Reproducibility of high-resolution laser speckle contrast imaging approaches to assess cutaneous microcirculation for wound healing monitoring in mice

Quantum machine learning enhanced laser speckle analysis for precise speed prediction

Laser speckle contrast imaging (LSCI) is an optical technique used to assess blood flow perfusion by modeling changes in speckle intensity, but it is generally limited to qualitative analysis due to difficulties in absolute quantification. Three-dimensional convolutional neural networks (3D CNNs) enhance the quantitative performance of LSCI by excelling at extracting spatiotemporal features from speckle data. However, excessive downsampling techniques can lead to significant information loss. To address this, we propose a hybrid quantum-classical 3D CNN framework that leverages variational quantum algorithms (VQAs) to enhance the performance of classical models. The proposed framework employs variational quantum circuits (VQCs) to replace the 3D global pooling layer, enabling the model to utilize the complete 3D information extracted by the convolutional layers for feature integration, thereby enhancing velocity prediction performance. We perform cross-validation on experimental LSCI speckle data and demonstrate the superiority of the hybrid models over their classical counterparts in terms of prediction accuracy and learning stability. Furthermore, we evaluate the models on an unseen test set and observe that the hybrid models outperform the classical models with up to 14.8% improvement in mean squared error (MSE) and up to 26.1% improvement in mean absolute percentage error (MAPE) evaluation metrics. Finally, our qualitative analysis shows that the hybrid models offer substantial improvements over classical models in predicting blood flow at both low and high velocities. These results indicate that the hybrid models possess more powerful learning and generalization capabilities.

Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing

OBJECTIVE

Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO2) in cutaneous tissues.

METHODS

A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red ([Formula: see text] nm) and near-infrared ([Formula: see text] nm), and StO2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance.

RESULTS

Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively.

CONCLUSION

By detecting the StO2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments. Clinical and Translational Impact Statement-This study supports efforts in monitoring StO2 levels, wound segmentation, and wound healing phase classification to improve the efficiency and accuracy of preclinical research in the field.

Heparinized Collagen Scaffolds Based on Schiff Base Bonds for Wound Dressings Accelerate Wound Healing without Scar

Skin wound healing is a complex process with multiple growth factors and cytokines participating and regulating each other. It is essential to develop novel wound dressings to accelerate the wound healing process. In this study, we developed the heparinized collagen scaffold materials (OL-pA), and the cross-linking reaction was based on the Schiff base reaction between pig acellular dermal matrix (pADM) and dialdehyde low molecular weight heparin (LMWH). Compared with pADM, the OL-pA modified by cross-linking still retained the triple helix structure of native collagen. When the dosage of the OL cross-linking agent was 12 wt %, the cross-linking density of OL-pA was 49.67%, the shrinkage temperature was 75.6 °C, the tensile strength was 14.62 MPa, the elongation at break was 53.14%, and the water contact angle was 25.1°, all of which were significantly improved compared with pADM. The cytocompatibility test showed that L929 cells adhered better on the surface of OL-pA scaffolds, and the proliferation ability of primary fibroblasts was enhanced. In vivo experiments showed that the OL-pA scaffolds could better accelerate wound healing, more effectively promote the positive expression of bFGF, PDGF, and VEGF growth factors, accelerate capillary angiogenesis, and promote wound scarless healing. In summary, the OL-pA scaffolds have more excellent hygrothermal stability, mechanical properties, hydrophilicity, and cytocompatibility. Especially the scaffolds have significant pro-healing properties for the full-thickness skin wound of rats and are expected to be a potential pro-healing collagen-based wound dressing.