Aptamers, valued for their stability, target affinity, and modifiability, have advanced biosensing, yet key challenges hinder their translation into practical sensing platforms. This work explores the future potential of aptasensing technologies acro...
The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, but traditional methods lack sufficient sensitivity, accuracy, and convenience. The CRISPR-colorimetric lateral flow assay (CLFA) integration offers a...
Flexible pressure sensors have become a research hotspot in human-computer interaction and material recognition due to their flexibility and high sensitivity. However, the identified information relies on traditional displays and cannot be dynamicall...
Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular architecture and molecular composition with unprecedented detail. Virtual staining techniques, which rely on computational algorithms to map molecular ...
Gastric cancer is one of the most common malignant tumors of the digestive system, with a high mortality rate due to late-stage diagnosis. Current clinical diagnosis relies on endoscopic biopsy and histopathological analysis, which are highly depende...
Tissue atlases provide foundational knowledge on the cellular organization and molecular distributions across molecular classes and spatial scales. Here, we construct a comprehensive spatiomolecular lipid atlas of the human kidney from 29 donor tissu...
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...
OBJECTIVE: This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance.
BACKGROUND: Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.
BACKGROUND: The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent complexity and specificity of medi...
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