AI Medical Compendium

Guided electron flow in anthraquinone-methoxy donor-acceptor1-acceptor2 covalent triazine frameworks enabling superior selective uranium capture.

Journal of colloid and interface science Jan 29, 2026

Rational design and synthesis of stable and efficient photocatalysts for selective U(VI) capture in water remains a great challenge due to the complicated water environment. Herein, considering the synergistic interaction between anthraquinone (-AQ, ... read more

Dynamic bidirectional data recomposition for efficient road garbage segmentation in semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society Jan 29, 2026

Deep neural networks excel in road garbage segmentation but require costly pixel-level annotations. Balancing accuracy and annotation costs is a key bottleneck in urban garbage management. Semi-supervised learning (SSL) reduces the dependence on anno... read more

Learning discriminative prototypes: Adaptive relation-aware refinement and patch-level contextual feature reweighting for few-shot classification.

Neural networks : the official journal of the International Neural Network Society Jan 29, 2026

Few-shot learning (FSL) aims to achieve efficient classification with limited labeled samples, providing an important research paradigm for addressing the model generalization issue in data-scarce scenarios. In the metric-based FSL framework, class p... read more

Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.

Plant communications Jan 29, 2026

With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively advancing toward an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques, leve... read more

Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes.

Artificial intelligence in medicine Jan 29, 2026

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, which can progress to more severe liver diseases such as liver fibrosis, cirrhosis and hepato... read more

Explainable reinforcement learning for glucose monitoring based on shapley value analysis.

Computer methods and programs in biomedicine Jan 29, 2026

BACKGROUND AND OBJECTIVE: Effective diabetes management requires continuous regulation of blood glucose in response to complex factors such as diet, activity, stress, and medication. Advances in continuous glucose monitoring and machine learning have... read more

Anti-Inflammatory Properties of Dendrobium: A Systematic Review of Pharmacological Mechanisms.

Journal of ethnopharmacology Jan 29, 2026

ETHNOPHARMACOLOGICAL RELEVANCE: In Traditional Chinese Medicine (TCM), Dendrobium species have long been utilized to alleviate various inflammatory symptoms, particularly those characterized by Yin deficiency with internal heat and stomach deficiency... read more

DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography.

NeuroImage Jan 29, 2026

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale studies. We introduce DeepMult... read more

Rapid Detection of NDM-Producing Carbapenem-Resistant Escherichia coli Using MALDI-TOF MS Combined with Machine Learning Techniques.

International journal of antimicrobial agents Jan 29, 2026

BACKGROUND: Carbapenem-resistant Escherichia coli (CREC) producing New Delhi metallo-beta-lactamase (NDM) poses a substantial threat to global public health. Prompt detection of CREC is essential for effective patient management and to curb the sprea... read more

Foundation models for ophthalmic imaging.

Survey of ophthalmology Jan 29, 2026

Foundation models represent a new frontier in ophthalmic artificial intelligence, enabling learning of transferable features from large unlabelled imaging datasets for flexible application to varying downstream tasks. We systematically analyze the ev... read more

Artificial Intelligence Medical Compendium