Zlobina, K. | Search bioRxiv

bioRxiv2024

Accelerating Wound Healing Through Deep Reinforcement Learning: A Data-Driven Approach to Optimal Treatment

Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of wound healing progression. Real-time monitoring allows for precise adjustment in treatment strategies that align with an individuals unique biological response. However, due to the complexities of human-drug interactions and a lack of predictive models it is challenging to determine just how one should adjust drug dosage to achieve the desired biological response. This work proposes an adaptive closed-loop control framework that integrates deep learning, optimal control, and reinforcement learning to update treatment strategies in real-time with the goal of accelerating wound closure. The proposed approach eliminates the need for mathematical modeling of complex nonlinear wound healing dynamics. We demonstrate the convergence of the controller via an in silico experimental setup, where the proposed approach successfully accelerates the wound healing process by 17.71%. Finally, we share the experimental setup and results of an in vivo implementation to highlight the translational potential of our work. Our data-driven model estimates a 40% acceleration in wound closure.

systems biology↗

bioRxiv2024

DeepMapper: Attention-Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of wound healing progression. Real-time monitoring allows for precise adjustment in treatment strategies that align with an individuals unique biological response. However, due to the complexities of human-drug interactions and a lack of predictive models it is challenging to determine just how one should adjust drug dosage to achieve the desired biological response. This work proposes an adaptive closed-loop control framework that integrates deep learning, optimal control, and reinforcement learning to update treatment strategies in real-time with the goal of accelerating wound closure. The proposed approach eliminates the need for mathematical modeling of complex nonlinear wound healing dynamics. We demonstrate the convergence of the controller via an in silico experimental setup, where the proposed approach successfully accelerates the wound healing process by 17.71%. Finally, we share the experimental setup and results of an in vivo implementation to highlight the translational potential of our work. Our data-driven model estimates a 40% acceleration in wound closure.

systems biology↗

bioRxiv2023

Method for Identifying Proteomic Biomarkers of Health

Classification of proteomic samples from sick and non-sick individuals is important for developing high-quality diagnostics of diseases. Creating a shortlist of proteins useful for diagnostics is challenging. In this manuscript a simple algorithm of creating a multidimensional biomarker of health based on scanning proteomics data is provided. The algorithm is applied to several existing publicly available datasets and demonstrates a 9-protein indicator of atopic dermatitis, and 6-protein indicator of heart failure.

bioinformatics↗

bioRxiv2023

Programmable delivery of fluoxetine via wearable bioelectronics for wound healing in vivo

The ability to deliver drugs with precise dosages at specific time points can significantly improve disease treatment while reducing side effects. Drug encapsulation for gradual delivery has opened up the doors for superior treatment regimen. To expand on this ability, programming bioelectronic devices to deliver small molecules enables ad-hoc personalized therapeutic profiles that are more complex than simple gradual release. Here, we introduce a wearable bioelectronic bandage with an integrated electrophoretic ion pump that affords on-demand drug delivery with precise dose control. Delivery of fluoxetine to wounds in mice resulted in a 27.2% decrease in the macrophage ratio (M1/M2) and a 39.9% increase in re-epithelialization, indicating a shorter inflammatory phase and faster overall healing. Programmable drug delivery using wearable bioelectronics in wounds introduces a broadly applicable strategy for the long-term delivery of a prescribed treatment regimen with minimal external intervention.

bioengineering↗

bioRxiv2021

Effective spatio-temporal regimes for wound treatment by way of macrophage polarization: A mathematical model

Wound healing consists of a sequence of biological processes often grouped into different stages. Interventions applied to accelerate normal wound healing must take into consideration timing with respect to wound healing stages in order to maximize treatment effectiveness. Macrophage polarization from M1 to M2 represents a transition from the inflammatory to proliferation stage of wound healing. Accelerating this transition may be an effective way to accelerate wound healing; however, it must be induced at the appropriate time. We search for an optimal spatio-temporal regime to apply wound healing treatment in a mathematical model of wound healing. In this work we show that to maximize effectiveness, treatment must not be applied too early or too late. We also show that effective spatial distribution of treatment depends on the heterogenity of the wound surface. In conclusion, this research provides a possible optimal regime of therapy that focuses on macrophage activity and a hypothesis of treatment outcome to be tested experimentally in future. Finding best regimes for treatment application is a first step towards development of intelligent algorithms of wound treatment that minimize healing time.

systems biology↗