Statistical analysis of the spontaneously emitted photon signals from palm and dorsal sides of both hands in human subjects

Modelling biophoton emission kinetics based on the initial intensity value in Helianthus annuus plants exposed to different types of stress

Biophoton radiation also referred to as ultra-weak photon emission (UPE) is used to denote a spontaneous and permanent photon emission associated with oxidative processes in cells and seems to universally occur in all living systems as a result of the generation of reactive oxygen species (ROS) that are produced under stress conditions. The measurement of this biophoton emission allows for a non-invasive approach in monitoring phenological stages throughout plant development which has direct relevance in agriculture research. In this study, the emission of photons emanating from sunflower (Helianthus annuus, L.) plants exposed to biotic and abiotic stress has been investigated. In healthy plants raised under controlled growth conditions UPE was low whereas in stressed individuals it considerably increased; particularly upon water stress. The kinetics of the signal is shown to reveal an exponential decay with characteristic dynamics, which appears to reflect different physiological states concomitantly setting in upon stress. The dynamics of the signal decay is shown to vary according to the type of stress applied (biotic vs. abiotic) hence suggesting a putative relationship between the kinetic traits of change in the signal intensity-decay and stress. Intriguingly, the determination of the change in the intensity of biophoton emission that ensued in a short time course was possible by using the initial biophoton emission intensity. The predictability level of the equations demonstrated the applicability of the model in a corroborative manner when employing it in independent UPE-measurements, thus permitting to forecast the intensity change in a very accurate way over a short time course. Our findings allow the notion that albeit stress confers complex and complicated changes on oxidative metabolism in biological systems, the employment of biophoton imaging offers a feasible method making it possible to monitor oxidative processes triggered by stress in a non-invasive and label-free way which has versatile applications especially in precision agriculture.

Integrating Ultra-Weak Photon Emission Analysis in Mitochondrial Research

Once regarded solely as the energy source of the cell, nowadays mitochondria are recognized to perform multiple essential functions in addition to energy production. Since the discovery of pathogenic mitochondrial DNA defects in the 1980s, research advances have revealed an increasing number of common human diseases, which share an underlying pathogenesis involving mitochondrial dysfunction. A major factor in this dysfunction is reactive oxygen species (ROS), which influence the mitochondrial-nuclear crosstalk and the link with the epigenome, an influence that provides explanations for pathogenic mechanisms. Regarding these mechanisms, we should take into account that mitochondria produce the majority of ultra-weak photon emission (UPE), an aspect that is often ignored - this type of emission may serve as assay for ROS, thus providing new opportunities for a non-invasive diagnosis of mitochondrial dysfunction. In this article, we overviewed three relevant areas of mitochondria-related research over the period 1960-2020: (a) respiration and energy production, (b) respiration-related production of free radicals and other ROS species, and (c) ultra-weak photon emission in relation to ROS and stress. First, we have outlined how these research areas initially developed independently of each other - following that, our review aims to show their stepwise integration during later stages of development. It is suggested that a further stimulation of research on UPE may have the potential to enhance the progress of modern mitochondrial research and its integration in medicine.

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.

Endogenous Chemiluminescence from Germinating Arabidopsis Thaliana Seeds

It is well known that all biological systems which undergo oxidative metabolism or oxidative stress generate a small amount of light. Since the origin of excited states producing this light is generally accepted to come from chemical reactions, the term endogenous biological chemiluminescence is appropriate. Apart from biomedicine, this phenomenon has potential applications also in plant biology and agriculture like monitoring the germination rate of seeds. While chemiluminescence capability to monitor germination has been measured on multiple agriculturally relevant plants, the standard model plant Arabidopsis thaliana has not been analyzed for this process so far. To fill in this gap, we demonstrate here on A. thaliana that the intensity of endogenous chemiluminescence increases during the germination stage. We showed that the chemiluminescence intensity increases since the second day of germination, but reaches a plateau on the third day, in contrast to other plants germinating from larger seeds studied so far. We also showed that intensity increases after topical application of hydrogen peroxide in a dose-dependent manner. Further, we demonstrated that the entropy of the chemiluminescence time series is similar to random Poisson signals. Our results support a notion that metabolism and oxidative reactions are underlying processes which generate endogenous biological chemiluminescence. Our findings contribute to novel methods for non-invasive and label-free sensing of oxidative processes in plant biology and agriculture.

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.

Ultra-weak photon emission in healthy subjects and patients with type 2 diabetes: evidence for a non-invasive diagnostic tool

BACKGROUND

Spontaneous ultra-weak photon emission (UPE) is a common phenomenon in biological systems and has been linked to pathological states. Researchers have always considered ultra-weak photon emission a potential non-invasive diagnostic tool, but its application in the medical field is stagnant due to the lack of relevant data for pathological states.

METHODS

Ultra-weak photon signals from five body sites (forehead, neck, heart, stomach, and navel) in fifty patients with type 2 diabetes and sixty age-matched healthy subjects were measured using a moveable whole-body biophoton detection system. Photon signal is measured for 10 min and detected in bins of 50 ms by a photomultiplier with a range of 290-630 nm. Each signal is a time series of 12 000 elements. Various parameters including photon intensity, Q value, squeezed state parameters (|α|, θ, ø, r) and SSI were analyzed.

RESULTS AND CONCLUSION

we found significant differences in the abovementioned parameters between groups, and all subjects could be clustered into two groups according to the results obtained by principal component analysis. Methods and results from this study could be useful for constructing a UPE database for a range of diseases, which would promote the application of UPE in clinical diagnosis in the future.

Crossing the Boundaries of Our Current Healthcare System by Integrating Ultra-Weak Photon Emissions with Metabolomics

The current healthcare system is hampered by a reductionist approach in which diagnostics and interventions focus on a specific target, resulting in medicines that center on generic, static phenomena while excluding inherent dynamic nature of biological processes, let alone psychosocial parameters. In this essay, we present some limitations of the current healthcare system and introduce the novel and potential approach of combining ultra-weak photon emission (UPE) with metabolomics technology in order to provide a dynamic readout of higher organizational systems. We argue that the combination of metabolomics and UPE can bring a new, broader, view of health state and can potentially help to shift healthcare toward more personalized approach that improves patient well-being.

Ultra-weak photon emission of hands in aging prediction

Aging has been one of the several topics intensely investigated during recent decades. More scientists have been scrutinizing mechanisms behind the human aging process. Ultra-weak photon emission is known as one type of spontaneous photon emission that can be detected with a highly sensitive single photon counting photomultiplier tube (PMT) from the surface of human bodies. It may reflect the body's oxidative damage. Our aim was to examine whether ultra-weak photon emission from a human hand is able to predict one's chronological age. Sixty subjects were recruited and grouped by age. We examined four areas of each hand: palm side of fingers, palm side of hand, dorsum side of fingers, and dorsum side of hand. Left and right hand were measured synchronously with two independent PMTs. Mean strength and Fano factor values of photon counts were utilized to compare the UPE patterns of males and females of different age groups. Subsequently, we utilized UPE data from the most sensitive PMT to develop an age prediction model. We randomly picked 49 subjects to construct the model, whereas the remaining 11 subjects were utilized for validation. The results demonstrated that the model was a good regression compared to the observed values (Pearson's r=0.6, adjusted R square=0.4, p=9.4E-7, accuracy=49/60). Further analysis revealed that the average difference between the chronological age and predicted age was only 7.6±0.8years. It was concluded that this fast and non-invasive photon technology is sufficiently promising to be developed for the estimation of biological aging.

Towards whole-body ultra-weak photon counting and imaging with a focus on human beings: a review

For decades, the relationship between ultra-weak photon emission (UPE) and the health state of the body is being studied. With the advent of systems biology, attention shifted from the association between UPE and reactive oxygen species towards UPE as a reflection of changed metabolic networks. Essential for this shift in thinking is the development of novel photon count statistical methods that more reflect the dynamics of the systems organization. Additionally, efforts to combine and correlate UPE data with other types of measurements such as metabolomics be key to understand the complexity of the human body. This review describes the history and developments in the area of human UPE research from a technical - methodological perspective, an experimental perspective and a theoretical perspective. There is ample evidence that human UPE research will allow a better understanding of the body as a complex dynamical system. The future lies in the further development of an integrated UPE and metabolomics platform for a personalized monitoring of changes of the system towards health or disease.

Attributes characterizing spontaneous ultra-weak photon signals of human subjects

Sixty visible range photon signals spontaneously emitted from the dorsal side of both hands of fifteen human subjects are analyzed with the aim of finding their attributes. The signals are of 30 min duration and detected in bins of 50 ms by two synchronized photo multipliers sensitive in the range (290-630 nm). Each signal is a time series of 36,000 elements. The attributes of its signal are determined from the statistical properties of time series. The mean and variance of time series determine the attributes signal strength and intercept (p₀) and slope (p₁) of the Fano Factor curve. The photon count distribution of the time series determines squeezed state parameters |α|, r, θ and ϕ, squeezed state index (SSI), and sum of the squares of residue (SSR). The correlation between simultaneously detected signals determines intercept (c₀) and slope (c₁) of their correlation curve. The variability of attributes is studied by calculating them in smaller intervals covering the entire signal. The profile of attribute at 12 sites in a subject is more informative and biologically relevant.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.