We report a detailed computational and experimental study of the interaction of
single-walled carbon nanotubes (SWCNTs) with the drug-metabolizing cytochrome P450
enzyme, CYP3A4. Dose-dependent inhibition of CYP3A4-mediated conversion of the model
compound, testosterone, to its major metabolite, 6β-hydroxy testosterone
was noted. Evidence for a direct interaction between SWCNTs and CYP3A4 was also
provided. The inhibition of enzyme activity was alleviated when SWCNTs were
pre-coated with bovine serum albumin. Furthermore, covalent functionalization of
SWCNTs with polyethylene glycol (PEG) chains mitigated the inhibition of CYP3A4
enzymatic activity. Molecular dynamics simulations suggested that inhibition of the
catalytic activity of CYP3A4 is mainly due to blocking of the exit channel for
substrates/products through a complex binding mechanism. This work suggests that
SWCNTs could interfere with metabolism of drugs and other xenobiotics and provides a
molecular mechanism for this toxicity. Our study also suggests means to reduce this
toxicity, eg., by surface modification.
Fast and accurate identification of source locations and release rates is particularly important for improving indoor air quality and ensuring the safety and health of people. Existing methods based on adjoint probability are difficult to distinguish the release rate of dynamic sources, and optimization algorithms based on regularization are limited to analysing only a small amount of potential pollutant source information. Therefore, this study proposed an algorithm combining adjoint equations and regularization models to identify the location and release intensity of pollutant sources in the entire computational domain of a room. Based on the validated indoor CFD computational model, we first obtained a series of response matrices corresponding to the sensor position by solving the adjoint equation, and then used the regularization method and Bayesian inference to extrapolate the release rate and location of dynamic pollutant source in the room. The results shown that the proposed algorithm is convenient and feasible to identify the location and intensity of the indoor pollutant source. Compared with the real source intensity, the identification of constant source intensity is lower than the error threshold (10%) in 97.4% of the time nodes, and the identification of periodic source is lower than the error threshold (10%) in 95.4% of the time nodes. This research provides a new method and perspective for the estimation of indoor pollutant source information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.