Spatiotemporal Spectroscopy of Fast Excited-State Diffusion in 2D Covalent Organic Framework Thin Films.

Covalent organic frameworks (COFs), crystalline and porous conjugated structures, are of great interest for sustainable energy applications. Organic building blocks in COFs with suitable electronic properties can feature strong optical absorption, whereas the extended crystalline network can establish a band structure enabling long-range coherent transport. This peculiar combination of both molecular and solid-state materials properties makes COFs an interesting platform to study and ultimately utilize photoexcited charge carrier diffusion.

Patients with relapsing-remitting multiple sclerosis show accelerated whole brain volume and thalamic volume loss early in disease.

Background: The aim of this study was to investigate the dynamics of annual whole brain volume loss (BVL/year) and annual thalamic volume loss (ThalaVL/year) in patients with relapsing-remitting multiple sclerosis (PwRRMS) during the course of the disease.

Methods: A longitudinal database of magnetic resonance imaging (MRI) scans of 195 healthy individuals (age range, 22.8-63.

Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.

Purpose: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the discrimination between MS patients with versus without disability progression.

Materials And Methods: A longitudinal normative database of healthy controls (n = 563), two mono-scanner MS cohorts (n = 414, 156) and a mixed-scanner cohort acquired for various indications (n = 216) were included retrospectively. Mean observation period from the baseline (BL) to the last follow-up (FU) MRI scan was 2-3 years.

An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.

Purpose: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether Artificial intelligence (AI) can provide high-quality ICH diagnostics and turnaround times suitable for routine radiological practice.

Methods: A convolutional neural network (CNN) was trained and validated to detect ICHs on DICOM images of cranial CT (CCT) scans, utilizing about 674,000 individually labeled slices.

The anti-aromatase and anti-estrogenic activity of plant products in the treatment of estrogen receptor-positive breast cancer.

Despite being the focal point of decades of research, female breast cancer (BC) continues to be one of the most lethal cancers in the world. Given that 80 % of all diagnosed BC cases are estrogen receptor-positive (ER+) with carcinogenesis driven by estrogen-ERα signalling, current standard of care (SOC) hormone therapies are geared towards modulating the function and expression levels of estrogen and its receptors, ERα and ERβ. Currently, aromatase inhibitors (AIs), selective ER modulators (SERMs) and selective ER degraders (SERDs) are clinically prescribed for the management and treatment of ER+ BC, with the anti-aromatase activity of AIs abrogating estrogen biosynthesis, while the anti-estrogenic SERMs and SERDs antagonise and degrade the ER, respectively.