Quercetin prevented diabetic nephropathy by inhibiting renal tubular epithelial cell apoptosis via the PI3K/AKT pathway.

Diabetic nephropathy (DN) is the most common and serious complication of diabetes, posing a significant threat to human health. Currently, safe and effective preventive strategies for DN are lacking. The study aimed to explore the preventive effect and the underlying mechanism of quercetin against DN. In the in vivo experiments, we established a mouse model of type 2 diabetes mellitus (T2DM) induced by a combination of high-fat diet (HFD) and streptozotocin (STZ) to explore the preventive effect of quercetin on DN and its protective role against renal tubular epithelial cell apoptosis. Subsequently, in vitro experiments using human tubular epithelial cells (HK-2 cells) were conducted to further validate the protective effects of quercetin on renal tubular epithelial cell apoptosis. Additionally, we employed RNA sequencing analysis (RNA-seq) and network pharmacology analysis to comprehensively elucidate the molecular mechanisms involved. In vivo, we observed a significant increase in the ratio of urinary microalbumin to creatinine in diabetic mice compared to control mice, accompanied by the activation of renal tubular epithelial cell apoptosis. Remarkably, all of these changes were reversed after quercetin treatment. In vitro, high-glucose-induced apoptosis in HK-2 cells was significantly attenuated by quercetin. Subsequent RNA sequencing analysis and network pharmacology analysis revealed that quercetin was most likely to inhibit high-glucose-induced HK-2 cell apoptosis through the PI3K/AKT signaling pathway. Western Blotting results further demonstrated that quercetin could inhibit the activation of the PI3K/AKT signaling pathway in HK-2 cells induced by high glucose. Our results supported that quercetin could prevent DN by inhibiting tubular epithelial cell apoptosis via the PI3K/AKT pathway. Quercetin might be a promising candidate for the prevention of DN.

Resolved Raman sideband cooling of a single optically trapped cesium atom.

We developed a resolved Raman sideband cooling scheme that can efficiently prepare a single optically trapped cesium (Cs) atom in its motional ground states. A two-photon Raman process between two outermost Zeeman sublevels in a single hyperfine state is applied to reduce the phonon number. Our scheme is less sensitive to the variation in the magnetic field than the commonly used scheme where the two outermost Zeeman sublevels belonging to the two separate ground hyperfine states are taken. Fast optical pumping with less spontaneous emission guarantees the efficiency of the cooling process. After cooling for 50 ms, 82% of the Cs atoms populate their three-dimensional ground states. Our scheme improves the long-term stability of Raman sideband cooling in the presence of magnetic field drift and is thus suitable for cooling other trapped atoms or ions with abundant magnetic sublevels.

An Investigation of Modular Composable Acoustic Metamaterials with Multiple Nonunique Chambers.

To make the sound absorber easy to fabricate and convenient for practical application, a modular composable acoustic metamaterial with multiple nonunique chambers (MCAM-MNCs) was proposed and investigated, which was divided into a front panel with the same perforated apertures and a rear chamber with a nonunique grouped cavity. Through the acoustic finite element simulation, the parametric studies of the diameter of aperture d, depth of chamber T0, and thickness of panel t0 were conducted, which could tune the sound absorption performances of MCAM-MNCs-1 and MCAM-MNCs-2 for the expected noise reduction effect. The effective sound absorption band of MCAM-MNCs-1 was 556 Hz (773-1329 Hz), 456 Hz (646-1102 Hz), and 387 Hz (564-951 Hz) for T = 30 mm, T = 40 mm, and T = 50 mm, respectively, and the corresponding average sound absorption coefficient was 0.8696, 0.8854, and 0.8916, accordingly, which exhibited excellent noise attenuation performance. The sound absorption mechanism of MCAM-MNCs was investigated by the distributions of the total sound energy density (TSED). The components used to assemble the MCAM-MNCs sample were fabricated by additive manufacturing, and its actual sound absorption coefficients were tested according to the transfer matrix method, which demonstrated its feasibility and promoted its actual application.

TGPS: dynamic point cloud down-sampling of the dense point clouds for Terracotta Warrior fragments.

The dense point clouds of Terracotta Warriors obtained by a 3D scanner have a lot of redundant data, which reduces the efficiency of the transmission and subsequent processing. Aiming at the problems that points generated by sampling methods cannot be learned through the network and are irrelevant to downstream tasks, an end-to-end specific task-driven and learnable down-sampling method named TGPS is proposed. First, the point-based Transformer unit is used to embed the features and the mapping function is used to extract the input point features to dynamically describe the global features. Then, the inner product of the global feature and each point feature is used to estimate the contribution of each point to the global feature. The contribution values are sorted by descending for different tasks, and the point features with high similarity to the global features are retained. To further learn rich local representation, combined with the graph convolution operation, the Dynamic Graph Attention Edge Convolution (DGA EConv) is proposed as a neighborhood graph for local feature aggregation. Finally, the networks for the downstream tasks of point cloud classification and reconstruction are presented. Experiments show that the method realizes the down-sampling under the guidance of the global features. The proposed TGPS-DGA-Net for point cloud classification has achieved the best accuracy on both the real-world Terracotta Warrior fragments and the public datasets.

ULD-Net: 3D unsupervised learning by dense similarity learning with equivariant-crop.

Although many recent deep learning methods have achieved good performance in point cloud analysis, most of them are built upon the heavy cost of manual labeling. Unsupervised representation learning methods have attracted increasing attention due to their high label efficiency. How to learn more useful representations from unlabeled 3D point clouds is still a challenging problem. Addressing this problem, we propose a novel unsupervised learning approach for point cloud analysis, named ULD-Net, consisting of an equivariant-crop (equiv-crop) module to achieve dense similarity learning. We propose dense similarity learning that maximizes consistency across two randomly transformed global-local views at both the instance level and the point level. To build feature correspondence between global and local views, an equiv-crop is proposed to transform features from the global scope to the local scope. Unlike previous methods that require complicated designs, such as negative pairs and momentum encoders, our ULD-Net benefits from the simple Siamese network that relies solely on stop-gradient operation preventing the network from collapsing. We also utilize the feature separability constraint for more representative embeddings. Experimental results show that our ULD-Net achieves the best results of context-based unsupervised methods and comparable performances to supervised models in shape classification and segmentation tasks. On the linear support vector machine classification benchmark, our ULD-Net surpasses the best context-based method spatiotemporal self-supervised representation learning (STRL) by 1.1% overall accuracy. On tasks with fine-tuning, our ULD-Net outperforms STRL under fully supervised and semisupervised settings, in particular, 0.1% accuracy gain on the ModelNet40 classification benchmark, and 0.6% medium intersection of union gain on the ShapeNet part segmentation benchmark.

Anti-inflammatory effects of ß-ionone-curcumin hybrid derivatives against ulcerative colitis.

A series of ß-ionone-curcumin hybrid derivatives were designed and chosen to merge the biological characteristics of two parent molecules and to obtain a leading compound with higher biological activity. Through the initial screening, the structure activity relationship of their hybrid derivatives as inhibitors of nitric oxide (NO) production showed that meta-substituted derivatives exhibited the best inhibitory activity, among which 1h was the best one. In lipopolysaccharide-induced Raw264.7 macrophage cells, 1h showed anti-inflammatory activity by inhibiting the productions of NO and reactive oxygen species, the expressions of Interleukin-1ß and tumor necrosis factor-α, and the translocation of nuclear factor (NF)-κB from the cytosol to the nucleus. Furthermore, molecular docking simulation displayed that 1h could interact with cluster of differentiation 14 to inhibit the toll-like receptor 4/NF-κB signaling. In dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) of mice, 100 mg/kg of 1h could significantly reduce the colon length shortening and protect against colon injury, liver injury and oxidative stress in DSS-induced UC of mice. Besides, 1h was safety in vivo. In conclusion, 1h was the potential anti-inflammatory agent, and further investigations were underway in our laboratory.

A defect-rich ultrathin MoS2/rGO nanosheet electrocatalyst for the oxygen reduction reaction.

The structural properties such as high specific surface area, good electrical conductivity, rich-defects of the catalyst surface guarantee outstanding catalytic performance and durability of oxygen reduction reaction (ORR) electrocatalysts. It is still a challenging task to construct ORR catalysts with excellent performance. Herein, we have reported column-like MoS2/rGO with defect-rich ultrathin nanosheets prepared by a convenient solvothermal method. The structure and composition of MoS2/rGO are systematically investigated. MoS2/rGO shows a remarkable electrocatalytic performance, which is characterized by an outstanding onset potential of 0.97 V, a half-wave potential of 0.83 V, noticeable methanol tolerance, and durability of 93.7% current retention, superior to commercial Pt/C. The ORR process occurring on MoS2/rGO is a typical four electron pathway. Therefore, this study achieves the design of a low-cost, highly efficient and stable nonprecious metal ORR electrocatalyst in alkaline media.

[Rule of acupoint selection for cancer pain based on association rule and entropy clustering].

OBJECTIVE: To analyze the rule of acupoint selection for cancer pain based on data mining. METHODS: The published literature regarding acupuncture for cancer pain in the recent 10 years was searched in PubMed, CNKI, VIP and Wanfang database. The acupoint selection was summarized and analyzed by TCMISS V2.5. RESULTS: Totally 68 literature was collected and 73 acupoint prescriptions were included, involving 117 acupoints. These acupoints were mainly in bladder meridian, stomach meridian, liver meridian and spleen meridian. Among them, 40 acupoints used more than 4 times were identified, and the top three acupoints were Zusanli (ST 36, 65 times), Neiguan (PC 6, 55 times) and Taichong (LR 3, 50 times). A total of 68 acupoint combinations used more than 19 times were identified, and the top three acupoint combinations were Zusanli (ST 36)-Neiguan (PC 6), Taichong (LR 3)-Zusanli (ST 36) and Zusanli (ST 36)-Sanyinjiao (SP 6). There were 103 acupoint combinations with strong association; based on the entropy clustering algorithm, 20 new acupoint combinations and 10 new acupoint prescriptions were obtained. CONCLUSION: The main meridians for cancer pain are bladder meridian, stomach meridian, liver meridian and spleen meridian, with Zusanli (ST 36), Neiguan (PC 6), Taichong (LR 3), Hegu (LI 4), Sanyinjiao (SP 6) and ashi points as core acupoints, and regulating spleen-stomach and treating qi-blood are the main principles.