Blackbox bio

This study compares treadmill-based DigiGait with free-movement BlackBox gait analysis following Achilles tendon injury in mice. Using velocity-matched comparisons, BlackBox demonstrated greater sensitivity to early functional changes, detecting alterations in stance and swing duration through postoperative day 14.

This study evaluated whether gabapentin alters spontaneous behaviors associated with chemotherapy-induced peripheral neuropathy (CIPN) in mice using the BlackBox automated behavioral analysis system. While paclitaxel treatment produced a reproducible behavioral phenotype, gabapentin affected several behaviors in both treated and control animals, suggesting these measures may not specifically reflect ongoing pain.

This study identifies acetylation of the axonal protein G3BP1 by the acetyltransferase ELP3 as a key mechanism that enhances axon regeneration following injury. The findings reveal a promising molecular target for promoting nerve repair and demonstrate the value of objective behavioral assessment for evaluating regenerative therapies.

This study demonstrates that intranasal delivery of oxytocin mRNA encapsulated in lipid nanoparticles (mRNA-LNPs) enhances social behavior while reducing pain-related responses in preclinical models. The findings highlight a promising non-invasive therapeutic strategy and demonstrate the importance of objective behavioral phenotyping for evaluating treatment efficacy.

This study investigates how neuropathic pain reshapes communication between the cortex and striatum over time. By combining advanced neuroscience techniques with objective behavioral analysis, the researchers demonstrate that chronic pain produces dynamic changes in neural circuitry that contribute to altered movement and behavior.

This study demonstrates that blood flow restriction (BFR) therapy improves muscle regeneration and shoulder function following rotator cuff injury in mice. Using the BlackBox behavioral analysis system, researchers objectively measured improvements in gait and weight-bearing, highlighting the value of automated behavioral phenotyping for evaluating rehabilitation strategies.

This study reveals how ascending spinal pathways are differentially modified during acute and chronic pain, providing new insight into how pain signals are processed and transmitted to the brain. The findings improve our understanding of pain neurobiology and support the development of more targeted therapeutic strategies.

This study demonstrates how deep behavioral phenotyping can objectively measure functional recovery following tibia fracture in mice. Using automated behavioral analysis, researchers identified subtle changes in movement and recovery that traditional assessment methods may overlook, providing a more comprehensive picture of fracture healing.

ARBEL is a machine learning platform that automatically classifies complex three-dimensional pain behaviors using light-based imaging and computer vision. By eliminating subjective manual scoring, the system enables rapid, objective, and reproducible behavioral assessment in preclinical pain research.

This study demonstrates that inhibiting non-muscle myosin II directly at the site of nerve injury significantly enhances axon regeneration. The findings provide new insight into mechanisms of neural repair and highlight the value of objective behavioral analysis for evaluating recovery following nervous system injury.

This comprehensive review examines the biological mechanisms underlying acute and chronic pain following bone fracture while evaluating current methods used to measure fracture pain in both clinical and preclinical research. The authors highlight opportunities for more objective, quantitative behavioral assessment to improve the development of future pain therapies.

This study demonstrates how the BlackBox system objectively quantifies persistent postural and motor impairments following spinal cord injury in mice. By combining machine learning–based pose estimation with automated weight-bearing analysis, researchers identified long-term behavioral deficits that traditional scoring methods failed to detect.

Researchers developed an automated machine learning platform capable of objectively detecting pain-related behaviors in freely moving rodents. The system accurately measured mechanical pain hypersensitivity and distinguished effective analgesia from sedation, demonstrating a powerful new approach for preclinical pain research.