Hierarchical-Coassembly-Enabled 3D-Printing of Homogeneous and Heterogeneous Covalent Organic Frameworks

Covalent organic frameworks (COFs) are crystalline polymers with permanent porosity. They are usually synthesized as micrometer-sized powders or two-dimensional thin films and membranes for applications in molecular storage, separation, and catalysis. In this work, we report a general method to integrate COFs with imine or β-ketoenamine linkages into three-dimensional (3D)-printing materials. A 3D-printing template, Pluronic F127, was introduced to coassemble with imine polymers in an aqueous environment. By limitation of the degree of imine polycondensation during COF formation, the amorphous imine polymer and F127 form coassembled 3D-printable hydrogels with suitable shear thinning and rapid self-healing properties. After the removal of F127 followed by an amorphous-to-crystalline transformation, three β-ketoenamine- and imine-based COFs were fabricated into 3D monoliths possessing high crystallinity, hierarchical pores with high surface areas, good structural integrity, and robust mechanical stability. Moreover, when multiple COF precursor inks were employed for 3D printing, heterogeneous dual-component COF monoliths were fabricated with high spatial precision. This method not only enables the development of COFs with sophisticated 3D macrostructure but also facilitates the heterogeneous integration of COFs into devices with interconnected interfaces at the molecular level

Table1_Establishing a glutamine metabolism-based model for predicting the prognosis of low-grade glioma.xlsx

Background: The natural history of patients with low-grade glioma (LGG) varies widely, but most patients eventually deteriorate, leading to poor prognostic outcomes. We aim to develop biological models that can accurately predict the outcome of LGG prognosis.Methods: Prognostic genes for glutamine metabolism were searched by univariate Cox regression, and molecular typing was constructed. Functional enrichment analysis was done to evaluate potential prognostic-related pathways by analyzing differential genes in different subtypes. Enrichment scores of specific gene sets in different subtypes were measured by gene set enrichment analysis. Different immune infiltration levels among subtypes were calculated using algorithms such as CIBERSORT and ESTIMATE. Gene expression levels of prognostic-related gene signatures of glutamine metabolism phenotypes were used to construct a RiskScore model. Receiver operating characteristic curve, decision curve and calibration curve analyses were used to evaluate the reliability and validity of the risk model. The decision tree model was used to determine the best predictor variable ultimately.Results: We found that C1 had the worst prognosis and the highest level of immune infiltration, among which the highest macrophage infiltration can be found in the M2 stage. Moreover, most of the pathways associated with tumor development, such as MYC_TARGETS_V1 and EPITHELIAL_MESENCHYMAL_TRANSITION, were significantly enriched in C1. The wild-type IDH and MGMT hypermethylation were the most abundant in C1. A five-gene risk model related to glutamine metabolism phenotype was established with good performance in both training and validation datasets. The final decision tree demonstrated the RiskScore model as the most significant predictor of prognostic outcomes in individuals with LGG.Conclusion: The RiskScore model related to glutamine metabolism can be an exceedingly accurate predictor for LGG patients, providing valuable suggestions for personalized treatment.</p

Tripodal Organic Cages with Unconventional CH···O Interactions for Perchlorate Remediation in Water

Perchlorate anions used in industry are harmful pollutants in groundwater. Therefore, selectively binding perchlorate provides solutions for environmental remediation. Here, we synthesized a series of tripodal organic cages with highly preorganized Csp3–H bonds that exhibit selectively binding to perchlorate in organic solvents and water. These cages demonstrated binding affinities to perchlorate of 105–106 M–1 at room temperature, along with high selectivity over competing anions, such as iodide and nitrate. Through single crystal structure analysis and density functional theory calculations, we identified unconventional Csp3–H···O interactions as the primary driving force for perchlorate binding. Additionally, we successfully incorporated this cage into a 3D-printable polymer network, showcasing its efficacy in removing perchlorate from water

Table2_Establishing a glutamine metabolism-based model for predicting the prognosis of low-grade glioma.xlsx

Background: The natural history of patients with low-grade glioma (LGG) varies widely, but most patients eventually deteriorate, leading to poor prognostic outcomes. We aim to develop biological models that can accurately predict the outcome of LGG prognosis.Methods: Prognostic genes for glutamine metabolism were searched by univariate Cox regression, and molecular typing was constructed. Functional enrichment analysis was done to evaluate potential prognostic-related pathways by analyzing differential genes in different subtypes. Enrichment scores of specific gene sets in different subtypes were measured by gene set enrichment analysis. Different immune infiltration levels among subtypes were calculated using algorithms such as CIBERSORT and ESTIMATE. Gene expression levels of prognostic-related gene signatures of glutamine metabolism phenotypes were used to construct a RiskScore model. Receiver operating characteristic curve, decision curve and calibration curve analyses were used to evaluate the reliability and validity of the risk model. The decision tree model was used to determine the best predictor variable ultimately.Results: We found that C1 had the worst prognosis and the highest level of immune infiltration, among which the highest macrophage infiltration can be found in the M2 stage. Moreover, most of the pathways associated with tumor development, such as MYC_TARGETS_V1 and EPITHELIAL_MESENCHYMAL_TRANSITION, were significantly enriched in C1. The wild-type IDH and MGMT hypermethylation were the most abundant in C1. A five-gene risk model related to glutamine metabolism phenotype was established with good performance in both training and validation datasets. The final decision tree demonstrated the RiskScore model as the most significant predictor of prognostic outcomes in individuals with LGG.Conclusion: The RiskScore model related to glutamine metabolism can be an exceedingly accurate predictor for LGG patients, providing valuable suggestions for personalized treatment.</p