Physical Properties and Biocompatibility of a Core-Sheath Structure Composite Scaffold for Bone Tissue Engineering In Vitro

Scaffolds play a critical role in the practical realization of bone tissue engineering. The purpose of this study was to assess whether a core-sheath structure composite scaffold possesses admirable physical properties and biocompatibility in vitro. A novel scaffold composed of poly(lactic-co-glycolic acid)/β-tricalcium phosphate (PLGA/β-TCP) skeleton wrapped with Type I collagen via low-temperature deposition manufacturing (LDM) was prepared, and bone mesenchymal stem cells (BMSCs) were used to evaluate cell behavior on the scaffold. PLGA/β-TCP skeleton was chosen as the control group. Physical properties were evaluated by pority ratio, compressive strength, and Young's modulus. Scanning electron microscope (SEM) was used to study morphology of cells. Hydrophilicity was evaluated by water absorption ratio. Cell proliferation was tested by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay (MTT). Osteogenic differentiation of BMSCs was evaluated by alkaline phosphates activity (ALP). The results indicated that physical properties of the novel scaffold were as good as those of the control group, hydrophilicity was observably better (P < 0.01) than that of control group, and abilities of proliferation and osteogenic differentiation of BMSCs on novel scaffold were significantly greater (P < 0.05) than those of control group, which suggests that the novel scaffold possesses preferable characteristics and have high value in bone tissue engineering

Single-image based deep learning for precise atomic defects identification

Defect engineering has been profoundly employed to confer desirable functionality to materials that pristine lattices inherently lack. Although single atomic-resolution scanning transmission electron microscopy (STEM) images are widely accessible for defect engineering, harnessing atomic-scale images containing various defects through traditional image analysis methods is hindered by random noise and human bias. Yet the rise of deep learning (DL) offering an alternative approach, its widespread application is primarily restricted by the need for large amounts of training data with labeled ground truth. In this study, we propose a two-stage method to address the problems of high annotation cost and image noise in the detection of atomic defects in monolayer 2D materials. In the first stage, to tackle the issue of data scarcity, we employ a two-state transformation network based on U-GAT-IT for adding realistic noise to simulated images with pre-located ground truth labels, thereby infinitely expanding the training dataset. In the second stage, atomic defects in monolayer 2D materials are effectively detected with high accuracy using U-Net models trained with the data generated in the first stage, avoiding random noise and human bias issues. In both stages, we utilize segmented unit-cell-level images to simplify the model's task and enhance its accuracy. Our results demonstrate that not only sulfur vacancies, we are also able to visualize oxygen dopants in monolayer MoS2, which are usually overwhelmed by random background noise. As the training was based on a few segmented unit-cell-level realistic images, this method can be readily extended to other 2D materials. Therefore, our results outline novel ways to train the model with minimized datasets, offering great opportunities to fully exploit the power of machine learning (ML) applicable to a broad materials science community

Roles of eddy generation and jet characteristics in setting the annual cycle of Siberian storm track

Abstract The Siberian storm track is one of the drivers of the East Asian extreme weather events. Using the daily JRA‐55 reanalysis data from 1980 to 2021, this study examines roles of eddy generation and jet characteristics in setting the annual cycle of Siberian storm track. It is found that there are two peaks of Siberian storm track intensity in boreal spring and autumn. The possible reason for such an annual cycle is explored by analyzing the maximum Eady growth rate over the Siberian region and jet characteristics. The stronger Siberian Eady growth rate in boreal spring and autumn, favoring a stronger baroclinic eddy generation, could contribute to the stronger intensity of Siberian storm tracks in these two seasons. Furthermore, the Siberian jet stream cores during boreal spring and autumn are located north of 50° N and resembles more an eddy‐driven jet. While in winter, the subtropical jet stream enhanced and the eddy‐driven jet becomes relatively weaker, which is less efficient to generate midlatitude baroclinic eddies. Besides, the eddy‐driven jet can modulate the horizontal wave propagations from upstream, which also plays a role in amplifying the spring and autumn Siberian storm tracks

The Role of Antioxidant Enzymes in the Ovaries

Proper physiological function of the ovaries is very important for the entire female reproductive system and overall health. Reactive oxygen species (ROS) are generated as by-products during ovarian physiological metabolism, and antioxidants are indicated as factors that can maintain the balance between ROS production and clearance. A disturbance in this balance can induce pathological consequences in oocyte maturation, ovulation, fertilization, implantation, and embryo development, which can ultimately influence pregnancy outcomes. However, our understanding of the molecular and cellular mechanisms underlying these physiological and pathological processes is lacking. This article presents up-to-date findings regarding the effects of antioxidants on the ovaries. An abundance of evidence has confirmed the various significant roles of these antioxidants in the ovaries. Some animal models are discussed in this review to demonstrate the harmful consequences that result from mutation or depletion of antioxidant genes or genes related to antioxidant synthesis. Disruption of antioxidant systems may lead to pathological consequences in women. Antioxidant supplementation is indicated as a possible strategy for treating reproductive disease and infertility by controlling oxidative stress (OS). To confirm this, further investigations are required and more antioxidant therapy in humans has to been performed

NPY and CGRP Inhibitor Influence on ERK Pathway and Macrophage Aggregation during Fracture Healing

Aim: The aims of this study are to investigate the effects of neurotransmitters NPY and CGRP on ERK signaling in fracture healing, and to identify the correlation between macrophage aggregation and fracture healing. Methods: Male Sprague-Dawley rats were used to build a fracture model. The neurotransmitter receptor inhibitors were injected intraperitoneally into the rats. Immunofluorescence staining and ELISA were employed to determine the expression of NPY and CGRP in fracture area and the peripheral blood, respectively. Micro-CT together with histological staining were utilized to assess the fracture healing conditions. Relative protein expression was determined using western blot. Immunofluorescence staining was used to detect the aggregation of macrophages in the injury area. Results: During fracture healing, the serum NPY and CGRP significantly increased. The levels of NPY and CGRP reached a peak in the 8th week and reduced significantly thereafter. NPY and CGRP inhibitors could inhibit fracture healing and down-regulate the phosphorylated ERK. Macrophages (NPY+ and CGRP+) aggregated in the injury area. Conclusion: NPY and CGRP participated in fracture healing, in which they were also shown to influence phosphorylated ERK expression. In addition, macrophages are involved in the fracture healing process