Clinical Outcome and Immune Recovery after Adoptive Infusion of BPX-501 Cells (donor iC9-transduced T cells) in Children with Wiskott-Aldrich Syndrome (WAS) Given Alfa/Beta T-Cell Depleted HLA-Haploidentical Hematopoietic Stem Cell Transplantation (HSCT)

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Texture analysis and multiple-instance learning for the classification of malignant lymphomas

Background and objectives: Malignant lymphomas are cancers of the immune system and are characterized by enlarged lymph nodes that typically spread across many different sites. Many different histological subtypes exist, whose diagnosis is typically based on sampling (biopsy) of a single tumor site, whereas total body examinations with computed tomography and positron emission tomography, though not diagnostic, are able to provide a comprehensive picture of the patient. In this work, we exploit a data-driven approach based on multiple-instance learning algorithms and texture analysis features extracted from positron emission tomography, to predict differential diagnosis of the main malignant lymphomas subtypes. Methods: We exploit a multiple-instance learning setting where support vector machines and random forests are used as classifiers both at the level of single VOIs (instances) and at the level of patients (bags). We present results on two datasets comprising patients that suffer from four different types of malignant lymphomas, namely diffuse large B cell lymphoma, follicular lymphoma, Hodgkin's lymphoma, and mantle cell lymphoma. Results: Despite the complexity of the task, experimental results show that, with sufficient data samples, some cancer subtypes, such as the Hodgkin's lymphoma, can be identified from texture information: in particular, we achieve a 97.0% of sensitivity (recall) and a 94.1% of predictive positive value (precision) on a dataset that consists in 60 patients. Conclusions: The presented study indicates that texture analysis features extracted from positron emission tomography, combined with multiple-instance machine learning algorithms, can be discriminating for different malignant lymphomas subtypes

Clinical Outcome after Adoptive Infusion of BPX-501 Cells (donor T cells transduced with iC9 suicide gene) in Children with Thalassemia Major (TM) Given Alfa/Beta T-Cell Depleted HLA-Haploidentical Stem Cell Transplantation (HSCT)

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Reliability assessment of the 2018 classification case definitions of peri-implant health, peri-implant mucositis, and peri-implantitis

Background: The purpose of this study was to evaluate the reliability and accuracy in the assignment of the case definitions of peri-implant health and diseases according to the 2018 Classification of Periodontal and Peri-implant Diseases and Conditions. MethodsTen undergraduate students, 10 general dentists, and 10 experts in implant dentistry participated in this study. All examiners were provided with clinical and radiographic documentation of 25 dental implants. Eleven out the 25 cases were also accompanied by baseline readings. Examiners were asked to define all cases using the 2018 classification case definitions. Reliability among examiners was evaluated using the Fleiss kappa statistic. Accuracy was estimated using percentage of complete agreement and quadratic weighted kappa for pairwise comparisons between each rater and a gold standard diagnosis. ResultsThe Fleiss kappa was 0.50 (95% CI: 0.48 to 0.51) and the mean quadratic weighted kappa value was 0.544. Complete agreement with the gold standard diagnosis was achieved in 59.8% of the cases. Expertise in implantology affected accuracy positively (p < 0.001) while the absence of baseline readings affected it negatively (p < 0.001). ConclusionBoth reliability and accuracy in assigning case definitions to dental implants according to the 2018 classification were mostly moderate. Some difficulties arose in the presence of specific challenging scenarios

Effects of Albumin Treatment on Systemic and Portal Hemodynamics and Systemic Inflammation in Patients With Decompensated Cirrhosis

BACKGROUND & AIMS: We investigated the effect of albumin treatment (20% solution) on hypoalbuminemia, cardiocirculatory dysfunction, portal hypertension, and systemic inflammation in patients with decompensated cirrhosis with and without bacterial infections. METHODS: We performed a prospective study to assess the effects of long-term (12 weeks) treatment with low doses (1 g/kg body weight every 2 weeks) and high doses (1.5 g/kg every week) of albumin on serum albumin, plasma renin, cardiocirculatory function, portal pressure, and plasma levels of cytokines, collecting data from 18 patients without bacterial infections (the Pilot-PRECIOSA study). We also assessed the effect of short-term (1 week) treatment with antibiotics alone vs the combination of albumin plus antibiotics (1.5 g/kg on day 1 and 1 g/kg on day 3) on plasma levels of cytokines in biobanked samples from 78 patients with bacterial infections included in a randomized controlled trial (INFECIR-2 study). RESULTS: Circulatory dysfunction and systemic inflammation were extremely unstable in many patients included in the Pilot-PRECIOSA study; these patients had intense and reversible peaks in plasma levels of renin and interleukin 6. Long-term high-dose albumin, but not low-dose albumin, was associated with normalization of serum level of albumin, improved stability of the circulation and left ventricular function, and reduced plasma levels of cytokines (interleukin 6, granulocyte colony-stimulating factor, interleukin 1 receptor antagonist, and vascular endothelial growth factor) without significant changes in portal pressure. The immune-modulatory effects of albumin observed in the Pilot-PRECIOSA study were confirmed in the INFECIR-2 study. In this study, patients given albumin had significant reductions in plasma levels of cytokines. CONCLUSIONS: In an analysis of data from 2 trials (Pilot-PRECIOSA study and INFECIR-2 study), we found that albumin treatment reduced systemic inflammation and cardiocirculatory dysfunction in patients with decompensated cirrhosis. These effects might be responsible for the beneficial effects of albumin therapy on outcomes of patients with decompensated cirrhosis. ClinicalTrials.gov, Numbers: NCT00968695 and NCT03451292

Sympathetic nervous activation, mitochondrial dysfunction and outcome in acutely decompensated cirrhosis: the metabolomic prognostic models (CLIF-C MET)

Background and aims Current prognostic scores of patients with acutely decompensated cirrhosis (AD), particularly those with acute-on-chronic liver failure (ACLF), underestimate the risk of mortality. This is probably because systemic inflammation (SI), the major driver of AD/ACLF, is not reflected in the scores. SI induces metabolic changes, which impair delivery of the necessary energy for the immune reaction. This investigation aimed to identify metabolites associated with short-term (28-day) death and to design metabolomic prognostic models. Methods Two prospective multicentre large cohorts from Europe for investigating ACLF and development of ACLF, CANONIC (discovery, n=831) and PREDICT (validation, n=851), were explored by untargeted serum metabolomics to identify and validate metabolites which could allow improved prognostic modelling. Results Three prognostic metabolites strongly associated with death were selected to build the models. 4-Hydroxy-3-methoxyphenylglycol sulfate is a norepinephrine derivative, which may be derived from the brainstem response to SI. Additionally, galacturonic acid and hexanoylcarnitine are associated with mitochondrial dysfunction. Model 1 included only these three prognostic metabolites and age. Model 2 was built around 4-hydroxy-3-methoxyphenylglycol sulfate, hexanoylcarnitine, bilirubin, international normalised ratio (INR) and age. In the discovery cohort, both models were more accurate in predicting death within 7, 14 and 28 days after admission compared with MELDNa score (C-index: 0.9267, 0.9002 and 0.8424, and 0.9369, 0.9206 and 0.8529, with model 1 and model 2, respectively). Similar results were found in the validation cohort (C-index: 0.940, 0.834 and 0.791, and 0.947, 0.857 and 0.810, with model 1 and model 2, respectively). Also, in ACLF, model 1 and model 2 outperformed MELDNa 7, 14 and 28 days after admission for prediction of mortality. Conclusions Models including metabolites (CLIF-C MET) reflecting SI, mitochondrial dysfunction and sympathetic system activation are better predictors of short-term mortality than scores based only on organ dysfunction (eg, MELDNa), especially in patients with ACLF

Sympathetic nervous activation, mitochondrial dysfunction and outcome in acutely decompensated cirrhosis: the metabolomic prognostic models (CLIF-C MET)

Background and aims: Current prognostic scores of patients with acutely decompensated cirrhosis (AD), particularly those with acute-on-chronic liver failure (ACLF), underestimate the risk of mortality. This is probably because systemic inflammation (SI), the major driver of AD/ACLF, is not reflected in the scores. SI induces metabolic changes, which impair delivery of the necessary energy for the immune reaction. This investigation aimed to identify metabolites associated with short-term (28-day) death and to design metabolomic prognostic models. Methods: Two prospective multicentre large cohorts from Europe for investigating ACLF and development of ACLF, CANONIC (discovery, n=831) and PREDICT (validation, n=851), were explored by untargeted serum metabolomics to identify and validate metabolites which could allow improved prognostic modelling. Results: Three prognostic metabolites strongly associated with death were selected to build the models. 4-Hydroxy-3-methoxyphenylglycol sulfate is a norepinephrine derivative, which may be derived from the brainstem response to SI. Additionally, galacturonic acid and hexanoylcarnitine are associated with mitochondrial dysfunction. Model 1 included only these three prognostic metabolites and age. Model 2 was built around 4-hydroxy-3-methoxyphenylglycol sulfate, hexanoylcarnitine, bilirubin, international normalised ratio (INR) and age. In the discovery cohort, both models were more accurate in predicting death within 7, 14 and 28 days after admission compared with MELDNa score (C-index: 0.9267, 0.9002 and 0.8424, and 0.9369, 0.9206 and 0.8529, with model 1 and model 2, respectively). Similar results were found in the validation cohort (C-index: 0.940, 0.834 and 0.791, and 0.947, 0.857 and 0.810, with model 1 and model 2, respectively). Also, in ACLF, model 1 and model 2 outperformed MELDNa 7, 14 and 28 days after admission for prediction of mortality. Conclusions: Models including metabolites (CLIF-C MET) reflecting SI, mitochondrial dysfunction and sympathetic system activation are better predictors of short-term mortality than scores based only on organ dysfunction (eg, MELDNa), especially in patients with ACLF

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

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

Notulae to the Italian alien vascular flora: 14

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates, published elsewhere, and corrections are provided as Suppl. materia

Notulae to the Italian alien vascular flora: 14

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates, published elsewhere, and corrections are provided as Suppl. material