Archivio Istituzionale della Ricerca - Università degli Studi di Pavia
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Biventricular electromechanical dysfunction and molecular remodeling in a rat model of advanced pulmonary arterial hypertension
No full textBackground: Pulmonary arterial hypertension (PAH) is a severe condition characterized by elevated pulmonary arterial pressure, leading to significant morbidity and mortality. Despite ongoing research, its pathophysiology remains incompletely understood. Traditionally, PAH has been regarded as predominantly affecting the right ventricle (RV), often overlooking its potential impact on the left ventricle (LV), particularly in patients with preserved LV ejection fraction (EF).
Methods: In this study, we investigate the late-stage effects of PAH on both electrical and mechanical functions, as well as their coupling, in each ventricle using the monocrotaline-treated rat model. Specifically, an integrative approach combining in-vivo epicardial potential mapping, in-situ video kinematic evaluation, and transcriptomic analysis was performed on rats injected with monocrotaline (MCT, n = 22) or saline solution (Physio, n = 16).
Results: Our findings reveal that PAH induces global increases in refractoriness from 88.8 ± 1.9 ms to 152.7 ± 3.9 ms and reductions in conduction velocity in the RV from 0.59 ± 0.01 m/s to 0.55 ± 0.01 m/s and from 0.28 ± 0.01 m/s to 0.25 ± 0.01 m/s along and across the fiber orientation, respectively. Notably, a significant increase in electromechanical delay from 24.9 ± 1.2 ms to 35.8 ± 5.2 ms was also observed in the RV. In the LV, PAH also results in increased refractoriness from 95.4 ± 3.0 ms to 140.0 ± 11.5 ms and reduced transverse conduction velocity by 14%, despite preserved EF. Transcriptomic analysis indicates that while both ventricles exhibit upregulation of extracellular matrix remodeling-related genes, the RV primarily shows downregulation of electromechanical-related genes. On the contrary, an upregulation of the inflammatory pathways was detected mainly in the LV, alongside a downregulation of mitochondrial metabolism-related genes.
Conclusions: Our findings revealed that both ventricles showed structural remodeling but only the RV underwent electromechanical alteration, while the LV displayed metabolic and inflammatory alteration. This was further validated by the preserved EF in the advanced stage of PAH. Our work highlights that a more comprehensive understanding of PAH pathophysiology can lead to targeted therapeutic strategies, challenging the conventional RV-centric perspective
What if a caring generation emerged from the climate crisis? A matter of discussion for development professionals|E se dalla crisi climatica emergesse una generazione che si prende cura? Una faccenda per professionisti dello sviluppo
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Living Materials for the Detection of Volatile Compounds to Monitor Agroindustrial Processes
No full textElectrochemical aptamer-based sensor for single-step quantification of glycated albumin in point-of-care diabetes and pre-diabetes management
No full textGlycated albumin (GA) provides unique advantages, offering a mid-term reflection of glycemic status over 2–3 weeks. GA is defined as the percentage ratio of glycated human serum albumin (GHSA) to total albumin, computed as the sum of GHSA and non-glycated human serum albumin (HSA). Current GA measurement methods rely on multi-step enzymatic assays requiring proteolytic digestion and separate quantification of GHSA and total albumin, limiting their applicability to point-of-care testing (POCT) platforms. In this study, we introduce an innovative electrochemical aptamer-based (E-AB) sensing method for GA monitoring. This approach enables single-step GA measurement using a single aptamer on a single electrode, without the need to quantify GHSA and HSA separately. This method leverages a novel analytical parameter, referred to as the “evolution”, which is based on the observation of distinct behaviors of the aptamer, revealing a rapid response for HSA and a slower and more complex interaction for GHSA. Using the evolution parameter, we developed a robust method for determining glycation ratios (10 %, 20 %, and 40 %) irrespective of total albumin concentration fluctuations within clinically relevant ranges (5.26–7.52 μM). The developed E-AB sensor shows statistically significant differences in the evolution signal (p – value < 0.05) across all three glycation ratios, with a coefficient of determination greater than 0.94. The sensor has a limit of detection (LOD) of 10.6 % across 9 replicate sensors. This work represents an advancement in GA monitoring, offering a rapid, accessible, and reliable tool suitable for decentralized diagnostic applications, including POCT and personal healthcare devices
Brain Magnetic Resonance Imaging Radiomic Signature and Machine Learning Model Prediction of Hepatic Encephalopathy in Adult Cirrhotic Patients
No full textBackground: Hepatic encephalopathy (HE) may arise as a possible consequence of cirrhosis. Magnetic resonance imaging (MRI) may reveal a T1-weighted hyperintensity in the globi pallidi, indicating the deposition of paramagnetic substances. The objective of this research was to implement a machine learning-based radiomic model to predict the diagnosis and severity of chronic hepatic encephalopathy in adult patients with cirrhosis. Methods: Between October 2018 and February 2020, brain magnetic resonance imaging (MRI) was conducted on adult patients, both with and without cirrhosis. The control population consisted of individuals who did not have a previous medical record of chronic liver disease. The grade of hepatic encephalopathy (HE) was determined by considering factors such as the presence of underlying liver disease, the severity of clinical symptoms, and the frequency of encephalopathic episodes. Radiomic texture analysis based on five machine learning algorithms was applied to axial T1-weighted MR images of bilateral lentiform nuclei. Using the area under the receiver operating characteristics curve, we determined the accuracy of the five machine learning-based algorithms in predicting the presence of HE and the HE grading. Results: The ultimate research cohort included 124 individuals, with 70 being cirrhotic patients and 54 being non-cirrhotic controls. Of the total number of patients, 38 had a previous occurrence of HE and, among them, 22 had a grade of HE greater than 1. The multilayer perceptron algorithm classified patients versus controls with an accuracy of 100%. The k-nearest neighbor (KNN) algorithm classified patients with or without HE with an accuracy of 76.5%. The multilayer perceptron algorithm classified HE grade (HE grade 1, HE grade ≥ 2) with an accuracy of 94.1%. Conclusions: The machine learning algorithms implemented provide a robust modeling technique for deriving valuable insights from brain MR images in cirrhotic patients and this can serve as an imaging tool valuable for the assessment of the burden of hepatic encephalopathy
Combined antiresorptive and new anabolic drug approach in osteogenesis imperfecta zebrafish models
No full textOsteogenesis imperfecta (OI) is a family of heritable collagen I–related skeletal disorders for which, to date, no definitive cure is available. Individuals with OI are mainly treated with bisphosphonates that enhance bone mass by inhibiting bone resorption. However, new strategies combining antiresorptive molecules with bone anabolic drugs are likely to provide valid alternatives for skeletal health, protecting physiological bone turnover. Recently, cellular stress has been identified as a therapeutic target in both dominant and recessive forms of OI characterized by overmodified collagen I. The chemical chaperone 4-phenylbutyrate (4PBA) successfully ameliorated cell homeostasis in both in vitro and in vivo OI models. In this study, dominant Chihuahua (Chi/+) and recessive p3h1−/− zebrafish OI models were treated for 2 mo either with the bisphosphonate alendronate (ALN) or with 4PBA or with a combination of the two. The treatment effect at the tissue level was evaluated by microCT analysis of the vertebral body, while histology and gene expression analyses allowed to dissect the consequences at a cellular level. Only ALN administration improved the vertebral thickness in the dominant Chi/+ model. The combined therapy synergistically improved osteoblast homeostasis and promoted the formation of mature extracellular collagen fibers in both models. All treatment conditions reduced osteoclast TRAP activity in Chi/+, whereas 4PBA and 4PBA + ALN had the opposite effect on p3h1−/−. Finally, 4PBA and the combination of ALN and 4PBA reduced osteocyte apoptosis only in p3h1−/−. Our data demonstrated for the first time in vivo a differential effect of the combination of an antiresorptive and a new anabolic compound in dominant and recessive OI zebrafish models, stressing the importance of identifying the specific causative molecular defect to define the best treatment option
Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI
No full textBackground: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. Methods: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. Results: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. Conclusions: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform
Preliminary Investigation of Real-Time Object Detection for Safe Robotic Navigation in Rehabilitation Scenarios
No full textObject detection, a cornerstone of computer vision powered by advancements in Convolutional Neural Networks (CNNs), plays a crucial role in enabling robots to perceive and interact with their surroundings, particularly in complex applications such as rehabilitation robotics. This paper investigates the viability of integrating the real-time-capable YOLOv11 architecture into the TIAGo robot for object detection tasks relevant to rehabilitation settings. Given the limitations of TIAGo's LiDAR - especially its fixed height, which hinders obstacle detection - this study explores whether YOLOv11 applied to RGB data from the robot's onboard camera can compensate for such perceptual gaps. We apply transfer learning to fine-tune various YOLOv11 variants (n, s, m, l, x) using a publicly available dataset of indoor scenes acquired with RGB-D camera, sensor frequently on board on assistive robot. Each model is evaluated in terms of detection accuracy (mAP50), inference time, and memory usage to assess its suitability for deployment under real-time constraints imposed by TIAGo's 30 Hz RGB-D camera. Considering the technical specifications of TIAGo, our results show that YOLOv11-s achieves the highest mAP50 (96.4%) but exceeds the frame rate requirement with an inference time of 34.6 ms, suggesting that optimization would be necessary for robotic purpose. The analysis highlights trade-offs between accuracy and computational efficiency and supports the feasibility of future integration of object detection within TIAGo's navigation framework for safe and effective rehabilitation assistance