ctDNA, circulating tumor DNA; HR, hazard ratio; CI, confidence interval. Active = ≥9 MET-h/week, inactive = <9 MET-h/week. Celecoxib use defined by assignment in CALGB/SWOG 80702. Models adjusted by T and N stage, age, sex, race, assigned cycles of fluorouracil, leucovorin, and oxaliplatin, KRAS and BRAF status, BMI, and Western diet.

Living donor liver transplantation (LDLT) is an established therapy with curative intent for pediatric and adult patients with acute liver failure and end-stage liver disease. Donor safety remains paramount and commences during preoperative evaluation and assessment. Given the importance of the topic, the International Liver Transplantation Society and International Living Donor Liver Transplantation Group consensus conference on Living Liver Donor Safety was convened in March 2025 (Toronto, Canada). Recommendations were based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus was followed. This report presents 28 recommendations from the working group focused on addressing clinically relevant questions related to the preoperative aspects of living donor safety including assessment of donor acceptability (age, body mass index and hepatic steatosis; medical conditions and contraindications), medical and surgical workup (liver function and procoagulant workup; anatomical considerations and safe remnant), and psychosocial evaluation (timing and team; underlying conditions; non-directed, paired, and anonymous directed donors; urgent living donor workups for acute liver failure).

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

SUMMARY Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of ‘disease indication — biological processes — molecular functions’, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

The success of living donor liver transplantation (LDLT) has bridged the growing gap between organ demand and availability in patients with liver failure or liver cancer. Safety of the living liver donor (LLD) is paramount, and astute intraoperative management plays a critical role in ensuring optimal outcomes for them. Given that intraoperative surgical and anesthesia practices vary widely across centers, the International Liver Transplantation Society (ILTS) and the International Living Donor Liver Transplantation Group (iLDLT Group) convened a global consensus conference in Toronto in March 2025 to develop guidelines for intraoperative management of the LLD. This manuscript details the recommendations on intraoperative considerations for the safety of LLD developed by Working Group II of the Consensus Scientific Committee, using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus. Working Group II focused on intraoperative monitoring, fluid and ventilation strategies, surgical techniques for vascular and biliary division, surgeon and center-specific experience, and intraoperative crises planning. These guidelines aim to support both established and emerging LDLT teams to ensure high-quality and safe intraoperative care for the LLD.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.