Utilisation patterns of radiotherapy among older patients: Insights from Portuguese National Cancer Registry data.

Introduction: Radiation therapy (RT) will be required by millions of those aged 70 or older by 2040 based on European growth in cancer incidence among this age group.

Purpose: This study evaluates the use of external radiotherapy (EBRT) using data from the Portuguese National Cancer Registry (RON), analysing various cancer locations and age groups. A comparative analysis was conducted between actual EBRT utilization and comorbidity-adjusted optimal rates.

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.

Introduction: White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets.

Solar Light CO Photoreduction Enhancement by Mononuclear Rhenium(I) Complexes: Characterization and Mechanistic Insights.

The catalytic efficacy of a novel mononuclear rhenium(I) complex in CO reduction is remarkable, with a turnover number (TON) of 1517 in 3 h, significantly outperforming previous Re(I) catalysts. This complex, synthesized via a substitution reaction on an aromatic ring to form a bromo-bipyridine derivative, = 2-bromo-6-(1-pyrazol-1-yl)pyridine, and further reacting with [Re(CO)Cl], results in the facial-tricarbonyl complex [ReL1(CO)Cl] (). The light green solid was obtained with an 80% yield and thoroughly characterized using cyclic voltammetry, nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, and ultraviolet-visible (UV-vis) spectroscopy.

Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights.

Prenatal alcohol exposure (PAE) refers to the exposure of the developing fetus due to alcohol consumption during pregnancy and can have life-long consequences for learning, behavior, and health. Understanding the impact of PAE on the developing brain manifests challenges due to its complex structural and functional attributes, which can be addressed by leveraging machine learning (ML) and deep learning (DL) approaches. While most ML and DL models have been tailored for adult-centric problems, this work focuses on applying DL to detect PAE in the pediatric population.