GCN-Based Framework for Materials Screening and Phase Identification.

This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.

Correction: Han et al. Genotypic Homogeneity in Distinctive Transforming Growth Factor-Beta-Induced (TGFBI) Protein Phenotypes. 2021, , 1230.

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A Bivalent Iridium(III) Complex Toolkit for Mitochondrial DNA G-Quadruplex-Targeted Theranostics.

The G-quadruplex (G4) is an important diagnostic and therapeutic target in cancers, but the development of theranostic probes for subcellular G4s remains challenging. In this work, we report three G4-targeted theranostic probes by conjugating a pyridostatin-derived G4 ligand to G4-specific iridium(III) complexes with desirable photophysical properties. These probes showed specifically enhanced luminescence to mitochondrial G4 in triple negative breast cancer (TNBC) cells.

Uncovering the surge: dengue fever on the rise.

Introduction: Dengue fever, traditionally a tropical disease, has shown a notable increase in incidence within the United States over recent decades. This paper focuses on the increase in dengue fever cases in Maryland during increasing temperature and humidity and the expanding geographical range of Aedes mosquitoes, the primary vectors for dengue virus transmission.

Methods: Electronic health data was used to identify patterns in dengue incidence from 2014 to 2024.