RESUMO
Polyurethanes and plastics have become ubiquitous in modern society, finding use in a wide variety of applications such as clothing, automobiles, and shoes. While these materials provide numerous benefits to human life, their persistence in the environment has caused ecological imbalances. Therefore, new processes are needed to make these materials more sustainable and re-usable. In 2011, Ludwik Leibler introduced a new class of covalent adaptable network (CAN) polymers called Vitrimers. Vitrimers possess self-repairing properties and are capable of being reprocessed due to dynamic exchange or breaking/recombination of covalent bonds, similar to thermoset materials. This study explores the synthesis of Vitrimers using waste polyurethane or plastics as feedstock. The raw materials were glycolysed to obtain the glycolysate, which was then used as a reagent for the Vitrimers synthesis. The main objective of this study was to achieve the maximum self-repairable rate of the prepared sample. The Taguchi orthogonal analysis was employed to guide the experiments. The optimized experimental conditions for polyurethane glycolysis were determined to be under ethylene glycol and catalyzed by sodium hydroxide at 180°C for 1 h, resulting in the highest hydroxyl concentration in the glycolysate. In the second stage of the experiment, the ratio of hexamethylene diisocyanate (HDI) to solvent was set to 2, HDI trimer to solvent was 2, and PGE/glycolysate was 0.5, with equal amounts of PEG and glycolysate used as the solvent. The reaction was carried out at 80°C for 1 h, achieving a self-repair ability of 47.5% in the prepared sample. The results of this study show that waste polyurethane or plastics can be effectively recycled and transformed into vitrimers with self-repairing properties. The use of glycolysis as a feedstock is a promising method for the sustainable recycling of polyurethane waste. The Taguchi orthogonal analysis is an effective approach for optimizing experimental conditions and improving the reproducibility of the results.
RESUMO
Immune checkpoint inhibitors (ICI) for early-stage non-small cell lung cancer (NSCLC) have been approved to improve outcomes and reduce recurrence. Biomarkers for patient selection are needed. In this paper, we proposed an inflammasome-based risk score (IRS) system for prognosis and prediction of ICI response for early-stage NSCLC. Cox regression analysis was used to identify significant genes (from 141 core inflammasome genes) for overall survival (OS) in a microarray discovery cohort (n = 467). IRS was established and independently validated by other datasets (n = 1320). We evaluated the inflammasome signaling steps based on five gene sets, which were IL1B-, CASP-1-, IL18-, GSDMD-, and inflammasome-regulated genes. Gene set enrichment analysis, the Kaplan-Meier curve, receiver operator characteristic with area under curve (AUC) analysis, and advanced bioinformatic tools were used to confirm the ability of IRS in prognosis and classification of patients into ICI responders and non-responders. A 30-gene IRS was developed, and it indicated good risk stratification at 10-year OS (AUC = 0.726). Patients were stratified into high- and low-risk groups based on optimal cutoff points, and high-risk IRS had significantly poorer OS and relapse-free survival. In addition, the high-risk group was characterized by an inflamed immunophenotype and higher proportion of ICI responders. Furthermore, expression of SLAMF8 was the key gene in IRS and indicated good correlation with biomarkers associated with immunotherapy. It could serve as a therapeutic target in the clinical setting of immunotherapy.