Ricerc@Sapienza

Our research investigates the mechanisms underlying cellular recovery, with a particular emphasis on the role of nutrients as active modulators of repair and adaptation processes, especially in neural models exposed to oxidative stress or harboring rare metabolic mutations.

We study how micronutrients—at both physiological and pharmacological doses—affect neural-like cells, focusing on metabolism as a potential upstream modulator rather than a downstream consequence. Using in vitro models and in silico analyses, we aim to identify molecular targets that may improve recovery from micronutrient deficiencies or act synergistically in metabolic disorders.

These efforts are supported by NMR-based metabolomics and structural analyses through Raman microscopy and fluorescence imaging. We also develop integrative, non-destructive approaches to explore biological processes in their native spatial context.

Raman microscopy plays a key role in capturing label-free molecular data; however, spectral overlap and complexity require sophisticated interpretative tools. To address this, we apply neuro-symbolic artificial intelligence, which merges data-driven learning with symbolic reasoning. This hybrid AI approach enables more transparent, structured, and biologically meaningful interpretations of spectral and spatial data.

Through correlative analyses combining spectral information, fluorescence imaging, and AI-driven modeling, we seek to uncover how molecular and metabolic pathways are modulated by micronutrients during recovery processes.

This interdisciplinary framework not only sheds light on the connections between metabolism, gene regulation, and cell architecture, but also builds scalable tools for predictive analysis. Ultimately, our goal is to support data-driven precision strategies and identify new molecular targets in the context of neural resilience and micronutrient-related diseases.