AIMC Journal:
APL bioengineering

Showing 1 to 6 of 6 articles

Cerebellar contribution to multisensory integration: A computational modeling exploration.

APL bioengineering
The remarkable ability of the human brain to create a coherent perception of reality relies heavily on multisensory integration-the complex process of combining inputs from different senses. While this mechanism is fundamental to our understanding of...

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning.

APL bioengineering
Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the electrophysiological changes underlying spontaneous recovery could offer critical insight into recovery...

Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications.

APL bioengineering
Prolonged exposure to high-stress environments can lead to mental illnesses such as anxiety disorders, depression, and posttraumatic stress disorder. Here, a wearable device utilizing photoplethysmography (PPG) technology is developed to noninvasivel...

Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug-protein-disease network-based deep learning.

APL bioengineering
Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillation (AF) often fail to account for the effects of medications and the complex interactions between drugs, proteins, and diseases. We developed an inte...

Decoding force-transmission linkages for therapeutic targeting and engineering.

APL bioengineering
Mechanosensing and mechanotransduction enable cells to perceive and respond to mechanical forces, underpinning essential physiological processes and disease pathways. Central to these phenomena are force-transmission supramolecular linkages, which un...

Classification of differentially activated groups of fibroblasts using morphodynamic and motile features.

APL bioengineering
Fibroblasts play essential roles in cancer progression, exhibiting activation states that can either promote or inhibit tumor growth. Understanding these differential activation states is critical for targeting the tumor microenvironment (TME) in can...