Marco Rolandi

This paper proposes a deep learning-based control framework for wound healing that accelerates closure by 17.71% in simulations and 40% in vivo.

This study uses mouse forebrain organoids to show how variations in cellular composition influence the self-organization of neural circuits, revealing intrinsic principles of forebrain circuit assembly.

This study compares fluoxetine delivery via a bioelectronic device to topical bolus dosing in swine wounds, showing higher tissue concentrations and safety with the device.

This paper develops a wireless bioelectronic actuator for wound healing in pigs, showing reduced M1/M2 macrophage ratio and increased re-epithelialization in treated wounds.

This paper develops a deep reinforcement learning framework for real-time wound healing treatment optimization, showing 17.71% acceleration in in silico experiments and 40% in vivo.

This paper develops a remote-controlled wireless bioelectronic system for delivering fluoxetine to promote wound healing in pigs, showing improved re-epithelialization and reduced inflammation.

The paper presents a wearable imaging system for automated wound monitoring in pigs, validated to capture and analyze wound progression using machine learning.

The paper demonstrates the use of a sliding mode controller in a bioelectronic device to deliver a pre-determined dosage of fluoxetine with precision over time.

This study uses deep learning to quantify innervation changes during wound healing in mice, finding a strong correlation between nerve fiber density and re-epithelization.