1,604 research outputs found

    CANCER TREATMENT BY TARGETING HDAC4 TRANSLOCATION INDUCED BY MICROSECOND PULSED ELECTRIC FIELD EXPOSURE: MECHANISTIC INSIGHTS THROUGH KINASES AND PHOSPHATASES

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    Epigenetic modifications, arising from sub-cellular shifts in histone deacetylase (HDAC) activity and localization, present promising strategies for diverse cancer treatments. HDACs, enzymes responsible for post-translational histone modifications, induce these epigenetic changes by removing acetyl groups from ε-N-acetyl-lysine residues on histones, thereby suppressing gene transcription. Within the HDAC group, class IIa HDACs are notable for their responsiveness to extracellular signals, bridging the gap between external stimuli, plasma membrane, and genome through nuclear-cytoplasmic translocation. This localization offers two significant mechanisms for cancer treatment: nuclear accumulation of HDACs represses oncogenic transcription factors, such as myocyte-specific enhancer factor 2C (MEF2C), triggering various cell death pathways. Conversely, cytoplasmic HDAC accumulation acts similarly to HDAC inhibitors by silencing genes. My dissertation introduces an innovative approach for glioblastoma and breast cancer treatment by investigating the application of microsecond pulsed electric fields. It particularly focuses on HDAC4, a class IIa HDAC overexpressed in these cancers. Beyond demonstrating HDAC4 translocation, my research delves into the intricate roles of kinases and phosphatases, shedding light on the underlying factors governing HDAC4 translocation

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Immune contexture monitoring in solid tumors focusing on Head and Neck Cancer

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    Forti evidenze dimostrano una stretta interazione tra il sistema immunitario e lo sviluppo biologico e la progressione clinica dei tumori solidi. L'effetto che il microambiente immunitario del tumore può avere sul comportamento clinico della malattia è indicato come "immunecontexture". Nonostante ciò, l'attuale gestione clinica dei pazienti affetti da cancro non tiene conto di alcuna caratteristica immunologica né per la stadiazione né per le scelte terapeutiche. Il tumore della testa e del collo (HNSCC) rappresenta il 7° tumore più comune al mondo ed è caratterizzato da una prognosi relativamente sfavorevole e dall'effetto negativo dei trattamenti sulla qualità della vita dei pazienti. Oltre alla chirurgia e alla radioterapia, sono disponibili pochi trattamenti sistemici, rappresentati principalmente dalla chemioterapia a base di platino-derivati o dal cetuximab. L'immunoterapia è una nuova strategia terapeutica ancora limitata al setting palliativo (malattia ricorrente non resecabile o metastatica). La ricerca di nuovi biomarcatori o possibili nuovi meccanismi target è molto rilevante quindi nel contesto clinico dell'HNSCC. In questa tesi ci si concentrerà sullo studio di tre possibili popolazioni immunitarie pro-tumorali studiate nell'HNSCC: i neutrofili tumore-associati (TAN), le cellule B intratumorali con fenotipo immunosoppressivo e i T-reg CD8+. Particolare attenzione è data all'applicazione di moderne tecniche biostatistiche e bioinformatiche per riassumere informazioni complesse derivate da variabili cliniche e immunologiche multiparametriche e per validare risultati derivati ​​in situ, attraverso dati di espressione genica derivati da dataset pubblici. Infine, la seconda parte della tesi prenderà in considerazione progetti di ricerca clinica rilevanti, volti a migliorare l'oncologia di precisione nell'HNSCC, sviluppando modelli predittivi di sopravvivenza, confrontando procedure oncologiche alternative, validando nuovi classificatori o testando l'uso di nuovi protocolli clinici come l'uso dell'immunonutrizione.Strong evidences demonstrate a close interplay between the immune system and the biological development and clinical progression of solid tumors. The effect that the tumor immune microenvironment can have on the clinical behavior of the disease is referred as the immuno contexture. Nevertheless, the current clinical management of patients affected by cancer does not take into account any immunological features either for the staging or for the treatment choices. Head and Neck Cancer (HNSCC) represents the 7th most common cancer worldwide and it is characterized by a relatively poor prognosis and detrimental effect of treatments on the quality of life of patients. Beyond surgery and radiotherapy, few systemic treatments are available, mainly represented by platinum-based chemotherapy or cetuximab. Immunotherapy is a new therapeutical strategy still limited to the palliative setting (recurrent not resectable or metastatic disease). The search for new biomarkers or possible new targetable mechanisms is meaningful especially in the clinical setting of HNSCC. In this thesis a focus will be given on the study of three possible pro-tumoral immune populations studied in HNSCC: the tumor associated neutrophils (TAN), intratumoral B-cells with a immunosuppressive phenotype and the CD8+ T-regs. Biostatistical and bioinformatical techniques are applied to summarize complex information derived from multiparametric clinical and immunological variables and to validate in-situ derived findings through gene expression data of public available datasets. Lastly, the second part of the thesis will take into account relevant clinical research projects, aimed at improving the precision oncology in HNSCC developing survival prediction models, comparing alternative oncological procedures, validating new classifiers or testing the use of novel clinical protocols as the use of immunnutrition

    A critical review on latest innovations and future challenges of electrochemical technology for the abatement of organics in water

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    Updated water directives and ambitious targets like the United Nations’ Sustainable Development Goals (SDGs) have emerged in the last decade to tackle water scarcity and contamination. Although numerous strategies have been developed to remove water pollutants, it is still necessary to enhance their effectiveness against toxic and biorefractory organic molecules. Comprehensive reviews have highlighted the appealing features of the electrochemical technologies, but much progress has been made in recent years. In this timely review, a critical discussion on latest innovations and perspectives of the most promising electrochemical tools for wastewater treatment is presented. The work describes the performance of electrocatalytic anodes for direct electrochemical oxidation, the oxidation mediated by electrogenerated active chlorine, the electrocatalytic reduction as well as coupled approaches for synchronous anodic and cathodic processes combined with homogeneous and heterogeneous catalysis. The last section is devoted to the assessment of scale-up issues and the increase in the technology readiness level

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Parental risk and resilience: How does evidence inform child maltreatment prevention and reduction?

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    Child Maltreatment is a global concern with a sequela of negative consequences. The Risk and Resilience Ecological Framework is used to enable synthesis of evidence from two systematic reviews, A and B, on evidence of factors that influence parental child maltreatment. Review A comprises non-interventional, empirical studies to determine parental risk and protective factor interplay, lending support to causal and correlational links to child maltreatment. Review B synthesises evidence from intervention evaluations on parental risk factors and intervention provision for child maltreatment. A total of 128 studies, 68 observational studies in Review A and 60 intervention evaluations in Review B, were systematically reviewed. Quality appraisal did not lead to exclusion of studies. Review A findings mirror prior evidence and highlight nuances such as memories of parental childhood maltreatment as risk, emotional support for mothers and companionship support for fathers as protective, and demarcate maltreatment type-specific factors, especially for physical abuse and neglect. A low representation of fathers, under-research of unique factors for sexual and emotional abuse and of macro-level protective factors were identified. Review B provides comprehensive data on potentially effective intervention components including child development education and parental emotional regulation. Behaviour Change Techniques Framework helped identify potentially optimal delivery techniques including Instruction on how to perform a behaviour and Social support (unspecified). Lack of cultural representation, sparsity of interventions targeting fathers, over-reliance on self-reporting measures and under-examination of macro-level intervention components were identified as gaps in knowledge. Both reviews underline a call for consensus in definitions and avoidance of umbrella terms. A final synthesis elucidated the complex interplay of multiple influences on parental child maltreatment. Findings offer valuable insight to move the field forward, inform researchers, policy, and practice to strengthen parental resilience to prevent and reduce child maltreatment

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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