Archivio Istituzionale della Ricerca - Università degli Studi di Pavia
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Studi strutturali e computazionali di elettroliti solidi per batterie ricaricabili agli ioni sodio
No full textMental health, coping and related risk factors during the first 2 years of the COVID-19 pandemic in children: Nationally representative, multi-wave, cross-sectional results from 12 countries from the global COH-FIT study
No full textFew multinational studies have assessed risk factors and coping strategies associated with the impact of the COVID-19 pandemic on children’s mental health over time. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is the largest transcontinental, multi-wave, cross-sectional survey collecting multi-nation data on well-being and psychopathology during the pandemic. We analyzed country-specific, general-population-based, representative COH-FIT data of 6067 children aged 6–13 years from 12 countries across repeated cross-sectional waves over a period of >2 years (Apr/2020–May/2022), addressing through current and retrospective assessment pre- to intra-pandemic changes in well-being (WHO-5) and general psychopathology scores (Pc) (0–100) in relation to COVID-related deaths, stringency index, eight a priori risk factors, and 16 coping strategies in different responders at each wave. From pre- to intra-pandemic, WHO-5 scores decreased (−4.59, 95 %CI=−6.18 to −2.99, p < 0.001), while PC-scores increased (+6.68, 95 %CI=4.48–8.88, p < 0.001) significantly, following distinct time patterns but both returning to near pre-pandemic levels. Changes in both scores varied by country. WHO-5 scores correlated strongly with PC and subdomain scores. Both score changes were significantly but minimally associated to COVID-19 deaths/stringency index. The proportion of children screening positive for depression increased from 3.9 % to 8.3 % (χ2=145.70, p < 0.001) and for major depression from 0.6 % to 2.2 % (χ2=68.64, p < 0.001) intrapandemic. WHO-5 and PC-score changes were significantly associated with female gender, school closure, and pre-existing physical and mental conditions, with cumulative effects. The five most frequently endorsed coping strategies were family contact (85.2 %), friends (67.3 %), outdoor play (54.0 %), pet interaction (51.5 %), and internet use (50.9 %). Identified risk groups and coping strategies can inform targeted interventions and global public health policy. Trial Registration: ClinicalTrials.gov; Identifier: NCT0438347
Asymptotic location and shape of the optimal favorable region in a Neumann spectral problem
No full textWe complete the study concerning the minimization of the positive principal eigenvalue associated with a weighted Neumann problem settled in a bounded regular domain Ω ⊂ R N , N ≥ 2 , for the weight varying in a suitable class of sign-changing bounded functions. Denoting with u the optimal eigenfunction and with D its super-level set, corresponding to the positivity set of the optimal weight, we prove that, as the measure of D tends to zero, the unique maximum point of u , P ∈ ∂ Ω , tends to a point of maximal mean curvature of ∂Ω. Furthermore, we show that D is the intersection with Ω of a C 1 , 1 nearly spherical set, and we provide a quantitative estimate of the spherical asymmetry, which decays like a power of the measure of D . These results provide, in the small volume regime, a fully detailed answer to some long-standing questions in this framework
PHARMACOLOGICAL AND CLINICAL BIOMARKERS IN PHARMACORESISTANT EPILEPSY: FROM THERAPEUTIC MONITORING TO TAILORED TREATMENTS
No full textA multiscale dosimetric approach to BNCT for Glioblastoma Multiforme: a theoretical and experimental journey from cell microdosimetry to patient treatment
No full textBiomarkers Identifying Tendency to Suicide (BITS): detection of biomarkers in adolescents with suicidal ideation or suicidal behavior for early prevention or intervention. A prospective cohort study
No full textAdolescent suicidality represents a major and persistent public health concern, with sustained increases in suicidal ideation and behaviors observed in recent years. Although suicide risk is widely conceptualized as a complex and multifactorial phenomenon, biological vulnerability factors remain insufficiently integrated into adolescent clinical assessment frameworks. In particular, neuroinflammatory processes, hypothalamic–pituitary–adrenal (HPA) axis dysregulation, and alterations in blood-brain barrier integrity have emerged as potentially relevant but underexplored contributors to suicidality. This prospective clinical cohort study adopts an integrative, multi-method approach to enhance the characterization of suicidality among help-seeking adolescents. We hypothesize that adolescents with suicidal ideation or suicidal behaviors will show distinct peripheral biomarker profiles compared with adolescents without suicidal concerns. The primary analyses focus on group differences at baseline in inflammatory cytokines, markers of systemic inflammation, HPA-axis activity, and blood-brain barrier-related biomarkers, as well as measures of suicidality severity and global functioning. Secondary outcomes include associations between biomarker levels and psychopathological severity and functioning, and longitudinal changes in biological and clinical measures, including transitions in suicidality status, over a 2-month follow-up. Participants will undergo a comprehensive psychodiagnostic assessment and standardized peripheral blood sampling at baseline and follow-up. By integrating biological measures with detailed psychological and behavioral profiling within a longitudinal design, this protocol aims to establish a framework for biomarker-informed risk stratification of high-risk adolescents, ultimately informing more precise and developmentally sensitive suicide prevention strategies
A Deep Learning Framework for Building Damage Assessment Using VHR SAR and Geospatial Data: Demonstration on the 2023 Türkiye Earthquake
No full textBuilding damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite imagery is commonly used for disaster mapping, its effectiveness is often hampered by cloud cover or the absence of pre-event acquisitions. To overcome these challenges, we introduce a novel multimodal deep learning (DL) framework for detecting building damage using single-date very high resolution (VHR) Synthetic Aperture Radar (SAR) imagery from the Italian Space Agency's (ASI) COSMO-SkyMed (CSK) constellation, complemented by auxiliary geospatial data. Our method integrates SAR image patches, OpenStreetMap (OSM) building footprints, digital surface model (DSM) data, and structural and exposure attributes from the Global Earthquake Model (GEM) to improve detection accuracy and contextual interpretation. Unlike existing approaches that depend on pre- and post-event imagery, our model utilizes only post-event data, facilitating rapid deployment in critical scenarios. The framework's effectiveness is demonstrated using a new dataset from the 2023 Kahramanmaraş earthquake in Türkiye, covering multiple cities with diverse urban settings. Results highlight that incorporating geospatial features significantly enhances detection performance and generalizability to previously unseen areas. By combining SAR imagery with detailed vulnerability and exposure information, our approach provides reliable and rapid building damage assessments without the dependency from available pre-event data. Moreover, the automated and scalable data generation process ensures the framework's applicability across diverse disaster-affected regions, underscoring its potential to support effective disaster management and recovery efforts
Project I: Development of a virus-free cell assay for the evaluation of viral protease inhibitors Project II: Role of microRNA hsa-mir-1307-3p in genome instability
No full textOne of the most promising strategies to counteract viral infections is the identification of molecular targets that play essential roles in viral life cycle. The development of new inhibitors involves in vitro screening of libraries of compounds, evaluating their activity against recombinant enzymes through biochemical assays. Hit compounds are then tested in cell-based systems to assess their cytotoxicity and antiviral efficacy. However, this step requires handling live viruses, which involves high biosafety levels facilities that are beyond the reach of most laboratories. In this PhD thesis, we have developed and optimized a novel virus-free, luminometric cell-based assay for evaluating inhibitors targeting two clinically relevant proteases: Mpro, the main protease of SARS-CoV-2, and NS3, the serine protease of West Nile Virus. In our assay, we engineered the NanoBiT luciferase joining the two subunits of the enzyme with a flexible spacer containing a cleavage site specific either for Mpro or NS3. In absence of the protease, the intact luciferase emits a bioluminescent signal, while expression of the viral protease leads to cleavage of the spacer, resulting in a loss of luciferase activity.
In this system, the presence of a protease inhibitor prevents the cleavage of the target
luciferase, thereby restoring the luminescent signal proportionally to the inhibitor’s potency. Our assay was used to evaluate two well-characterized Mpro inhibitors, GC376 and Nirmatrelvir and a small library of novel compounds. Our virus-free systems provide a powerful and versatile platform for the screening and characterization of protease inhibitors, with potential applications in both academic research and pharmaceutical development.
MicroRNAs (miRNAs) are key regulators of gene expression, and their dysregulation contributes to both tumorigenesis and cellular response to viral infections. Due to their involvement in cancer progression and viral infection, miRNAs are promising therapeutic targets. Among them, the human miRNA hsa-miR-1307-3p has been reported to act as an oncogenic miRNA. The aim of this thesis was to investigate the role of hsa-miR-13073p in genomic stability and its involvement in regulation of cellular stress response. We demonstrated that inhibition of hsa-miR-1307-3p affects cell viability, activates DNA damage response markers, and decreases mitotic activity. To further investigate the role
of hsa-miR-1307-3p, two stable cell lines were generated: one overexpressing hsa-miR1307-3p and another carrying a non-targeting control sequence. Cells treated with the hsa-miR-1307-3p inhibitor in combination with etoposide, a potent topoisomerase II inhibitor that induces double-strand breaks, showed a synergistic cytotoxic effect,
supporting the therapeutic potential of targeting hsa-miR-1307-3p in combination with DNA-damaging agents.
In silico analysis predicted high‐confidence binding sites for hsa-miR-1307-3p within the 5’‐UTR of three proteins involved in DNA:RNA hybrids metabolism: DDX5, DDX1, and RNaseH2B. We demonstrated that overexpression of hsa-miR-1307-3p significantly reduced the expression of DDX5 and RNaseH2B, suggesting a role in DNA:RNA hybrids processing. Immunofluorescence assays confirmed this effect, showing an accumulation of DNA:RNA hybrids in cells overexpressing hsa-miR-1307-3p, while inhibition of the miRNA decreased hybrids levels. Finally, flow cytometry analysis excluded cell cycle arrest as the underlying cause of altered protein expression, confirming a direct regulatory role for hsa-miR-1307-3p.
Overall, this work identifies hsa-miR-1307-3p as a possible regulator of genomic stability and DNA damage response. These findings expand our understanding of its oncogenic role and support its potential as a therapeutic target in combination with genotoxic agents.One of the most promising strategies to counteract viral infections is the identification of molecular targets that play essential roles in viral life cycle. The development of new inhibitors involves in vitro screening of libraries of compounds, evaluating their activity against recombinant enzymes through biochemical assays. Hit compounds are then tested in cell-based systems to assess their cytotoxicity and antiviral efficacy. However, this step requires handling live viruses, which involves high biosafety levels facilities that are beyond the reach of most laboratories. In this PhD thesis, we have developed and optimized a novel virus-free, luminometric cell-based assay for evaluating inhibitors targeting two clinically relevant proteases: Mpro, the main protease of SARS-CoV-2, and NS3, the serine protease of West Nile Virus. In our assay, we engineered the NanoBiT luciferase joining the two subunits of the enzyme with a flexible spacer containing a cleavage site specific either for Mpro or NS3. In absence of the protease, the intact luciferase emits a bioluminescent signal, while expression of the viral protease leads to cleavage of the spacer, resulting in a loss of luciferase activity.
In this system, the presence of a protease inhibitor prevents the cleavage of the target
luciferase, thereby restoring the luminescent signal proportionally to the inhibitor’s potency. Our assay was used to evaluate two well-characterized Mpro inhibitors, GC376 and Nirmatrelvir and a small library of novel compounds. Our virus-free systems provide a powerful and versatile platform for the screening and characterization of protease inhibitors, with potential applications in both academic research and pharmaceutical development.
MicroRNAs (miRNAs) are key regulators of gene expression, and their dysregulation contributes to both tumorigenesis and cellular response to viral infections. Due to their involvement in cancer progression and viral infection, miRNAs are promising therapeutic targets. Among them, the human miRNA hsa-miR-1307-3p has been reported to act as an oncogenic miRNA. The aim of this thesis was to investigate the role of hsa-miR-13073p in genomic stability and its involvement in regulation of cellular stress response. We demonstrated that inhibition of hsa-miR-1307-3p affects cell viability, activates DNA damage response markers, and decreases mitotic activity. To further investigate the role
of hsa-miR-1307-3p, two stable cell lines were generated: one overexpressing hsa-miR1307-3p and another carrying a non-targeting control sequence. Cells treated with the hsa-miR-1307-3p inhibitor in combination with etoposide, a potent topoisomerase II inhibitor that induces double-strand breaks, showed a synergistic cytotoxic effect,
supporting the therapeutic potential of targeting hsa-miR-1307-3p in combination with DNA-damaging agents.
In silico analysis predicted high‐confidence binding sites for hsa-miR-1307-3p within the 5’‐UTR of three proteins involved in DNA:RNA hybrids metabolism: DDX5, DDX1, and RNaseH2B. We demonstrated that overexpression of hsa-miR-1307-3p significantly reduced the expression of DDX5 and RNaseH2B, suggesting a role in DNA:RNA hybrids processing. Immunofluorescence assays confirmed this effect, showing an accumulation of DNA:RNA hybrids in cells overexpressing hsa-miR-1307-3p, while inhibition of the miRNA decreased hybrids levels. Finally, flow cytometry analysis excluded cell cycle arrest as the underlying cause of altered protein expression, confirming a direct regulatory role for hsa-miR-1307-3p.
Overall, this work identifies hsa-miR-1307-3p as a possible regulator of genomic stability and DNA damage response. These findings expand our understanding of its oncogenic role and support its potential as a therapeutic target in combination with genotoxic agents
Development of deep learning-based methods for signal and image processing in ultrasound medical imaging
No full textQuesto elaborato tratta lo sviluppo e la validazione di metodologie basate su deep learning (DL) applicate alle diverse fasi della catena di acquisizione e formazione delle immagini ecografiche, con l’obiettivo di migliorarne la qualità e l’automazione, dal segnale grezzo fino all’interpretazione clinica. L’imaging a ultrasuoni rappresenta una delle tecniche diagnostiche più diffuse per la sua natura non invasiva, portabilità e capacità di acquisizione in tempo reale. Tuttavia, il processo che trasforma gli echi acustici in immagini clinicamente utili è complesso e soggetto a diverse limitazioni, tra cui il rumore, i compromessi tra qualità e complessità hardware e la forte dipendenza dall’esperienza dell’operatore. I metodi tradizionali di elaborazione, basati su modelli fisici deterministici, offrono robustezza e interpretabilità, ma si adattano con difficoltà a condizioni operative variabili e vincoli tecnici. In questo contesto, il DL ha introdotto un approccio data-driven capace di apprendere relazioni complesse direttamente dai dati e di ottimizzare in modo congiunto le diverse fasi della catena di imaging. Le reti neurali possono migliorare la qualità del segnale e dell’immagine, ridurre il rumore e automatizzare compiti diagnostici, avvicinandosi alle prestazioni di operatori esperti. Una prima linea di ricerca ha riguardato la soppressione del rumore elettronico di commutazione, un artefatto introdotto dall’hardware che degrada i segnali a radiofrequenza (RF). È stato sviluppato un approccio DL nel dominio delle frequenze capace di riconoscere e rimuovere il rumore direttamente dai dati grezzi, supportato da un protocollo sperimentale per la raccolta di segnali rumorosi e puliti. Successivamente, il DL è stato applicato al miglioramento dell’imaging a apertura sintetica (Synthetic Aperture), una tecnica che combina più trasmissioni per aumentare la risoluzione spaziale. Sono stati proposti due approcci complementari: il primo, di tipo beamforming, mappa i dati RF acquisiti con configurazioni semplificate in immagini equivalenti a quelle di sistemi più complessi; il secondo, invece, opera nel dominio dell’immagine, migliorando le ricostruzioni ottenute con metodi convenzionali attraverso una mappatura da immagine a immagine. Un’ulteriore area di studio ha riguardato l’imaging tridimensionale con array sparsi, dove è stato introdotto un metodo di fusione adattiva basato su DL per combinare i risultati di diversi beamformer, ottimizzando la qualità e la coerenza delle ricostruzioni volumetriche. Infine, la ricerca ha affrontato la segmentazione automatica delle immagini ecocardiografiche, fondamentale per la valutazione della funzione cardiaca. Sono stati esplorati modelli DL, in particolare architetture basate su Vision Transformer, che mostrano una maggiore capacità di catturare relazioni spaziali a lungo raggio e di garantire coerenza anatomica rispetto alle reti convolutive tradizionali. Nel complesso, la tesi dimostra come l’integrazione del DL nella catena di imaging ecografico possa rendere l’ecografia una tecnica più accurata, adattiva e automatizzata, migliorando la qualità diagnostica e riducendo la dipendenza dall’operatore.This dissertation focuses on the development and validation of deep learning (DL)-based methodologies applied to the different stages of the ultrasound imaging acquisition and reconstruction chain, with the goal of improving image quality and automation, from raw signal processing to clinical interpretation.
Ultrasound imaging is one of the most widespread diagnostic modalities due to its non-invasiveness, portability, and real-time acquisition capability. However, the process that transforms acoustic echoes into clinically meaningful images is complex and subject to several limitations, including noise contamination, trade-offs between image quality and hardware complexity, and strong dependence on the operator’s experience. Traditional signal processing methods, typically based on deterministic physical models, are robust and interpretable but often struggle to adapt to varying operating conditions and hardware constraints.
In this context, deep learning introduces a data-driven approach capable of learning complex relationships directly from data and jointly optimizing the different stages of the imaging chain. Neural networks can enhance signal and image quality, reduce noise, and automate diagnostic tasks, achieving performance levels comparable to those of experienced operators.
A first line of research addressed the suppression of electronic switching noise, an artifact introduced by the acquisition hardware that degrades the radio-frequency (RF) signals. A DL-based approach operating in the frequency domain was developed to identify and remove this noise directly from raw data, supported by an experimental protocol designed to collect paired noisy and clean signals.
Subsequently, DL was applied to Synthetic Aperture (SA) imaging, a technique that combines multiple transmissions to improve spatial resolution. Two complementary approaches were proposed: the first, a beamforming-based method, maps RF data acquired with simplified configurations into images equivalent to those obtained with more complex systems; the second operates in the image domain, enhancing reconstructions produced by conventional methods through image-to-image mapping.
Another research direction focused on three-dimensional imaging using sparse arrays, where a DL-based adaptive fusion framework was introduced to combine the outputs of multiple beamformers, optimizing the quality and consistency of volumetric reconstructions.
Finally, the research addressed the automatic segmentation of echocardiographic images, a key step for the quantitative assessment of cardiac function. DL models were explored, with particular attention to Vision Transformer architectures, which demonstrated superior ability to capture long-range spatial dependencies and ensure anatomical consistency compared to traditional convolutional networks.
Overall, the dissertation demonstrates how integrating deep learning into the ultrasound imaging chain can make ultrasound a more accurate, adaptive, and automated modality, enhancing diagnostic quality while reducing operator dependency
Transatlantic Crossings for Experts’ Training: Italian Agricultural Economics’ Round-trip from Naples to the United States
No full textThis report focuses on a group of Italian agricultural experts based at the University of Naples, headed by economist Manlio Rossi-Doria, during the postwar years. Rossi-Doria joined transatlantic knowledge exchange networks associated with the American initiative to enhance cultural and intellectual ties in Western European countries during the early Cold War.
In the latter half of the 1950s, with support from the Rockefeller Foundation, he became a visiting fellow at the Department of Agriculture at the University of California, Berkeley. During this period, he and a team of close associates established a Center for Advanced Training and Research in Agricultural Economics at the University of Naples, inspired by similar research centers in the United States. The Ford Foundation provided financial backing for this initiative, and the Center officially opened in 1959. This research report aims to understand how this Italian expert and his disciples, with essential support from American philanthropic institutions, revitalized and internationalized economics and social sciences in Italy, while also contributing to the establishment of postgraduate education and research programs in that country