Chemically Stable Guanidinium Covalent Organic Framework for the Efficient Capture of Low-Concentration Iodine at High Temperatures.

The capture of radioactive I2 vapor from nuclear waste under industrial operating conditions remains a challenging task, as the practical industrial conditions of high temperature (≥150 °C) and low I2 concentration (∼150 ppmv) are unfavorable for I2 adsorption. We report a novel guanidinium-based covalent organic framework (COF), termed TGDM, which can efficiently capture I2 under industrial operating conditions. At 150 °C and 150 ppmv I2, TGDM exhibits an I2 uptake of ∼30 wt %, which is significantly higher than that of the industrial silver-based adsorbents such as Ag@MOR (17 wt %) currently used in the nuclear fuel reprocessing industry. Characterization and theoretical calculations indicate that among the multiple types of adsorption sites in TGDM, only ionic sites can bond to I2 through strong Coulomb interactions under harsh conditions. The abundant ionic groups of TGDM account for its superior I2 capture performance compared to various benchmark adsorbents. In addition, TGDM exhibits exceptionally high chemical and thermal stabilities that fully meet the requirements of practical radioactive I2 capture (high-temperature, humid, and acidic environment) and differentiate it from other ionic COFs. Furthermore, TGDM has excellent recyclability and low cost, which are unavailable for the current industrial silver-based adsorbents. These advantages make TGDM a promising candidate for capturing I2 vapor during nuclear fuel reprocessing. This strategy of incorporating chemically stable ionic guanidine moieties in COF would stimulate the development of new adsorbents for I2 capture and related applications.

Identification of an endoplasmic reticulum stress-related prognostic risk model with excellent prognostic and clinical value in oral squamous cell carcinoma.

BACKGROUND: Recently, endoplasmic reticulum stress related gene (ERS) markers have performed very well in predicting the prognosis of tumor patients. METHODS: The differentially expressed genes in Oral squamous cell carcinoma (OSCC) were obtained from TCGA and GTEx database. Three prognosis-related and differentially expressed ERSs were screened out by Least Absolute Selection and Shrinkage Operator (Lasso) regression to construct a prognostic risk model. Receiver Operating Characteristic Curve (ROC), riskplots and survival curves were used to verify the model's accuracy in predicting prognosis. Multi-omics analysis of immune infiltration, gene mutation, and stem cell characteristics were performed to explore the possible mechanism of OSCC. Finally, we discussed the model's clinical application value from the perspective of drug sensitivity. RESULTS: Three genes used in the model (IBSP, RDM1, RBP4) were identified as prognostic risk factors. Bioinformatics analysis, tissue and cell experiments have fully verified the abnormal expression of these three genes in OSCC. Multiple validation methods and internal and external datasets confirmed the model's excellent performance in predicting and discriminating prognosis. Cox regression analysis identified risk score as an independent predictor of prognosis. Multi-omics analysis found strong correlations between risk scores and immune cells, cell stemness index, and tumor mutational burden (TMB). It was also observed that the risk score was closely related to the half maximal inhibitory concentration of docetaxel, gefitinib and erlotinib. The excellent performance of the nomogram has been verified by various means. CONCLUSION: A prognostic model with high clinical application value was constructed. Immune cells, cellular stemness, and TMB may be involved in the progression of OSCC.