Search

International literature is increasingly disclosing the relevance of cultural aspects within the processes of teaching and learning mathematics. Knowledge is inextricably linked to the activities in which the subjects engage, and must be considered in relation to the socio-cultural context wherein the activity takes place. Literature reveals the relationship between the background culture (e.g. language, nationality, etc.) of prospective elementary teachers and their beliefs about mathematics and its teaching. In this paper, we define culture with reference to the wider discussion from cross-cultural psychology literature about cultural values, and we investigate if and how differences in individuals’ cultural values are related to prospective teachers’ beliefs about being successful in mathematics and in its teaching. We adopt a questionnaire from the studies by Schwartz to measure participants’ values. We assign each prospective teacher of our sample to a cluster of beliefs and we analyse how the beliefs of prospective teachers are related to their values. Results show that cultural values and beliefs about mathematics are related, while this is not the case for beliefs about the teaching of this subject.

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment.

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.

The worldwide growing concern for environmental protection compels businesses to incorporate green orientation in their strategies. Crowdfunding has opened new possibilities to attract investors by encompassing green concerns in their agenda. This study investigates the relationship between green orientation and UK crowdfunding campaign success. We have used both qualitative and quantitative approaches to build the green orientation index, for which we have chosen 210 green campaigns out of 720 crowdfunding campaigns. Then we performed a cluster analysis to identify three clusters (social enthusiast, green fan, and tech-oriented) of green-oriented crowdfunding campaign. We performed a predictive validity analysis to identify which cluster is more effective in ensuring the crowdfunding’s performance. The results show that tech-oriented obtain the highest value and share a positive relationship with the amount raised and crowdfunding’s success. Social enthusiast effectively attracts more crowd through the social platform and thereby successfully raises funds. Green orientation, however, does not affect crowdfunding success or overfunding, which shows that there is a huge difference between the clusters, suggesting that the green orientation itself is not considered so important for the crowdfunding campaigns. It also highlights a crucial point the in the crowdfunding’s objectives, the realization of the environmental concern, society, and the long-term sustainability concern is not promising.

Introduction: Both variable- and person-centered approaches identify grandiose and vulnerable themes in pathological narcissism (PN). However, person-centered results rely on cross-sectional data, preventing identification of subtypes of individuals through transitory self-states. Methods: We perform a cluster analysis on the joint trajectory of daily ratings of grandiose (GN) and vulnerable narcissism (GN), collected during a 28-day experience sampling (N = 196 participants). Results: The best partition – identified by multiple criteria – includes three clusters: a “low PN” cluster displays below-average levels of both daily GN and VN; a “high VN” cluster displays average daily GN and above-average levels of daily VN; a “high GN” cluster shows above-average levels of daily GN and below-average levels of daily VN. Significant inter-cluster differences emerge on both daily and trait measures of narcissism, and on trait measures of self-esteem and shame, but less sharply on impairment in personality structure. There is no inter-cluster difference on the variability and instability of daily narcissism. Discussion: We conclude that the constructs of daily GN and VN define corresponding groups of individuals with either high GN or VN, the latter group being more distressed. Yet, this group is also less “pure” in its narcissistic characterization, showing at least some levels of trait and state GN.

Purpose: Whilst operating in the context of a high-income economy, the Italian labour market is affected by the considerable challenges of vertical mismatch, skill gaps and skill shortages. In such a context, the aim of this empirical study is to explore current university provision regarding the formal qualification of the oenologist, in order to assess its alignment with the professionalism demands from the wine sector. Design/Methodology/Approach: The following was deployed to analyse university provision with a mixed method approach including social network analysis, cluster analysis and desk analyses based on descriptive statistics. Findings: The comparative analysis outlined in the research on the basis of different educational activities shows that some degree courses would appear to be interchangeable. Furthermore, the study also shows a partial alignment between university educational provision and the skills required by stakeholders, thereby demonstrating the need to make changes in study programmes. Practical implications: The authors hope that the evidence-based suggestions proffered herein may be used as a scientific basis to support the formulation of education policy at the ministerial level and as a guideline to plan and update academic curricula. Theoretical implications: This research contributes to the scientific debate relating to the educational preparation of university graduates and their employability according to the requirements of the professional wine sector. It enriches the theoretical framework of evidence-based educational research, offering alternative avenues for interpretation about the similarity profiles of educational provision. Originality/Values: To the best of our knowledge, the research laid out in this paper is innovative in the field of educational literature due to its use of different methodologies and techniques to obtain results, thereby proposing a wider and alternative use of well known analytical methods.

Background No simple staging system has emerged for basal cell carcinomas (BCCs), since they do not follow the TNM process, and practitioners failed to agree on simple clinical or pathological criteria as a basis for a classification. Operational classification of BCCs is required for decision‐making, trials and guidelines. Unsupervised clustering of real cases of difficult‐to‐treat BCCs (DTT‐BCCs; part 1) has demonstrated that experts could blindly agree on a five groups classification of DTT‐BCCs based on five patterns of clinical situations. Objective Using this five patterns to generate an operational and comprehensive classification of BCCs. Method Testing practitioner's agreement, when using the five patterns classification to ensure that it is robust enough to be used in the practice. Generating the first version of a staging system of BCCs based on pattern recognition. Results Sixty‐two physicians, including 48 practitioners and the 14 experts who participated in the generation of the five different patterns of DTT‐BCCs, agreed on 90% of cases when classifying 199 DTT‐BCCs cases using the five patterns classification (part 1) attesting that this classification is understandable and usable in practice. In order to cover the whole field of BCCs, these five groups of DTT‐BCCs were added a group representing the huge number of easy‐to‐treat BCCs, for which sub‐classification has little interest, and a group of very rare metastatic cases, resulting in a four‐stage and seven‐substage staging system of BCCs. Conclusion A practical classification adapted to the specificities of BCCs is proposed. It is the first tumour classification based on pattern recognition of clinical situations, which proves to be consistent and usable. This EADO staging system version 1 will be improved step by step and tested as a decision tool and a prognostic instrument.

This study investigates whether young people in low- and middle-income countries (LMICs) have experienced processes of destandardization of the life course similar to those observed in high-income societies. We provide two contributions to the relevant literature. First, we use data from 263 Demographic and Health Surveys (DHS) across 69 LMICs, offering the richest comparative account to date of women's transition to adulthood (TTA) patterns in the developing world. In so doing, we adopt sequence analysis and shift the focus from individual life-course events—namely first sexual intercourse, first union, and first birth—to a visually appealing approach that allows us to describe interrelations among events. By focusing on the analysis of trajectories rather than the occurrence of single events, the study provides an in-depth focus on the timing of events, time intervals between events, and how experiencing (or not) one event might have consequences for subsequent markers in the TTA in cross-national comparative perspective. Second, we identify clusters of TTA and explore their changes across cohorts by region and household location of residence (rural vs. urban). We document significant differences by macro-regions, yet relative stability across cohorts. We interpret the latter as suggestive of cultural specificities that make the TTA resistant to change and slow to converge across regions, if converging at all. Also, we find that much of the difference across cluster typologies ensues from variation related to when the transition begins (early vs. late), rather than from the duration between events, which tends to be uniformly quick across three out of four clusters.

From March 23rd to May 3rd 2020, Italy underwent a complete lockdown in the attempt to contain the spread of the pandemic due to Covid-19 outbreak. During this period, a new kind of environment has been experienced in all cities, resulting in an abatement of traffic noise levels. Consequently, due to the prohibition of all non-essential activities, traffic noise dynamics changed as well. In this paper, we analyse the data recorded from the permanent noise monitoring network installed in the pilot area of the city of Milan, Italy. The results show how, besides a dramatic reduction of the noise levels (about 6 dB on average), also the noise pattern was profoundly changed. This is particularly important in the framework of DYNAMAP, a statistically based European project able to predict traffic noise over an extended area based on the noise recorded by limited number of monitoring stations. The change of the traffic dynamics, resulting in different noise patterns of the normalized hourly median profiles for each sensor, pose some limitations about the use of such predicting tool during extraordinary situations such as that experienced during a lockdown.

Building on the policy debate about the effectiveness of the public sector in spurring environmental innovation, the present paper investigates whether and to what extent firms pursuing different approaches to environmental innovation differently respond to policy measures (among regulatory, technology-push and demand-pull instruments). Relying on data provided by the 2012 Italian Community Innovation Survey (CIS), the empirical exercise adopts a clustering approach to identify divergent innovation trajectories (EI modes) and a logistic regression model to explore the institutional drivers of distinct EI modes. The results from 1.807 manufacturing firms indicate that the role of public policies in stimulating EI practices changes in sign and magnitude across EI policy tools and modes. This study contributes to literature by providing an enriched and more nuanced view of environmental innovation processes, with important implications for the theorization of distinct classes of policy aimed at fostering the transition towards increased sustainability.

Gaussian Mixture Models (GMMs) are one of the most widespread methodologies for model-based clustering. They assume a multivariate Gaussian distribution for each component of the mixture, centered at the mean vector and with volume, shape and orientation derived by the covariance matrix. To reduce the large number of parameters produced by the covariance matrices, parsimonious parameterizations of the latter were proposed in literature, e.g., the eigen-decomposition and the parsimonious GMMs based on mixtures of probabilistic principal component analyzers and mixtures of factor analyzers. We introduce a new parameterization of a covariance matrix by defining an extended ultrametric covariance matrix and we implement it into a GMM. This structure can be used to describe multidimensional phenomena which are characterized by nested latent concepts having different levels of abstraction, from the most specific to the most general. The proposal is able to pinpoint a hierarchical structure on variables for each component of the GMM, thus identifying a different characterization of a multidimensional phenomenon for each component (cluster, subpopulation) of the mixture. At the same time, it defines a new parsimonious GMM since the ultrametric covariance structure reconstructs the relationships among variables with a limited number of parameters. The proposal is applied on synthetic and real data. On the former it shows good performance in terms of classification when compared to the other existing parameterizations, and on the latter it also provides insight into the hierarchical relationships among the variables for each cluster.

IntroductionFunctional Motor Disorders (FMDs) represent nosological entities with no clear phenotypic characterization, especially in patients with multiple (combined FMDs) motor manifestations. A data-driven approach using cluster analysis of clinical data has been proposed as an analytic method to obtain non-hierarchical unbiased classifications. The study aimed to identify clinical subtypes of combined FMDs using a data-driven approach to overcome possible limits related to “a priori” classifications and clinical overlapping.MethodsData were obtained by the Italian Registry of Functional Motor Disorders. Patients identified with multiple or “combined” FMDs by standardized clinical assessments were selected to be analyzed. Non-hierarchical cluster analysis was performed based on FMDs phenomenology. Multivariate analysis was then performed after adjustment for principal confounding variables.ResultsFrom a study population of n = 410 subjects with FMDs, we selected n = 188 subjects [women: 133 (70.7%); age: 47.9 ± 14.4 years; disease duration: 6.4 ± 7.7 years] presenting combined FMDs to be analyzed. Based on motor phenotype, two independent clusters were identified: Cluster C1 (n = 82; 43.6%) and Cluster C2 (n = 106; 56.4%). Cluster C1 was characterized by functional tremor plus parkinsonism as the main clinical phenotype. Cluster C2 mainly included subjects with functional weakness. Cluster C1 included older subjects suffering from anxiety who were more treated with botulinum toxin and antiepileptics. Cluster C2 included younger subjects referring to different associated symptoms, such as pain, headache, and visual disturbances, who were more treated with antidepressants.ConclusionUsing a data-driven approach of clinical data from the Italian registry, we differentiated clinical subtypes among combined FMDs to be validated by prospective studies.

COVID-19 may have a heterogeneous onset, especially in older age. However, whether and how COVID-19 signs and symptoms may present and aggregate together according to sociodemographic and health factors is unclear, as well as their prognostic value. This study included 981 COVID-19 inpatients who participated in the GeroCovid Observational study. Signs/symptoms at disease onset, sociodemographic, health, cognitive status, and mobility were systematically recorded. Clusters of signs/symptoms were identified through agglomerative hierarchical clustering. The associations of single signs/symptoms and symptom clusters with longer hospitalization (≥16 days) and in-hospital mortality were explored through logistic and Cox regressions. The signs/symptoms most reported in our sample (age 78.3 ± 9.39 years; 49.4% women) were fever (62.5%), cough (45.5%), and dyspnea (62.7%). Atypical symptoms were reported by up to one-third of patients, and delirium by 9.1%. Atypical symptoms were more frequent with advancing age and with lower pre-COVID-19 cognitive and mobility levels. Older men more likely reported respiratory symptoms than women. Dyspnea (hazard ratio [HR] = 1.47, 95% confidence interval [CI]: 1.02-2.12), tachypnea (HR = 1.53, 95% CI: 1.14-2.07), low oxygen saturation (HR = 1.95, 95% CI: 1.32-2.88) and delirium (HR = 1.60, 95% CI: 1.13-2.28) were associated with higher in-hospital mortality. Four symptom clusters were identified. Compared with the mild respiratory symptoms cluster, the severe clinical impairment cluster was associated with higher mortality (HR = 2.57, 95% CI: 1.58-4.18). The severe clinical impairment and aspecific symptoms clusters were associated with longer hospitalization (odds ratio [OR] = 2.38, 95% CI: 1.56-3.63, and OR = 1.75, 95% CI: 1.08-2.83, respectively). Multiple health aspects influence COVID-19 clinical presentation. A symptom clusters approach may help predict adverse health outcomes in older patients. In addition to respiratory symptoms, delirium is independently associated with mortality risk. ClinicalTrials.gov (NCT04379440).

The paper investigates whether standard innovation and different types of eco-innovation activities have different effects on firm employment growth. Heterogeneity in terms of growth effects is analyzed by considering how the influence of different innovation strategies varies across firms grouped by their pace of growth. Relying on a sample of 3000 Italian manufacturing firms observed between 2012 and 2016, we find that innovative companies tend to grow faster than their non-innovative counterparts. However, when distinct paces of growth are taken into account, the employment growth impact of innovation will depend on the specific types of innovation strategies pursued by companies. In particular, more complex eco-innovation activities have no impact on employment growth for high-growth firms. This finding suggests that, on average, high-growth firms tend to be too small and too young to be able to expand through more complex EI strategies which, to be handled, require a wide range of cognitive, technological, and financial capabilities.The online version contains supplementary material available at 10.1007/s40888-022-00263-x.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive deterioration of cognitive functions. Cortical and hippocampal hyperexcitability intervenes in the pathological derangement of brain activity leading to cognitive decline. As key regulators of neuronal excitability, the voltage-gated K+ channels (KV) might play a crucial role in the AD pathophysiology. Among them, the KV2.1 channel, the main α subunit mediating the delayed rectifier K+ currents (IDR) and controlling the intrinsic excitability of pyramidal neurons, has been poorly examined in AD. In the present study, we investigated the KV2.1 protein expression and activity in hippocampal neurons from the Tg2576 mouse, a widely used transgenic model of AD. To this aim we performed whole-cell patch-clamp recordings, Western blotting, and immunofluorescence analyses. Our Western blotting results reveal that KV2.1 was overexpressed in the hippocampus of 3-month-old Tg2576 mice and in primary hippocampal neurons from Tg2576 mouse embryos compared with the WT counterparts. Electrophysiological experiments unveiled that the whole IDR were reduced in the Tg2576 primary neurons compared with the WT neurons, and that this reduction was due to the loss of the KV2.1 current component. Moreover, we found that the reduction of the KV2.1-mediated currents was due to increased channel clustering, and that glutamate, a stimulus inducing KV2.1 declustering, was able to restore the IDR to levels comparable to those of the WT neurons. These findings add new information about the dysregulation of ionic homeostasis in the Tg2576 AD mouse model and identify KV2.1 as a possible player in the AD-related alterations of neuronal excitability.

Background & Aims: Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification in primary biliary cholangitis (PBC). Methods: ML was applied to an international dataset of PBC patients. The dataset was split into a derivation cohort (training set) and a validation cohort (validation set), and key clinical features were analysed. The outcome was a composite of liver-related death or liver transplantation. ML and standard survival analysis were performed. Results: The training set was composed of 11,819 subjects, while the validation set was composed of 1,069 subjects. ML identified four clusters of patients characterized by different phenotypes and long-term prognosis. Cluster 1 (n = 3566) included patients with excellent prognosis, whereas Cluster 2 (n = 3966) consisted of individuals at worse prognosis differing from Cluster 1 only for albumin levels around the limit of normal. Cluster 3 (n = 2379) included young patients with florid cholestasis and Cluster 4 (n = 1908) comprised advanced cases. Further sub-analyses on the dynamics of albumin within the normal range revealed that ursodeoxycholic acid-induced increase of albumin >1.2 x lower limit of normal (LLN) is associated with improved transplant-free survival. Conclusions: Unsupervised ML identified four novel groups of PBC patients with different phenotypes and prognosis and highlighted subtle variations of albumin within the normal range. Therapy-induced increase of albumin >1.2 x LLN should be considered a treatment goal.

We evaluate the level of mobility services and infrastructures in Milan to identify which areas are best equipped to serve citizens. We explore the overall degree of smart mobility by ranking the 88 administrative districts according to their transportation services. A statistical analysis both quantifies and groups the neighborhoods by their degree of mobility. We first built a set of composite indicators, including the AMPI and the Static Jevons Index. The robustness of the index is validated through a sensitivity analysis of behavior when varying the underlying indicators. A spatial cross-correlation analysis is conducted to contextualize the degree of mobility estimated in the neighborhoods with respect to some infrastructural variables. Second, the composite indices are used to cluster the districts into homogeneous groups with similar mobility levels. The results show that, whether using the indices individually or in combination, the cluster analyses successfully distinguish key areas of the city, such as the interchange hubs, university zones, city center, workplaces, and suburbs. We identify four classes of districts characterized by increasing levels of smart mobility, and highlight critical differences between the city center and the peripheral areas of Milan.

BackgroundBipolar disorder (BD) is recognized to be at high risk for developing negative psychopathological sequelae to potentially traumatic events. Nevertheless, scant data are still available about the effects of the COVID-19 emergency on the clinical course of BD. The present study examined prospectively the development and trajectories of post-traumatic stress, depressive, and anxiety symptoms among subjects with BD that were followed in an outpatient psychiatric clinic at the time of pandemic onset.MethodsA cohort of 89 subjects with BD was enrolled during the first wave of the COVID-19 pandemic, and assessed at baseline (T0), 2-months (T1), and 6-months (T2) follow-up. A K-means cluster analysis was used to identify distinct trajectories of depressive, anxiety, and post-traumatic stress symptoms during the three time points.ResultsWe identified three trajectories: the Acute reaction (13.5%); the Increasing severity (23.6%); and the Low symptoms (62.9%) groups, respectively. In the Acute reaction group a significant prevalence of female gender was reported with respect to the Low symptoms one. Subjects in the Increasing severity group reported significantly lower employment rate, and higher rate of relatives at risk for COVID-19 medical complications. Subjects in the Increasing Severity group reported higher rates of previous hospitalization and manic symptoms at baseline than those included in the Low symptoms one.ConclusionsOur results describe three distinct symptom trajectories during the COVID-19 emergency in a cohort of subjects suffering from BD, suggesting the need of a long-term follow-up for detecting the impact of the COVID-19 pandemic in this vulnerable population.

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.

In this study, we propose to analyse students’ drawings using the Semiotic Cultural Psychological Theory (SCPT), a socio-cultural psychological model that integrates semiotics with dynamic systems theory. According to the SCPT framework, students-generated drawings can be analysed in terms of generalised meanings, i.e., implicit assumptions about the represented content. We applied SCPT to identify a set of generalised meanings about seasonal changes and to investigate whether they depend on students’ grade level. We used a draw-and-explain task to collect drawings and written explanations about seasonal changes produced by 495 Italian 8th grade (N = 196) and 9th grade (N = 299) students. The analysis was based on Multiple Correspondence Analysis and Cluster Analysis. Five generalised meanings about seasonal changes were identified: 1) Climate; 2) Static Earth – Sun; 3) Moving Earth – Sun; 4) Rays – Earth; 5) Moving Earth–Orbit– Sun. The emerging generalised meanings were significantly associated with students’ written explanations and grade level. Moreover, generalised meanings were informed by the cultural conventions adopted in textbook representations about seasonal changes. Findings suggest that the SCPT framework can be productively used to analyse students-generated drawings about natural phenomena and to detect specific features of textbook representations that can impair the development of appropriate scientific knowledge.

The present study proposed an application of the Rahim’ Model of Conflict Management, and aimed to explore the styles of handling interpersonal conflicts with students adopted by teachers from five European countries (Italy, Spain, Germany, Belgium, Austria), identifying specific patterns and evaluating potential differences according to teachers' Gender, Age, Working Seniority and Country of belonging. Overall, 589 secondary school teachers completed a questionnaire consisting of Socio-demographic characteristics and the Rahim Organizational Conflict Inventory-II (ROCI-II, Form B). Non-hierarchical k-means cluster analysis was employed to derive patterns of conflict management, identifying four patterns labelled as Multi-strategic and Engaged, Multi-strategic and Solution-Oriented, Control-Oriented and Avoidant, and Mediating. Significant differences between countries were found in the numbers of teachers grouped across the four patterns. Findings identified stable and meaningful patterns for evaluating teachers' styles of management of interpersonal conflicts with students and for promoting teachers’ effectiveness in conflict management in the European school context.

Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement. The estimated trajectories were used for a subsequent K-medians cluster analysis to categorize the longitudinal pain score patterns into distinct clusters. For each cluster, a mixture regression model estimated the association between pain score and time to discharge adjusting for confounding. The fitted regression model generated the pain trajectory pattern for given cluster. Finally, regression analyses examined the association between pain trajectories and patient outcomes. A total of 3442 surgeries were identified with a median of 22 pain scores at an academic hospital during 2009–2016. Four pain trajectory patterns were identified and one was associated with higher rates of outcomes. In conclusion, we described a novel approach with fast implementation to model patients’ pain experience using electronic health records. In the era of big data science, clinical research should be learning from all available data regarding a patient’s episode of care instead of focusing on the “average” patient outcomes.

Background Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients’ clinical phenotypes and analyse the differential clinical course. Methods We performed a hierarchical cluster analysis based on Ward’s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients’ prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27–3.62; HR 3.42, 95%CI 2.72–4.31; HR 2.79, 95%CI 2.32–3.35), and Cluster 1 (HR 1.88, 95%CI 1.48–2.38; HR 2.50, 95%CI 1.98–3.15; HR 2.09, 95%CI 1.74–2.51) reported a higher risk for the three outcomes respectively. Conclusions In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.

In the cruise industry, there has recently been a considerable increase of interest in managing the on-board servicescape by recognizing the physical environment as an important driver of competitive advantage to provide a memorable tourism experience. However, few studies have focused on this issue in the specific context of the cruise industry, and even fewer studies have used this dimension as basis for clustering passengers. Adopting an experiential marketing approach, the aim of this study was to profile cruise passengers based on their perceived on-board servicescape and experience. Exploratory factor analysis (EFA) and Hierarchical Cluster Analysis were applied to data from a sample of 417 cruise passengers interviewed in 2017 at the port of call of Naples (Italy). The EFA revealed interesting insights by disconfirming the existing literature through the identification of new latent subfactors. Four clusters profiling interesting passenger types that are useful for customizing the on-board experience were identified. Moreover, analysis of variance (ANOVA) was employed to determine whether the different types of cruisers in their response to behavioral intentions (i.e. WOM and e-WOM). The empirical findings enrich the existing literature on the cruise experience by introducing an original model of segmentation; they provide some practical insights for cruise managers to develop customized on-board service offerings. Finally, research opportunities based on these findings are also discussed.

BACKGROUND Recent evidence suggests that neutralizing antibodies (nAbs) to severe acute respiratory syndrome coronavirus 2 may persist over time; however, knowledge regarding pediatric subjects is limited. METHODS A single-center, prospective observational study was conducted on 57 family clusters of coronavirus disease 2019, including children of neonatal and pediatric age attending the University Hospital of Padua (Italy). For each patient, blood samples were collected for both the quantification of nAbs through a plaque reduction neutralizing test and the detection of antinucleocapsid-spike protein immunoglobulin G and/or immunoglobulin M. RESULTS We analyzed 283 blood samples collected from 152 confirmed coronavirus disease 2019 cases (82 parents and 70 children or older siblings of median age of 8 years, interquartile range: 4–13), presenting asymptomatic or with mildly symptomatic disease. Despite the decrease of immunoglobulin G over time, nAbs were found to persist up to 7 to 8 months in children, whereas adults recorded a modest declining trend. Interestingly, children aged CONCLUSIONS Mild and asymptomatic severe acute respiratory syndrome coronavirus 2 infections in family clusters elicited higher nAbs among children.

Background No simple classification system has emerged for 'advanced basal cell carcinomas', and more generally for all difficult-to-treat BCCs (DTT-BCCs), due to the heterogeneity of situations, TNM inappropriateness to BCCs, and different approaches of different specialists. Objective To generate an operational classification, using the unconscious ability of experts to simplify the great heterogeneity of the clinical situations into a few relevant groups, which drive their treatment decisions. Method Non-supervised independent and blinded clustering of real clinical cases of DTT-BCCs was used. Fourteen international experts from different specialties independently partitioned 199 patient cases considered 'difficult to treat' into as many clusters they want (≤10), choosing their own criteria for partitioning. Convergences and divergences between the individual partitions were analyzed using the similarity matrix, K-mean approach, and average silhouette method. Results There was a rather consensual clustering of cases, regardless of the specialty and nationality of the experts. Mathematical analysis showed that consensus between experts was best represented by a partition of DTT-BCCs into five clusters, easily recognized a posteriori as five clear-cut patterns of clinical situations. The concept of 'locally advanced' did not appear consistent between experts. Conclusion Although convergence between experts was not granted, this experiment shows that clinicians dealing with BCCs all tend to work by a similar pattern recognition based on the overall analysis of the situation. This study thus provides the first consensual classification of DTT-BCCs. This experimental approach using mathematical analysis of independent and blinded clustering of cases by experts can probably be applied to many other situations in dermatology and oncology.

Higher education institutions are recognised as settings where the community’s awareness of sustainable mobility can be strengthened or reshaped. The first objective of the present study was to identify groups of commuters based on their modal choice in a large higher education institution in Italy. The second objective was to compare the groups on socio-demographic and psychosocial variables, specifically attitudes, personal norms, personal constraints, and travel satisfaction. The cluster analysis revealed five different types of commuters: car-oriented, two-wheeled urban users, pedestrians, long-distance commuters, and regular bus users. Attitudes, personal constraints and norms, and satisfaction differed in the five groups of commuters. The present study provides insights for behavioural change programmes and organizational policies on sustainable mobility.

Background Among people living with HIV (PLWH), the prevalence of non-HIV related co-morbidities is increasing. Aim of the present study is to describe co-morbidity and multi-morbidity, their clustering mode and the potential disease-disease interactions in a cohort of Italian HIV patients. Methods Cross-sectional analysis conducted by the Coordinamento Italiano per lo Studio di Allergia e Infezioni da HIV (CISAI) on adult subjects attending HIV-outpatient facilities. Non-HIV co-morbidities included: cardiovascular disease, diabetes mellitus, hypertension, oncologic diseases, osteoporosis, probable case of chronic obstructive pulmonary disease (COPD), hepatitis C virus (HCV) infection, psychiatric illness, kidney disease. Multi-morbidity was defined as the presence of two or more co-morbidities. Results One thousand and eighty-seven patients were enrolled in the study (mean age 47.9 ± 10.8). One hundred-ninety patients (17.5%) had no co-morbidity, whereas 285 (26.2%) had one condition and 612 (56.3%) were multi-morbid. The most recurrent associations were: 1) dyslipidemia + hypertension (237, 21.8%); 2) dyslipidemia + COPD (188, 17.3%); 3) COPD + HCV-Ab+ (141, 12.9%). Multi-morbidity was associated with older age, higher body mass index, current and former smoking, CDC stage C and longer ART duration. Conclusions More than 50% of PLHW were multi-morbid and about 30% had three or more concurrent comorbidities. The identification of common patterns of comorbidities address the combined risks of multiple drug and disease-disease interactions.

Multivariate statistical analysis is a useful method for supporting the interpretation of experimental data, particularly in the case of large datasets. In the present study, cluster analysis and factor analysis are used to support the hydrochemical characterization of groundwater and surface water in an area located in the Oglio River basin (N Italy). During a field survey performed in Fall 2015, 58 groundwater, 20 river (Oglio River and its main tributaries), 1 Lake Iseo and 7 spring samples were collected for chemical analysis. Results of multivariate statistical analysis led to the identification of the following 5 main clusters which characterize the hydrological system: (1) higher plain groundwater and springs, characterized by an oxidized hydrofacies with higher NO3, (2) lower plain groundwater, characterized by a reduced hydrofacies with higher As, Fe and Mn, (3) Oglio River, (4) Oglio River tributaries and (5) outliers. This characterization will bear the improvement of the hydrogeological conceptual model of the area, also oriented to groundwater/surface water interactions, that, in turn, will support the numerical flow modeling of the system