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As United States Food and Drug Administration-approved implantable material, carbon fiber-reinforced polyetheretherketone (CFRPEEK) possesses an adjustable elastic modulus similar to cortical bone and is a prime candidate to replace surgical metallic implants. The bioinertness and inferior osteogenic properties of CFRPEEK, however, limit its clinical application as orthopedic/dental implants. In this study, CFRPEEK-nanohydroxyapatite ternary composites (PEEK/n-HA/CF) with variable surface roughness have been successfully fabricated. The effect of surface roughness on their in vitro cellular responses of osteoblast-like MG-63 cells (attachment, proliferation, apoptosis, and differentiation) and in vivo osseointegration is evaluated. The results show that the hydrophilicity and the amount of Ca ions on the surface are significantly improved as the surface roughness of composite increases. In cell culture tests, the results reveal that the cell proliferation rate and the extent of osteogenic differentiation of cells are a function of the size of surface roughness. The composite with moderate surface roughness significantly increases cell attachment/proliferation and promotes the production of alkaline phosphatase (ALP) activity and calcium nodule formation compared with the other groups. More importantly, the PEEK/n-HA/CF implant with appropriate surface roughness exhibits remarkably enhanced bioactivity and osseointegration in vivo in the animal experiment. These findings will provide critical guidance for the design of CFRPEEK-based implants with optimal roughness to regulate cellular behaviors, and to enhance biocompability and osseointegration. Meanwhile, the PEEK/n-HA/CF ternary composite with optimal surface roughness might hold great potential as bioactive biomaterial for bone grafting and tissue engineering applications.

Background When inflammation occurs in periodontal tissues, a dynamic cellular crosstalk interacts between gingival fibroblasts and bone marrow mesenchymal stem cells (BMSCs), which plays a crucial role in the biological behaviour and differentiation of the cells. Recently, flavonoids are increasingly recognized for their therapeutic potential in modulating inflammation and osteogenic differentiation. Owing to their varied molecular structures and mechanisms, there are more needs that flavonoid compounds should be identified by extensive screening. However, current drug research mostly relies on static, single-type cell cultures. In this study, an innovative bionic microfluidic chip system tailored for both soft and hard tissues was developed to screen for flavonoids suitable for treating periodontitis. Methods This study developed a microfluidic system that bionically simulates the soft and hard structures of periodontal tissues. Live/dead staining, reactive oxygen species (ROS) staining, and RT-qPCR analysis were employed. These techniques evaluated the effects of flavonoid compounds on the levels of inflammatory factors and ROS contents in HGF and HBMSC under LPS stimulation. Additionally, the impact of these compounds on osteogenic induction in HBMSC and the exploration of the underlying mechanisms were assessed. Results The microfluidic chip used in this study features dual chambers separated by a porous membrane, allowing cellular signal communication via bioactive factors secreted by cells in both layers under perfusion. The inflammatory response within the chip under LPS stimulation was lower compared to individual static cultures of HGF and HBMSC. The selected flavonoids-myricetin, catechin, and quercetin-significantly reduced cellular inflammation, decreased ROS levels, and enhanced osteogenic differentiation of BMSCs. Additionally, fisetin, silybin, and icariside II also demonstrated favorable outcomes in reducing inflammation, lowering ROS levels, and promoting osteogenic differentiation through the Wnt/β-catenin pathway. Conclusions The bionic microfluidic chip system provides enhanced capabilities for drug screening and evaluation, delivering a more precise assessment of drug efficacy and safety compared to traditional in vitro methods. This study demonstrates the efficacy of flavonoids in influencing osteogenic processes in BMSCs primarily through the Wnt/β-catenin pathway. These results uncover the potential of flavonoids as therapeutic medicine for treating periodontitis, meriting further research and development. Graphical Abstract

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R 2 ), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R 2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R 2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R 2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R 2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Fowl adenovirus serotype 4 (FAdV-4) is a non-enveloped double-stranded DNA virus with a 43–45 kb genome. This characteristic makes it a promising viral vector for expressing other antigens in developing multi-valent or multi-series vaccines in the poultry industry. To create an easy-to-use reverse genetics system for manipulating FAdV-4 genomic DNA, a full-length infectious clone of FAdV-4 was constructed using lambda Red-mediated recombination in Escherichia coli DH10B. Viable viruses were successfully rescued after the transfection of linearised infectious clones into LMH cells. The rescued viruses showed the same cytopathic effect and growth kinetics as wild-type FAdV-4 viruses. Based on the FAdV-4 infectious clone, the hexon coding sequence of the high-pathogenicity FAdV-4 was replaced by that of the nonpathogenic FAdV-4 using lambda Red-mediated recombination combined with rpsL counter selection without leaving extra sequences after engineering. The rescued recombinant virus was highly attenuated and showed low pathogenicity to 21-day-old SPF chickens. Hereto, the easy-to-use reverse genetics system for FAdV-4 was successfully established. With this platform, the genomic DNA of FAdV-4 can be manipulated and purified in DH10B, making it quicker and easier to generate a recombinant FAdV-4 virus to develop multi-valent/multi-series vaccines.

Background Heat shock protein B7 ( HSPB7 ), which belongs to small heat shock protein family, has been reported to be involved in diverse biological processes and diseases. However, whether HSPB7 regulates osteogenic differentiation of human adipose derived stem cells (hASCs) remains unexplored. Methods The expression level of HSPB7 during the osteogenesis of hASCs was examined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot analysis. Lentivirus transfection was used to knock down or overexpress HSPB7 , which enabled us to investigate the effect of HSPB7 on osteogenic differentiation of hASCs. U0126 and extracellular signal-regulated protein kinase 1/2 (ERK1/2) siRNA were used to identify the mechanism of the HSPB7 /ERK1/2 axis in regulating osteogenic differentiation of hASCs. Moreover, ectopic bone formation in nude mice and osteoporosis mice model was used to investigate the effect of HSPB7 on osteogenesis in vivo . Results In this study, we found the expression of HSPB7 was significantly downregulated during the osteogenic differentiation of hASCs. HSPB7 knockdown remarkably promoted osteogenic differentiation of hASCs, while HSPB7 overexpression suppressed osteogenic differentiation of hASCs both in vitro and in vivo. Moreover, we discovered that the enhancing effect of HSPB7 knockdown on osteogenic differentiation was related to the activation of extracellular signal-regulated protein kinase (ERK) signaling pathway. Inhibition of ERK signaling pathway with U0126 or silencing ERK1/2 effectively blocked the stimulation of osteogenic differentiation induced by HSPB7 knockdown. Additionally, we found that HSPB7 expression was markedly increased in mouse bone marrow mesenchymal stem cells (mBMSCs) from the osteoporotic mice which suggested that HSPB7 might be utilized as a potential target in the development of effective therapeutic strategies to treat osteoporosis and other bone diseases. Conclusion Taken together, these findings uncover a previously unrecognized function of HSPB7 in regulating osteogenic differentiation of hASCs, partly via the ERK signaling pathway.

Abstract Oral soft tissue defects remain difficult to treat owing to the limited efficacy of available treatment materials. Although the injectable platelet-rich fibrin (i-PRF) is a safe, autologous source of high levels of growth factors that is often employed to promote the regeneration of oral soft tissue, its effectiveness is restrained by difficulties in intraoperative shaping together with the burst-like release of growth factors. We herein sought to develop a bioactive bioink composed of i-PRF, alginate and gelatin capable of promoting the regeneration of the oral soft tissue. This bioink was successfully applied in 3D bioprinting and exhibited its ability to be shaped to individual patient needs. Importantly, we were also able to significantly prolong the duration of multiple growth factors release as compared to that observed for i-PRF. The growth factor bioavailability was further confirmed by the enhanced proliferation and viability of printed gingival fibroblasts. When deployed in vivo in nude mice, this bioink was further confirmed to be biocompatible and to drive enhanced angiogenic activity. Together, these data thus confirm the successful production of an i-PRF-containing bioink, which is suitable for the individualized promotion of the regeneration of oral soft tissue. Graphical Abstract

In order to solve the problem of mowing between plants in Xinjiang trunk orchards, an obstacle avoidance mower suitable for trunk orchard planting mode was designed. The whole structure, working principle and main parameter design of the obstacle avoidance mower are introduced. The finite element analysis and kinematic analysis of the cutter are carried out on the premise of using a Y-shaped cutter and its arrangement, and the condition that the inter-row mower does not leak is determined. Through the modal analysis of the frame, the range of the first six natural frequencies of the frame is determined and compared with the frequency of the main excitation source of the machine to determine the rationality of the frame design. On the premise of simplifying the inter-plant obstacle avoidance mechanism into a two-dimensional model for kinematics analysis, the motion parameters of the key components of the machine were determined. At the same time, the virtual kinematics simulation single-factor test of the designed inter-plant obstacle avoidance device was carried out with the help of ADAMS 2020 software. Through the reduction in and calculation of the motion trajectory of the simulation test, it was finally determined that the forward speed of the machine, the elastic coefficient of the reset spring and the compression speed of the hydraulic cylinder were the main influencing factors of the inter-plant obstacle avoidance mower. The orthogonal test was designed and the optimal solution of the three test factors was determined. The optimal solution is taken for further field test verification. The results show that when the tractor forward speed is 1.5 km∙h−1, the hydraulic cylinder compression speed is 225 mm∙s−1, and the elastic coefficient of the reset spring is 29 N∙mm−1, the average leakage rate between the orchard plants is 7.64%, and the obstacle avoidance pass rate is 100%. The working stability is strong and meets the design requirements.

This clinical report aimed to compare the scanning accuracy of SRA, and a novel extended scan body in edentulous patients. Through quantitative analysis, the study provided data support for oral prosthodontics and digital treatments. Edentulous patients with six Straumann bone-level implants were selected. The scan data was compared to the standard model derived from traditional impressions to evaluate scanning accuracy. The SRA scan bodies showed lower accuracy (46 ± 45 μm), while the Segma scan bodies with an extended structure achieved significantly higher accuracy (20 ± 2 μm). The extended structure effectively reduced image stitching errors in challenging intraoral regions, improving accuracy and speed. The Segma scan bodies with an extended structure accurately replicated spatial position information of implants in edentulous patients, providing insights for oral prosthodontics and digital treatments to enhance restoration quality.

Background Pathogens including duck-origin avian influenza virus (AIV), duck-origin Newcastle disease virus (NDV) and duck Tembusu virus (DTMUV) posed great harm to ducks and caused great economic losses to the duck industry. In this study, we aim to develop a triplex real-time polymerase chain reaction (PCR) assay to detect these three viruses as early as possible in the suspicious duck flocks. Results The detection limit of the triplex real-time PCR for AIV, NDV, and DTMUV was 1 × 10 1 copies/μL, which was at least 10 times higher than the conventional PCR. In addition, the triplex assay was highly specific, and won’t cross-react with other duck pathogens. Besides, the intra-day relative standard deviation and inter-day relative standard deviation were lower than 4.44% for these viruses at three different concentrations. Finally, a total of 120 clinical samples were evaluated by the triplex real-time PCR, the conventional PCR and virus isolation, and the positive rates for these three methods were 20.83, 21.67, 19.17%, respectively. Taking virus isolation as the gold standard, the diagnostic specificity and positive predictive value of the three viruses were all above 85%, while the diagnostic sensitivity and negative predictive value of the three viruses were all 100%. Conclusion The developed triplex real-time PCR is fast, specific and sensitive, and is feasible and effective for the simultaneous detection of AIV, NDV, and DTMUV in ducks.

In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky–Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R2 of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves.