Recent updates on 1,2,3-, 1,2,4-, and 1,3,5-triazine hybrids (2017-present): The anticancer activity, structure-activity relationships, and mechanisms of action.

Cancer is one of the leading causes of death across the world, and the prevalence and mortality rates of cancer will continue to grow. Chemotherapeutics play a critical role in cancer therapy, but drug resistance and side effects are major hurdles to effective treatment, evoking an immediate need for the discovery of new anticancer agents. Triazines including 1,2,3-, 1,2,4-, and 1,3,5-triazine have occupied a propitious place in drug design and development due to their excellent pharmacological profiles. Mechanistically, triazine derivatives could interfere with various signaling pathways to induce cancer cell death. Hence, triazine derivatives possess potential in vitro and in vivo efficacy against diverse cancers. In particular, triazine hybrids are able to overcome drug resistance and reduce side effects. Moreover, several triazine hybrids such as brivanib (indole-containing pyrrolo[2,1-f][1,2,4]triazine), gedatolisib (1,3,5-triazine-urea hybrid), and enasidenib (1,3,5-triazine-pyridine hybrid) have already been available in the market. Accordingly, triazine hybrids are useful scaffolds for the discovery of novel anticancer chemotherapeutics. This review focuses on the anticancer activity of 1,2,3-, 1,2,4-, and 1,3,5-triazine hybrids, together with the structure-activity relationships and mechanisms of action developed from 2017 to the present. The enriched structure-activity relationships may be useful for further rational drug development of triazine hybrids as potential clinical candidates.

Research on the Corrosion Behavior of Q235 Pipeline Steel in an Atmospheric Environment through Experiment.

Low-carbon steel pipelines are frequently used as transport pipelines for various media. As the pipeline transport industry continues to develop in extreme directions, such as high efficiency, long life, and large pipe diameters, the issue of pipeline reliability is becoming increasingly prominent. This study selected Q235 steel, a typical material for low-carbon steel pipelines, as the research object. In accordance with the pipeline service environment and the accelerated corrosion environment test spectrum, cyclic salt spray accelerated corrosion tests that simulated the effects of the marine atmosphere were designed and implemented. Corrosion properties, such as corrosion weight loss, morphology, and product composition of samples with different cycles, were characterized through appearance inspection, scanning electron microscopy analysis, and energy spectrum analysis. The corrosion behavior and mechanism of Q235 low-carbon steel in the enhanced corrosion environment were studied, and the corrosion weight loss kinetics of Q235 steel was verified to conform to the power function law. During the corrosion process, the passivation film on the surface of the low-carbon steel and the dense and stable α-FeOOH layer formed after the passivation film was peeled off played a role in corrosion resistance. The passivation effect, service life, and service limit of Q235 steel were studied and determined, and an evaluation model for quick evaluation of the corrosion life of Q235 low-carbon steel was established. This work provides technical support to improve the life and reliability of low-carbon steel pipelines. It also offers a theoretical basis for further research on the similitude and relevance of cyclic salt spray accelerated corrosion testing.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.