Natural ursolic acid based self-therapeutic polymer as nanocarrier to deliver natural resveratrol for natural therapy of acute kidney injury.

Acute kidney injury (AKI) is a common kidney disease associated with excessive reactive oxygen species (ROS). Unfortunately, due to the low kidney targeting and undesired side effects, the existing antioxidant and anti-inflammatory drugs are unavailable for AKI management in clinic. Therefore, it's essential to develop effective nanodrugs with high renal targeting and biocompatibility for AKI treatment. Herein, we reported a novel nanodrug for AKI treatment, utilizing poly(ursolic acid) (PUA) as a bioactive nanocarrier and resveratrol (RES) as a model drug. The PUA polymer was synthesized form ursolic acid with intrinsic antioxidant and anti-inflammatory activities, and successfully encapsulated RES through a nanoprecipitation method. Subsequently, we systemically investigated the therapeutic potential of RES-loaded PUA nanoparticles (PUA NPs@RES) against AKI. In vitro results demonstrated that PUA NPs@RES effectively scavenged ROS and provided substantial protection against H2O2-induced cellular damage. In vivo studies revealed that PUA NPs significantly improved drug accumulation in the kidneys and exhibited favorable biocompatibility. Furthermore, PUA NPs alone exhibited additional anti-inflammatory and antioxidant effect, synergistically enhancing therapeutic efficacy in AKI mouse models when combined with RES. Overall, our study successfully developed an effective nanodrug using self-therapeutic nanocarriers, presenting a promising option for the treatment of AKI.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning.

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding, agricultural production, and diverse research applications. Nevertheless, the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion. This drawback obstructed the accurate extraction of phenotypic parameters. Hence, this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques. The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network. The findings demonstrated that our network is stable and robust, as it can effectively complete diverse leaf point cloud morphologies, missing ratios, and multi-missing scenarios. A novel framework is presented for 3D plant reconstruction using a single-view RGB-D (Red, Green, Blue and Depth) image. This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions. Additionally, the extracted leaf area parameters, based on triangular mesh, were compared with the measured values. The outcomes revealed that prior to the point cloud completion, the R2 value of the flowering Chinese Cabbage's estimated leaf area (in comparison to the standard reference value) was 0.9162. The root mean square error (RMSE) was 15.88 cm2, and the average relative error was 22.11%. However, post-completion, the estimated value of leaf area witnessed a significant improvement, with an R2 of 0.9637, an RMSE of 6.79 cm2, and average relative error of 8.82%. The accuracy of estimating the phenotypic parameters has been enhanced significantly, enabling efficient retrieval of such parameters. This development offers a fresh perspective for non-destructive identification of plant phenotypes.