Journal of chemical information and modeling
Apr 24, 2026
X-ray diffraction (XRD) is a powerful analytical technique for identifying crystalline phases in unknown mixtures. However, traditional phase identification methods are time-consuming and require substantial human intervention. To accelerate this pro... read more
BACKGROUND: Maxillofacial skeletal defects lead to functional impairments, aesthetic disfigurement, and psychosocial burdens, while traditional surgical approaches face challenges in precision and personalization. Recent advancements in artificial in... read more
IntroductionEEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric study investigates EEGLAB publications in the Asia-Pacific and Arabian regions, focusing on Scopus a... read more
BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpretable machine learning framework and evaluate its predictive accuracy. METHODS: A retrospective cohor... read more
Virchows Archiv : an international journal of pathology
Apr 24, 2026
The evaluation of HER2 gene amplification is a time-consuming process that requires quantifying a large number of signals in cancer cells to ensure reproducible results, a task that can be assisted by image analysis (IA) tools. This study aimed to de... read more
CRISPR/Cas9 technologies are now routinely used in plant research, with guide RNA (gRNA) design being a critical determinant of genome editing success. However, rational design of highly active gRNAs is challenging due to complex sequence and biochem... read more
Environmental monitoring and assessment
Apr 24, 2026
Coastal erosion, driven by climate change, sea-level rise, and human activities, poses critical risks to coastal ecosystems and communities worldwide. Understanding how the scientific community has investigated shoreline change is essential for advan... read more
Graph Neural Networks (GNNs) have emerged as a novel paradigm that enables scientists to model complex relational data in medical applications, offering unique advantages over traditional deep learning (DL) approaches for non-Euclidean domains. This ... read more
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