Periodontal ligament cells-derived exosomes promote osteoclast differentiation via modulating macrophage polarization.

Several studies have demonstrated that exosomes (Exos) are involved in the regulation of macrophage polarization and osteoclast differentiation. However, the characteristics as well as roles of exosomes from human periodontal ligament cells (hPDLCs-Exos) in M1/M2 macrophage polarization and osteoclast differentiation remain unclear. Here, periodontal ligament cells were successfully extracted by method of improved Type-I collagen enzyme digestion. hPDLCs-Exos were extracted by ultracentrifugation. hPDLCs-Exos were identified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA) and western blotting (WB). Osteoclast differentiation was evaluated by real-time quantitative polymerase chain reaction (RT-qPCR), WB and tartrate-resistant acid phosphatase (TRAP) staining. M1/M2 macrophage polarization were evaluated by RT-qPCR and WB. The results showed hPDLCs-Exos promoted osteoclast differentiation and M2 macrophage polarization, but inhibited M1 macrophage polarization. Moreover, M1 macrophages inhibited osteoclast differentiation, whereas M2 macrophages promoted osteoclast differentiation. It has shown that hPDLCs-Exos promoted osteoclast differentiation by inhibiting M1 and promoting M2 macrophage polarization.

Aqueous Dispersion of Aramid Nanofibers Achieved by Using Tannic Acid for Ultrahigh Strength Films.

Polymer nanofibers have established a robust foundation and possess immense potential in various emerging fields such as sensors and biotechnology. In this study, aqueous dispersions of aramid nanofibers (ANFs) were successfully prepared by using tannic acid (TA). Morphological analysis revealed that TA effectively prevented self-aggregation of ANFs, and preserved the nanofiber structure during TA-assisted solvent exchange. Subsequently, the ANF and TA/ANF films were fabricated using casting and vacuum-assisted filtration techniques. Notably, the tensile strength of the casting TA/ANF film reached 393.8 MPa, exhibiting a remarkable improvement of 41.3% compared to that of the pure ANF film. These exceptional mechanical properties can be attributed to the well-dispersed nanostructures, hydrogen-bonding interactions, zigzag structures, and fiber-bridging effects. Furthermore, the TA/ANF film demonstrated superior ultraviolet (UV) shielding capabilities, visible transparency properties, and excellent resistance to chemical reagents. The above-mentioned interesting findings demonstrate its potential as a nanofiber-reinforced material for poly(vinyl alcohol) (PVA) composites.

Identification of EGFR R776H germline mutations in a patient with multifocal lung adenocarcinoma: A case report and literature review.

The advancement of molecular pathology techniques has led to the discovery of rare EGFR mutations for targeted therapy in lung cancer. Additionally, a substantial body of evidence indicates a connection between the development of lung cancer and genetic variations in the EGFR gene. Here, we present a case report of a patient with multifocal lung adenocarcinoma who possessed a rare germline mutation, EGFR R776H. An investigation into the family history of the patient exposed the notable incidence of lung adenocarcinoma, indicating a plausible genetic vulnerability to the ailment. To be specific, the patient's older brother and sister both suffered from lung cancer, which underlines the hereditary predisposition. Furthermore, it should be noted that the patient's daughter has inherited the germline mutation and also presented with multiple lung ground-glass nodules, emphasizing the clinical importance of this genetic variation. Following the lobectomy, the patient received treatment with almonertinib, a third-generation EGFR tyrosine kinase inhibitor (TKI), and at the latest follow-up, the patient has achieved partial remission. This case highlights the significance of taking into account germline possibilities when multiple lesions carry the same mutation. It stresses the importance of acquiring a comprehensive family history and performing genetic testing on leukocytes. Moreover, for the infrequent EGFR R776H mutation, third generation EGFR-TKIs may be a viable option.

Multiple free-radical-trapping and hydrogen-bonding-enhanced polyurethane foams with long-lasting flame retardancy, aging resistance, and toughness.

Flexible polyurethane foam (FPUF) is a ubiquitous material utilized in furniture cushions, mattresses, and various technical applications. Despite the widespread use, FPUF faces challenges in maintaining long-lasting flame retardancy and aging resistance, particularly in harsh environments, while retaining mechanical robustness. Here, we present a novel approach to address these issues by enhancing FPUF through multiple free-radical-trapping and hydrogen-bonding mechanisms. A hindered amine phosphorus-containing polyol (DTAP) was designed and chemically introduced into FPUF. The distinctive synergy between hindered amine and phosphorus-containing structures enables the formation of multiple hydrogen bonds with urethane, while also effectively capturing free radicals across a broad temperature spectrum. As a result, incorporating only 5.1 wt% of DTAP led to the material successfully passing vertical burning tests and witnessing notable enhancements in tensile strength, elongation at break, and tear strength. Even after enduring accelerated thermal aging for 168 hours, the foam maintained exceptional flame retardancy and mechanical properties. This study offers novel insights into material enhancement, simultaneously achieving outstanding long-lasting flame retardancy, toughness, and anti-aging performance.

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning.

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.

CFViSA: A comprehensive and free platform for visualization and statistics in omics-data.

INTRODUCTION: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics. OBJECTIVES: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community. METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program. RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods. CONCLUSION: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.