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Il clustering è una tecnica fondamentale in molte discipline scientifiche, utilizzata nell'analisi di innumerevoli tipi di dati. Il suo obiettivo primario è di suddividere un insieme di dati non etichettato in gruppi distinti e intrinseci, rivelando potenziali strutture sottostanti o segmentando i dati in base all'importanza definita dal ricercatore. Tra l'ampia gamma di tipi di dati che possono essere usati nel clustering, questa tesi si concentra sui dati di tipo misto, caratterizzati dalla presenza simultanea di diverse scale di misurazione all'interno di un singolo set di dati. Esempi di tali dati possono essere tratti dall'ambito medico, dove il record di un paziente potrebbe includere misurazioni numeriche come età o indice di massa corporea, indicatori binari per la presenza o l'assenza di sintomi specifici, dati categorici come il gruppo sanguigno e valori ordinali come la valutazione della gravità del dolore. Sebbene i dati di tipo misto presentino una ricca fonte di informazioni, la loro eterogeneità pone sfide complesse, in particolare nel clustering, dove è cruciale trovare un equilibrio tra questi diversi tipi di dati. In aggiunta alle sfide di usare il clustering su dati di tipo misto, emerge un ulteriore strato di complessità dato dagli errori intrinseci presenti nei dati raccolti nel mondo reale. È un'ipotesi idealistica per gli algoritmi di clustering tradizionali aspettarsi dati senza errori. Fattori come le imperfezioni nella raccolta di dati dovute al ricordo umano o alle inesattezze degli strumenti rendono infatti tali errori ampiamente presenti. Sebbene questi errori di misurazione abbiano il potenziale di influenzare profondamente l'accuratezza dei risultati del clustering, non c'è certezza che i metodi di clustering disponibili e più utilizzati siano robusti rispetto al recupero del cluster quando il modello di dati è nascosto dagli errori, poiché gli algoritmi non sono generalmente testati in queste situazioni. In particolare, l', Clustering is a cornerstone technique in many scientific disciplines, employed in the analysis of myriad data types. Its primary goal is to partition an unlabeled dataset into distinct, intrinsic groupings, revealing potential underlying structures or segmenting data based on researcher-defined significance. The emphasis is therefore on data understanding. Among the wide range of data types that can be clustered, this thesis focuses on mixed-type data, characterized by the simultaneous presence of diverse measurement scales within a singular dataset. Examples of such data can be drawn from healthcare, where a patient's record might encompass numerical measurements such as age or body mass index, binary indicators for the presence or absence of specific symptoms, categorical data like blood type, and ordinal values such as pain severity ratings. While mixed-type data presents a rich reservoir of information, its heterogeneity poses complex challenges, particularly in clustering, where striking a balance among these diverse data types is crucial. Building on the challenges of clustering mixed-type data, an additional layer of complexity arises from the inherent errors present in real-world data collection. It is an idealistic assumption for traditional clustering algorithms to expect error-free data, as the variability inherent in human recall or sensor inaccuracies underscores the ubiquity of such perturbations. While these measurement errors have the potential to profoundly influence the accuracy of clustering results, there is no assurance that the available and most used clustering methods are robust with respect to cluster recovery when the data pattern is hide by errors, since algorithms are not generally tested in these situations. Especially the intersection of measurement errors and mixed-type data clustering remains an underexplored domain. In this thesis, we investigated the relatively uncharted territory of mixed-data clustering in the presence of measurem

The vacuolar proton-translocating ATPase (V-ATPase) is a transmembrane multi-protein complex fundamental in maintaining a normal intracellular pH. In the tumoral contest, its role is crucial since the metabolism underlying carcinogenesis is mainly based on anaerobic glycolytic reactions. Moreover, neoplastic cells use the V-ATPase to extrude chemotherapy drugs into the extra-cellular compartment as a drug resistance mechanism. In glioblastoma (GBM), the most malignant and incurable primary brain tumor, the expression of this pump is upregulated, making it a new possible therapeutic target. In this work, the bafilomycin A1-induced inhibition of V-ATPase in patient-derived glioma stem cell (GSC) lines was evaluated together with temozolomide, the first-line therapy against GBM. In contrast with previous published data, the proposed treatment did not overcome resistance to the standard therapy. In addition, our data showed that nanomolar dosages of bafilomycin A1 led to the blockage of the autophagy process and cellular necrosis, making the drug unusable in models which are more complex. Nevertheless, the increased expression of V-ATPase following bafilomycin A1 suggests a critical role of the proton pump in GBM stem components, encouraging the search for novel strategies to limit its activity in order to circumvent resistance to conventional therapy.

Purpose: Antibodies against SARS-CoV-2 spike (anti-S) may confer protection against symptomatic COVID-19. Whether their level predicts progression among those with COVID-19 pneumonia remains unclear. Methods: We conducted a retrospective cohort study to assess predictors of anti-S levels and whether anti-S titer is associated with death or mechanical ventilation (MV). Adults hospitalized for COVID-19 pneumonia between July 2021 and July 2022 were enrolled if anti-S had been measured within 72 h of admission. Predictors of anti-S level were explored using multivariable quantile regression. The association between anti-S levels and 30-day death/MV was investigated via multivariable logistic regression. Analyses were stratified by vaccine status. Results: The median anti-S level was 1370 BAU/ml in 328 vaccinated and 15.5 BAU/ml in 206 unvaccinated individuals. Among the vaccinated, shorter symptom duration (p = 0.001), hematological malignancies (p = 0.002), and immunosuppressive therapy (p = 0.004) were associated with lower anti-S levels. In the unvaccinated group, symptom duration was the only predictor of anti-S levels (p < 0.001). After 30 days, 134 patients experienced death or MV. Among vaccinated individuals, higher anti-S levels correlated significantly with lower death/MV risk (per log2 increase, OR 0.88, 95%CI 0.81-0.97), irrespective of age and solid malignancies. Among unvaccinated, a marginally protective effect was observed (OR 0.86, 95%CI 0.73-1.01), independent of age, immunosuppressive therapy, and diabetes. Adjustment for monoclonal antibody treatment strengthened the association (OR 0.81, 95%CI 0.68-0.96). Conclusion: This study suggests that levels of anti-S antibodies can predict critical or fatal outcomes in COVID-19 pneumonia patients, regardless of vaccination. Whether anti-S Ab could guide risk assessment and vaccination boosting merits further evaluation.

Background: Nonpharmacological approaches are recommended as first-line treatment for the neuropsychiatric symptoms of persons living with dementia. Tailored Activity Program is a non-pharmacological approach which has shown to reduce neuropsychiatric symptoms and functional dependence of persons living with dementia at the community, as well as caregiver’s distress. The aim of the study is to assess the feasibility of a project in which the Tailored Activity Program is delivered partially at an acute hospital ward and partially at the patient's home. Material and Methods: The study is aimed at enrolling 50 dyads (patients/caregivers) from the memory clinic, acute geriatric ward, or short-stay emergency department at the IRCCS San Gerardo dei Tintori, Monza (Italy). Inclusion criteria are: the patient’s clinical diagnosis of dementia, clinically detectable neuropsychiatric symptoms (Neuro Psychiatric Inventory, NPI score >6 in at least one item), the ability to participate in activities of daily living (Katz’s index ≥ 2), the presence of a caregiver (aged ≥ 21 years; minimum 4 hours of daily care), and the willingness of the dyad patient/caregiver to participate in the project. Treatment is delivered both i) in hospital (2 sessions) and ii) at home (6 sessions), including occupational therapy, telemedicine and nursing care provided once a week. The following variables are measured: Neuro Psychiatric Inventory, Time Up and Go Test, Katz’s Index and the level of caregiver’s sense of competence with the Sense of Competence Questionnaire. Data are collected both at baseline, at the end of the treatment, with a follow-up scheduled at 4 months after enrollment. Primary outcome measure is the dyad's retention rate at 4-month follow-up. Secondary outcomes are the reduction in the patient’s frequency and severity of NPS; the improvement in the patient’s Time Up and Go Test score; the improvement in the patient's levels of autonomy and participation in activities of daily

Diagnostic criteria for juvenile myelomonocytic leukemia (JMML) are currently well defined, however in some patients diagnosis still remains a challenge. Flow cytometry is a well established tool for diagnosis and follow-up of hematological malignancies, nevertheless it is not routinely used for JMML diagnosis. Herewith, we characterized the CD34+ hematopoietic precursor cells collected from 31 children with JMML using a combination of standardized EuroFlow antibody panels to assess the ability to discriminate JMML cells from normal/reactive bone marrow cell as controls (n=29) or from cells of children with other hematological diseases mimicking JMML (n=9). CD34+ precursors in JMML showed markedly reduced B-cell and erythroid-committed precursors compared to controls, whereas monocytic and CD7+ lymphoid precursors were significantly expanded. Moreover, aberrant immunophenotypes were consistently present in CD34+ precursors in JMML, while they were virtually absent in controls. Multivariate logistic regression analysis showed that combined assessment of the number of CD34+CD7+ lymphoid precursors and CD34+ aberrant precursors or erythroid precursors had a great potential in discriminating JMMLs versus controls. Importantly our scoring model allowed highly efficient discrimination of truly JMML versus patients with JMML-like diseases. In conclusion, we show for the first time that CD34+ precursors from JMML patients display a unique immunophenotypic profile which might contribute to a fast and accurate diagnosis of JMML worldwide by applying an easy to standardize single eight-color antibody combination.

Background: Prone positioning (PP) is routinely used among patients with COVID-19 requiring mechanical ventilation (MV). However, its utility among spontaneously breathing patients is still debated. Methods: In an open-label randomised controlled trial, we enrolled patients hospitalised with mild COVID-19 pneumonia, whose PaO2/FiO2 ratio (P/F) was >200 mmHg and who did not require MV or Continuous Positive Airway Pressure (CPAP) at hospital admission. Patients were randomised 1:1 to PP on top of standard of care (intervention group) versus standard of care only (controls). The primary composite outcome included death, MV, CPAP and P/F <200 mmHg; secondary outcomes were oxygen weaning and hospital discharge. Results: Sixty-one subjects were enrolled, 29 adjudicated to PP and 32 to the control group. By day 28, 24/61 (39.3%) patients met the primary outcome: 16 because of P/F ratio <200 mmHg, 5 CPAP and 3 MV. Three patients died. Using an intention-to-treat approach, 15/29 patients in PP group versus 9/32 controls met the primary outcome, corresponding to a significantly higher risk of progression among those randomised to PP (HR 2.38 [95%CI 1.04-5.43]; p=0.040). Using an as-treated approach, which included in the intervention group only patients who maintained PP for ≥3 h·day-1, no significant differences were found between the two groups (HR 1.77; [95%CI 0.79-3.94]; p=0.165). Also, we did not find any statistically significant difference in terms of time to oxygen weaning or hospital discharge between study arms, in any of the analyses conducted. Conclusions: We observed no clinical benefit from PP among spontaneously breathing patients with COVID-19 pneumonia requiring conventional oxygen therapy.

Diagnostic criteria for juvenile myelomonocytic leukemia (JMML) are currently well defined, however in some patients diagnosis still remains a challenge. Flow cytometry is a well-established tool for diagnosis and follow-up of hematological malignancies, nevertheless it is not routinely used for JMML diagnosis. Herewith, we characterized the CD34+ hematopoietic precursor cells collected from 31 children with JMML using a combination of standardized EuroFlow antibody panels to assess the ability to discriminate JMML cells from normal/reactive bone marrow cell as controls (n=29) or from cells of children with other hematological diseases mimicking JMML (n=9). CD34+ precursors in JMML showed markedly reduced B and erythroid-committed precursors compared to controls, whereas monocytic and CD7+ lymphoid precursors were significantly expanded. Moreover, aberrant immunophenotypes were consistently present in CD34+ precursors in JMML, while they were virtually absent in controls. Multivariate logistic regression analysis showed that combined assessment of the number of CD34+CD7+ lymphoid precursors and CD34+ aberrant precursors or erythroid precursors had a great potential in discriminating JMMLs vs controls. Importantly our scoring model allowed highly efficient discrimination of truly JMML vs patients with JMML-like diseases. In conclusion, we show for the first time that CD34+ precursors from JMML patients display a unique immunophenotypic profile which might contribute to a fast and accurate diagnosis of JMML worldwide by applying an easy to standardize single 8-color antibody combination.

Background It is not known whether, in children and adolescents with alterations in weight and/or blood pressure (BP), lifestyle modifications are associated with an improvement of early cardiac damage.Methods In a pediatric population referred for excess weight, high BP, or both (n = 278, 10.6 (2.3) years), echocardiography was performed at enrollment and after 15 months of follow-up, during which participants received nonpharmacological treatment, based on correcting unhealthy lifestyles and improving dietary habits. Left ventricular mass was indexed for height (g/m(2.7), LVMI), and an LVMI value higher than or equal to age- and gender-specific 95(th) percentile was the criterion for defining left ventricular hypertrophy (LVH). Multiple linear and logistic regression analyses were carried out to determine associations between changes in BMI and BP z-scores and changes of LVMI values and LVH prevalence, from baseline to follow-up.Results At baseline, 33.1% of study participants were hypertensive, 52.9% obese, and 36.3% had LVH. At follow-up, the prevalence of hypertension, obesity, and LVH was 18.7%, 30.2%, and 22.3%, respectively (p < 0.001 for all). A decrease in LVMI from 37.1 to 35.2 g/m(2.7) (p < 0.001) was observed. Only delta BMI z-score positively related to an improvement of LVMI. Reductions of BMI (OR = 0.22, 95% CI 0.07-0.64) and diastolic BP (OR = 0.64, 95% CI 0.42-0.93) z-scores from baseline to follow-up and family history of hypertension (OR = 0.36, 95% CI 0.16-0.78) were associated with a lower prevalence of LVH.Conclusions In a pediatric population at cardiovascular risk, changing incorrect lifestyle and dietary habits is associated with both reduction of BMI and BP values and regression of early cardiac damage.

Background: Magnetic resonance cholangiopancreatography (MRCP) is the gold standard for diagnosis of patients with primary sclerosing cholangitis (PSC). The semi-quantitative MRCP-derived Anali scores proposed for risk stratification, have poor-to-moderate inter-reader agreement. Aims: To evaluate the prognostic performance of quantitative MRCP metrics in PSC. Methods: This is a retrospective study of PSC patients undergoing MRCP. Images were processed using MRCP+ software (Perspectum Ltd, Oxford) that provides quantitative biliary features, semi-automatically extracted by artificial intelligence-driven analysis of MRCP-3D images. The prognostic value of biliary features has been assessed for all hepato-biliary complications. Results: 87 PSC patients have been included in the analysis. Median follow-up from MRCP to event/censoring of 30.9 months (Q1-Q3=13.6–46.6). An adverse outcome occurred in 27 (31.0%) patients. The number of biliary strictures (HR=1.05 per unit, 95%CI 1.02–1.08, p < 0.0001), spleen length (HR=1.16 per cm, 95%CI 1.01–1.34, p = 0.039), adjusted for height, age at MRCP, and time from diagnosis to MRCP predicted higher risk of hepatobiliary complications. These were incorporated into a the quantitative MRCP-derived PSC (qMRCP-PSC) score (C-statistic=0.80). After 3-fold cross-validation, qMRCP-PSC outperformed the Anali score in our cohort (C-statistic of 0.78 vs 0.64) and enabled the discrimination of survival of PSC patients (log-rank p < 0.0001). Conclusions: The qMRCP-PSC score identified patients at higher risk of hepatobiliary complications and outperformed the available radiological scores. It represents a novel quantitative biomarker for disease monitoring and a potential surrogate endpoint for clinical trials.