Unveiling the mechanisms of peracetic acid activation by iron-rich sludge biochar for sulfamethoxazole degradation with wide adaptability.

Advanced oxidation processes (AOPs) based on peracetic acid (PAA) has been extensively concerned for the degradation of organic pollutants. In this study, metallic iron-modified sludge biochar (Fe-SBC) was employed to activate PAA for the removal of sulfamethoxazole (SMX). The characterization results indicated that FeO and Fe2O3 were successfully loaded on the surface of the sludge biochar (SBC). Fe-SBC/PAA system achieved 92% SMX removal after 30 min. The pseudo-first-order kinetic reaction constant of the Fe-SBC/PAA system was 7.34 × 10-2 min-1, which was 2.4 times higher than the SBC/PAA system. The degradation of SMX was enhanced with increasing the Fe-SBC dosage and PAA concentration. Apart from Cl-, NO3- and SO42- had a negligible influence on the degradation of SMX. Quenching experiments and electron paramagnetic resonance (EPR) techniques identified the existence of reactive species, of which CH3C(O)OO•, 1O2, and O2•- were dominant reactive species in Fe-SBC/PAA system. The effect of different water matrices on the removal of SMX was investigated. The removal of SMX in tap water and lake water were 79% and 69%, respectively. Four possible pathways for the decay of SMX were presented according to the identification of oxidation products. In addition, following the ecological structure-activity relationship model (ECOSAR) procedure and the germination experiments with lettuce seeds to predict the toxicity of the intermediates. The acute and chronic ecotoxicity of SMX solution was dramatically diminished by processing with Fe-SBC/PAA system. In general, this study offered a prospective strategy for the degradation of organic pollutants.

Discovery of novel EGFR-PROTACs capable of degradation of multiple EGFR-mutated proteins.

Although three generations of Epidermal growth factor receptor (EGFR) - TK inhibitors have been approved for the treatment of Non-small-cell lung cancers (NSCLC), their clinical application is still largely hindered by acquired drug resistance mediated new EGFR mutations and side effects. The Proteolysis targeting chimera (PROTAC) technology has the potential to overcome acquired resistance from mutant EGFR through a novel mechanism of action. In this study, we developed the candidate degrader IV-3 by structural modifications of the lead compound 13, which exhibited limited antiproliferative activity against HCC-827 cells. Compared to compound 13, IV-3 exhibited remarkable anti-proliferative activity against HCC-827 cells, NCI-H1975 cells, and NCI-H1975-TM cells (IC50 = 0.009 µM, 0.49 µM and 3.24 µM, respectively), as well as significantly inducing degradation of EGFR protein in these cell lines (DC50 = 17.93 nM, 0.25 µM and 0.63 µM, respectively). Further investigations confirmed that IV-3 exhibited superior anti-tumor activity in all xenograft tumor models through the degradation of mutant EGFR protein. Moreover, IV-3 showed no inhibitory activity against A431 and A549 cells expressing wild-type EGFR, thereby eliminating potential toxic side effects emerging from wild-type EGFR inhibition. Overall, our study provides promising insights into EGFR-PROTACs as a potential therapeutic strategy against EGFR-acquired mutation.

Deep Multi-Constraint Soft Clustering Analysis for Single-Cell RNA-Seq Data via Zero-Inflated Autoencoder Embedding.

Clustering cells into subgroups plays a critical role in single cell-based analyses, which facilitates to reveal cell heterogeneity and diversity. Due to the ever-increasing scRNA-seq data and low RNA capture rate, it has become challenging to cluster high-dimensional and sparse scRNA-seq data. In this study, we propose a single-cell Multi-Constraint deep soft K-means Clustering(scMCKC) framework. Based on zero-inflated negative binomial (ZINB) model-based autoencoder, scMCKC constructs a novel cell-level compactness constraint by considering association between similar cell, to emphasize the compactness between clusters. Besides, scMCKC utilizes pairwise constraint encoded by prior information to guide clustering. Meanwhile, a weighted soft K-means algorithm is leveraged to determine the cell populations, which assigns the label based on affinity between data and clustering center. Experiments on eleven scRNA-seq datasets demonstrate that scMCKC is superior to the state-of-the-art methods and notably improves cluster performance. Moreover, we validate the robustness on human kidney dataset, which demonstrates that scMCKC exhibits comprehensively excellent performance on clustering analysis. The ablation study on eleven datasets proves that the novel cell-level compactness constraint is conductive to the clustering results.