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Zirconia ceramics (ZiC) has become a biomaterial with good clinical application prospects due to its good biocompatibility, stability, and excellent mechanical properties. In this study, bioactive glass (BioS) was undertaken as the modifier, and the ZiC precursor solution was penetrated on the surface of the ZiC body or the ZiC particles were coated with the BioS precursor solution, so as to obtain the ZiC with biological activity by introducing the BioS phase. While the material was characterized, its mechanical properties and biological activity were analyzed by simulating the mineralization of the body fluid, including the cell adhesion, proliferation, and differentiation of the material. The ZiC was applied as an implant for restoration of patients with teeth loss caused by periodontal disease. In the test, after surface infiltration of BioS sol or coating of ZiC particles, a very thin BioS phase can be formed on the surface of ZiC after high temperature sintering. The in vitro mineralization test showed that the activation of the grain boundary promoted the deposition of calcium and phosphorus layers on the surface of the sample. The mechanical test revealed that the relative density, flexural strength, and fracture toughness of the material decreased with the infiltration and coating of BioS. However, under proper preparation conditions, the modified ZiC still showed good mechanical properties and can promote the spreading, adhesion, and differentiation of mouse bone marrow mesenchymal stem cells (mBMSCs). Clinical trials proved that after the modified ZiC implant material was implanted in patients with teeth loss, the structure was not loose or broken, and the intraoral prosthesis could function normally. After treatment, the level of IL-1β in the gingival crevicular fluid of the patient decreased obviously (P P

Filopodia, a finger-like structure and actin-rich plasma-membrane protrusion at the leading edge of the cell, has important roles in cell motility. However, the mechanisms of filopodia generation are not well-understood via the actin-related protein 2/3 (ARP2/3) complex in Non-Small Cell Lung Cancer (NSCLC) cells. We previously have demonstrated that PRR11 associates with the ARP2/3 complex to regulate cytoskeleton-nucleoskeleton assembly and chromatin remodeling. In this study, we further demonstrate that PRR11 involves in filopodia formation, focal adhesion turnover and cell motility through ARP2/3 complex. Cell phenotype assays revealed that the silencing of PRR11 increased cellular size and inhibited cell motility in NSCLC cells. Mechanistically, PRR11 recruited and co-localized with Arp2 at the membrane protrusion to promote filopodia formation but not lamellipodia formation. Notably, PRR11 mutant deletion of the proline-rich region 2 (amino acid residues 185–200) abrogated the effect of filopodia formation. In addition, PRR11-depletion inhibited filopodial actin filaments assembly and increased the level of active integrin β1 in the cell surface, whereas reduced the phosphorylation level of focal adhesion kinase (FAKY397) to repress focal adhesion turnover and cell motility in NSCLC cells. Taken together, our findings indicate that PRR11 has critical roles in controlling filopodia formation, focal adhesion turnover and cell motility by recruiting ARP2/3 complex, thus dysregualted expression of PRR11 potentially facilitates tumor metastasis in NSCLC cells.

The actin cytoskeleton is extremely dynamic and supports diverse cellular functions in many physiological and pathological processes, including tumorigenesis. However, the mechanisms that regulate the actin-related protein 2/3 (ARP2/3) complex and thereby promote actin polymerization and organization in cancer cells are not well-understood. We previously implicated the proline-rich 11 (PRR11) protein in lung cancer development. In this study, using immunofluorescence staining, actin polymerization assays, and siRNA-mediated gene silencing, we uncovered that cytoplasmic PRR11 is involved in F-actin polymerization and organization. We found that dysregulation of PRR11 expression results in F-actin rearrangement and nuclear instability in non-small cell lung cancer cells. Results from molecular mechanistic experiments indicated that PRR11 associates with and recruits the ARP2/3 complex, facilitates F-actin polymerization, and thereby disrupts the F-actin cytoskeleton, leading to abnormal nuclear lamina assembly and chromatin reorganization. Inhibition of the ARP2/3 complex activity abolished irregular F-actin polymerization, lamina assembly, and chromatin reorganization due to PRR11 overexpression. Notably, experiments with truncated PRR11 variants revealed that PRR11 regulates F-actin through different regions. We found that deletion of either the N or C terminus of PRR11 abrogates its effects on F-actin polymerization and nuclear instability and that deletion of amino acid residues 100–184 or 100–200 strongly induces an F-actin structure called the actin comet tail, not observed with WT PRR11. Our findings indicate that cytoplasmic PRR11 plays an essential role in regulating F-actin assembly and nuclear stability by recruiting the ARP2/3 complex in human non-small cell lung carcinoma cells.

In the exploration and production of fossil resources, the characterization of rock facies is critical for estimations of rock physical properties, such as porosity and permeability, and for reservoir detection and simulation. We propose a new machine learning (ML) algorithm for characterizing rock facies using a convolutional neural network (CNN) with feature engineering and data padding strategies. In the new ML algorithm, we extend rock feature data from 1-dimensional “profile” to 2-dimensional maps by padding the original dataset. The 2-dimensional padded rock facies map enables the CNN to capture the inherent geological features while keeping the local continuities. In this new ML algorithm, we only need a simple CNN design and structure to efficiently achieve accurate classification of rock facies. We test the feasibility of applying this new algorithm using a verifiable well logging dataset from the Panoma gas field in southwest Kansas. The results show that our new ML algorithm with a simple CNN structure has achieved higher accuracy in classifications of rock facies in comparison with the CNN results of the 2016 SEG ML contest. This new ML algorithm has application potential in automatic rock facies characterization with high accuracy and efficiency.

To provide a reference for finding a reasonable evaluation method for treatment effect of radiofrequency ablation (RFA), computed tomography (CT) image optimized by the intelligent segmentation algorithm was utilized to evaluate the liver condition of hepatocellular carcinoma (HCC) patients after RFA and to estimate the patient’s prognosis. Eighty-eight patients with HCC who needed RFA surgery after diagnosis in our hospital were selected. The CT images before optimization were set as the control group; the CT images after optimization were set as the observation group. Comprehensive diagnosis was taken as the gold standard to compare the ablation range and residual lesions under CT scans before and after surgery. The results showed that the consistency of the two sets of CT images was compared with comprehensive diagnosis under different diameters of the lesion. The difference between the two groups was not statistically considerable when the diameter of the lesion was less than 50 mm ( P > 0.05 ). For lesions larger than 50 mm in diameter, the consistency of the observation group (83%) was remarkably higher than that of the control group (40%), and the difference was substantial ( P < 0.05 ). The kappa value of the observation group was 0.84 and that of the control group was 0.78. The kappa value of observation group was better than the control group, with considerable difference ( P < 0.05 ). In conclusion, the diagnostic effect of CT image based on intelligent segmentation algorithm was superior to conventional diagnosis when the diameter of the lesion was larger than 50 mm. Moreover, the overall improvement rate of patients after RFA treatment was far greater than the recurrence rate, indicating that the clinical adoption of RFA was very meaningful.

Background: Transcatheter aortic valve replacement (TAVR) has become an essential alternate option for people suffering from aortic stenosis. However, the efficacy and safety of TAVR for elderly population (aged over 80 years) is still unclear. Methods: We plan to perform a systematic review and meta-analysis of clinical controlled trials and propensity-match cohort studies to evaluate the short- and long-term outcomes in elderly aortic stenosis patients who undergo a transcatheter or surgical aortic valve replacement. We will search PubMed, EMBASE, and Cochrane Library using a comprehensive strategy. The related conference proceedings and reference lists of the included studies will also be checked to identify additional studies. Two reviewers will screen retrieved records, extract information, and assess the risk of bias independently. STATA software will be used to conduct data synthesis. There is no requirement of ethical approval and informed consent. Results: This study will be submitted to a peer-reviewed journal for publication. Conclusion: This is the first systematic assessment of TAVR for elderly patients with aortic stenosis. We hope it will provide a relatively comprehensive reference for clinical practice and future relevant clinical trials. Ethics and dissemination: Ethics approval and patient consent are not required as this study is a systematic review and meta-analysis. PROSPERO registration number: CRD42019140857. Study protocol registry: The protocol has been registered in PROSPERO, which is an International Prospective Register of Systematic Reviews. The registration number is CRD42019140857

Additional file 1: Supplementary Figures S1, S2, and S3.

Supplemental Digital Content is available in the text, Observational studies have reported that Vitamin D deficiency and the risk type 2 diabetes are associated, but the causation is unclear. Mendelian randomization (MR) involving genetic variants as instrument variables (IVs) overcomes the reverse-casualty and unmeasured confounding. However, with limited sample size and IVs, previous MR studies showed inconsistent results. Leveraging by a largely increased sample size for both stages, we aim to provide an updated and precise estimate for the causality between Vitamin D and type 2 diabetes. A 2-sample multi-IVs MR was performed. IVs for circulating 25-hydroxyvitamin D (25(OH)D) were obtained from a genome-wide association study from UK biobank involving 329,247 subjects of European ancestry. The causal effect of 25(OH)D and type 2 diabetes was estimated using traditional inverse variance weighting and MR pleiotropy residual sum and outlier (MR-PRESSO) framework which provides a robust estimate by systematically filtering out IVs identified with potential pleiotropy effects. A higher genetically instrumented 25(OH)D was causally linked to reduced risk of type 2 diabetes risk by MR-PRESSO [odds ratio (OR) per standard deviation (SD) = 0.950, 95% confidence interval (CI) = 0.913–0.988, P = .010] after removing 13 (13/193) invalid IVs. In addition, we confirmed the causal role Vitamin D using 2 synthesis-related single-nucleotide polymorphisms (SNPs) which are consistent with previous MR studies [OR per SD = 0.894, 95% CI = 0.816–0.979, P = .016]. With a largely improved sample size, our results confirmed that genetically increased 25(OH)D concentration reduced the risk of type 2 diabetes and provided a more precise estimate for the effect size. The updated result empowers the role of Vitamin D and provides nontrivial evidence for interventional studies.

Shale gas contributes significantly to current global energy consumption, and an accurate estimation of geological gas-in-place (GIP) determines an optimal production plan. As the dominant form of storage, adsorbed gas in shale formation is of primary importance to be assessed. This paper summarizes adsorption models into traditional pressure/density dependent isothermal models, pressure and temperature unified model, and machine learning based models. Using a comprehensive experimental dataset, these models are applied to simulate shale gas adsorption under in-situ conditions. Results show that the modified Dubinin-Radushkevich (DR) model provides the optimal performance in traditional isothermal models. Pressure and temperature unified models make a breakthrough in isothermal conditions and can extrapolate the predictions beyond test ranges of temperature. Well-trained machine learning models not only break the limit of the isothermal condition and types of shale formation, but can also provide reasonable extrapolations beyond test ranges of temperature, total organic carbon (TOC), and moisture. Four popular machine learning algorithms are used, which include artificial neural network (ANN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The XGBoost model is found to provide the best results for predicting shale gas adsorption, and it can be conveniently updated for broader applications with more available data. Overall, this paper demonstrates the capability of machine learning for prediction of shale gas adsorption, and the well-trained model can potentially be built into a large numerical frame to optimize production curves of shale gas.

The aim of this study was to investigate expression of CD147 and MMP-9 in salivary duct carcinoma (SDC) so as to determine whether these two genes may be correlated with poor prognosis of SDC. We examined the significance of the CD147 and MMP-9 expression in SDC (n = 35), non-cancerous salivary tissue (n = 20) in previously untreated patients using immunohistochemical staining. Furthermore, we analyzed the correlation between the expression of these two genes and various clinicopathologic factors including survival status of patients with SDC. Positive stain of CD147 and MMP-9 was seen in all 35 cases of tumor samples. A statistical correlation was observed between CD147 and MMP-9 expression in SDC tissues. The incidences of high expression were 45.71% for CD147 and 51.43% for MMP-9 in 35 SDC tissues, respectively. High expression of CD147 and MMP-9 was significantly correlated with clinical feature and shorter progression-free survival (PFS) (P CD147 = 0.031; P MMP-9 = 0.020) and overall survival (OS) (P CD147 = 0.044; P MMP-9 = 0.013). CD147 and MMP-9 expression is correlated with invasion, metastasis and shorter PFS/OS of SDC. Patients with high expression of CD147 and MMP-9 had poor prognosis than SDC patients with low expression.