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Piccirillo, C.

Publications and source records attributed to Piccirillo, C..

2 recordsLinked to original sources

A Continuously Oxygenated Macroencapsulation System Enables High-Density Packing and Delivery of Insulin-Secreting Cells

The encapsulation of insulin-secreting cells within immuno-protective systems holds significant promise for curative treatment of type 1 diabetes without immunosuppression. A major challenge, however, remains the inadequate oxygen tension within the encapsulation systems, which compromises the survival and function of encapsulated cells and necessitates low packing density and impractically large systems to deliver a curative cell mass. In this study, we present a novel cell encapsulation system capable of generating oxygen via the electrolysis of tissue moisture to provide a continuous oxygen supply to densely packed insulin-secreting cells. Our system comprises a miniaturized implantable electrochemical oxygen generator (iEOG) and a scalable cylindrical cell encapsulation pouch, designed in a linear configuration to facilitate minimally invasive implantation and retrieval. The oxygen generation from the system was shown to be precisely controlled, stable, and capable of supporting clinically relevant doses of pancreatic islets. In vitro studies demonstrated that the oxygenated system effectively maintained the viability and function of insulinoma cell aggregates and human pancreatic islets at densities of 60,000 islet equivalents per mL (IEQ/mL) or 4,200 IEQ/cm{superscript 2} under a hypoxic cell culture condition (1% O ). In an allogeneic rat model, the oxygenated systems containing pancreatic islets implanted into the poorly vascularized but clinically attractive subcutaneous space at a density of 60,000 IEQ/mL successfully reversed diabetes for up to about 3 months without the need for immunosuppression, while animals implanted with non-oxygenated systems remained diabetic. Most ([~] 90%) of the pancreatic islets encapsulated in the continuously oxygenated systems were found viable and functional upon retrieval. These findings suggest the feasibility of using continuous oxygenation to support insulin-secreting cells at high loading densities in subcutaneous space, enabling the development of an encapsulation system with clinically practical dimensions.

bioengineering↗

Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis

BackgroundOur prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. MethodsOne hundred seventy-one MS patients (age: 42.67{+/-}10.41, 74% females) were included. The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test (BVMT) were used to assess cognition. The Network Modification tool was performed to estimate SC, which was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. Ridge regression was performed to predict cognition using regional eSC and eFC, along with demographics and clinical information as well as conventional MRI metrics. Baseline cognition was added to the models that were used to predict the follow-up cognition. Spearmans correlation (r) was used to assess the prediction accuracy. ResultsThe highest accuracy was obtained when predicting follow-up SDMT using regional eSC or eFC (median r=0.58 for eSC and r=0.56 for eFC). Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Baseline SDMT, clinical subtype, and age were the most important non-connectome metrics in predicting follow-up SDMT. ConclusionsOur findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.

neuroscience↗