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Resourceful beyond-graphene two-dimensional (2D) carbon crystals have been proposed/synthesized; however, the fundamental knowledge of their melting thermodynamics remains lacking. Here, the structural and thermodynamic properties of nine contemporary 2D carbon crystals upon heating are investigated using first-principle-based ReaxFF molecular dynamics simulations. Those 2D carbon crystals show distinct evolution of energetic and Lindemann index that distinguish their thermal stabilities. There are two or three critical temperatures at which structural transformation occurs for non-hexagon-contained 2D carbon allotropes. Analysis of polygons reveals that non-hexagon-contained 2D carbon crystals show thermally induced hex-graphene transitions via mechanisms such as bond rotations, dissociation, and reformation of bonds. The study provides new insights into the thermodynamics and pyrolysis chemistry of 2D carbon materials, as well as structural transitions, which is of great importance in the synthesis and application of 2D materials in high-temperature processing and environment.

In practical engineering, concrete structures usually work with cracks, and seismic loads often deviate from the center of the cracks. Therefore, exploring the strain rate effect on the fracture performance of concrete under small eccentricity is of great significance. In order to explore the dynamic fracture properties of concrete under different loading positions, considering four strain rates and four eccentric displacement factors, combined with digital image correlation (DIC), a hydraulic servo machine is used to conduct three-point bending beam fracture tests on concrete. By conducting tests under different working conditions, the concrete fracturing morphology, load-crack mouth opening displacement (P -CMOD) curves, and mechanical characteristic values were obtained. The influence of strain rate and loading position on concrete fracture properties is analyzed. The results show that the initial fracture load and unstable load of concrete fracture increase with the increase of loading eccentricity displacement and loading strain rate. A model for the relationship between concrete unstable load, loading eccentricity, and strain rate is established based on the experimental results. Under the coupling effect of high-strain rate and high-loading eccentricity, the crack propagation speed of concrete increases sharply after reaching the peak load. The fracture toughness of concrete increases linearly with the increase of loading eccentricity and strain rate. The unstable toughness of concrete shows a trend of first increasing and is then almost unchanged with the increase of strain rate, and gradually increasing with the increase of loading eccentricity displacement. At the same loading position, the fracture energy of concrete increases with the increase of strain rate, with a maximum growth rate of 40.5%. At the same strain rate, the fracture energy of concrete gradually increases with the increase of loading eccentricity, with a maximum growth rate of 56.6%. The research results provide a theoretical basis for the initiation and development of concrete cracks under medium to low strain rates and small eccentric loads. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma is a paradigm of cancer‐associated immunosuppression, limiting the effects of immunotherapeutic strategies. Thus, identifying the molecular mechanisms underlying immune surveillance evasion is critical. Recently, the preferential expression of inhibitory natural killer (NK) cell receptor CD161 on glioma‐infiltrating cytotoxic T cells was identified. Focusing on the molecularly annotated, large‐scale clinical samples from different ethnic origins, the data presented here provide evidence of this immune modulator's essential roles in brain tumor biology. Methods: Retrospective RNA‐seq data analysis was conducted in a cohort of 313 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 603 patients in The Cancer Genome Atlas (TCGA) database. In addition, single‐cell sequencing data from seven surgical specimens of glioblastoma patients and a model in which patient‐derived glioma stem cells were cocultured with peripheral lymphocytes, were used to analyze the molecular evolution process during gliomagenesis. Results: CD161 was enriched in high‐grade gliomas and isocitrate dehydrogenase (IDH)‐wildtype glioma. CD161 acted as a potential biomarker for the mesenchymal subtype of glioma and an independent prognostic factor for the overall survival (OS) of patients with glioma. In addition, CD161 played an essential role in inhibiting the cytotoxicity of T cells in glioma patients. During the process of gliomagenesis, the expression of CD161 on different lymphocytes dynamically evolved. Conclusion: The expression of CD161 was closely related to the pathology and molecular pathology of glioma. Meanwhile, CD161 promoted the progression and evolution of gliomas through its unique effect on T cell dysfunction. Thus, CD161 is a promising novel target for immunotherapeutic strategies in glioma treatment. [ABSTRACT FROM AUTHOR]

Aims: We aimed to create a tumor recurrent‐based prediction model to predict recurrence and survival in patients with low‐grade glioma. Methods: This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO‐COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. Results: This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low‐grade gliomas. A predictive recurrent signature, built by the LASSO‐COX algorithm, was significantly associated with overall survival and progression‐free survival in low‐grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. Conclusion: An individualized prediction model was created to predict 1‐, 2‐, 3‐, 5‐, and 10‐year survival and recurrent rate of patients with low‐grade glioma, which may serve as a potential tool to guide postoperative individualized care. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma is the most malignant tumor of the central nervous system. Several prediction models have been produced to aid in prognosis assessment. Age, a primary decision factor for prognosis, is associated with increased genomic alterations, however the exact link between increased age and poor prognosis is unknown. Objective: In this study, we aimed to reveal the underlying cause of poor prognosis in elderly patients. Methods: This study included data on 616 primary GBM tumor samples from the CGGA and TCGA databases and 41 nontumor brain tissue samples obtained from GSE53890. Hallmarks and clinicopathological characteristics were evaluated in both tumor and nontumor brain tissues. The association between choice of treatment regimen and age was measured using ANOVA and Student's t test. Results: Age was a robust predictor of poor prognosis in glioma. No age‐related hallmarks of cancer were detected, including pathological characteristics or mutations. However, treatment choice was strongly significantly different between old and young patients. Combined chemo‐radiation therapy could benefit old and young GBM patients, however, old patients are currently less likely to choose it. Conclusion: The vast divergence in prognosis between young and old GBM patients is largely caused by choice of treatment rather than age‐related tumor genomic characteristics. Postoperative standard radio‐ and chemotherapy provide strong benefits to primary glioblastoma patients of all ages. [ABSTRACT FROM AUTHOR]