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Phytotechnologies used for cleaning up urban and suburban polluted soils (i.e., brownfields) have shown some weakness in the excessive extent of the timeframe required for them to be effectively operating. This bottleneck is due to technical constraints, mainly related to both the nature of the pollutant itself (e.g., low bio-availability, high recalcitrance, etc.) and the plant (e.g., low pollution tolerance, low pollutant uptake rates, etc.). Despite the great efforts made in the last few decades to overcome these limitations, the technology is in many cases barely competitive compared with conventional remediation techniques. Here, we propose a new outlook on phytoremediation, where the main goal of decontaminating should be re-evaluated, considering additional ecosystem services (ESs) related to the establishment of a new vegetation cover on the site. The aim of this review is to raise awareness and stress the knowledge gap on the importance of ES associated with this technique, which can make phytoremediation a valuable tool to boost an actual green transition process in planning urban green spaces, thereby offering improved resilience to global climate change and a higher quality of life in cities. This review highlights that the reclamation of urban brownfields through phytoremediation may provide several regulating (i.e., urban hydrology, heat mitigation, noise reduction, biodiversity, and CO2 sequestration), provisional (i.e., bioenergy and added-value chemicals), and cultural (i.e., aesthetic, social cohesion, and health) ESs. Although future research should specifically be addressed to better support these findings, acknowledging ES is crucial for an exhaustive evaluation of phytoremediation as a sustainable and resilient technology.

In this paper, we study human–AI collaboration protocols, a design-oriented construct aimed at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We applied this construct in two user studies involving 12 specialist radiologists (the knee MRI study) and 44 ECG readers of varying expertise (the ECG study), who evaluated 240 and 20 cases, respectively, in different collaboration configurations. We confirm the utility of AI support but find that XAI can be associated with a “white-box paradox”, producing a null or detrimental effect. We also find that the order of presentation matters: AI-first protocols are associated with higher diagnostic accuracy than human-first protocols, and with higher accuracy than both humans and AI alone. Our findings identify the best conditions for AI to augment human diagnostic skills, rather than trigger dysfunctional responses and cognitive biases that can undermine decision effectiveness.

Few studies analyzed left atrial (LA) peak atrial longitudinal strain (PALS) determinants, particularly across heart failure (HF) stages. We aimed to analyze the pathophysiological and clinical PALS correlates in a large multicentric prospective study. This is a multicenter prospective observational study enrolling 745 patients with HF stages. Data included PALS and left ventricular global longitudinal strain (LV-GLS). Exclusion criteria were: valvular prosthesis; atrial fibrillation; cardiac transplantation; poor acoustic window. Median global PALS was 17% [24–32]. 29% of patients were in HF-stage 0/A, 35% in stage-B, and 36% in stage-C. Together with age, the echocardiographic determinants of PALS were LA volume and LV-GLS (overall model R2 = 0.50, p < 0.0001). LV-GLS had the strongest association with PALS at multivariable analysis (beta: −3.60 ± 0.20, p < 0.0001). Among HF stages, LV-GLS remained the most important PALS predictor (p < 0.0001) whereas age was only associated with PALS in lower HF-stage 0/A or B (R = − 0.26 p < 0.0001, R = − 0.23 p = 0.0001). LA volume increased its association to PALS moving from stage 0/A (R = − 0.11; P = 0.1) to C (R = − 0.42; P < 0.0001). PALS was the single most potent echocardiographic parameter in predicting the HF stage (AUC for B vs. 0/A 0.81, and AUC vs. 0/A for C 0.76). PALS remained independently associated with HF stages after adjusting for ejection fraction, E/e′ ratio, and mitral regurgitation grade (p < 0.0001). Although influenced by LV-GLS and LA size across HF stages, PALS is incrementally and independently associated with clinical status. LA function may reflect a substantial part of the hemodynamic consequences of ventricular dysfunction.

Purpose: To test reliability and reproducibility of ESASO morphologic OCT-based classification of diabetic maculopathy (DM). Methods: This is a multi-center cross-sectional study including a coordination center (CC) and 18 participating centers (PCs). After instruction on the correct use of ESASO Classification, the validation process was carried out in two consecutive stages. In the first retrospective phase, we evaluated the concordance between PCs and CC in the staging of OCT images collected during PCs’ daily activity (608 images). In a second prospective phase, we analyzed the inter-observer agreement of staging assigned by each PCs to OCT images selected by the CC (22 images). Results: The overall concordance achieved in the retrospective phase was 89.8% (Kappa = 0.83 (95% CI: 0.78–0.87); p<0.0001). In 99.5% of cases, concordance did not differ by more than one stage. In the prospective phase, PCs reached an inter-operator agreement of 93.0% (Krippendorff's Alpha = 0.953, 95% CI: 0.929–0.977, p<0.0001). Any discrepancy among the 22 images was within one stage. Conclusion: The results achieved in this study confirm that ESASO OCT-based Classification can be considered as an easy and reproducible method to stage DM during clinical practice. A diffused use of a common and validated method to describe the progression of retinal damage in DM may offer several clinical and scientific advantages.

Background: The COVID-19 pandemic carries a high burden of morbidity and mortality worldwide. We aimed to identify possible predictors of in-hospital major cardiovascular (CV) events in COVID-19. Methods: We retrospectively included patients hospitalized for COVID-19 from 10 centers. Clinical, biochemical, electrocardiographic, and imaging data at admission and medications were collected. Primary endpoint was a composite of in-hospital CV death, acute heart failure (AHF), acute myocarditis, arrhythmias, acute coronary syndromes (ACS), cardiocirculatory arrest, and pulmonary embolism (PE). Results: Of the 748 patients included, 141(19%) reached the set endpoint: 49 (7%) CV death, 15 (2%) acute myocarditis, 32 (4%) sustained-supraventricular or ventricular arrhythmias, 14 (2%) cardiocirculatory arrest, 8 (1%) ACS, 41 (5%) AHF, and 39 (5%) PE. Patients with CV events had higher age, body temperature, creatinine, high-sensitivity troponin, white blood cells, and platelet counts at admission and were more likely to have systemic hypertension, renal failure (creatinine ≥ 1.25 mg/dL), chronic obstructive pulmonary disease, atrial fibrillation, and cardiomyopathy. On univariate and multivariate analysis, troponin and renal failure were associated with the composite endpoint. Kaplan–Meier analysis showed a clear divergence of in-hospital composite event-free survival stratified according to median troponin value and the presence of renal failure (Log rank p < 0.001). Conclusions: Our findings, derived from a multicenter data collection study, suggest the routine use of biomarkers, such as cardiac troponin and serum creatinine, for in-hospital prediction of CV events in patients with COVID-19.

Although conscious aspects of attachment representations can be effectively assessed in middle childhood, the few available self-reports are based on different operationalizations of attachment and do not always show adequate psychometric properties. The current study aimed to develop and evaluate the psychometric properties of the Attachment in Middle Childhood Questionnaire (AMCQ). Using three independent samples of Italian children (Mage = 10 years, 52% = girls), in three interrelated studies we (1) selected a pool of items from existing questionnaires, adapted them to the same response format, and subjected them to exploratory factor analysis; (2) performed confirmatory factor analyses on the retained items; and (3) used structural equation modeling to assess the factor structure, external validity, and invariance across gender and age groups. The final 15-item questionnaire comprised two dimensions (anxiety and avoidance) and a supplementary scale (security). Overall, results supported the reliability and validity of the AMCQ for Italian children.

The use of phototrophic organisms in wastewater treatment is gaining increasing interest, due to the possibility of exploiting photosynthetic oxygenation, thus lowering treatment costs and environmental impacts. Mixed consortia of phototrophic organisms and aerobic bacteria rapidly develop in photobioreactors receiving wastewaters; in this case, the complex mutualistic interactions among microorganisms cannot be easily described, making difficult to forecast removal efficiencies and biomass productivities. Respirometric methods are applied for evaluating bacterial activity in activated sludge (conventional respirometry) or photosynthetic activity in phototrophic organisms (photorespirometry). Photo-respirometric methods can be also exploited for monitoring algae-bacteria systems and for calibrating mathematical models, allowing for a better comprehension of the interactions involved and a more accurate prediction of system efficiencies. A general protocol and a series of guidelines for conducting photorespirometric tests on algal-bacterial suspensions are here proposed, based on the review of experimental protocols applied to evaluate the effects of several parameters on these systems.