Pencovich, Niv

Abstract Reconstructing gene expression atlases for human tissues is challenging due to limited access to healthy samples from live donors. Neurologically deceased donors often show ischemic changes, while tissues near diseased regions may have altered gene expression. The liver, with its unique regenerative capacity, allows analysis from live healthy donors (LHDs). Using spatial transcriptomics (Visum, Visium HD and MERFISH), we analyzed 16 liver samples: eight from young LHDs and eight from patients with liver pathology, sampling ‘adjacent normal’ tissue. LHD livers displayed significant gene expression differences from ‘adjacent normal’ tissues. Hepatocytes exhibited marked zonation along the porto-central axis of liver lobules, with key functions pericentrally shifted compared to other mammals. Our atlas identified dynamic programs in early steatotic hepatocytes, showing transitions from lipid uptake in low-lipid regions to insulin hypersensitivity in high-lipid regions. This study presents a spatial gene expression reference for the healthy human liver and insights into hepatocyte adaptations in steatosis.

Background. Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. Methods. We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data. Results. Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). Conclusions. The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

Background: The value of platelet characteristics as a prognostic factor in patients with pancreatic adenocarcinoma (PDAC) remains unclear. Methods: We assessed the prognostic ability of post-splenectomy thrombocytosis in patients who underwent left pancreatectomy for PDAC. Perioperative platelet count ratio (PPR), defined as the ratio between the maximum platelet count during the first five days following surgery and the preoperative level, was assessed in relation to long-term outcomes in patients who underwent left pancreatectomy for PDAC between November 2008 and October 2022. Results: A comparative cohort of 245 patients who underwent pancreaticoduodenectomy for PDAC was also evaluated. The median PPR among 106 patients who underwent left pancreatectomy was 1.4 (IQR1.1, 1.8). Forty-six had a PPR ≥ 1.5 (median 1.9, IQR1.7, 2.4) and 60 had a PPR < 1.5 (median 1.2, IQR1.0, 1.3). Patients with a PPR ≥ 1.5 had increased median overall survival (OS) compared to patients with a PPR < 1.5 (40 months vs. 20 months, p < 0.001). In multivariate analysis, PPR < 1.5 remained a strong predictor of worse OS (HR 2.24, p = 0.008). Among patients who underwent pancreaticoduodenectomy, the median PPR was 1.1 (IQR1.0, 1.3), which was significantly lower compared to patients who underwent left pancreatectomy (p > 0.001) and did not predict OS. Conclusion: PPR is a biomarker for OS after left pancreatectomy for PDAC. Further studies are warranted to consolidate these findings.

The impact of bariatric surgery (BS) on kidney transplantation (KT) outcomes in patients with obesity remains controversial. We systematically searched MEDLINE, EMBASE, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials for studies reporting outcomes of KT recipients that underwent prior BS. Common/random effects meta-analyses were performed to obtain summary ratios of the postoperative outcomes. Eighteen eligible studies involving 315 patients were identified. Sleeve gastrectomy was the most common BS type (65.7%) followed by Roux-en-Y gastric bypass (27.6%) and gastric banding (4.4%). Across studies that provided the data, the %excess weight loss from BS to KT was 62.79% (95% confidence interval [CI], 52.01-73.56; range, 46.2%-80.3%). The rates of delayed graft function and acute rejection were 16% (95% CI, 7%-28%) and 16% (95% CI, 11%-23%) in 14 and 11 studies that provided this data, respectively. The rates of wound, urinary, and vascular complications following KT were 5% (95% CI, 0%-13%),19% (95% CI, 2%-42%), and 2% (95% CI, 0%-5%), in 12, 9, and 11 studies that provided this data, respectively. Follow-up time after KT was reported in 11 studies (61.1%) and ranged from 16 mo to >5 y. Graft loss was reported in 14 studies with an average of 3% (95% CI, 1%-6%). Four studies that included a comparator group of patients with obesity who did not undergo BS before KT showed comparable outcomes between the groups. We conclude that currently there is a paucity of robust evidence to suggest that pretransplant BS has a major effect on post-KT outcomes. High-quality studies are needed to fully evaluate the impact of BS on KT outcomes.