The increase and spread of antibiotic-resistant bacteria (ARB) in aquatic environments and the dissemination of antibiotic resistance genes (ARGs) greatly impact environmental and human health. It is necessary to understand the mechanism of action of ARB and ARGs to formulate measures to solve this problem. This study aimed to determine the mechanism of antibiotic resistance spread during sub-lethal ozonation of ARB with different antibiotic resistance targets, including proteins, cell walls, and cell membranes. ARB conjugation and transformation frequencies increased after exposure to 0-1.0 mg/L ozone for 10 min. During sub-lethal ozonation, compared with control groups not stimulated by ozone, the conjugative transfer frequencies of E. coli DH5α (CTX), E. coli DH5α (MCR), and E. coli DH5α (GEN) increased by 1.35-2.02, 1.13-1.58, and 1.32-2.12 times, respectively; the transformation frequencies of E. coli DH5α (MCR) and E. coli DH5α (GEN) increased by 1.49-3.02 and 1.45-1.92 times, respectively. When target inhibitors were added, the conjugative transfer frequencies of antibiotics targeting cell wall and membrane synthesis decreased 0.59-0.75 and 0.43-0.76 times, respectively, while that for those targeting protein synthesis increased by 1-1.38 times. After inhibitor addition, the transformation frequencies of bacteria resistant to antibiotics targeting the cell membrane and proteins decreased by 0.76-0.89 and 0.69-0.78 times, respectively. Cell morphology, cell membrane permeability, reactive oxygen species, and antioxidant enzymes changed with different ozone concentrations. Expression of most genes related to regulating different antibiotic resistance targets was up-regulated when bacteria were exposed to sub-lethal ozonation, further confirming the target genes playing a crucial role in the inactivation of different target bacteria. These results will help guide the careful utilization of ozonation for bacterial inactivation, providing more detailed reference information for ozonation oxidation treatment of ARB and ARGs in aquatic environments.

Computer-aided drug design (CADD) is a computational approach used to discover, develop, and analyze drugs and active molecules with similar biochemical properties. Molecular simulation technology has significantly accelerated drug research and reduced manufacturing costs. It is an optimized drug discovery method that greatly improves the efficiency of novel drug development processes.
This review discusses the development of molecular simulations of effective cancer inhibitors and traces the main outcomes of in silico studies by introducing representative categories of six important anticancer targets. The authors provide views on this topic from the perspective of both medicinal chemistry and artificial intelligence, indicating the major challenges and predicting trends.
The goal of introducing CADD into cancer treatment is to realize a highly efficient, accurate, and desired approach with a high success rate for identifying potent drug candidates. However, the major challenge is the lack of a sophisticated data-filtering mechanism to verify bottom data from mixed-quality references. Consequently, despite the continuous development of algorithms, computer power, and interface optimization, specific data filtering mechanisms will become an urgent and crucial issue in the future.