Why Companies Do Not Renew Their EMAS Registration? An Exploratory Research

The Eco-Management and Audit Scheme (EMAS) is the official Environmental Management System (EMS) issued by the European Union (EU). Italy is the country where EMAS is most widespread, accounting for over 1000 registered organizations. Since entry into the force of the Regulation in 1997, the number of registrations has constantly grown until 2008, when the figures started to drop. The phenomena are due to both the decrease of the annual registration rate and the lack of renewals. According to the Italian Institute for Environmental Protection and Research (ISPRA), in recent years, an increasing number of organizations decided to withdraw from EMAS registration. The purpose of this paper is to analyze the reasons of this negative trend. The first step consisted of a literature review concerning the main barriers, difficulties, and costs incurred by EMAS-registered organizations. Subsequently, this information was integrated with data about the evolution of EMAS registrations and the results of a previous survey, which involved the entire population of registered firms. The present exploratory research highlighted economic and operational domains concerning the cancellation trends that deserve a deeper investigation, which will be conducted through a questionnaire addressed to Italian firms that did not renew the registration in the last lustrum. The intended output will allow us to identify stakeholders' priority intervention areas in order to suggest an operative strategy to reduce EMAS cancellation rates, addressed to Member States (MS) Competent Bodies

life cycle approach a critical review in the tourism sector

The concept of sustainability comes from the scientific literature that defines sustainable the management of a resource if, being known his ability to play, is not exceeded in its exploitation beyond a certain threshold defined critical natural capital [1]. In the past, economic growth has been achieved at the expense of natural resource depletion, without stocks being allowed to regenerate. Ecosystems have been widely degraded and biodiversity has been lost at an unprecedented pace [2,3]. In this sense, the concept of sustainability and development are not compatible with the degradation of heritage and natural resources (non-renewable and potentially exhaustible), but also with concepts mostly related to ethical and social values such as the violation of the human dignity and freedom, with poverty and economic decline and the lack of recognition of the rights and equal opportunities [4,5]. The three basic components of sustainability are therefore: - the ability to generate income and employment for the people's livelihood (economic sustainability) - the ability to generate conditions of human well-being, understood as the territory security, an equal distribution of health and civil rights (social sustainability); - the ability to maintain the same level of quality and reproducibility of natural resources (environmental sustainability

Superinfections caused by carbapenem-resistant Enterobacterales in hospitalized patients with COVID-19: a multicentre observational study from Italy (CREVID Study)

Objectives To describe clinical characteristics and outcomes of COVID-19 patients who developed secondary infections due to carbapenem-resistant Enterobacterales (CRE). Methods Retrospective observational study including COVID-19 patients admitted to 12 Italian hospitals from March to December 2020 who developed a superinfection by CRE. Superinfection was defined as the occurrence of documented bacterial infection >48 h from admission. Patients with polymicrobial infections were excluded. Demographic, clinical characteristics and outcome were collected. Isolates were classified as KPC, metallo-beta-lactamase (MBL) and OXA-48-producing CRE. A Cox regression analysis was performed to identify factors independently associated with 30 day mortality. Results Overall, 123 patients (median age 66 years, IQR 59-75) were included. The majority of infections occurred in the ICU (81, 65.9%), while 42 (34.1%) in medical wards. The most common types of infection were bloodstream infections (BSI) (n = 64, 52%), followed by urinary-tract infections (UTI) (n = 28, 22.8%), hospital-acquired/ventilator-associated pneumonia (HAP/VAP) (n = 28, 22.8%), intra-abdominal infections (n = 2, 1.6%) and skin infections (n = 1, 0.8%). Sixty-three (51.2%) infections were caused by KPC-, 54 (43.9%) by MBL-, and 6 (4.8%) by OXA-48-producing CRE. Thirty-day mortality was 33.3% (41/123). On Cox regression analysis, HAP/VAP compared with UTI (HR 7.23, 95% CI 2.09-24.97, P = 0.004), BSI compared with UTI (HR 3.96, 95% CI, 1.33-11.77, P = 0.004), lymphopenia on admission (HR 3, 95% CI 1.44-6.26, P = 0.003) and age (HR 1.05, 95% CI 1.02-1.08, P = 0.002) were predictors of 30 day mortality. Conclusions Superinfections by CRE were associated with high risk of 30 day mortality in patients with COVID-19. HAP/VAP was the strongest predictor of death in these patients

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

BACKGROUND Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). METHODS We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

SIMPLE SUMMARY: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. ABSTRACT: Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

Italian real life experience with ibrutinib: Results of a large observational study on 77 relapsed/refractory mantle cell lymphoma

Although sometimes presenting as an indolent lymphoma, mantle cell lymphoma (MCL) is an aggressive disease, hardly curable with standard chemo-immunotherapy. Current approaches have greatly improved patients' outcomes, nevertheless the disease is still characterized by high relapse rates. Before approval by EMA, Italian patients with relapsed/refractory MCL were granted ibrutinib early access through a Named Patient Program (NPP). An observational, retrospective, multicenter study was conducted. Seventyseven heavily pretreated patients were enrolled. At the end of therapy there were 14 complete responses and 14 partial responses, leading to an overall response rate of 36.4%. At 40 months overall survival was 37.8% and progression free survival was 30%; disease free survival was 78.6% at 4 years: 11/14 patients are in continuous complete response with a median of 36 months of follow up. Hematological toxicities were manageable, and main extra-hematological toxicities were diarrhea (9.4%) and lung infections (9.0%). Overall, 4 (5.2%) atrial fibrillations and 3 (3.9%) hemorrhagic syndromes occurred. In conclusions, thrombocytopenia, diarrhea and lung infections are the relevant adverse events to be clinically focused on; regarding effectiveness, ibrutinib is confirmed to be a valid option for refractory/relapsed MCL also in a clinical setting mimicking the real world

Clinical characteristics and risk factors associated with COVID-19 severity in patients with haematological malignancies in Italy: a retrospective, multicentre, cohort study

Several small studies on patients with COVID-19 and haematological malignancies are available showing a high mortality in this population. The Italian Hematology Alliance on COVID-19 aimed to collect data from adult patients with haematological malignancies who required hospitalisation for COVID-19

Circular economy inspired imaginaries for sustainable innovations

In this chapter, Narayan and Tidström draw on the concept of imaginaries to show how Circular Economy (CE) can facilitate values that enable sustainable innovation. Innovation is key for sustainability, however, understanding and implementing sustainable innovation is challenging, and identifying the kind of actions that could direct sustainable innovations is important. The findings of this study indicate that CE-inspired imaginaries enable collaboration and by relating such imaginaries to common and shared social and cultural values, intermediaries could motivate actors into taking actions that contribute to sustainable innovation.fi=vertaisarvioitu|en=peerReviewed