Glucose tolerance stages in Cystic Fibrosis are idenfied by a unique pattern of defects of Beta-cell function

To assess the order of severity of the defects of three direct determinants of glucose regulation, i.e., beta-cell function, insulin clearance and insulin sensitivity, in patients with CF categorized according their glucose tolerance status, including early elevation of mid-OGTT glucose values (>140 and < 200 mg/dL), named AGT140

Has COVID-19 Delayed the Diagnosis and Worsened the Presentation of Type 1 Diabetes in Children?

Objective: To evaluate whether the diagnosis of pediatric type 1 diabetes or its acute complications changed during the early phase of the coronavirus disease 2019 (COVID-19) pandemic in Italy. Research design and methods: This was a cross-sectional, Web-based survey of all Italian pediatric diabetes centers to collect diabetes, diabetic ketoacidosis (DKA), and COVID-19 data in patients presenting with new-onset or established type 1 diabetes between 20 February and 14 April in 2019 and 2020. Results: Fifty-three of 68 centers (77.9%) responded. There was a 23% reduction in new diabetes cases in 2020 compared with 2019. Among those newly diagnosed patient who presented in a state of DKA, the proportion with severe DKA was 44.3% in 2020 vs. 36.1% in 2019 (P = 0.03). There were no differences in acute complications. Eight patients with asymptomatic or mild COVID-19 had laboratory-confirmed severe acute respiratory syndrome coronavirus 2. Conclusions: The COVID-19 pandemic might have altered diabetes presentation and DKA severity. Preparing for any "second wave" requires strategies to educate and reassure parents about timely emergency department attendance for non-COVID-19 symptoms

Diabetic ketoacidosis at the onset of disease during a national awareness campaign: a 2-year observational study in children aged 0-18 years

After a previous survey on the incidence of diabetic ketoacidosis (DKA) at onset of type 1 diabetes in children in 2013-2014 in Italy, we aimed to verify a possible decline in the incidence of DKA at onset during a national prevention campaign

The Silent Epidemic of Diabetic Ketoacidosis at Diagnosis of Type 1 Diabetes in Children and Adolescents in Italy During the COVID-19 Pandemic in 2020

To compare the frequency of diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes in Italy during the COVID-19 pandemic in 2020 with the frequency of DKA during 2017-2019

Diagnosis, treatment and prevention of pediatric obesity: consensus position statement of the Italian Society for Pediatric Endocrinology and Diabetology and the Italian Society of Pediatrics

The Italian Consensus Position Statement on Diagnosis, Treatment and Prevention of Obesity in Children and Adolescents integrates and updates the previous guidelines to deliver an evidence based approach to the disease. The following areas were reviewed: (1) obesity definition and causes of secondary obesity; (2) physical and psychosocial comorbidities; (3) treatment and care settings; (4) prevention.The main novelties deriving from the Italian experience lie in the definition, screening of the cardiometabolic and hepatic risk factors and the endorsement of a staged approach to treatment. The evidence based efficacy of behavioral intervention versus pharmacological or surgical treatments is reported. Lastly, the prevention by promoting healthful diet, physical activity, sleep pattern, and environment is strongly recommended since the intrauterine phase

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

SIMPLE SUMMARY: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. ABSTRACT: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs