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Background: Transjugular intrahepatic portosystemic shunt (TIPS) is an effective intervention for managing complications of portal hypertension, particularly acute variceal bleeding (AVB). While effective in reducing portal pressure and preventing rebleeding, TIPS is associated with a considerable risk of overt hepatic encephalopathy (OHE), a complication that significantly elevates mortality rates.

Aim: To develop a machine learning (ML) model to predict OHE occurrence post-TIPS in patients with AVB using a 5-year dataset.

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Many different types of nanoparticles have been developed for photothermal therapy (PTT), but directly comparing their efficacy as heaters and determining how they will perform when localized at depth in tissue remains complex. To choose the optimal nanoparticle for a desired hyperthermic therapy, it is vital to understand how efficiently different nanoparticles extinguish laser light and convert that energy to heat. In this paper, we apply photothermal mass conversion efficiency (η ) as a metric to compare nanoparticles of different shapes, sizes, and conversion efficiencies.

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Purpose: Therapeutic options for patients with brain metastases (BM) increase. While these lead to considerable survival effects in subgroups, there is limited knowledge about characteristics, prognosticators and treatment effects in patients with BM and short survival.

Methods: Patients with a survival time of ≤ 6 months (short-term survivors, STS), diagnosed with BM between 2009-2021 at a large tertiary cancer center were analysed.

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Background: Extant literature indicates autistic students have lower school attendance compared to the general population. However, there remains considerable heterogeneity between studies, a lack of large population-based studies beyond the UK and US, and insufficient consideration of age and sex differences in attendance rates. The aim of this study was to examine school attendance among autistic compared with non-autistic students including stratification by age and sex.

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Correcting scale distortion in RNA sequencing data.

BMC Bioinformatics

January 2025

Department of Radiology, University of Minnesota, Minneapolis, MN, 55455, USA.

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used for population-based studies designed to identify genetic determinants of various diseases. Naturally, the accuracy of these tests should be verified and improved if possible.

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