University of Udine
Archivio istituzionale della ricerca - Università degli Studi di UdineNot a member yet
91472 research outputs found
Sort by
Cytomegalovirus-RNA Accurately Identifies Clinically Significant Infection Needing Preemptive Therapy in Liver Transplanted Children: A Proof-of-Concept Study
Get PDFPreemptive therapy (PET) is safe and effective in controlling Cytomegalovirus (CMV) infection after pediatric liver transplantation (LT) and allows to observe the kinetics of quantitative CMV-DNA viral load till it reaches the treatment thresholds. While early detection of low-to-moderate CMV-DNA levels may not indicate active viral replication, awaiting the viral load to exceed the treatment threshold may lead to viremic breakthroughs and CMV disease. We assessed the capacity of quantitative CMV-RNA (UL21.5 mRNA) to identify active viral replication and its accuracy in identifying clinically significant CMV infection (csCMVi) needing PET in LT children. One-hundred and forty-four comparative quantitative CMV-RNA and CMV-DNA determinations were obtained from 12 children followed prospectically for 6 months after LT. Of 52 CMV-DNA-positive specimens, 17 (32%) were also CMV-RNA-positive, while CMV-RNA was undetectable in CMV-DNA-negative specimens. All children with csCMVi had early detectable CMV-RNA, peaking simultaneously to CMV-DNA (median CMV-DNA: 65 906 cp/mL; median CMV-RNA: 767 cp/mL); conversely, none of those with persistently low DNAemia proved CMV-RNA-positive. In this first pilot study, CMV-RNA had 100% sensitivity and specificity in identifying children needing PET after pediatric LT. The early detection of CMV-RNA marks significant CMV infection/reactivation, thus allowing to avoid unnecessary antiviral treatment
Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study
Get PDFIntroduction: Artificial Intelligence (AI) chatbots, which generate human-like responses based on extensive data, are becoming important tools in healthcare by providing information on health conditions, treatments, and preventive measures, acting as virtual assistants. However, their performance in aligning with clinical practice guidelines (CPGs) for providing answers to complex clinical questions on lumbosacral radicular pain is still unclear. We aim to evaluate AI chatbots' performance against CPG recommendations for diagnosing and treating lumbosacral radicular pain. Methods: We performed a cross-sectional study to assess AI chatbots' responses against CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinical questions based on these CPGs were posed to the latest versions (updated in 2024) of six AI chatbots: ChatGPT-3.5, ChatGPT-4o, Microsoft Copilot, Google Gemini, Claude, and Perplexity. The chatbots' responses were evaluated for (a) consistency of text responses using Plagiarism Checker X, (b) intra- and inter-rater reliability using Fleiss' Kappa, and (c) match rate with CPGs. Statistical analyses were performed with STATA/MP 16.1. Results: We found high variability in the text consistency of AI chatbot responses (median range 26%–68%). Intra-rater reliability ranged from “almost perfect” to “substantial,” while inter-rater reliability varied from “almost perfect” to “moderate.” Perplexity had the highest match rate at 67%, followed by Google Gemini at 63%, and Microsoft Copilot at 44%. ChatGPT-3.5, ChatGPT-4o, and Claude showed the lowest performance, each with a 33% match rate. Conclusions: Despite the variability in internal consistency and good intra- and inter-rater reliability, the AI Chatbots' recommendations often did not align with CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinicians and patients should exercise caution when relying on these AI models, since one to two-thirds of the recommendations provided may be inappropriate or misleading according to specific chatbots
Carolingian Collections of Gregory the Great’s Letters and the So-Called Collectio Pauli
No full textDeep resequencing unveils novel SNPs, InDels, and large structural variants for the clonal fingerprinting of sweet orange [Citrus sinensis (L.) Osbeck]
Get PDFThe large phenotypic variability characterizing the sweet orange [Citrus sinensis (L.) Osbeck] germplasm arose from spontaneous somatic mutations and led to the diversification of major groups (common, acidless, Navel, and pigmented). Substantial divergence also occurred within each varietal group. The genetic basis of such variability (i.e., ripening time, fruit shape, color, acidity, and sugar content) is largely uncharacterized, and therefore not exploitable for molecular breeding. Moreover, the clonal nature of all sweet orange accessions hinders the traceability of propagation material and fruit juice using low-density molecular markers. To build a catalog of somatic mutations in Italian varieties, 20 accessions were sequenced at high coverage. This allowed the identification of single nucleotide polymorphisms (SNPs), structural variants (SVs), and large hemizygous deletions, specific to clones or varietal groups. A panel of 239 SNPs was successfully used for genotyping 221 sweet orange accessions, allowing them to be clustered into varietal groups. Furthermore, genotyping of SNPs and SVs was extended to leaf and juice samples of commercial varieties belonging to two varietal groups (Moro and Tarocco) collected from 26 sites in Southern Italy, confirming the usefulness of the identified markers for the identification of specific clones. Interestingly, we found that the insertion of the transposable element VANDAL in the gene exons significantly affected the level of allelic-specific expression. Finally, the markers developed in the present work contribute to unraveling the origin and diversification of sweet oranges, representing a reliable and efficient molecular tool for the unambiguous fingerprint of somatic mutants and an asset for the traceability of orange plant material and fruit juice
Sostenibilità e pratiche leali di informazione
No full textNonostante la Strategia Farm to Fork assegni un ruolo centrale alle scelte d'acquisto dei consumatori finali per promuovere la sostenibilità nel settore alimentare, l'assenza di una definizione di alimento sostenibile e l'incertezza in merito agli indicatori ai quali ricondurre le diverse dimensioni di cui si compone tale concetto finiscono per favorire la diffusione di strategie di marketing opache, di cui non sempre è facile stabilire la lealtà. Nell'indagare la comunicazione di sostenibilità nel settore alimentare, sembra opportuno, quindi, riconsiderare il complesso rapporto fra il reg. UE n. 1169/2011 e la dir. 2005/29/CE, chiedendosi in quali termini la mancanza di indici di riferimento per la valutazione della sostenibilità dei prodotti alimentari possa incidere sull'applicazione dei parametri sui quali si struttura il divieto di realizzare pratiche sleali business to consumer. A tal proposito, risultano di particolare interesse le novità introdotte dalle dir. UE 2024/825, che, nel modificare le direttive 2005/29/CE e 2011/83/UE, ha inteso "responsabilizzare" i consumatori nella transizione verde. In particolare, alla luce delle ambiziose finalità del Green Deal si pone la necessità di comprendere se le disposizioni di cui alla dir. UE 2024/825 possano avere un'incidenza positiva sul profilo della tutela dei consumatori finali di alimenti rispetto alla diffusione di pratiche di greenwashing e di fairwashing, oppure se vi sia l'esigenza di apprestare soluzioni normative in parte diverse, che tengano conto della specificità del settore alimentare
Toward Human-AI Co-Creativity? An Exploration of Early Adopters’ Perspectives and Experiences with GenAI in the Creative Industries
No full textThis study investigates the evolving dynamics of human-AI co-creativity in the context of creative industries, focusing on the mutual shaping of Generative Artificial Intelligence (GenAI) systems and their users. Through 30 in-depth semi-structured interviews with early adopters in Italy, we explore the socio-technical practices, perceptions, and challenges associated with integrating GenAI tools into creative workflows. Our findings reveal an unprecedented level of interpretative flexibility in how creatives appropriate these technologies, with each user developing unique relationships and co-creation processes with AI. We identify diverse interpretations of GenAI’s role, ranging from autonomous creator to supportive tool, and highlight the opportunities and concerns surrounding its adoption. The study demonstrates that the extreme differentiation in socio-technical practices may be a defining characteristic of AI in creative contexts, potentially leading to novel interpretative frameworks and emergent properties. This research contributes to our understanding of the complex interplay between human creativity and artificial intelligence, offering insights into the future landscape of cultural production in an AI-augmented world
Writing a gendered history of English language teaching in Italy, 1861-1922
No full textAfter the Unification of Italy in late nineteenth century, state schooling was made partially accessible to girls, at the same time as women were granted some limited possibilities to become teachers in the new national education sector. This chapter draws on research conducted in the fields of the historiography of Italian education sector and the historiography of language teaching with the aim of delineating the broader historical context in which women’s careers as language teachers in post-Unification Italy can be studied. The first section reviews the debates concerning girls and women entering the new school system after 1861. The second section offers a more focused examination of the role and status, in the new hierarchy of Italian school teachers, of male and female modern foreign language teachers in the period between 1861 and 1922
The impact of pole use on vertical cost of transport and foot force during uphill treadmill walking before and after a simulated trail running competition
No full textPurpose: Trail running poles are widely used among trail runners but their effects on cost of transport and biomechanics under fatigued conditions remains understudied. This study aimed to evaluate the effects of pole use on the walking vertical cost of transport (CoTvert) and foot force (FF) before and after a simulated trail running competition (STRC). Methods: Sixteen trail runners (V ̇O2: 61.0 ± 8.3 ml/kg/min; ITRA performance index: 634 ± 107 points) performed walking trials with (PW) and without poles (CW) on an incline treadmill (18.6 degrees) before (PRE) and after (POST) a STRC. The course covered 31.2 km with 2086 m of elevation gain and was completed under race-simulated conditions. CoTvert and FF were measured using instrumented insoles, and axial pole force was recorded during PW. Results: The STRC was completed in 4:25:33 ± 0:39:51 (hh:mm:ss) at an average heart rate (HR) of 81.4 ± 3.8% of HRmax. Walking CoTvert showed significant time and condition effects, with higher values without poles at POST (+ 2.50 ± 2.62%, p = 0.0183). Rating of perceived exertion (RPE) was lower with poles at both PRE and POST (p = 0.0022 and p = 0.0187, respectively). FF was significantly lower with poles at PRE (p = 0.0140) and POST (p < 0.0001). Poling force decreased at POST compared to PRE (p = 0.0026). Conclusions: The main findings are that (1) CoTvert increases after STRC; (2) walking CoTvert and FF are lower with pole use and (3) upper limb force decreases at POST. These results support the use of poles in long-lasting events to reduce CoT, redistribute workload and possibly mitigate the fatigue effects
Algorithmic Drift: A simulation framework to study the effects of recommender systems on user preferences
No full textUser navigation on social media platforms is often driven by recommendation algorithms. A growing body of literature questions whether these recommendation systems may exacerbate detrimental phenomena, perpetrate intrinsic biases, and alter user preferences in the long-term. Driven by this premise, the present study formalizes the concept of “algorithmic drift”, further introducing a novel framework and two metrics to quantify it. Our methodology involves a simulation process that models user behavior through random walks, reflecting user navigation under the influence and guidance of recommendation systems. This approach highlights that each user may respond differently to such stimuli, varying in both resistance to recommendation influence and inertia in selecting new steps in the random walk. The proposed metrics measure the drift in user behavior and item consumption over time in the random walks. We conduct a comprehensive evaluation over both synthetic and real-world datasets to validate the framework's ability to measure drift across different parameter settings. All code and data used in our experimentation are publicly accessible online.