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Pierantoni, L.

Publications and source records attributed to Pierantoni, L..

2 recordsLinked to original sources

Microfluidic Extraction of Extracellular Vesicle-Associated Nucleic Acids for Downstream Molecular Analysis

Aim: To develop a microfluidic workflow for extracting extracellular vesicle (EV)-associated nucleic acids (NAs) from biofluids, such as blood plasma and serum, for downstream molecular analysis. Methods: Healthy serum and plasma samples were spiked with EVs obtained from Hec-1-A cells carrying defined mutations and characterized in accordance with MISEV guidelines. The spiked samples were then processed using either the standard centrifugation-based ExoGAG protocol or a microfluidic workflow. The latter combines ExoGAG mediated aggregation of EVs with mechanical entrapment of the resulting EV aggregates in a microfluidic filtration device (EVNA-chip). The retained material was washed and on-chip lysed to release its NA cargo. NAs were subsequently purified using a commercial silica column, producing an output suitable for molecular analysis through droplet digital PCR (ddPCR). Results: The EVNA-chip recovery yield was 42.0 +/- 4.28% and 38.1 +/- 1.46% for double-stranded DNA (dsDNA) and microRNA respectively. The recovered dsDNA remained compatible with ddPCR detection of the KRAS p.G12D variant (c.35G>A) and with the assessment of MSI-associated alterations using BAT25, BAT26, NR21, NR24, and MONO27. The method was demonstrated for pilot cohort of patients with endometrial cancer. The RNA workflow supported reverse transcription and was demonstrated for the quantification of miR-X, a potential marker of Fabry disease, using patient samples and healthy controls. Conclusion: Although recovery was lower than with the standard protocol (~45% for DNA and for RNA), the centrifugation-free EVNA-chip workflow demonstrates the compatibility of the method with downstream NA molecular analyses and is advantageous for the development of an integrated, automated platform for EV-based liquid biopsy applications.

bioengineering↗

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

ObjectivePeripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon the implantation procedure and the subjects anatomy. Currently, few alternatives exist for the determination of the target subject structural and functional anatomy, and statistical characterizations from cadaveric samples are limited because of their high cost. We propose an optimization workflow that can guide both the pre-surgical planning and the determination of maximally selective multisite stimulation protocols for implants consisting of several intraneural electrodes, and we characterize its performance in silico. We show that the availability of structural and functional information leads to very high performances and allows taking informed decisions on neuroprosthetic design. ApproachWe employ hybrid models (HMs) of neuromodulation in conjunction with a machine learning-based surrogate model to determine fiber activation under electrical stimulation, and two steps of optimization through particle swarm optimization (PSO) to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost. Main resultsOur method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode (TIME) implants, the optimal number of electrodes to implant, their optimal insertion, and a set of multipolar stimulation protocols that lead in silico to selective activation of all the muscles innervated by the human median nerve. SignificanceWe show how to use effectively HMs for optimizing personalized neuroprostheses for motor function restoration. We provide in-silico evidences about the potential of multipolar stimulation to increase greatly selectivity. We also show that the knowledge of structural and functional anatomies of the target subject leads to very high selectivity and motivate the development of methods for their in vivo characterization.

bioengineering↗