Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.
Objective: Gastrostomy tubes (G-tubes) provide long-term feeding assistance to children with severe feeding dysfunction. Although there are a host of complications that occur at home with current pediatric G-tube feeding, their prevalences and outcomes remain relatively unstudied. This study aims to identify and describe such complications. Methods: A dual-round survey was administered to 98 participants through the Feeding Tube Awareness Foundation, a 501(c)(3) organization that supports parents and caretakers of G-tube-fed children. Information was collected broadly regarding G-tube complications, causes, and attitudes toward such complications. Results: Infection (56%), itching/irritation/redness (52%), and leakage (51%) were the leading G-tube related complications. The average time that G-tubes were replaced was 3.4 ± 1.2 months as compared to the typical recommended period of up to 6 months. Of the caretakers who had not experienced G-tube displacement, 7.9% wanted to see a change in current G-tubes to address the issue, compared with 75% of those who had experienced displacement. This 67.1% differential in caretakers’ attitudes toward G-tubes based on their prior experience with a particular complication was the largest gap among all other listed complications. Conclusions: G-tube complications are prevalent and varied. A sizable portion of G-tube users experience complications severe enough to require intervention. Of these, G-tube displacement is particularly critical and frequently precedes other prevalent complications, namely gastric leakage, infection, and tissue granulation.
Introduction:Targeted temperature management (TTM) has been associated with greater likelihood of neurological recovery among comatose survivors of cardiac arrest. However, the efficacy of TTM is not consistently observed, possibly due to heterogeneity of therapeutic response. The aim of this study is to determine if models leveraging multi-modal data available in the first 12 hours after ICU admission (hyperacute phase) can predict short-term outcome after TTM.Methods:Adult patients receiving TTM after cardiac arrest were selected from a multicenter ICU database. Predictive features were extracted from clinical, physiologic, and laboratory data available in the hyperacute phase. Primary endpoints were survival and favorable neurological outcome, determined as the ability to follow commands (motor Glasgow Coma Scale [mGCS] of 6) upon discharge. Three machine learning (ML) algorithms were trained: generalized linear models (GLM), random forest (RF), and gradient boosting (XG). Models with optimal features from forward selection were 10-fold cross-validated and resampled 10 times.Results:Data were available on 310 cardiac arrest patients who received TTM, of whom 183 survived and 123 had favorable neurological outcome. The GLM performed best, with an area under the receiver operating characteristic curve (AUROC) of 0.86 ± 0.04, sensitivity 0.75 ± 0.09, and specificity 0.77 ± 0.07 for the prediction of survival and an AUROC of 0.85 ± 0.03, sensitivity 0.71 ± 0.10, and specificity 0.80 ± 0.12 for the prediction of favorable neurological outcome. Features most predictive of both endpoints included lower serum chloride concentration, higher serum pH, and greater neutrophil counts.Conclusion:In patients receiving TTM after cardiac arrest, short-term outcomes can be accurately discriminated using ML applied to data routinely collected in the first 12 hours after ICU admission. With validation, hyperacute prediction could enable personalized approach to clinical decision-making in the post-cardiac arrest setting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.