Exposure to nickel oxide nanoparticles (NiONPs), which have been widely produced and applied in industry, leads to adverse pulmonary and systemic effects. The aim of this study is to investigate the involvement of apoptosis and ferroptosis in NiONPsinduced acute lung injury (ALI). Intratracheal instillation of NiONPs into mice elevated the levels of pro-inflammatory cytokines, neutrophils, and proteins in the bronchoalveolar lavage fluid, and triggered apoptosis and ferroptosis in the lung tissues. Consistently, NiONPs-induced apoptosis and ferroptosis were observed in in vitro experiments using human lung epithelial cells. Activating transcription factor 3 (ATF3), a stress-inducible transcription factor, was upregulated by NiONPs exposure in both murine lung tissues and human lung epithelial cells. Moreover, human lung epithelial cells with ATF3 deficiency exhibited a lower level of apoptosis and ferroptosis when exposed to NiONPs. Collectively, our findings demonstrated that ATF3 was responsive to NiONPs exposure, and promoted NiONPs-induced apoptosis and ferroptosis in lung epithelial cells, indicating that ATF3 is a potential biomarker and therapeutic target for NiONPs-associated ALI.

Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation...

Two platinum(II) complexes, DN603 and DN604, were designed and prepared by using 3-oxocyclobutane-1,1-dicarboxylate as a ligand. The compounds were prepared according to the concept that incorporation of a functionalized moiety in the leaving ligand that did not affect its coordination bonding to the metal atom would play a key role in the anticancer activity of the resulting platinum complex. The newly prepared compounds were found to show potent in vitro anticancer activity comparable to cisplatin and oxaliplatin; especially DN604, which exhibited low acute toxicity similar to carboplatin, and presented acceptable solubility and stability in water. Chemical and biological results indicated that the functionalized moiety, uncoordinated, led to potent anticancer activity and low apparent toxicity of the platinum complexes by affecting the kinetic properties of the compounds.

A series of platinum(II) complexes, characteristic of chiral trans-bicyclo[2.2.2]octane-7,8-diamine as ligand possessing dicyclic steric hindrance, were designed and synthesized. Biological evaluation showed that almost all complexes had cytotoxic activity against the tested cancer cell lines, among which most of chiral (R,R)-enantiomeres had stronger cytotoxicity than their (S,S)-counterparts, and 2a, [trans-bicyclo[2.2.2]octane-7R,8R-diamine](oxalato-O,O')platinum(II), is the most effective agent. Significantly, its counterpart, 2b, was much more sensitive to cisplatin resistant SGC7901/CDDP cancer cell line at a higher degree than 2a. Docking study and agarose gel electrophoresis revealed that the interaction of 2a with DNA was similar to that of oxaliplatin. Western blot analysis demonstrated that 2a could induce a better effect than cisplatin on a mitochondrial-dependent apoptosis pathway. Kinetic study indicated that the dicyclic ligand can accelerate the reaction rate of the complex.

Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation is an important concept in physical and organic chemistry, describing the interaction of solutes and solvents. This interaction leads to a solvation complex, a molecular complex similar to a reactant-reagent complex. In this study, we introduced the SolvBERT model, which reads the solute and solvents through the SMILES representation of the solvation complex. SolvBERT is pretrained in an unsupervised learning fashion using a large database of computational solvation free energies. The pretrained model can be used to predict the experimental solvation free energy or solubility, depending on the fine-tuning database. To the best of our knowledge, this multi-task prediction capability has not been observed in previously developed graph-based models for predicting the properties of molecular complexes. Furthermore, the performance of our SolvBERT in predicting solvation free energy is comparable to the state-of-the-art graph-based model DMPNN, mainly due to the clustering feature of the pretraining phase of the model, as demonstrated by the TMAP visualization algorithm.