The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles. This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Pupillary hippus, i.e., the spontaneous bilateral oscillation of pupil diameter under constant illumination, provides a non-invasive window into autonomic and central nervous system dynamics. Despite decades of research, a unified framework linking its spectral features, physiological underpinnings, and analytical complexity is still missing. In this narrative review, we synthesize spectral and nonlinear pupillometry studies to propose a paradigm-aware taxonomy of hippus frequency bands that integrates evidence from autonomic tests and cognitive-arousal paradigms. Across the literature, low-frequency components tend to covary with respiratory/vasomotor autonomic rhythms, while higher-frequency fluctuations and complexity indices are more sensitive to cognitive load, visual fatigue, and pathological states but remain methodologically heterogeneous. Finally, we explore contexts in which hippus-based biomarkers show clinical potential, with a main focus on glaucoma as an emerging translational model, while underscoring the methodological and translational gaps that currently hinder their validation and routine clinical adoption. Physiology, spectral metrics, and complexity-based analyses are integrated to lay the foundations of a coherent framework.

The human diving reflex is an innate cardiorespiratory adjustment triggered during apnea that concurrently activates both sympathetic and parasympathetic branches of the autonomic nervous system to maintain physiological stability under stress. Although pupil dilation and constriction are antagonistically regulated by these branches, the effect of the diving reflex on pupil diameter oscillations, known as hippus, remains unexplored. Here, we compared hippus in healthy participants during breathing or apnea either with (Wet) or without (Dry) facial immersion in cold water. In both apneic conditions, hippus exhibited reduced power in the low-frequency band (< 0.25 Hz). Notably, during Wet apnea, we observed a reallocation of power towards higher frequencies (> 0.25 Hz) and increased entropy of fluctuations, indicating a shift in autonomic balance and greater signal complexity during the diving reflex. This pilot study highlights pupil dynamics as a sensitive and non-invasive probe of autonomic adjustments underlying the human diving reflex.

Introduction: Perception operates as rhythmically structured sampling in which temporal predictions determine when incoming signals are weighted. Fixational eye movements carry opposing consequences, enhancing acuity yet inducing brief peri-saccadic suppression, suggesting that their timing is paced by expected, salient rhythms. Auditory scenes can be parsed into competing streams that unfold over time. If fixation dynamics are shaped by temporal expectation, and auditory streaming imposes a percept-dependent temporal structure on otherwise identical acoustics, then fixational eye movements might provide a window into how listeners parse sound over time. We asked whether fixational eye movements reflect the perceived rather than the physical temporal organization of an ambiguous ABA– pattern. Methods: While listeners fixated and either attended High, Low, or All tones (Experiment 1, n = 15) or freely reported their percept (Experiment 2, n = 15), we recorded binocular eye position (500 Hz) and quantified microsaccade (MS) dynamics and eye-velocity spectra. Results: Across both experiments, eye-velocity spectra showed a percept-dependent redistribution between 2 and 4 Hz, with relative power shifting with the instructed/reported stream. A normalized 4–2 Hz index (ΔPSD) separated Low-tone from High-tone percepts across procedures. Time-resolved analyses further revealed within-trial waxing-and-waning of 2 vs. 4 Hz dominance, consistent with bistable fluctuations in maintaining a stream. Moreover, microsaccade reaction time (msRT), aligned to the onset of the sound sequence, differed significantly depending on the percept. Discussion: These findings extend oculomotor inhibition beyond discrete events, positioning fixation dynamics as a sensitive, report-free marker of auditory scene organization. We discuss mechanistic links to temporal attention and active sensing, and implications for a multisensory timing framework.

Purpose Purpose: The ocular surface (OS) microbiome is influenced by various factors and impacts ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. Methods The 16S rRNA gene sequencing was performed on OS samples collected using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Results Nationality, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis showed higher microbial richness in Spanish subjects compared to Italian subjects and higher biodiversity in sports practitioners. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. ML approach confirmed the significance of nationality in classifying microbial profiles. Conclusion This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health. Key Points Identify confounding factors influencing the ocular microbiome composition; Characterize the ocular surface microbiome; Analyse 16S rRNA gene sequencing data from ocular surface samples; Perform Diversity Analysis (i.e.; Alpha-diversity and Beta-diversity) and Difference Abundance Analysis;