Organic acid leaching is an effective and sustainable method for simultaneously recovering critical metals from ternary lithium batteries (T-LIBs). However, current methods overlook the structural impact of organic acids and rely on inefficient trial...
Underwater intelligent systems demand flexible materials with robustness, environmental tolerance, and functional integration. Hydrogels, which combine flexibility with conductivity, show promise but often fail under harsh conditions. Herein, an anti...
The Fenton reaction is a widely used advanced oxidation process for water purification, valued for its simplicity and effectiveness in degrading refractory organic pollutants. However, accurately modeling its degradation kinetics remains challenging ...
Weather conditions are closely related to human health, yet effective methods for communicating the joint health risks associated with weather-related factors remain limited, especially when accounting for the complex interactions among weather expos...
The evolution of biosensors demands synergistic improvements in signal transduction and data processing. We present a universal biosensing platform that combines dual-mode signal responses from silver-modulated gold nanorods (AuNRs) and gold-silver n...
BACKGROUND: Ovarian cancer (OC) demonstrates the poorest prognosis among gynecological malignancies, with five-year survival rates below 45%, primarily due to late-stage diagnosis. To address this challenge, we systematically identified OC-specific d...
OBJECTIVE: To identify the potential diagnostic biomarkers and therapeutic targets of atherosclerotic carotid artery stenosis (ACAS), a comprehensive bioinformatics analysis was conducted to identify its related key genes and regulatory networks.
BACKGROUND: We aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomy...
OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.
BACKGROUND: Identifying potential associations among food, gut microbiota and disease is fundamental for elucidating interaction mechanisms and advancing personalized healthy dietary strategies. While computational methods have been extensively appli...
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