Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.
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 CD31hiEMCNhi type H-positive capillary formation. Lastly, ICA effectively accelerated the rate of bone formation in the defect area. CONCLUSION ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs.
We present a comparative study of two nearby type Ia supernovae (SNe Ia), 2018xx and 2019gbx, that exploded in NGC 4767 and MCG-02-33-017 at a distance of 48 Mpc and 60 Mpc, respectively. The B -band light curve decline rate for SN 2018xx is estimated to be 1.48 ± 0.07 mag and for SN 2019gbx it is 1.37 ± 0.07 mag. Despite the similarities in photometric evolution, quasi-bolometric luminosity, and spectroscopy between these two SNe Ia, SN 2018xx has been found to be fainter by about ∼0.38 mag in the B -band and has a lower 56 Ni yield. Their host galaxies have similar metallicities at the SN location, indicating that the differences between these two SNe Ia may be associated with the higher progenitor metallicity of SN 2018xx. Further inspection of the near-maximum-light spectra has revealed that SN 2018xx has relatively strong absorption features near 4300 Å relative to SN 2019gbx. The application of the code TARDIS fitting to the above features indicates that the absorption features near 4300 Å appear to be related to not only Fe II /Mg II abundance but possibly to the other element abundances as well. Moreover, SN 2018xx shows a weaker carbon absorption at earlier times, which is also consistent with higher ejecta metallicity.
A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. Experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, a high consistency between the region of detected clump and that of simulated clump, and is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises $^{13}CO$ ($J = 1-0$) emission line of the MWISP within $11.7^{\circ} \leq l \leq 13.4^{\circ}$, $0.22^{\circ} \leq b \leq 1.05^{\circ}$ and 5 km s$^{-1}$ $\leq v \leq$ 35 km s$^{-1}$ and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.
As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. Impressed by the capability of the ChatGPT, many people are wondering about its limits: can GPT-5 (or other future GPT variants) help ChatGPT unify all AIGC tasks for diversified content creation? Toward answering this question, a comprehensive review of existing AIGC tasks is needed. As such, our work comes to fill this gap promptly by offering a first look at AIGC, ranging from its techniques to applications. Modern generative AI relies on various technical foundations, ranging from model architecture and self-supervised pretraining to generative modeling methods (like GAN and diffusion models). After introducing the fundamental techniques, this work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future. Moreover, we summarize their significant applications in some mainstream industries, such as education and creativity content. Finally, we discuss the challenges currently faced and present an outlook on how generative AI might evolve in the near future.
Based on the statistical self-similar fractal characteristics of the microstructure of porous media, the total flow rate and permeability of Newtonian fluids in the rough fracture network and rough matrix pores are derived, respectively. According to the connection structure between fractures and pores, the permeability analysis model of fluids in a matrix-embedded fracture network is established. The comparison between the predicted values of the model and the experimental data shows that the predicted values of the permeability of the rough fracture network and the rough matrix pores decrease with the increase in the relative roughness of the fractures and matrix pores, and are lower than the experimental data. Meanwhile, the predicted total flow rate of a rough fractured dual porous media is lower than that of a smooth fractal model and experimental data. In addition, it is also found that the larger the average inclination angle and the relative roughness of the fracture network, the smaller the permeability of the fractured dual porous media, and the relative roughness of the fracture network has a far greater influence on fluid permeability in the fractured dual porous media than the relative roughness of the matrix pores.
Generative AI has demonstrated impressive performance in various fields, among which speech synthesis is an interesting direction. With the diffusion model as the most popular generative model, numerous works have attempted two active tasks: text to speech and speech enhancement. This work conducts a survey on audio diffusion model, which is complementary to existing surveys that either lack the recent progress of diffusion-based speech synthesis or highlight an overall picture of applying diffusion model in multiple fields. Specifically, this work first briefly introduces the background of audio and diffusion model. As for the text-to-speech task, we divide the methods into three categories based on the stage where diffusion model is adopted: acoustic model, vocoder and end-to-end framework. Moreover, we categorize various speech enhancement tasks by either certain signals are removed or added into the input speech. Comparisons of experimental results and discussions are also covered in this survey.
The detection and analysis of molecular clumps can lead to a better understanding of star formation in the Milky Way. Herein, we present a molecular-clump-detection method based on improved YOLOv5 joint Density Peak Clustering (DPC). The method employs a two-dimensional (2D) detection and three-dimensional (3D) stitching strategy to accomplish the molecular-clump detection. In the first stage, an improved YOLOv5 is used to detect the positions of molecular clumps on the Galactic plane, obtaining their spatial information. In the second stage, the DPC algorithm is used to combine the detection results in the velocity direction. In the end, the clump candidates are positioned in the 3D position-position-velocity (PPV) space. Experiments show that the method can achieve a high recall of 98.41% in simulated data made up of Gaussian clumps added to observational data. The efficiency of the strategy has also been demonstrated in experiments utilizing observational data from the Milky Way Imaging Scroll Painting (MWISP) project.
Previous studies have revealed that melatonin could play a role in anti-osteoporosis and promoting osteogenesis. However, the effects of melatonin treatment on osteoporotic bone defect and the mechanism underlying the effects of melatonin on angiogenesis are still unclear. Our study was aimed to investigate the potential effects of melatonin on angiogenesis and osteoporotic bone defect. Bone marrow mesenchymal stem cells (BMSCs) were isolated from the femur and tibia of rats. The BMSC osteogenic ability was assessed using alkaline phosphatase (ALP) staining, alizarin red S staining, qRT-PCR, western blot, and immunofluorescence. BMSC-mediated angiogenic potentials were determined using qRT-PCR, western blot, enzyme-linked immunosorbent assay, immunofluorescence, scratch wound assay, transwell migration assay, and tube formation assay. Ovariectomized (OVX) rats with tibia defect were used to establish an osteoporotic bone defect model and then treated with melatonin. The effects of melatonin treatment on osteoporotic bone defect in OVX rats were analyzed using micro-CT, histology, sequential fluorescent labeling, and biomechanical test. Our study showed that melatonin promoted both osteogenesis and angiogenesis in vitro . BMSCs treated with melatonin indicated higher expression levels of osteogenesis-related markers [ALP, osteocalcin (OCN), runt-related transcription factor 2, and osterix] and angiogenesis-related markers [vascular endothelial growth factor (VEGF), angiopoietin-2, and angiopoietin-4] compared to the untreated group. Significantly, melatonin was not able to facilitate human umbilical vein endothelial cell angiogenesis directly, but it possessed the ability to promote BMSC-mediated angiogenesis by upregulating the VEGF levels. In addition, we further found that melatonin treatment increased bone mineralization and formation around the tibia defect in OVX rats compared with the control group. Immunohistochemical staining indicated higher expression levels of osteogenesis-related marker (OCN) and angiogenesis-related markers (VEGF and CD31) in the melatonin-treated OVX rats. Then, it showed that melatonin treatment also increased the bone strength of tibia defect in OVX rats, with increased ultimate load and stiffness, as performed by three-point bending test. In conclusion, our study demonstrated that melatonin could promote BMSC-mediated angiogenesis and promote osteogenesis–angiogenesis coupling. We further found that melatonin could accelerate osteoporotic bone repair by promoting osteogenesis and angiogenesis in OVX rats. These findings may provide evidence for the potential application of melatonin in osteoporotic bone defect.
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