Lidocaine-loaded nanoparticles are versatile nanomaterials that may be used in pain treatment due to their wound healing properties. The current study describes a wound dressing formulation focused on lidocaine-loaded dextran/ethylene glycol nanoparticles (an anesthetic drug). The lidocaine-loaded dextran/ethylene glycol membranes were fabricated using lidocaine solutions inside the dextran/ethylene glycol medium. The influence of various experimental conditions on dextran/ethylene glycol nanoparticle formations were examined. The sizes of dextran/ethylene glycol and lidocaine-loaded dextran/glycol nanoparticles were examined through the HR-SEM. Moreover, the efficacy antibacterial activity of dextran/glycol and lidocaine-loaded dextran/ethylene glycol nanoparticles was evaluated against the microorganisms grampositive and negative. Furthermore, we observed the In Vivo wound healing of wounds in skin using a mice model over a 16 days period. In this difference to the wounds of untreated mouse, quick healing was observed in the lidocaine-loaded dextran/glycol nanoparticles-treated wounds with fewer injury. These results specify that lidocaine-loaded dextran/ethylene glycol nanoparticles-based dressing material could be a ground-breaking nanomaterial having wound repair and implantations potential required for wound injury in pain management, which was proven using an animal model.

The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.