Xu’s research while affiliated with Cogswell Polytechnical College and other places

Polycaprolactone (PCL) is one of the most widely used three-dimensional (3D) printing materials with excellent biocompatibility and mechanical properties. However, its hydrophobic nature hinders cell adhesion and proliferation. Polydopamine (PDA) has been shown to promote proliferation and induce osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) on polymer surfaces. Despite this, the impact of varying PDA coating thicknesses on the osteogenic differentiation of BMSCs has been minimally explored. In this paper, PCL scaffolds were fabricated using 3D printing technology, and PDA-coated PCL scaffolds (PDA-PCL-0, PDA-PCL-3, PDA-PCL-6, PDA-PCL-24) were prepared by immersing the scaffolds in an aqueous dopamine solution for fixed time points (0, 3, 6, 24 h) under constant shaking. The scaffolds were characterized and subjected to physicochemical performance tests to evaluate their effects on BMSC proliferation, adhesion, and osteogenic differentiation. The results showed that PDA-PCL-6 scaffolds exhibited significant immunomodulatory properties, promoting BMSC proliferation, adhesion, and osteogenic differentiation more effectively than the other groups. In vivo validation experiments, including micro-computed tomography, hematoxylin and eosin staining, Masson staining, and immunohistochemical analysis of bone morphologenetic protein 2 (BMP-2) and type I collagen (COL-I), confirmed that PDA-PCL-6 scaffolds significantly enhanced bone regeneration, histocompatibility, and hemocompatibility compared to uncoated scaffolds at 1, 2, and 3 months postoperation. In conclusion, our results indicate that a PDA coating obtained through 6-h immersion significantly enhances the biocompatibility and osteoinductive properties of PCL scaffolds, providing a promising strategy for bone defect repair.

Researchers have developed smart shape-memory materials that adapt their structure or function to external stimuli. The demand for dynamic oral bone tissue repair is driven by continuous changes in bone and surrounding tissues during the repair process, such as tooth growth, movement, reconstruction of oral soft tissues, and skeletal differences in alveolar and craniofacial bones. These changes challenge the mechanical stability of bone implants and the precision of printing. Consequently, 4D printing technology introduces “time,” allowing pre-programmed changes in material shape or functionality, which enables scaffolds to respond to complex oral environments intelligently, achieving dynamic repair of bone and surrounding tissues. Despite its theoretical benefits in oral bone tissue engineering, the study and use of 4D printing technology is still in its infancy. This review explores the recent advances in 4D printing in dentistry, discussing skeletal structure, etiology of bone defects, and bone repair mechanisms. It also provides an overview of the materials, cells, and growth factors used in 4D printing bone tissue engineering. Thus, by reviewing existing studies, this review provides valuable insights for the future development of 4D printing technology in oral bone tissue engineering.

Chemotherapy has already proven widely effective in treating cancer. Chemotherapeutic agents usually include DNA damaging agents and non-DNA damaging agents. Assessing genotoxic effect is significant during chemotherapy drug development, since the ability to attack DNA is the major concern for DNA damaging agents which relates to the therapeutic effect, meanwhile genotoxicity should also be evaluated for chemotherapy agents' safety especially for non-DNA damaging agents. However, currently applicability of in vitro genotoxicity assays is hampered by the fact that genotoxicity results have comparatively high false positive rates. γ-H2AX has been shown to be a bifunctional biomarker reflecting both DNA damage response and repair. Previously, we developed an in vitro genotoxicity assay based on γ-H2AX quantification using mass spectrometry. Here, we employed the assay to quantitatively assess the genotoxic effects of 34 classic chemotherapy agents in HepG2 cells. Results demonstrated that the evaluation of cellular γ-H2AX could be an effective approach to screen and distinguish types of action of different classes of chemotherapy agents. In addition, two crucial indexes of DNA repair kinetic curve, i.e., k (speed of γ-H2AX descending) and t 50 (time required for γ-H2AX to drop to half of the maximum value) estimated by our developed online tools were employed to further evaluate nine representative chemotherapy agents, which showed a close association with therapeutic index or carcinogenic level. The present study demonstrated that mass spectrometric quantification of γ-H2AX may be an appropriate tool to preliminarily evaluate genotoxic effects of chemotherapy agents. (2024), Assessing genotoxic effects of chemotherapy agents by a robust in vitro assay based on mass spectrometric quantification of γ-H2AX in HepG2 cells.

In this paper, we consider the CM line bundle on the K-moduli space, i.e., the moduli space parametrizing K-polystable Fano varieties. We prove it is ample on any proper subspace parametrizing reduced uniformly K-stable Fano varieties that conjecturally should be the entire moduli space. As a corollary, we prove that the moduli space parametrizing smoothable K-polystable Fano varieties is projective. During the course of proof, we develop a new invariant for filtrations that can be used to test various K-stability notions of Fano varieties.

Background: Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China. But the successfully implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. This study aims to evaluate and compare the performance of four different machine-learning algorithms on predicting breast cancer among Chinese women, using 10 breast cancer risk factors. Objective: This study aims to evaluate and compare the performance of four different machine-learning algorithms on predicting breast cancer among Chinese women, using 10 breast cancer risk factors. Methods: A dataset consisting of 7,127 breast cancer cases and 7,127 matched healthy controls was used for model training and testing. We utilized repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results: The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher AUCs, sensitivity and accuracy than Logistic Regression. Among the three novel machine-learning algorithms, XGBoost (AUC=0.742) outperformed Deep Neural Network (0.728) and Random Forest (AUC=0.728). Main residence, number of live births, menopause status, age and age at first birth were presented as top ranked variables in the three novel machine-learning algorithms. Conclusions: The novel machine-learning algorithms, especially XGBoost can be used to develop breast cancer prediction models to help identify women at high risk of breast cancer in developing countries.

This work was motivated by a study of particle size effects on pyrolysis kinetics and models of polystyrene particle. Micro-size polystyrene particles with four different diameters, 5, 10, 15, and 50 µm, were selected as experimental materials. Activation energies were obtained by isoconversional methods, and pyrolysis model of each particle size and heating rate was examined through different reaction models by the Coats–Redfern method. To identify the controlling model, the Avrami–Eroféev model was identified as the controlling pyrolysis model for polystyrene pyrolysis. Accommodation function effect was employed to modify the Avrami–Eroféev model. The model was then modified to f() = n0.39n − 1.15(1 − )[−ln(1 − )]1 − 1/n, by which the polystyrene pyrolysis with different particle sizes can be well explained. It was found that the reaction model cannot be influenced by particle geometric dimension. The reaction rate can be changed because the specific surface area will decrease with particle diameter. To separate each step reaction and identify their distributions to kinetics, distributed activation energy method was introduced to calculate the weight factor and kinetic triplets. Results showed that particle size has big impacts on both first and second step reactions. Smaller size particle can accelerate the process of pyrolysis reaction. Finally, sensitivity analysis was brought to check the sensitivity and weight of each parameter in the model.

The aerosol absorption Ångström exponent (AAE) is widely used to indicate aerosol absorption spectrum variations and is an important parameter for characterizing aerosol optical absorption properties. This study discusses the relationship between aerosol AAEs and their colors numerically. By combining light scattering simulations, a two-stream radiative transfer model, and an RGB (Red, Green, and Blue) color model, aerosol colors that can be sensed by human eyes are numerically generated with both the solar spectrum and human eye response taken into account. Our results indicate that the responses of human eyes to visible light might be more significant than the incident spectrum in the simulation of aerosol color in the atmosphere. Using the improved numerical simulation algorithm, we obtain the color change of absorption aerosols with different AAEs. When the AAE value is small, the color of the aerosol is generally black and gray. When the AAE value increases to approximately 2 and the difference between the light transmittances at wavelengths of 400 nm and 730 nm is greater than 0.2, the aerosol will appear brown or yellow

Preparation of aluminosilicate ferrierite (FER) zeolite nanosheets with controllable thickness in the presence of a sole organic ammonium is attractive, but still challenging. In this report, with the employment of N,N-diethyl-cis-2,6-dimethylpiperidinium (DMP) as both a structure directing agent and crystal growth inhibitor, aluminosilicate FER zeolite nanosheets, with a variety of crystal thicknesses, ranging from 6 to 200 nm, are successfully synthesized under hydrothermal conditions. Very interestingly, the amount of DMP in the starting gel is the key factor for crystal thickness control of aluminosilicate FER zeolite nanosheets. The obtained FER products, with different thicknesses, are well characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), N2 sorption, thermogravimetric analysis (TG), inductively coupled plasma (ICP), and magic angle spinning nuclear magnetic resonance (MAS NMR) techniques. This simple strategy might provide a novel avenue for the synthesis of other zeolite nanosheets with controllable thickness.

Anthocyanin contributes to the coloration of pear fruit and enhances plant defenses. Members of the ethylene response factor (ERF) family play vital roles in hormone and stress signaling and are involved in anthocyanin biosynthesis. Here, PbERF22 was identified from the lanolin-induced red fruit of ‘Zaosu’ pear (Pyrus bretschneideri Rehd.) using a comparative transcriptome analysis. Its expression level was up- and down-regulated by methyl jasmonate and 1-methylcyclopropene plus lanolin treatments, respectively, which indicated that PbERF22 responded to the jasmonate- and ethylene-signaling pathways. In addition, transiently overexpressed PbERF22 induced anthocyanin biosynthesis in ‘Zaosu’ fruit, and a quantitative PCR analysis further confirmed that PbERF22 facilitated the expression of anthocyanin biosynthetic structural and regulatory genes. Moreover, a dual luciferase assay showed that PbERF22 enhanced the activation effects of PbMYB10 and PbMYB10b on the PbUFGT promoter. Therefore, PbERF22 responses to jasmonate and ethylene signals and regulates anthocyanin biosynthesis. This provides a new perspective on the correlation between jasmonate–ethylene crosstalk and anthocyanin biosynthesis.

Improving the genetic process of growth traits is one of the major goals in the beef cattle industry, as it can increase meat production and reduce the cost of raising animals. Although several quantitative trait loci affecting growth traits in beef cattle have been identified, the genetic architecture of these economically important traits remains elusive. This study aims to map single nucleotide polymorphisms (SNPs) and genes associated with birth weight (BW), yearling weight (YW), average daily gain from birth to yearling (BYADG), and body weight at the age of 18 months (18MW) in a Chinese Simmental beef cattle population using a weighted, single-step, genome-wide association study (wssGWAS). Phenotypic and pedigree data from 6022 animals and genotypes from 744 animals (596,297 SNPs) were used for an association analysis. The results showed that 66 genomic windows explained 1.01–20.15% of the genetic variance for the four examined traits, together with the genes near the top SNP within each window. Furthermore, the identified genomic windows (>1%) explained 50.56%, 57.71%, 61.78%, and 37.82% of the genetic variances for BW, YW, BYADG, and 18MW, respectively. Genes with potential functions in muscle development and regulation of cell growth were highlighted as candidates for growth traits in Simmental cattle (SQOR and TBCB for BW, MYH10 for YW, RLF for BYADG, and ARHGAP31 for 18MW). Moreover, we found 40 SNPs that had not previously been identified as being associated with growth traits in cattle. These findings will further advance our understanding of the genetic basis for growth traits and will be useful for the molecular breeding of BW, YW, BYADG, and 18MW in the context of genomic selection in beef cattle.