Biology subjects

Cavinato, L.

Publications and source records attributed to Cavinato, L..

2 recordsLinked to original sources

bioRxiv2025

Functional interaction of hybrid extracellular vesicle-liposome nanoparticles with target cells: absence of toxicity

Building on the success of COVID-19 vaccine development, lipid nanoparticles (LNPs) have emerged as leading vehicles for mRNA delivery in a range of therapeutic applications. Naturally-occurring extracellular vesicles (EVs), which share similar physical properties with LNPs, present a promising alternative platform because of their relative stability and lower immunogenicity. A key challenge common to both EVs and LNPs is enabling efficient vesicle - cell interactions and establishing a polarized permeability pathway required for effective cargo transfer. Membrane recognition and intercalation are essential for the function and delivery capacity of both systems, regardless of their complexity. In this study, we leveraged recent advances to create hybrid extracellular vesicles (HEVs) by using LNPs to load mRNA into EVs. We characterized HEV formation using Forster resonance energy transfer (FRET), cryo-electron microscopy (Cryo-EM), and super-resolution microscopy, and demonstrated their ability to deliver mRNA to recipient cells. In both, in vitro and in vivo models, HEVs exhibited superior transfection efficiency compared to conventional LNPs composed of synthetic lipids, while significantly reducing LNPs cytotoxicity - a not-well-recognized limitation of synthetic lipid-based systems. These results highlight HEVs as a safer and more effective alternative for mRNA and small molecule delivery. Future therapeutic strategies could involve isolating EVs from patients, hybridizing them with synthetic lipid carriers loaded with therapeutic cargo, and reintroducing them for personalized treatment.

cell biology↗

bioRxiv2023

Dual Adversarial Deconfounding Autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data

Medical imaging represents the primary tool for investigating and monitoring several diseases, including cancer. The advances in quantitative image analysis have developed towards the extraction of biomarkers able to support clinical decisions. To produce robust results, multi-center studies are often set up. However, the imaging information must be denoised from confounding factors - known as batch-effect - like scanner-specific and center-specific influences. Moreover, in non-solid cancers, like lymphomas, effective biomarkers require an imaging-based representation of the disease that accounts for its multi-site spreading over the patients body. In this work, we address the dual-factor deconfusion problem and we propose a deconfusion algorithm to harmonize the imaging information of patients affected by Hodgkin Lymphoma in a multi-center setting. We show that the proposed model successfully denoises data from domain-specific variability while it coherently preserves the spatial relationship between imaging descriptions of peer lesions, which is a strong prognostic biomarker for tumor heterogeneity assessment. This harmonization step allows to significantly improve the performance in prognostic models, enabling building exhaustive patient representations and delivering more accurate analyses. This work lays the groundwork for performing large-scale and reproducible analyses on multi-center data that are urgently needed to convey the translation of imaging-based biomarkers into the clinical practice as effective prognostic tools. The code is available on GitHub at this link

bioinformatics↗