Icariin accelerates Bone Regeneration By inducing Osteogenesis-Angiogenesis Coupling in Rats With Type 1 Diabetes Mellitus

BACKGROUND: Icariin (ICA), a natural flavonoid compound monomer, has multiple pharmacological activities. However, its effect on bone defect in the context of type 1 diabetes mellitus (T1DM) has not yet been examined. AIM: to explore the role and potential mechanism of ICA on bone defect in the context of T1DM. METHODS: The effects of ICA on osteogenesis and angiogenesis were evaluated by alkaline phosphatase staining, alizarin red S staining, quantitative real-time polymerase chain reaction, Western blot, and immunofluorescence. Angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis. A bone defect model was established in T1DM rats. The model rats were then treated with ICA or placebo and micron-scale computed tomography, histomorphometry, histology, and sequential fluorescent labeling were used to evaluate the effect of ICA on bone formation in the defect area. RESULTS: ICA promoted bone marrow mesenchymal stem cell (BMSC) proliferation and osteogenic differentiation. The ICA treated-BMSCs showed higher expression levels of osteogenesis-related markers (alkaline phosphatase and osteocalcin) and angiogenesis-related markers (vascular endothelial growth factor A and platelet endothelial cell adhesion molecule 1) compared to the untreated group. ICA was also found to induce osteogenesis-angiogenesis coupling of BMSCs. In the bone defect model T1DM rats, ICA facilitated bone formation and CD31 CONCLUSION: ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs

Miyun 232 MHz survey 1: Fields centered at: alpha:00(h)41(m), delta:41 deg 12 min and alpha: 07(h)00(m),delta:35 deg 00 min

A new meter-wave survey of sky region north of declination +30 deg is carried out with the Miyun 232 MHz Synthesis Radio Telescope (MSRT). The instrument, observation, and method of data reduction are briefly described. A preliminary catalog, first of a series, for two 8 deg. x 8 deg. regions centered respectively at 35 deg. is presented. On the average 4 - 5 sources per square degree are recorded with position accuracy of 5 sec. / S(Jy). BGPW scale is adopted for the flux density calibration. The accuracy of flux determination is limited by background fluctuation which is about 30 mJy. The catalog is complete for sources with flux larger than 0.25 Jy. The total number of sources listed in the paper amounts to 687. Several extended sources, sources with convex spectra, and one GPS source were found. Spectra of sources with flux larger than 0.5 Jy were also given

Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure

Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting the patterns of objective systems. This phenomenon is particularly pronounced in research domains associated with deep learning. However, investigations of causal relationships based on statistical and informational theories have posed an interesting and valuable challenge to large-scale models in the recent decade. Macroscopic models with fewer parameters can outperform their microscopic counterparts with more parameters in effectively representing the system. This valuable situation is called "Causal Emergence." This paper introduces a quantification framework, according to the Effective Information and Transition Probability Matrix, for assessing numerical conditions of Causal Emergence as theoretical constraints of its occurrence. Specifically, our results quantitatively prove the cause of Causal Emergence. By a particular coarse-graining strategy, optimizing uncertainty and asymmetry within the model's causal structure is significantly more influential than losing maximum information due to variations in model scales. Moreover, by delving into the potential exhibited by Partial Information Decomposition and Deep Learning networks in the study of Causal Emergence, we discuss potential application scenarios where our quantification framework could play a role in future investigations of Causal Emergence.Comment: 18 pages, 14 figure

Developing a Model for Chloride Ions Transport in Cement Concrete under Dynamic Flexural Loading and Dry-Wet Cycles

Chloride ions attack is the main factor leading to the degradation of concrete durability, while the diffusion process would be significantly aggravated under the dynamic flexural loading and dry-wet cycles. In this paper, the influence coefficients of dynamic flexural loading on chloride/water diffusion coefficients were established, based on the relationship between the dynamic flexural loading and the chloride ions diffusion coefficient of concrete. Based on the model of chloride ions transporting in dry-wet cycle environment, the transport model of chloride ions in concrete under the dynamic flexural loading and dry-wet cycles was established. The effects of different factors on the chloride ions transport law in concrete were analyzed through laboratory test. The results showed that the model was in good agreement with the experimental results. The theory and assumptions proposed applied in the model of chloride ions transport in concrete under the dynamic flexural loading and dry-wet cycles had certain rationality and scientificity

Loss of monoacylglycerol O-acyltransferase 2 can be compensated for by diacylglycerol O-acyltransferases 1 and 2 resulting in a negligible influence on mammary cancer development found in a mouse model and verified in human tissues

Background: Dietary fat absorption involves the re-esterification of digested triacylglycerol in the enterocytes, it is a biological process catalyzed by monoacylglycerol O-acyltransferase 2 (MOGAT2, aka MGAT2), which is highly expressed in the small intestine. A previous study showed that the loss of the Mogat2 gene can prevent high-fat diet-induced obesity in mice. Obesity is associated with an increased risk of several types of cancer including a postmenopausal mammary tumor. Methods: We collected 147 patients with triple-negative breast adenocarcinoma to explore the relationship between MOGAT2 expression and overall patient survival. The TCGA data were also retrieved for analyzing the prognostic values of MOGAT2 mRNA level as well as the relationships between MOGAT2 and DGAT1/2 mRNA levels. We also used a Mogat2-deficient mouse mammary tumor model by crossing Mogat2-deficient mice with MMTV-PyMT mice to examine the effect of MOGAT2 on mammary tumor development. Results: In human triple-negative breast adenocarcinoma, elevated expression of MOGAT2 correlated with a poorer patient prognosis. Obesity could be induced by a relatively high-fat diet (37% of calories from fat) in the mice with or without Mogat2 knockout. Mammary tumor development was deteriorated by a relatively high-fat diet regardless of Mogat2 deficiency. As a compensation mechanism, upregulation of diacylglycerol O-acyltransferases 1 and 2 (Dgat1 and Dgat2) in the Mogat2 deficient mice was found. Consistently, in human normal tissues adjacent to breast cancer, an inverse correlation between MOGAT2 mRNA level and DGAT1/2 mRNA levels was also found. Conclusions: Elevated expression of MOGAT2 in triple-negative breast adenocarcinoma predicts poorer patient overall survival. With the compensation of Dgat1 and Dgat2, Mogat2 deficiency alone cannot prevent fat diet-induced obesity, nor prevent mammary tumor development in a mouse model