AIMC Topic: Humans

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High-entropy nanozyme biosensors: Machine learning-assisted design and stimulus-responsive applications.

Colloids and surfaces. B, Biointerfaces
High-entropy nanozymes (HENs) have emerged as a revolutionary class of bio-inspired catalysts that integrate multi-enzyme mimetic activities with environmental responsiveness, creating transformative opportunities for next-generation biosensing techn...

Artificial intelligence for solving pediatric clinical cases: A Retrieval-Augmented approach utilizing Llama3.2 and structured references.

International journal of medical informatics
BACKGROUND: The "hallucinations" of Large Language Models (LLMs) raise concerns about their accuracy in pediatrics. This study aimed to evaluate whether integrating information from the Nelson Textbook of Pediatrics through a Retrieval-Augmented Gene...

Expanding point cloud statistical shape model applications: Generalized vascular modeling for population-level hemodynamic simulations.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Population-scale hemodynamic research faces limitations due to the trade-off between computationally expensive patient-specific Computational Fluid Dynamics (CFD) and overly idealized cylindrical models. To overcome this, we...

Uncertainty-based cardiac image registration using variational autoencoder with nonuniformly spaced control points.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plague...

A flexible two-stage anonymization framework for narrative medical records adapting to various language models.

Computers in biology and medicine
The healthcare sector increasingly relies on Electronic Health Records (EHRs) for efficient and high-quality patient care by providing rapid access to comprehensive medical information. However, these records contain sensitive patient data that must ...

Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models.

Computers in biology and medicine
Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex infor...

LG-TriCapsNet: A lightweight graph capsule framework with nearest neighbor graphs for multi-disease EEG classification.

Computers in biology and medicine
EEG signal classification for neurological disorders is a very critical task in the healthcare field, demanding accuracy and efficiency. Due to the diversity of these disorders and the complexity of the EEG signals, the task of diagnosing these disor...

MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.

Interdisciplinary sciences, computational life sciences
Emerging evidence highlights long non-coding RNAs (lncRNAs) as pivotal regulators demonstrating significant linkages with diverse human pathologies through expression dynamics and regulatory cascades. This research endeavors to establish an algorithm...

Biomarker risk stratification with capsule sponge in the surveillance of Barrett's oesophagus: prospective evaluation of UK real-world implementation.

Lancet (London, England)
BACKGROUND: Endoscopic surveillance is the clinical standard for Barrett's oesophagus, but its effectiveness is inconsistent. We have developed a test comprising a pan-oesophageal cell collection device coupled with biomarkers to stratify patients in...