A Biomass-Inspired Hydrogel Patch for Intelligent Pain Monitoring and On-Demand Analgesia

TL;DR

A smart, biomass-based hydrogel patch is developed for real-time pain monitoring and on-demand treatment using AI and local anesthetic.

Contribution

The patch integrates pain sensing, AI-assisted evaluation, and on-demand therapy using a biomass-derived hydrogel.

Findings

The hydrogel patch showed excellent stretchability (534.22%), adhesion, and conductivity.

It achieved ~100% accuracy in monitoring pain signals in patients with musculoskeletal conditions.

The patch provided prolonged analgesia in a mouse model with minimal side effects.

Abstract

The development of efficacious pain management strategies remains a pivotal challenge, requiring the creation of sustainable, biomass-derived interfaces for real-time techniques. Existing assessment approaches are either invasive, rendering them inappropriate for extended home-based monitoring, or dependent on patient-reported subjective evaluations. In this study, we fabricated a multifunctional biomass-inspired polydopamine-based hydrogel (polyvinyl alcohol [PVA]/polyacrylamide [PAM]/lithium chloride [LiCl]/polydopamine [PDA]/lidocaine hydrochloride [LiH]) wearable patch. Encapsulating lidocaine, a local anesthetic, this biomass-composite patch integrated pain-sensing-assisted assessment and treatment functionalities. It exhibited remarkable properties, including good stretchability (534.22%), low modulus (0.044 kPa), fine tissue adhesion (1.82 kPa), high conductivity (3.90 S m−1), rapid self-healing ability, and antibacterial properties. The patch enabled accurate sensing of diverse motion-related signals. Combined with deep learning algorithms, patients diagnosed with scapulohumeral periarthritis and lumbar diseases were recruited as volunteers for pain signal monitoring and evaluation (accuracy rate ~100%). Moreover, the hydrogel patch prolonged local photothermal analgesia in paw withdrawal threshold (>31% vs. Ctrl) and cumulative pain score (<10) by using a mouse plantar incision pain model. PVA/PAM/LiCl/PDA-based hydrogels elicited no detectable skin irritation or sensitization under the tested conditions. Therefore, this work not only pioneers the construction of a wearable integrated patch for pain management featuring “AI-assisted sensing evaluation” and “on-demand therapy”, but also provides a highly promising intelligent solution based on biomass-derived patches for the objective and prospective assessment and treatment of various types of pain.

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