Anti-osteoporosis activity of Astragalus membranaceus Bunge extract in experimental rats

Purpose: To investigate the anti-osteoporosis effect of Astragalus membranaceus (Fisch.) Bunge. extract (AMBE) in experimental rats.Method: Female Sprague-Dawley rats were randomly divided into six groups: control group, ovariectomy (OVX) with vehicle group, OVX with 17β-estradiol (E2, 25 μg/kg/day) group, and OVX with AMBE doses (60, 120 and 240 mg/kg/day) groups. Daily oral administration of AMBE or E2 was started 4 weeks after OVX and lasted for 16 weeks. The bone mineral density (BMD) of L4 vertebrae and right femurs was evaluated. The length of each femur was measured with a micrometer, and the center of diaphysis was determined. Three representative L4 vertebrae were selected to evaluate trabecular microarchitecture. Serum alkaline phosphatase (ALP), urinary calcium (U-Ca), urinary phosphorus (UP), urinary creatinine (Cr) and osteocalcin (OC) levels were measured.Results: AMBE dose-dependently inhibited the bone mineral density (BMD) reduction of L4 vertebrae (0.27 ± 0.03 g/cm2, p < 0.05) and femurs (0.23 ± 0.03 g/cm2, p < 0.05) caused by OVX and prevented the deterioration of trabecular microarchitecture (p < 0.05), which were accompanied by a significant decrease in skeletal remodeling (p < 0.05) as evidenced by the lower levels of bone turnover markers. A higher dosage of AMBE treatment (240 mg/kg/day) increased U-Ca/Cr (0.27 ± 0.03 mmol/mmol), ALP (137.23 ± 16.72 U/L), U-P/Cr (4.18 ± 0.27 mmol/mmol) and OC (8.47 ± 0.26 mmol/L) levels (both p < 0.05).Conclusion: The findings of this study indicate that AMBE prevents OVX-induced osteoporosis in rats.Keywords: Astragalus membranaceus (Fisch.) Bunge, Osteoporosis, Ovariectomy, Bone mineral densit

Effect of Liuweibuqi capsule, a Chinese patent medicine, on the JAK1/STAT3 pathway and MMP9/TIMP1 in a chronic obstructive pulmonary disease rat model

AbstractObjectiveTo observe effect of Liuweibuqi Capsule, a Traditional Chinese Medicine (TCM), on the janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and matrix metalloproteinases (MMPs) in a chronic obstructive pulmonary disease (COPD) rat model with lung deficiency in terms of TCM's pattern differentiation.MethodsRats were randomly divided into a normal group, model group, Liuweibuqi group, Jinshuibao group, and spleen aminopeptidase group (n= 10). Aside from the normal group, all rats were exposed to smoke plus lipopolysaccharide tracheal instillation to establish the COPD model with lung deficiency. Models were established after 28 days and then the normal and model groups were given normal saline (0.09 g/kg), Liuweibuqi group was given Liuweibuqi capsule (0.35 g/kg), Jinshuibao group was given Jinshuibao capsules (0.495 g/kg), and the spleen group was given spleen aminopeptidase (0.33 mg/kg), once a day for 30 days. Changes in symptoms, signs, and lung histology were observed. Lung function was measured with a spirometer. Serum cytokines were detected using enzyme-linked immunosorbent assay, and changes in the JAK/STAT pathway, MMP-9, and MMPs inhibitor 1 (TIMP1) were detected by immunohistochemistry, RT-PCR, and western blotting, respectively.ResultsCompared with the normal group, lung tissue was damaged, and lung function was reduced in the model control group. Additionally, the levels of interleukin (IL)-1β, γ interferon (IFN-γ), and IL-6 were higher, while IL-4 and IL-10 were lower in the model control group than those in the normal group. The expressions of JAK1, STAT3, p-STAT3, and MMP-9 mRNA and protein in lung tissue were higher, and TIMP1 mRNA and protein was lower in the model group compared with the normal group. After treatment, compared with the model group, the expression of inflammatory cytokines was lower in each treatment group, and expressions of JAK/STAT pathway, MMPs were lower. Compared with the positive control groups, the Jinshuibao and spleen aminopeptidase groups, lung function was better, and JAK1, STAT3, and p-STAT3 protein were lower and TIMP1 was higher in the Liuweibuqi group.ConclusionLiuweibuqi capsules can improve the symptoms of COPD possibly by regulating the expression of the JAK1/STAT3 pathway and MMP9/TIMP1

Dynamics of opinion formation in a small-world network

The dynamical process of opinion formation within a model using a local majority opinion updating rule is studied numerically in networks with the small-world geometrical property. The network is one in which shortcuts are added to randomly chosen pairs of nodes in an underlying regular lattice. The presence of a small number of shortcuts is found to shorten the time to reach a consensus significantly. The effects of having shortcuts in a lattice of fixed spatial dimension are shown to be analogous to that of increasing the spatial dimension in regular lattices. The shortening of the consensus time is shown to be related to the shortening of the mean shortest path as shortcuts are added. Results can also be translated into that of the dynamics of a spin system in a small-world network.Comment: 10 pages, 5 figure

Ethyl 4-{2,6-dichloro-4-[3-(2,6-difluoro­benzo­yl)ureido]phen­oxy}butanoate

The title compound, C20H18Cl2F2N2O5, is considered to belong to the fourth generation of insecticides with properties such as high selectivity, low acute toxicity for mammals and high biological activity. An intramolecular N—H⋯O hydrogen bond results in the formation of a six-membered ring. In the crystal structure, intermolecular N—H⋯O and C—H⋯F hydrogen bonds link the molecules

Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the `uncertainty accumulation' and `temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy

Quantum molecular dynamic simulations of warm dense carbon monoxide

Using quantum molecular dynamic simulations, we have studied the thermophysical properties of warm dense carbon monoxide under extreme conditions. The principal Hugoniot, which is derived from the equation of state, shows excellent agreement with available experimental data up to 67 GPa. The chemical decomposition of carbon monoxide has been predicted at 8 GPa by means of pair correlation function. Based on Kubo-Greenwood formula, the dc electrical conductivity and the optical reflectivity are determined, and the nonmetal-metal transition for shock compressed carbon monoxide is observed around 43 GPa

Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills

Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement learning (RL) is well suited for these safety-critical tasks, it still struggles to plan over extended periods. In this work, we present a skill-based framework that enhances offline RL to overcome the long-horizon vehicle planning challenge. Specifically, we design a variational autoencoder (VAE) to learn skills from offline demonstrations. To mitigate posterior collapse of common VAEs, we introduce a two-branch sequence encoder to capture both discrete options and continuous variations of the complex driving skills. The final policy treats learned skills as actions and can be trained by any off-the-shelf offline RL algorithms. This facilitates a shift in focus from per-step actions to temporally extended skills, thereby enabling long-term reasoning into the future. Extensive results on CARLA prove that our model consistently outperforms strong baselines at both training and new scenarios. Additional visualizations and experiments demonstrate the interpretability and transferability of extracted skills