Analyzing efficacy and safety of anti-fungal blue light therapy via kernel-based modeling the reactive oxygen species induced by light

Analyzing efficacy and safety of anti-fungal blue light therapy via kernel-based modeling the reactive oxygen species induced by light

Wang, T. (TU Delft Electronic Components, Technology and Materials; Chinese Academy of Sciences)
Dong, Jianfei (Chinese Academy of Sciences)
Zhang, Kouchi (TU Delft Electronic Components, Technology and Materials)

2022

Objective: The goal of this study is to investigate the efficacy, safety, and mechanism of ABL for inactivating Candida albicans (C. albicans), and to determine the best wavelength for treating candida infected disease, by experimental measurements and dynamic modeling. Methods: The changes in reactive oxygen species (ROS) in C. albicans and human host cells under the irradiation of 385, 405, and 415nm wavelengths light with irradiance of 50mW/cm2 were measured. Moreover, a kernel-based nonlinear dynamic model, i.e., nonlinear autoregressive with exogenous inputs (NARX), was developed and applied to predict the concentration of light-induced ROS, whose kernels were selected by a newly developed algorithm based on particle swarm optimization (PSO). Results: The ROS concentration was increased respectively about 10-12 times in C. albicans and about 3-6 times in human epithelial cells by the ABL treatment with the same fluence of 90J/cm2. The NARX models were respectively fitted to the data from the experiments on both types of cells. Besides, four different kernel functions, including Gaussian, Laplace, linear and polynomial kernels, were compared in their fitting accuracies. The errors with the Laplace kernel turned out to be only 0.2704 and 0.0593, as respectively fitted to the experimental data of the C. albicans and human host cells. Conclusion: The results demonstrated the effectiveness of the NARX modeling approach, and revealed that the 415nm light was more effective as an anti-fungal treatment with less damage to the host cells than the 405 or 385nm light. Significance: The kernel-based NARX model identification algorithm offers opportunities for determining the effective and safe light dosages in treating various fungal infection diseases.

Anti-fungal blue light therapy
Kernel selection
NARX modeling
Nonlinear dynamics
Reactive oxygen species

http://resolver.tudelft.nl/uuid:5552a9ca-2976-4b96-bb30-07dd77c3d049

https://doi.org/10.1109/TBME.2022.3146567

2023-07-01

0018-9294

IEEE Transactions on Biomedical Engineering, 69 (8), 2433-2442

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

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© 2022 T. Wang, Jianfei Dong, Kouchi Zhang