Heparin-Loaded Composite Coatings on Porous Stent from Pure Magnesium for Biomedical Applications.

Challenges associated with drug-releasing stents used in percutaneous transluminal coronary angioplasty (PTCA) encompass allergic reactions, prolonged endothelial dysfunction, and delayed stent clotting. Although absorbable stents made from magnesium alloys seem promising, fast in vivo degradation and poor biocompatibility remain major challenges. In this study, zirconia (ZrO2) layers were used as the foundational coat, while calcium phosphate (CaP) served as the surface layer on unalloyed magnesium specimens. Consequently, the corrosion current density was decreased to 3.86, from 13.3 µA/cm2. Moreover, a heparin-controlled release mechanism was created by co-depositing CaP, gelatin (Gel), and heparin (Hep) on the specimens coated with CaP/ZrO2, thereby boosting magnesium's blood compatibility and prolonging the heparin-releasing time. Techniques like X-ray diffractometry (XRD), focused ion beam (FIB) system, toluidine blue testing, UV-visible spectrometry, field emission scanning electron microscopy (FESEM), and surrogate tests for endothelial cell viability were employed to examine the heparin-infused coatings. The drug content rose to 484.19 ± 19.26 µg/cm2 in multi-layered coatings (CaP-Gel-Hep/CaP-Hep/CaP/ZrO2) from 243.56 ± 55.18 µg/cm2 in a single layer (CaP-Hep), with the controlled release spanning beyond 28 days. Also, cellular viability assessments indicated enhanced biocompatibility of the coated samples relative to those without coatings. This suggests the potential of magnesium samples after coating ZrO2 and CaP with Gel as candidates for porous biodegradable stents or even scaffolds in biomedical applications.

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net.

In response to the difficulty of traditional image processing methods to quickly and accurately extract regions of interest from non-contact dorsal hand vein images in complex backgrounds, this study proposes a model based on an improved U-Net for dorsal hand keypoint detection. The residual module was added to the downsampling path of the U-Net network to solve the model degradation problem and improve the feature information extraction ability of the network; the Jensen-Shannon (JS) divergence loss function was used to supervise the final feature map distribution so that the output feature map tended to Gaussian distribution and improved the feature map multi-peak problem; and Soft-argmax is used to calculate the keypoint coordinates of the final feature map to realize end-to-end training. The experimental results showed that the accuracy of the improved U-Net network model reached 98.6%, which was 1% better than the original U-Net network model; the improved U-Net network model file was only 1.16 M, which achieved a higher accuracy than the original U-Net network model with significantly reduced model parameters. Therefore, the improved U-Net model in this study can realize dorsal hand keypoint detection (for region of interest extraction) for non-contact dorsal hand vein images and is suitable for practical deployment in low-resource platforms such as edge-embedded systems.