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Anti-fungal blue light (ABL) therapies have been studied and applied in treating various diseases caused by fungal infection. The existing work has been mainly devoted to study the effect of various light dosages on the fungal viability and on the induced cytotoxic reactive oxygen species (ROS) in the pathogens. While in vivo experimental studies have also been reported, there is still no work targeted on quantifying the effect of light on prohibiting the pathogens from invading into the deeper sites in the skin of their host. This can be attributed to the lack of methods to analyze the tissue section images, which are the main means of examining infected tissues. This work has been devoted to solve such problems, so as to improve dosimetric analyses of ABL therapies on treating fungal infections. Specifically, the invasion depth of the fungi and their ratios to the tissue in four bins at different depths inside the skin were extracted from the tissue section images. The significance of the treatment with different dosages on inhibiting the fungi was also tested by each of these depth-related metrics. The ABL experiments using 415-nm-wavelength LED light were performed on BALB/c mice, whose skin was infected by Candida albicans (C. albicans). The proposed methods were applied to the tissue sections of the experimental animals. The results clearly verified that the fluence up to 180J/cm2 can significantly prohibit the fungal infection into the skin in terms of almost all the newly proposed metrics. [ABSTRACT FROM AUTHOR]

Anti-fungal blue light (ABL) therapies have been widely studied to treat various microbial infections in the literature. The blue light with wavelengths ranging from 400 to 470 nm has been reported to be effective to inhibit various kinds of bacteria and fungi. The existing studies usually report the viability rates of the pathogens under the irradiation of the blue light with different dosage parameters. However, to the best of our knowledge, there is still no work especially focusing on studying the effect of ABL therapies on treating candida vaginitis, where it is important to study the viability of both the Candida albicans (C. albicans) and the human vaginal epithelial cells. It is the purpose of this work to conduct ABL experiments on both of these two cells, analyze the effects, and determine the best ABL wavelength out of three candidates, i.e., 405-nm, 415-nm, and 450-nm wavelength. The viability rates of the C. albicans and the human vaginal epithelial cells irradiated by the three blue LED light sources were measured, whose irradiance (power density) were all set to 50 mW/cm2. The dynamic viability models of the C. albicans and the epithelial cells were built based on the experimental data. Moreover, in this work, we also built a functional relationship between the viability of these two types of cells, by which we further compared the effects of the blue light irradiation on both the C. albicans and vaginal epithelial cells. The experimental data showed that when an approximately 80% inhibiting rate of the C. albicans was achieved, the survival rates of the epithelial cells were 0.6700, 0.7748, and 0.6027, respectively for the treatment by the 405-nm, 415-nm, and 450-nm wavelength light. On the other hand, by simulating the functional relationship between the viability of the two types of cells, the survival rates of the epithelial cells became 0.5783, 0.6898, and 0.1918 respectively using the 405-nm, 415-nm and 450-nm wavelength light, when the C. albicans was completely inhibited. Therefore, both the experimental data and the model simulation results have demonstrated that the 415-nm light has a more effective anti-fungal result with less damage to the epithelial cells than the 405-nm and 450-nm light. [ABSTRACT FROM AUTHOR]

Light emitting diodes (LEDs) have become the main light sources for general lighting, due to their high lumen efficiency and long life time. Moreover, their high bandwidth and the availability of diverse wavelength contents ranging from ultraviolet to infrared empower them with great controllability in tuning brightness, pulse durations and spectra. These parameters are the essential ingredients of the applications in medical imaging and therapies. Despite the fast advances in both LED technologies and their applications, few reviews have been seen to link the controllable emission properties of LEDs to these applications. The objective of this paper is to bridge this gap by reviewing the main control techniques of LEDs that enable creating enhanced lighting patterns for imaging and generating effective photon doses for photobiomodulation. This paper also provides the basic mechanisms behind the effective LED therapies in treating cutaneous and neurological diseases. The emerging field of optogenetics is also discussed with a focus on the application of LEDs. The multidisciplinary topics reviewed in this paper can help the researchers in LEDs, imaging, light therapy and optogenetics better understand the basic principles in each other's field; and hence to stimulate the application of LEDs in health care. [ABSTRACT FROM AUTHOR]

This paper introduces the application of neural networks based on Bayesian inference, the automatic relevance determination algorithm for selecting relevant features and designing neural estimators for tool wear estimation in face-milling processes. Two types of neural networks are studied: Bayesian support vector machines for regression (BSVR) and Bayesian multilayer perceptrons (BMLP). Sixteen well-known features derived from pre-processing of the milling force signal are considered. The force signal samples are collected from 20 milling experiments under various machining conditions. The feature extraction and selection procedure is then applied to the sampled force signal. The feature selection results from the two neural networks are found to be quite similar. The average force has been proven to be the most relevant feature for tool wear estimation in both cases, among a set of six other features in each case, with each set differing by only one feature. The comparison among the generalization capabilities of the entire, selected, and rejected features shows that the selected features are relatively more relevant to tool wear processes in both cases. The comparison between the estimation results from the two neural networks using the corresponding relevant feature set shows that the BSVR method is more accurate in estimating flank wear than BMLP, but at the cost of a higher computing load. [ABSTRACT FROM AUTHOR]