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Biology subjects

Simphor, E.

Publications and source records attributed to Simphor, E..

2 recordsLinked to original sources

Comparison of extracellular vesicles isolation methods reveals method-dependent protein and miRNA profiles in saliva

Salivary extracellular vesicles (EVs) represent a powerful, non-invasive source of biomarkers for disease diagnosis and monitoring. Their molecular cargo reflects systemic and local physiological states, offering a window into neurological and inflammatory disorders. However, the diversity of EV isolation protocols and the possibility that each enriches distinct EV subpopulations remains a major barrier to reproducibility and data comparability. We conducted a comprehensive comparison of three EV isolation methods: ultracentrifugation (UC), PEG-based precipitation (Q), and immunoaffinity capture (M) to evaluate their impact on EV yield, purity, and molecular composition. Salivary EVs from healthy volunteers were analysed using proteomic and small-RNA sequencing approaches. Principal component analysis revealed clear isolation method-dependent clustering, where M-derived EVs displayed the most distinct profile. UC and Q produced broader proteomic repertoires with higher total protein content, whereas M-isolated EVs exhibited greater purity and enrichment of trafficking-and lysosome-associated proteins. Over 731 miRNAs selected, 28 were consistently altered across methods and 65 uniquely enriched in M isolates. RT-qPCR confirmed key directional trends. These 93 method-dependent miRNAs have predicted targets associated with synaptic structure and neurodegenerative pathways. These findings show that isolation methodology deeply shapes salivary EV cargo and suggest that immunoaffinity capture can isolate specific EV populations meeting diagnostic requirements.

molecular biology↗

Capturing in saliva real time emotions induced by a fragrance: an objective emotional assessment based on multiplex molecular biomarker profiles

This study introduces a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional methods such as self-reports, physiological monitoring, or neuroimaging are often limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers a practical alternative, reflecting rapid neuroendocrine changes linked to emotional states. We analyzed four key salivary biomarkers: cortisol (stress, HPA-axis activity), alpha-amylase (sympathetic activation), dehydroepiandrosterone (resilience), and oxytocin (social bonding, emotional regulation) to capture multidimensional emotional responses. Two clinical studies (N=30, N=63) and one consumer study (N=80) exposed healthy volunteers to six fragrances, with saliva collected before, 5 minutes after, and 20 minutes after olfactory stimulation. Subjective ratings of happiness, relaxation, confidence, and dynamism were also obtained via questionnaires. Rigorous analytical validation accounted for reproducibility, circadian variation and sample stability. Biomarker patterns revealed fragrance-specific emotional profiles, with distinct subgroups of participants whose biomarker dynamics correlated with specific emotional states. Increased oxytocin and decreased cortisol consistently aligned with happiness and relaxation, whereas distinct biomarker combinations predicted confidence or dynamism. Classification and regression tree analysis demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort (N=80) using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. These findings establish salivary biomarker profiling as a reliable tool for decoding real-time emotional responses. Beyond scientific insights into affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders. Expanding the biomarker panel and integrating molecular data with neuroimaging or autonomic measures could further elucidate the interplay between central olfactory processing and peripheral physiology.

neuroscience↗