Biology subjects

Gabrielli, A.

Publications and source records attributed to Gabrielli, A..

3 recordsLinked to original sources

bioRxiv2025

Role of Hypertrophic Adipocytes, Collagen VI and CD38 in Fat Fibrosis of Patients with Obesity

Fat fibrosis correlates to metabolic consequences in patients with obesity, and is due to three types of collagen: I and III (fibrillar) and VI (non-fibrillar). In this sudy the extent of fibrosis in obese patients (n 50) was significant only in visceral parenchymal fat (4.7% vs 2.5% in controls (n 15) P<0.0001) and not in subcutaneous fat. Electron microscopy, in vivo and in vitro data, suggested that obese adipocytes are responsible for fibrillar collagen (I and III) production. COL6 (gene producing the non fibrillar form) resulted less expressed. In line, patients with COL6 mutations, showed increased fibrotic tissue even in subcutaneous fat: about 6.5 times vs controls in the patient with the severe form (Ullrich) and 2.8 times in two patients with the milder form (Bethlem). Approximately 15% of obese adipocytes were dead (perilipin1 negative), and consequent infiltrating macrophages showed hyperexpression of CD38, an ectoenzyme implicated in systemic fibrosis. Correlations with gene expression confirmed the importance also of myofibroblasts and the extracellular matrix peptidase D. All together our data support a role for obese adipocytes in the fibrillar collagen production and evidentiate collagen VI and CD38 as new molecular determinants, reinforcing the idea of a multi-factorial origin of fat fibrosis.

cell biology↗

bioRxiv2024

Linearizing and forecasting: a reservoir computing route to digital twins of the brain

Exploring the dynamics of a complex system, such as the human brain, poses significant challenges due to inherent uncertainties and limited data. In this study, we enhance the capabilities of noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework, demonstrating their effectiveness in creating autonomous in silico replicas - digital-twins - of brain activity. Our findings reveal that the poles of the Laplace transform of high-dimensional inferred lRNNs are directly linked to the spectral properties of observed systems and to the kernels of auto-regressive models. Applying this theoretical framework to resting-state fMRI, we successfully predict and decompose BOLD signals into spatiotemporal modes of a low-dimensional latent state space confined around a single equilibrium point. lRNNs provide an interpretable proxy for clustering among subjects and different brain areas. This adaptable digital-twin framework not only enables virtual experiments but also offers computational efficiency for real-time learning, highlighting its potential for personalized medicine and intervention strategies.

neuroscience↗

bioRxiv2024

Partial Correlation as a Tool for Mapping Functional-Structural Correspondence in Human Brain Connectivity

Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared to partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging (fMRI) brain activity time series under various regularization (a.k.a. noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from Density Weighted Imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neuro-genetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗