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Defining the mechanistic basis of neutrophil mediated sensitisation of sensory neurons in fibromyalgia syndrome
Full text linkPrevious work in my group has shown a key role for neutrophils in mediating the spatiotemporal spread of neuronal and behavioural hypersensitivity in murine models of chronic widespread pain (CWP) and fibromyalgia syndrome (FMS) (Caxaria et al., 2023). My thesis seeks to determine a mechanistic basis for neutrophil-mediated neuronal sensitisation. I have used calcium imaging techniques to demonstrate sensitisation of sensory neurons following exposure to neutrophils derived from FMS patients. Mass spectrometry and NETosis assays also reveal distinct phenotypic differences between FMS and PFC neutrophils which may underpin their pro-nociceptive capacity. Following i.v. adoptive transfer of FMS neutrophils into naive recipient mice, microscopy techniques demonstrate that neutrophils are trafficked towards dorsal root ganglia in a spatially restricted manner within the leptomeningeal layers, rather than direct neutrophil infiltration into the sensory ganglia bodies. Furthermore, I also observe neutrophil infiltration into trigeminal ganglia. My doctoral studies aimed to define the mechanism of neutrophil mediated sensitisation of sensory neurons in the absence of direct contact between infiltrating neutrophils and neuronal soma. I investigated the role of neutrophil derived extracellular vesicles (NDEV) as potential mediators of pain in FMS. I first optimised a protocol to isolate NDEVs from unstimulated neutrophils, confirmed using western blot and transmission electron scanning microscopy. I measured size and concentration of NDEVs derived from FMS patients and PFCs using nanoparticle flow cytometry and observed increased NDEV release in FMS derived neutrophils. To determine their pro-nociceptive capacity, I used calcium imaging to demonstrate increased neuronal activation following exposure of sensory neurons to NDEVs isolated from FMS neutrophils. Furthermore, I used pharmacological blockade of NDEV generation to attenuate the capacity of FMS neutrophils to sensitise sensory neurons ex vivo and in vivo, highlighting the essential role of NDEVs driving pain pathogenesis in FMS
CAR-T Cell Exhaustion in Cancer over the Past Decade: Mitochondrial Metabolism as a Target for Counteraction.
No full textChimeric antigen receptor T (CAR-T) cell therapy has emerged as a transformative advancement in cancer immunotherapy, but remains limited by multiple challenges. The exhaustion of T cells represents a critical obstacle limiting the success of immunotherapeutic interventions. Targeting mitochondrial metabolism offers a promising approach to mitigate exhaustion and enhance CAR-T persistence. Mechanistically, mitochondrial dysfunction within the tumor microenvironment disrupts energy metabolism, reactive oxygen species (ROS) homeostasis, and cell survival, impairing CAR-T function. Here, we review the current challenges facing the clinical application of CAR-T therapy in cancers and summarize mitochondrial-centered approaches to overcome some of these obstacles by optimizing mitochondrial metabolic pathways. We emphasize the essential role of mitochondrial metabolism in augmenting therapeutic efficacy and persistence of CAR-T cells. Future breakthroughs will depend on robust clinical evidence and precise metabolic modulation to enhance CAR-T therapies
Ventricular free-wall rupture, ventricular pseudoaneurysm and papillary muscle rupture complicating acute myocardial infarction: a Clinical Consensus Statement of the ESC Working Group on Cardiovascular Surgery, the Association for Acute CardioVascular Care (ACVC) of the ESC, the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC, the ESC Working Group on Pulmonary Circulation & Right Ventricular Function, the European Association of Preventive Cardiology (EAPC) of the ESC, the Heart Failure Association (HFA) of the ESC, the European Association of Cardiovascular Imaging (EACVI) of the ESC, and the European Association of Cardio-Thoracic Surgery (EACTS)
No full textMulti-omics identifies oxidative stress, prothrombotic pathways, and lactoperoxidase variants as key factors in COVID-19 severity.
No full textBACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected over 26 million individuals in Italy, resulting in ∼200,000 COVID-19-related deaths. Unravelling host genetic factors underlying disease severity is key to understanding progression mechanisms. METHODS: We applied multi-omics approaches to investigate genetic susceptibility to COVID-19 severity in the Italian population. We combined an exome-wide case-control study of rare germline variants (215 severe/critically ill patients vs 1755 controls) with transcriptomic (differential gene expression and alternative splicing) analyses of 59 hospitalised patients to identify signatures associated with severe respiratory outcomes (ICU admission). FINDINGS: Rare variant analysis revealed significant associations with genes implicated in oxidative stress and mitochondrial dysfunction, including MTERF1 (FDR = 7.69 × 10-5), TDP1 (FDR = 3.23 × 10-7), and LPO (FDR = 1.58 × 10-2). Pathway analyses confirmed enrichment in "reactive oxygen species", "oxidative phosphorylation", and "inflammatory response" pathways. Transcriptomics showed a proinflammatory profile in hospitalised patients (N = 24) and a prothrombotic signature in ICU-admitted individuals (N = 35), reflecting disease progression. Genomic and transcriptomic data integration highlighted LPO, encoding the antimicrobial enzyme lactoperoxidase, as the only gene both significantly enriched for damaging variants and upregulated in ICU-admitted cases (log2FC = 0.57, FDR = 0.028). Notably, we confirmed the genetic association with severity in independent cohorts (1873 cases vs 508,532 controls; meta-analysis p = 0.0050, OR = 3.44, 95% CI = 1.71-6.89). We propose that LPO haploinsufficiency may impair host capacity to neutralise ROS, contributing to COVID-19 progression. INTERPRETATION: In conclusion, our multi-omics analysis implicates oxidative stress and mitochondrial dysfunction as central to COVID-19 severity, identifying LPO as a candidate susceptibility gene. FUNDING: Banca Intesa San Paolo, EU Next-Generation EU-MUR-PNRR (INF-ACT, PE00000007), Dolce & Gabbana
Hybrid solvers for reactor modelling: matrix-based and matrix-free approaches on voxel-dominated meshes
Full text linkSimulating neutronics and thermal hydraulics within nuclear reactor cores is computationally intensive, not only because of the complexity of the governing equations but also because of the intricate geometries involved. Solving the Boltzmann transport and Navier-Stokes equations for a full core representation typically relies on unstructured meshes, which, while highly flexible, can substantially increase computational costs regarding memory and solving time. Cartesian meshes with Finite Elements (FE) offer a faster alternative, potentially improving computational speed by an order of magnitude due to direct memory addressing. However, they necessitate finer grids to accurately capture the boundary details of non-Cartesian surfaces, which can offset these gains by increasing solver times. To address this challenge, a new meshing algorithm is proposed in conjunction with hybrid, matrix-based and matrix free, solver technologies. It employs a geometry-conforming boundary method using voxel-dominated Cartesian meshes. This method enables accurate boundary representation at arbitrary resolutions, which can be adjusted to resolve the physics to the desired level of accuracy rather than strictly to capture geometric detail. This is combined with a hybrid solver for fluid flows to different regions of a problem in order to increase efficiency when resolving the boundary. This article demonstrates the method’s application to Computational Fluids Dynamics (CFD) and neutronics problems relevant to reactor physics, showcasing its accuracy, convergence, numerical stability, and suitability for handling complex geometries.</jats:p
Robust Inverse Regression for Multivariate Elliptical Functional Data
No full textFunctional data have received significant attention as they frequently
appear in modern applications, such as functional magnetic resonance imaging
(fMRI) and natural language processing. The infinite-dimensional nature of functional data makes it necessary to use dimension reduction techniques. Most existing techniques, however, rely on the covariance operator, which can be affected
by heavy-tailed data and unusual observations. Therefore, in this paper, we consider a robust sliced inverse regression for multivariate elliptical functional data.
For that reason, we introduce a new statistical linear operator, called the conditional spatial sign Kendall’s tau covariance operator, which can be seen as an
extension of the multivariate Kendall’s tau to both the conditional and functional
settings. The new operator is robust to heavy-tailed data and outliers, and hence
can provide a robust estimate of the sufficient predictors. We also derive the
convergence rates of the proposed estimators for both completely and partially
observed data. Finally, we demonstrate the finite sample performance of our
estimator using simulation examples and a real dataset based on fMRI
Mono- and cross-lingual evaluation of representation language models on less-resourced languages
No full textThe current dominance of large language models in natural language processing is based on their contextual awareness. For text classification, text representation models, such as ELMo, BERT, and BERT derivatives, are typically fine-tuned for a specific problem. Most existing work focuses on English; in contrast, we present a large-scale multilingual empirical comparison of several monolingual and multilingual ELMo and BERT models using 14 classification tasks in nine languages. The results show, that the choice of best model largely depends on the task and language used, especially in a cross-lingual setting. In monolingual settings, monolingual BERT models tend to perform the best among BERT models. Among ELMo models, the ones trained on large corpora dominate. Cross-lingual knowledge transfer is feasible on most tasks already in a zero-shot setting without losing much performance
Design and Integration of a 15-DOF Myoelectric Prosthetic Hand with Tendon-Driven Actuation and Force Feedback Control
No full textThis paper presents the design and integration of a 15-degree-of-freedom (DOF) myoelectric prosthetic hand as a modular solution for mobile assistive robotics. The system combines tendon-driven actuation and surface electromyography (sEMG) control with real-time sensor fusion using force-sensitive resistors (FSRs) and inertial measurement units (IMUs). Designed for seamless integration with mobile platforms and assistive exoskeletons, the hand enables dynamic object interaction across varying environments. Modular 3D-printed construction using PLA and TPU allows for cost-effective fabrication and structural compliance. Embedded sensors facilitate adaptive grip modulation, compensating for terrain changes and object variations. Experimental validation on a powered robotic base demonstrates stable grip performance, responsive force control, and robust manipulation in motion. The proposed hand achieves 94.8% grip stability and 92% task completion across varied surfaces and object types. These results highlight the design’s potential as a scalable, low-cost solution for real-world loco-manipulation tasks in mobile assistive settings