Artificial Intelligence Medical Compendium

Poor sleep quality amplifies pain experience. However, it remains uncertain regarding whether subjectively or physiologically assessed sleep parameters better predict pain, given reported discrepancies between the subjective and physiological measure... read more 

OBJECTIVE: Existing deep learning (DL) approaches for assessing temporomandibular disorders (TMD) are limited by underutilization of magnetic resonance imaging (MRI) in some tasks, a narrow focus on single-task detection, and predominant reliance on ... read more 

Target identification is pivotal for developing novel therapeutics in cancer and other diseases. Traditional experiment screening methods are constrained by low throughput and the complexity of biological systems. Multi-omics technologies offer a tra... read more 

Filamentous fungi have emerged as ideal chassis cells for high-value products such as industrial enzymes, therapeutic proteins, and antibiotics, due to their broad substrate adaptability, efficient protein secretion capacity, and well-developed post-... read more 

Monoclonal antibodies (mAbs) are key therapeutics for diseases like cancer and autoimmunity. The production of mAbs relies on cell culture, in which the culture medium for high productivity and activity is essential. Despite the traditional manual an... read more 

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Reducing the material cost of inorganic solid-state electrolytes is crucial to advancing all-solid-state batteries (ASSBs) for next-generation energy storage applications. The halospinel Li2Sc2/3Cl4 solid electrolyte (SE) possesses a high ionic condu... read more 

Early diagnosis significantly improves survival rates for hepatocellular carcinoma (HCC), yet traditional methods face limitations, including specialized instruments/personnel and prolonged reporting cycles. While lateral flow immunoassay (LFA) offer... read more 

Disordered fibrous networks play vital mechanical roles but are difficult to model due to their sparse connectivity and complex nonaffine deformation. We introduce a physics-informed, graph-learning-based Network Mechanics Prediction (GNMP) that pred... read more 

When soft wearable electronic devices are deployed on the body, body motion-induced artifacts pose a major challenge for accurately decoupling concurrent stimuli such as normal, shear forces, and elongational strain. This study introduces an ion-base... read more