Environmental sustainability in intensive care: An international survey of intensive care professionals'views, practices and proposals to the European Society of Intensive Care Medicine

Background: The intensive care unit (ICU) is a high-resource area, generating more waste and greenhouse gas (GHG) emissions than standard hospital wards. Environmental sustainability is important for healthcare professionals worldwide, prompting scientific societies to call for urgent action. To respond to this global need, the European Society of Intensive Care Medicine (ESICM) conducted an international survey assessing intensive care professionals' attitudes and practices towards environmental sustainability. Methods: Intensive care professionals completed an online survey between 21 October 2023, and 5 January 2024. The survey, featuring 21 questions assessing attitudes and practices towards environmental sustainability and proposals for actions from ESICM, was disseminated during the 36th ESICM Congress via National Intensive Care Societies and ESICM's social media. Results: We received 635 responses from 48 countries. Four hundred seventy (80 %) respondents acknowledged a responsibility to be aware of the environmental impact of intensive care practice, and 372 (63.5 %) disagreed or were uncertain about their knowledge level to guide practice. Four hundred thirty-seven (84 %) lacked formal training on making sustainable choices. Ninety-five per cent used non-sterile gloves in ICUs, and 63 % were willing to reduce use to support sustainable practices. Two proposed actions for ESICM to improve environmental sustainability were increasing knowledge on ecology (91/187, 49 %) and raising awareness in the field (36/187,20 %). Conclusion: This survey highlighted the need for scientific societies, notably ESICM, to provide robust support and structured education on environmental sustainability. Intensive care professionals acknowledged the environmental impact of intensive care and seemed eager to invest in their education on this topic

Mixtures of probabilistic logic programs

Structure learning (SL) is a fundamental task in Statistical Relational Artificial Intelligence, where the goal is to learn a program from data. Among the possible target languages, there is Probabilistic Logic Programming. Mixture models have recently gained attention thanks to their effectiveness in modeling complex distributions by combining simpler ones. In this paper, we propose learning a mixture of probabilistic logic programs to handle SL. Our method consists of three steps: 1) generating mixture components with a specific structure, 2) applying parameter learning to each component, and 3) optimizing the weights associated with each component. Furthermore, to possibly reduce the number of components and mitigate overfitting, we also explore the use of L1 and L2 regularization. Empirical results obtained by considering both the full set of components and only a fraction of them demonstrate that our approach, despite being seemingly simple, is competitive with state-of-the-art solvers

Città stratificate. Spazio, tempo e memoria urbana / Layered Cities. Space, time and urban memory

Il contributo affronta il tema della città stratificata intesa come oggetto complesso, polisemico e multitemporale sviluppando temi e riflessioni della critica contemporanea sulla realtà urbana, attraverso l’esercizio di approcci culturali sintetizzabili secondo una triplice azione: “conoscere”, “attualizzare” ed “esercitare la memoria”The paper addresses the theme of the layered city, understood as a complex, polysemic, and multi-temporal object, by developing themes and reflections from contemporary criticism on urban reality. This is achieved through the exercise of cultural approaches that can be summarized in a threefold action: 'to know,' 'to actualize,' and 'to exercise memory

Incrocio di sguardi. Riflessioni e confronti per una educazione de-coloniale

Queste brevi riflessioni sono ispirate alle cronache che sia i nativi sia gli invasori hanno scritto del loro incontro-scontro. Non si tratta di un saggio di storia in senso stretto, se mai è il tentativo di studiare il quadro di riferimento con cui i perdenti hanno interpretato l’incontro e lo scontro di due civiltà, che è a dire l’incontro- scontro di due sistemi di pensiero. La comparazione in educazione è anche questo: il confronto fra cosmogonie differenti, fra organizzazioni simboliche che appartengono a logiche difformi. E le concatenazioni di conseguenze che, ai primi reciproci avvistamenti, tali logiche scatenano

Risk of clinical events in virologically suppressed people with HIV switching to a two-drug regimen vs. remaining on a three-drug regimen: a target trial emulation

Background: Guidelines support the switch to a two-drug regimen (2DR) in virologically suppressed people with HIV (PWH) on a three-drug regimen (3DR). Randomized clinical trials have not included clinical outcomes in study endpoints. We provide estimates of 3-year clinical risk by means of a target trial emulation using the data of a large cohort of PWH in Italy. Methods: PWH from the Icona Foundation Study who were virologically suppressed (HIV-RNA ≤50 copies/mL) for ≥6 months on a 3DR on or after November 2014, were enrolled (database closure on July 31, 2024). PWH were classified according to therapeutic strategies: switching to 2DR (protease inhibitors or dolutegravir plus lamivudine or dolutegravir plus rilpivirine) or remaining on 3DR (any combination). The primary endpoint was the time to the first clinical composite event (cardiovascular disease [CVD], cancer [AIDS and non-AIDS related], or death). We calculated the difference in 3-year risk between therapeutic strategies, estimated using a weighted non-parametric Kaplan–Meier estimator. Findings: 7672 participants entered the analysis: 629 (8.2%) switching to 2DR and 7043 (91.8%) remaining on 3DR. Over the 3-year follow-up, 408 events were registered (64 CVD, 234 cancer, and 110 deaths). The 3-year adjusted risk estimate was 2.55 (95% CI 1.72, 5.33) in 2DR vs. 4.69 (95% CI 4.48, 6.17) in 3DR. The difference (−2.15% [95% CI −3.56%, −0.20%]) in favor of 2DR was mainly driven by events of non-AIDS related cancer and mortality. Interpretation: This study provides evidence that virologically suppressed PWH can be safely switched to 2DR, and may slightly reduce the 3-year risk of a composite clinical outcome. Funding: The Icona Foundation Study is supported by unrestricted grants from Gilead Sciences, ViiV Healthcare, Merck Sharpe & Dohme

Lifecycle Environmental and Circular Assessment in Building Product Manufacturing: Systemic Approach for Data Optimization of Custom Prefabricated Façade Modules

In un panorama caratterizzato dalla necessità impellente del settore delle costruzioni di mitigare il cambiamento climatico e di trasformare i propri processi in ottica circolare, il presente studio si propone di sviluppare un approccio innovativo per la progettazione di strumenti di supporto agli attori della filiera edilizia. Tali strumenti, personalizzabili, interoperabili e automatizzati, consentiranno una valutazione ecologica dei prodotti. L’obiettivo è quello di supportare gli operatori del settore nel rispondere alle urgenze del nostro tempo. Tra cui: rendere sostenibili i processi edilizi, conformarsi alle normative nazionali e internazionali orientate al raggiungimento degli obiettivi climatici, ed implementare modelli di business alternativi compatibili con i principi dell’economia circolare. Lo studio illustra un approccio metodologico sistemico, mirato all’ottimizzazione della gestione dei dati nell’industria manifatturiera di prodotti modulari e personalizzati per facciate architettoniche prefabbricate. L’originalità risiede nello sfruttamento dei dati progettuali e di filiera, spesso già disponibili nei sistemi aziendali per scopi gestionali, per guidare decisioni strategiche in ottica ambientale lungo il ciclo di vita dei prodotti. L’integrazione di strumenti informatici come i Building Information Models o Computer-aided Technologies con framework digitali consente di migliorare l’accessibilità, l’organizzazione e la valorizzazione delle informazioni, favorendo pratiche edilizie sostenibili. La metodologia proposta si articola in tre fasi principali. La prima consiste nell’osservazione e nell’analisi del contesto di riferimento, volta a identificare le informazioni necessarie per i progettisti e i costruttori. In particolare, si definiscono: il tipo di dati richiesti secondo un approccio esigenziale basato sulle prestazionali richieste, la loro tipica localizzazione nei modelli informatici attualmente in uso, e le modalità per correlare tali dati sulla base di un inventario meta-progettuale dei moduli di facciata. La seconda fase prevede la progettazione di un framework basato su modelli semantici del tipo Entity-Relationship che ottimizzi l’elaborazione dei dati individuati per la valutazione di impatto ambientale e di economia circolare nelle fasi progettuali più critiche. L’ultima fase consiste nella validazione del sistema sviluppato mediante l’implementazione di una prova di fattibilità operativa. Nonostante i vantaggi della prefabbricazione modulare, sia in termini di possibilità di progettazione per lo smontaggio sia di integrazione di componenti virtuosi, la valutazione del loro impatto rimane complessa. Le parti interessate necessitano di strumenti che supportino l’intero ciclo di vita del prodotto, dalla progettazione alla dismissione e riutilizzo. Anche attraverso attività sperimentali svolte nell’ambito di progetti di ricerca interdisciplinari e di larghi obiettivi, questo lavoro dimostra come l’ottimizzazione dei dati di filiera possa innovare i processi, in particolare nelle fasi iniziali di progettazione e di fine vita. Tali innovazioni possono contribuire al controllo sistematico e al mitigamento degli impatti degli involucri edilizi industrializzati. In conclusione, il presente lavoro pone le basi per lo sviluppo di strumenti integrati e ad alte prestazioni a servizio dell’industria manifatturiera per l’edilizia. L’applicazione dell’approccio proposto, replicabile nella sua essenza al variare dei contesti e delle risorse disponibili, ha il potenziale di generare sistemi performanti, rispondendo in modo puntuale alle esigenze della filiera. Inoltre, in prospettiva, tali strumenti potrebbero incentivare una collaborazione ecosistemica lungo l’intera catena del valore, integrare valutazioni di sostenibilità sociale e contribuire alla definizione di linee guida più efficaci per l’implementazione di modelli di sviluppo sostenibile.Within a context marked by the pressing necessity for the construction sector to mitigate climate change and transform its production processes towards circular thinking, this study aims to develop an innovative approach to the design of advanced tools for supporting stakeholders in the construction value chain. These tools, customisable, interoperable, and automated, will enable an effective environmental assessment of products. The objective is to assist sector operators in addressing the critical challenges of our time, including making construction processes sustainable, complying with national and international regulations aligned with climate goals, and implementing alternative business models consistent with circular economy principles. The study outlines a systemic methodological approach aimed at optimising data management within the manufacturing sector for modular and custom prefabricated architectural façades. Its originality lies in leveraging foreground design and supply chain data—often already available within enterprise digital systems for management purposes—to guide environmentally informed strategic decisions across the entire lifecycle of the products. Integrating tools, such as Building Information Models and Computer-Aided Technologies, with digital frameworks enhances the accessibility, organisation, and utility of information, thereby promoting sustainable practices. The proposed methodology is structured into three main phases. The first entails a thorough observation and analysis of the reference context to identify the information required by designers and builders. Specifically, it defines the type of data needed based on a performance design perspective considering utter requirements, data typical location within current digital models, and the modalities for interrelating such data based on an meta-design inventory for façade modules. The second phase involves the development of a framework, grounded in semantic models such as Entity-Relationship schema, to optimise the processing of identified data for environmental and circular economy assessments during the most critical design phases. The final phase consists of validating the developed system through the implementation of an operational proof of concept. Despite the demonstrated advantages of module prefabrication—including design for disassembly capabilities and the achievable integration of low-impact components—their environmental assessment remains complex. Stakeholders require effective tools to support the entire product lifecycle, from design to decommissioning and reuse. Drawing on experimental activities conducted within interdisciplinary research initiatives addressing a wide range of objectives, this study demonstrates how optimising manufacturing and supply chain data can significantly innovate the design and production processes, particularly during the early design and end-of-life phases. These innovations can contribute to systematic performance monitoring and the mitigation of environmental impacts associated with industrialised building envelopes. In conclusion, this study lays the groundwork for the development of integrated, high-performance tools serving construction manufacturing. The application of the proposed approach, replicable across varying contexts and technological resources, has the potential to generate efficient systems that address the specific needs of the sector. Furthermore, in the longer term, such tools could foster ecosystem-wide collaboration along the entire value chain towards value networking, integrate social sustainability assessments, and support the formulation of more effective guidelines for implementing sustainable development models

Volume Tolerance and Prognostic Impact of Hematoma Expansion in Deep and Lobar Intracerebral Hemorrhage

BACKGROUND: The prognostic impact of intracerebral hemorrhage (ICH) volume varies according to location, with smaller volume tolerance in deep ICH, and hematoma expansion (HE) contributes to final ICH volume. We tested the hypothesis that HE influences outcome only when the final ICH volume achieves a critical threshold that differs according to ICH location. METHODS: Retrospective analysis of patients with supratentorial ICH admitted at 10 centers in North America and China (development cohort) and Europe (replication cohort). HE was defined as growth >33% and/or >6 mL. Location-specific (lobar versus deep) volume cutoffs for the prediction of poor outcomes were derived using receiver operating characteristic curves and the Youden index. The prognostic impact of HE stratified by location and final volume was explored with logistic regression (poor outcome: 90-day modified Rankin Scale score of 4-6), accounting for age, Glasgow Coma Scale, baseline volume, intraventricular hemorrhage, and admission center. RESULTS: We identified 1774 patients with ICH in the development cohort and 1746 in the replication cohort. A total of 1058 (mean age, 68 years; 47.8% men) and 1423 (mean age, 71 years; 44.7% men) subjects met the inclusion criteria, respectively. The optimal final ICH volume cutoff for poor outcome differed by location: ≥36 mL for lobar and ≥17 mL for deep ICH. HE with final volume below the cutoff was not associated with higher odds of poor outcome compared with patients without HE (adjusted odds ratio, 1.85 [95% CI, 0.78-4.38]; P=0.163 in lobar ICH; adjusted odds ratio, 0.85 [95% CI, 0.38-1.89]; P=0.685 in deep ICH). The combination of HE and final volume over the critical threshold was, however, significantly associated with poor prognosis, and the magnitude of this effect was substantial (adjusted odds ratio, 8.55 [95% CI, 2.87-25.48]; P<0.001 in lobar ICH; adjusted odds ratio, 10.34 [95% CI, 2.86-37.44]; P<0.001 in deep ICH). These findings were confirmed in the replication cohort. CONCLUSIONS: HE significantly impacts severe outcomes only when the final ICH volume exceeds a critical target threshold, and this threshold is lower in deep versus lobar ICH. These findings might inform clinical practice and future trials

Identifying Research Priorities for Cognition in CKD A Delphi Study

Background Cognition is a research priority for people living with CKD, but identification of critical research questions is lacking. This study aimed to determine which cognition-related research questions are most important to CKD stakeholders. Methods A modified Delphi technique with three survey rounds was used. The study sample included three panels (People with lived CKD experience, Researchers, and Clinicians) recruited through international patient and kidney research networks, kidney societies, and snowball sampling with email invitations. Survey rounds were distributed electronically through Research Electronic Data Capture. In round 1 (October 2021–May 2022), respondents contributed three important research questions regarding cognition in CKD (free text). After deduplication and qualitative synthesis, respondents ranked the importance of these questions on a nine-point Likert scale in round 2 (February–April 2023). Questions with mean and median ratings of .7 by at least two respondent panels or rated critically important by the lived experience panel were reranked in round 3 (August–September 2023) and assessed for consensus to identify the final list of priority research questions. Results Respondents (n5152) identified 125 and 44 discrete questions after rounds 1 and 2, respectively. The final shortlist included 27 questions in eight categories. The most critical research question identified was “What factors prevent cognitive impairment in people receiving dialysis?” Overall, respondents prioritized questions focusing on prevention and treatment of cognitive impairment. Scores between the panels were significantly different for 16 questions. Those with lived CKD experience prioritized quality of life, researchers emphasized developing interventions to mitigate cognitive impairment, and clinicians prioritized the effect of CKD treatment on cognitive impairment. Conclusions Through an established consensus methodology involving key stakeholder groups, we identified 27 critical research questions about cognition in CKD. These questions should guide future study design and outcome selection

The lithic legacy of Loreto: a technological perspective on early hominin toolmaking during the Lower Palaeolithic

The site of Loreto (Venosa basin, Southern Italy) dates to between MIS 15 and 13, a pivotal phase in the human peopling of Europe. This period marks a moment of change in the Lower Palaeolithic, preceding the glacial break of MIS 12, and the subsequent technological renewal that, from MIS 11 onward, culminated in the transition to the Middle Palaeolithic. Between MIS 15–13, the number of archaeological sites increased, alongside the emergence of bifaces, retouched tools, and more structured land-use strategies. The lithic assemblage from Loreto exemplifies this shift. The technological analysis presented here, focused on layer A, sheds light on hominin behavioural strategies. Core management includes discoid, centripetal, SSDA (Système par surface de débitage alternée), and core-on-flake reduction sequences. Retouched flakes are particularly noteworthy, featuring elaborately worked scrapers and pointed tools with edge rejuvenation, scalariform retouch, and possible soft-hammer use. Beyond its affinities with other coeval sites, the Loreto assemblage may reflect a degree of innovation often linked to biface production, thereby expanding our understanding of Acheulean technical variability. It also contributes to broader discussions on the diversity of lithic strategies adopted by hominins prior to the onset of MIS 11

Artificial intelligence & nuclear medicine: an emerging partnership

This letter addresses a recently published article evaluating the performance of 3D U-Net–based deep learning models for automated lesion segmentation in PET/CT imaging. The study represents a significant advancement in the integration of artificial intelligence (AI) into Nuclear Medicine. By comparing volumetric, MIP-based, and hybrid segmentation approaches using [18F]FDG and [68Ga]Ga-PSMA radiotracers, the authors demonstrate that hybrid models can enhance lesion detection and contouring accuracy. These findings underscore the potential of AI-based segmentation to improve consistency and reduce the manual workload in clinical PET/CT interpretation. We consider this work a pivotal step toward the clinical adoption of AI tools, offering tangible benefits for routine practice and radiomic analysis, while preserving the essential supervisory role of the Nuclear Medicine Physician