arXiv subjects

A. Gemignani

Publications and source records attributed to A. Gemignani.

3 recordsLinked to original sources

arXiv2026

Biophoton Emission from Palm during Meditation: A Multi-Method Complexity Analysis

Biophotons are ultra-weak photon emissions in the visible spectrum produced by living organisms. While extensively studied in plants, germinating seeds, and cell cultures, no systematic multi-method complexity analysis of human ultraweak photon emission (UPE) under physiological modulation has been reported. We address this gap by applying a comprehensive analytical framework to UPE measurements from the right palm of a human subject. Three independent sessions were conducted on different days, each comprising four consecutive 15-minute phases: Dark reference, pre-meditation resting state (Pre), structured meditation based on the Sama Vritti box-breathing protocol, and post-meditation recovery (Post). Photon count series are analysed with four complementary methods: distributional statistics (Fano factor, skewness, tail Expected Shortfall); multiscale Fano factor and Allan deviation; stripe-filtered Diffusion Entropy Analysis (DEA); and Renyi entropy with a Time Reversal test. The methods show complementary sensitivities, converging on a coherent picture: a systematic reduction of emission intermittency during meditation, consistently detected across all three sessions. Stripe-filtered DEA places the emission in the non-ergodic renewal regime with a Pre-to-Meditation decrease of the scaling exponent. Renyi analysis reveals two effects: reduced marginal amplitude burstiness (Tdir) and increased sequential pattern structure (Tseq), interpreted as entrainment to the Sama Vritti rhythm. These findings are consistent with cardiac complexity transitions during meditation reported by Tuladhar et al. and with EEG reorganization during Sama Vritti breathing by Zaccaro et al., suggesting a coordinated multi-channel physiological response. The results establish a proof-of-concept framework for complexity analysis of human UPE under physiological modulation.

physics.bio-ph↗

arXiv2025

First experimental measurements of biophotons from Astrocytes and Glioblastoma cell cultures

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch in 1924 as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed by the collaboration at the National Laboratories of Frascati (LNF-INFN) and at the University of Rome II - Tor Vergata. The statistical analyses of the data collected revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatuses. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects potentially related to intercellular signalling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences that emerged from the application of Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the foundation for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and cellular communication research.

physics.bio-ph↗

arXiv2003

Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition

Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.

q-bio.TO↗