Effects of Ginsenosides on Cardiomyocytes and NF in Type 2 Diabetes Rats B Expression

Objective: To explore the clinical medicinal value of ginsenosides. Methods: 24 male type 2 diabetes rats aged 7 weeks were taken as the research object, and the myocardial cell morphology, infammatory factor content and NF in each group were observed by grouping them with diferent doses- κ B expression. Result: The swelling degree of cells in the CP Rg50 group was alleviated most signifcantly, with a signifcant reduction in deep staining of the nucleus, a signifcant reduction in cell shrinkage, and a basic trend towards normal cell morphology. Meanwhile, compared to the control group, the CP Rg50 and CP Rg25 groups showed signifcant diferences in IL-1 levels β/ IL-6, TNF- α It also signifcantly decreased horizontally (P0.05). Conclusion: Ginsenoside Rg1 has signifcant efects in the treatment of cardiomyopathy and is worth promoting in clinical practice

Scalable Label Distribution Learning for Multi-Label Classification

Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of relevant labels. Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios. Moreover, most existing methods design learning processes associated with the number of labels, which makes their computational complexity a bottleneck when scaling up to large-scale output space. To tackle these issues, we propose a novel MLC learning method named Scalable Label Distribution Learning (SLDL) for multi-label classification which can describe different labels as distributions in a latent space, where the label correlation is asymmetric and the dimension is independent of the number of labels. Specifically, SLDL first converts labels into continuous distributions within a low-dimensional latent space and leverages the asymmetric metric to establish the correlation between different labels. Then, it learns the mapping from the feature space to the latent space, resulting in the computational complexity is no longer related to the number of labels. Finally, SLDL leverages a nearest-neighbor-based strategy to decode the latent representations and obtain the final predictions. Our extensive experiments illustrate that SLDL can achieve very competitive classification performances with little computational consumption

Sustained release of VEGF from PLGA nanoparticles embedded thermo-sensitive hydrogel in full-thickness porcine bladder acellular matrix

We fabricated a novel vascular endothelial growth factor (VEGF)-loaded poly(lactic-co-glycolic acid) (PLGA)-nanoparticles (NPs)-embedded thermo-sensitive hydrogel in porcine bladder acellular matrix allograft (BAMA) system, which is designed for achieving a sustained release of VEGF protein, and embedding the protein carrier into the BAMA. We identified and optimized various formulations and process parameters to get the preferred particle size, entrapment, and polydispersibility of the VEGF-NPs, and incorporated the VEGF-NPs into the (poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (Pluronic®) F127 to achieve the preferred VEGF-NPs thermo-sensitive gel system. Then the thermal behavior of the system was proven by in vitro and in vivo study, and the kinetic-sustained release profile of the system embedded in porcine bladder acellular matrix was investigated. Results indicated that the bioactivity of the encapsulated VEGF released from the NPs was reserved, and the VEGF-NPs thermo-sensitive gel system can achieve sol-gel transmission successfully at appropriate temperature. Furthermore, the system can create a satisfactory tissue-compatible environment and an effective VEGF-sustained release approach. In conclusion, a novel VEGF-loaded PLGA NPs-embedded thermo-sensitive hydrogel in porcine BAMA system is successfully prepared, to provide a promising way for deficient bladder reconstruction therapy

Unified description of the productions of Dˉ∗D\bar{D}^*D and Dˉ∗D∗\bar{D}^*D^* molecules in BB decays

The exotic states $X(3872)$ and $Z_c(3900)$ have long been conjectured as isoscalar and isovector $\bar{D}^*D$ molecules, respectively. In this letter, we propose a unified framework to understand the productions of $\bar{D}^*D$ molecules as well as their heavy quark spin symmetry partners, $\bar{D}^*D^*$ molecules, in $B$ decays. We show that the large isospin breaking of the ratio $\mathcal{B}[B^+ \to X(3872) K^+]/\mathcal{B}[B^0 \to X(3872) K^0]$ can be attributed to the isospin breaking of the $\bar{D}^*D$ neutral and charged components. Because of this, the branching fractions of $Z_c(3900)$ in $B$ decays are smaller than the corresponding ones of $X(3872)$ by at least one order of magnitude, which naturally explains the non-observation of $Z_{c}(3900)$ in $B$ decays. Furthermore, we predict a hierarchy for the productions fractions of all the $\bar{D}^*D$ and $\bar{D}^*D^*$ molecules in $B$ decays, which are consistent with all the existing data and can help elucidate the internal structure of the $XZ$ states around the $\bar{D}^*D$ and $\bar{D}^*D^*$ mass thresholds, if confirmed by future experiments

Generation of spatially-separated spin entanglement in a triple quantum dot system

We propose a novel method for the creation of spatially-separated spin entanglement by means of adiabatic passage of an external gate voltage in a triple quantum dot system.Comment: 10 pages, 6 figure

A Novel Cross-Perturbation for Single Domain Generalization

Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. However, the limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. To address this, data perturbation (augmentation) has emerged as a crucial method to increase data diversity. Nevertheless, existing perturbation methods often focus on either image-level or feature-level perturbations independently, neglecting their synergistic effects. To overcome these limitations, we propose CPerb, a simple yet effective cross-perturbation method. Specifically, CPerb utilizes both horizontal and vertical operations. Horizontally, it applies image-level and feature-level perturbations to enhance the diversity of the training data, mitigating the issue of limited diversity in single-source domains. Vertically, it introduces multi-route perturbation to learn domain-invariant features from different perspectives of samples with the same semantic category, thereby enhancing the generalization capability of the model. Additionally, we propose MixPatch, a novel feature-level perturbation method that exploits local image style information to further diversify the training data. Extensive experiments on various benchmark datasets validate the effectiveness of our method