Analyzing Efficacy and Safety of Anti-Fungal Blue Light Therapy via Kernel-Based Modeling the Reactive Oxygen Species Induced by Light

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 415 nm wavelengths light with irradiance of 50
$mW/cm^2$
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 90
$J/cm^2$
. 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 415 nm light was more effective as an anti-fungal treatment with less damage to the host cells than the 405 or 385 nm 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.