Biological Auto(chemi)luminescence Imaging of Oxidative Processes in Human Skin.

Oxidative processes in all types of organisms cause the chemical formation of electronically excited species, with subsequent ultraweak photon emission termed biological auto(chemi)luminescence (BAL). Imaging this luminescence phenomenon using ultrasensitive devices could potentially enable monitoring of oxidative stress in optically accessible areas of the human body, such as skin. Although oxidative stress induced by UV light has been explored, for chemically induced stress, there is no in vivo-quantified imaging of oxidative processes in human skin using BAL under the controlled extent of oxidative stress conditions. Furthermore, the mechanisms and dynamics of BAL from the skin have not been fully explored. Here, we demonstrate that different degrees of chemically induced oxidative stress on the skin can be spatially resolved quantitatively through noninvasive label-free BAL imaging. Additionally, to gain insight into the underlying mechanisms, a minimal chemical model of skin based on a mixture of lipid, melanin, and water was developed and used to show that it can be used to reproduce essential features of the response of real skin to oxidative stress. Our results contribute to novel, noninvasive photonic label-free methods for quantitative sensing of oxidative processes and oxidative stress.

Short-time fractal analysis of biological autoluminescence.

Biological systems manifest continuous weak autoluminescence, which is present even in the absence of external stimuli. Since this autoluminescence arises from internal metabolic and physiological processes, several works suggested that it could carry information in the time series of the detected photon counts. However, there is little experimental work which would show any difference of this signal from random Poisson noise and some works were prone to artifacts due to lacking or improper reference signals. Here we apply rigorous statistical methods and advanced reference signals to test the hypothesis whether time series of autoluminescence from germinating mung beans display any intrinsic correlations. Utilizing the fractional Brownian bridge that employs short samples of time series in the method kernel, we suggest that the detected autoluminescence signal from mung beans is not totally random, but it seems to involve a process with a negative memory. Our results contribute to the development of the rigorous methodology of signal analysis of photonic biosignals.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Photocount statistics of ultra-weak photon emission from germinating mung bean.

Ultra-weak photon emission (UPE) is an endogenous bioluminescence phenomenon present in all biological samples with an active oxidative metabolism, even without an external pre-illumination. To verify the potential of UPE for non-invasive monitoring of metabolism and growth in germinating plants, the aim of this study was to investigate the UPE from a model system - germinating mung bean seedlings (Vigna radiata) - and analyze the statistical properties of UPE during the growth in two different conditions of imbibition (pure water and 1% sucrose). We found that in all days and in both conditions, photocount distributions of UPE time series follow the negative binomial distribution whose parameters changed during the growth due to the increasing ratio of signal-to-detector dark count. Correspondingly for both groups, the mean values of UPE increased during the seedlings growth, while the values of Fano factor show a decreasing trend towards 1 during the 6day period. While our results do not show any significant difference in hypocotyl length and weight gain between the two groups of mung seedlings, there is an indication of a tiny suppressing effect of sucrose on UPE intensity. We believe that UPE can be exploited for a sensitive non-invasive analysis of oxidative metabolism during the plant development and growth with potential applications in agricultural research.