Sull'autodepurazione degli ortaggi

    &nbsp

Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective

Combining a hardware approach with a multiple classifier method can deeply improve system performance, since the multiple classifier system can successfully enhance the classification accuracy with respect to a single classifier, and a hardware implementation would lead to systems able to classify samples with high throughput and with a short latency. To the best of our knowledge, no paper in the literature takes into account the multiple classifier scheme as additional design parameter, mainly because of lack of efficient hardware combiner architecture. In order to fill this gap, in this paper we will first propose a novel approach for an efficient hardware implementation of the majority voting combining rule. Then, we will illustrate a design methodology to suitably embed in a digital device a multiple classifier system having Decision Trees as base classifiers and a majority voting rule as combiner. Bagging, Boosting and Random Forests will be taken into account. We will prove the effectiveness of the proposed approach on two real case studies related to Big Data issues

Psychosocial burden and professional and social support in patients with hereditary transthyretin amyloidosis (ATTRv) and their relatives in Italy

Hereditary transthyretin amyloidosis (hATTR), alias ATTR variant (ATTRv) is a severe and disabling disease causing sensory and motor neuropathy, autonomic dysfunction, and cardiomyopathy. The progressive decline of patient's functional autonomy negatively affects the patient's quality of life and requires increasing involvement of relatives in the patient's daily life. Family caregiving may become particularly demanding when the patient is no longer able to move independently. This study is focused on the psychosocial aspects of ATTRv from the patient and relative perspectives. In particular, it explored: the practical and psychological burdens experienced by symptomatic patients with ATTRv and their key relatives and the professional and social network support they may rely on; whether burden varied in relation to patients' and relatives' socio-demographic variables, patients' clinical variables, and perceived professional and social network support; and, any difference in burden and support between patients and their matched relatives

Management of the corpse with suspect, probable or confirmed COVID-19 respiratory infection \u2013 Italian interim recommendations for personnel potentially exposed to material from corpses, including body fluids, in morgue structures and during autopsy practice.

non disponibil

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Clinical autonomic nervous system laboratories in Europe: a joint survey of the European Academy of Neurology and the European Federation of Autonomic Societies

© 2022 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Background and purpose: Disorders of the autonomic nervous system (ANS) are common conditions, but it is unclear whether access to ANS healthcare provision is homogeneous across European countries. The aim of this study was to identify neurology-driven or interdisciplinary clinical ANS laboratories in Europe, describe their characteristics and explore regional differences. Methods: We contacted the European national ANS and neurological societies, as well as members of our professional network, to identify clinical ANS laboratories in each country and invite them to answer a web-based survey. Results: We identified 84 laboratories in 22 countries and 46 (55%) answered the survey. All laboratories perform cardiovascular autonomic function tests, and 83% also perform sweat tests. Testing for catecholamines and autoantibodies are performed in 63% and 56% of laboratories, and epidermal nerve fiber density analysis in 63%. Each laboratory is staffed by a median of two consultants, one resident, one technician and one nurse. The median (interquartile range [IQR]) number of head-up tilt tests/laboratory/year is 105 (49-251). Reflex syncope and neurogenic orthostatic hypotension are the most frequently diagnosed cardiovascular ANS disorders. Thirty-five centers (76%) have an ANS outpatient clinic, with a median (IQR) of 200 (100-360) outpatient visits/year; 42 centers (91%) also offer inpatient care (median 20 [IQR 4-110] inpatient stays/year). Forty-one laboratories (89%) are involved in research activities. We observed a significant difference in the geographical distribution of ANS services among European regions: 11 out of 12 countries from North/West Europe have at least one ANS laboratory versus 11 out of 21 from South/East/Greater Europe (p = 0.021). Conclusions: This survey highlights disparities in the availability of healthcare services for people with ANS disorders across European countries, stressing the need for improved access to specialized care in South, East and Greater Europe.info:eu-repo/semantics/publishedVersio

EFAS/EAN survey on the influence of the COVID-19 pandemic on European clinical autonomic education and research

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Purpose: To understand the influence of the coronavirus disease 2019 (COVID-19) pandemic on clinical autonomic education and research in Europe. Methods: We invited 84 European autonomic centers to complete an online survey, recorded the pre-pandemic-to-pandemic percentage of junior participants in the annual congresses of the European Federation of Autonomic Societies (EFAS) and European Academy of Neurology (EAN) and the pre-pandemic-to-pandemic number of PubMed publications on neurological disorders. Results: Forty-six centers answered the survey (55%). Twenty-nine centers were involved in clinical autonomic education and experienced pandemic-related didactic interruptions for 9 (5; 9) months. Ninety percent (n = 26/29) of autonomic educational centers reported a negative impact of the COVID-19 pandemic on education quality, and 93% (n = 27/29) established e-learning models. Both the 2020 joint EAN-EFAS virtual congress and the 2021 (virtual) and 2022 (hybrid) EFAS and EAN congresses marked higher percentages of junior participants than in 2019. Forty-one respondents (89%) were autonomic researchers, and 29 of them reported pandemic-related trial interruptions for 5 (2; 9) months. Since the pandemic begin, almost half of the respondents had less time for scientific writing. Likewise, the number of PubMed publications on autonomic topics showed the smallest increase compared with other neurological fields in 2020-2021 and the highest drop in 2022. Autonomic research centers that amended their trial protocols for telemedicine (38%, n = 16/41) maintained higher clinical caseloads during the first pandemic year. Conclusions: The COVID-19 pandemic had a substantial negative impact on European clinical autonomic education and research. At the same time, it promoted digitalization, favoring more equitable access to autonomic education and improved trial design.info:eu-repo/semantics/publishedVersio

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project