Dessert freddi: criteri per la loro classificazione [Cold dessert: criteria for their classification]

The expression “cold desserts” names a heterogeneous group of sweet products, which includes gelati, ice cream, pudding, mousse and more. The present work develop an inventory of all the sweet products falling under the definition of “cold desserts”. To reach this aim, several databases (Mintel GNPD), pastry’s books, industrial, as well as artisanal, products are going to be consulted. Moreover, the research has the objective to advance a rational organization of the sweet products comprised by the “cold dessert”’s definition. Thanks to such a logical organization, it will be possible to develop homogenous groupings of products, according to their characteristics. The variables with the greatest discriminating and grouping power employed in this research are: the product’s temperature of consumption, its structure, and the modalities through which it incorporates the air. The main difference between industrial and artisanal gelato is indeed in the modalities through which the air is incorporated in the products and in their consequently different rheological properties

PITYOPHAGUS QUERCUS REITTER, 1877, A NEW SAPROXYLIC SAP BEETLE FOR THE ITALIAN FAUNA (Coleoptera, Nitidulidae)

During ecological investigations on saproxylic beetle communities of central Italy (Latium), at Bosco Polverino (a mixed evergreen/deciduous forest fragment dominated by cork oaks), and at Allumiere (a small fragment of beech forest surrounded by turkey oak stands), the authors found three specimens of Pityophagus quercus Reitter, 1877 (Coleoptera, Nitidulidae). These are the first known records of this species in Italy, and the first one in association with an evergreen oak, Quercus suber. This discovery led us to review both bionomical and faunistic data so far available on this exceedingly rare and poorly known species

Laparoscopic TME with APPEAR (Anterior and Perineal PlanE for ultra-low Anterior Resection of the Rectum) technique for distal rectal cancer

n/

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the “Sant”Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6–7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system

Digital detection of exosomes by interferometric imaging

Exosomes, which are membranous nanovesicles, are actively released by cells and have been attributed to roles in cell-cell communication, cancer metastasis, and early disease diagnostics. The small size (30–100 nm) along with low refractive index contrast of exosomes makes direct characterization and phenotypical classification very difficult. In this work we present a method based on Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS) that allows multiplexed phenotyping and digital counting of various populations of individual exosomes (>50 nm) captured on a microarray-based solid phase chip. We demonstrate these characterization concepts using purified exosomes from a HEK 293 cell culture. As a demonstration of clinical utility, we characterize exosomes directly from human cerebrospinal fluid (hCSF). Our interferometric imaging method could capture, from a very small hCSF volume (20 uL), nanoparticles that have a size compatible with exosomes, using antibodies directed against tetraspanins. With this unprecedented capability, we foresee revolutionary implications in the clinical field with improvements in diagnosis and stratification of patients affected by different disorders.This work was supported by Regione Lombardia and Fondazione Cariplo through POR-FESR, project MINER (ID 46875467); Italian Ministry of Health, Ricerca Corrente. This work was partially supported by The Scientific and Technological Research Council of Turkey (grant #113E643). (Regione Lombardia; 46875467 - Fondazione Cariplo through POR-FESR, project MINER; Italian Ministry of Health, Ricerca Corrente; 113E643 - Scientific and Technological Research Council of Turkey)Published versio

Role of cerebrospinal fluid biomarkers to predict conversion to dementia in patients with mild cognitive impairment: a clinical cohort study

Abstract Background: Cerebrospinal fluid (CSF) levels assessment of Aβ1-42 and Tau proteins may be accurate diagnostic biomarkers for the differentiation of preclinical Alzheimer's disease (AD) from age-associated memory impairment, depression and other forms of dementia in patients with mild cognitive impairment (MCI). The aim of our study was to explore the utility of CSF biomarkers in combination with common cognitive markers as predictors for the risk of AD development, and other forms of dementia, and the time to conversion in community patients with MCI. Methods: A group of 71 MCI patients underwent neurological assessment, extended neuropsychological evaluation, routine blood tests, ApoE determination, and lumbar puncture to dose t-tau, p-tau181, Aβ1-42. We investigated baseline CSF and neuropsychological biomarker patterns according to groups stratified with later diagnoses of AD conversion (MCI-AD), other dementia (MCI-NAD) conversion, or clinical stability (sMCI). Results: Baseline Aβ1-42 CSF levels were significantly lower in MCI-AD patients compared to both sMCI and MCI-NAD. Additionally, p-tau181 was higher in the MCI-AD group compared to sMCI. The MCI-AD subgroup analysis confirmed the role of Aβ1-42 in its predictive role of time to conversion: rapid converters had lower Aβ1-42 levels compared to slow converters. Logistic regression and survival analysis further supported the key predictive role of baseline Aβ1-42 for incipient AD and dementia-free survival. Conclusions: Our results confirm the key role of CSF biomarkers in predicting patient conversion from MCI to dementia. The study suggests that CSF biomarkers may also be reliable in a real world clinical setting

Comparing the capacity of nurses and nursing students in assessing patient problems during clinical internship: A descriptive comparative study

Objective: No studies were found in the literature which compared the capacity of nurses and nursing students to assess patient problems using the clinical cases followed during internship. Therefore, the aim of this study was to formulate a method for comparing these skills in cases followed during a practical clinical internship.Methods: The sample studied was made up of students of the degree course in nursing during their internship and by community nurses, both trained in using assessment. Each student identified a patient and carried out an assessment of the problems according to the functional patterns of M. Gordon; the nurses also simultaneously carried out the same activity without comparing their work with that of the students. A method was formulated for evaluating the correctness of the two evaluations.Results: The results relative to the assessment showed a percentage of correctness of 85.77% for the students and 91.28% for the nurses with a statistically significant difference (p = .027).Conclusions: The results obtained demonstrated that the students in the last year of their degree course in nursing had developed a good capacity of assessment during their internship in clinical practice in the community in line with the capacity of the nurses who taught them

Detailed design requirements of the TES spectrometer

This document is not to be reproduced, modified, adapted, republished, translated in any material form in whole or in part without the prior written permission of the authors.This document reports the set of requirements for the design and construction of the TES X-ray spectrometer prototype to be realized in the frame of the AHEAD2020 project WP15. These requirements have been identified, discussed and agreed by the WP15 partecipants during a series of meeting from the project start. For the main scientific requirements a verification method is proposed within this document to provide a guide for the test and integration phase. The document also shows some design implementation details with the purpose of better describe what the requirements are aimed to

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21–65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures

EPURAEA DEUBELI REITTER, 1898, A CONFIRMED SAPROXYLIC SAP BEETLE FOR THE ITALIAN FAUNA (Coleoptera, Nitidulidae)

During ecological investigations on saproxylic beetle communities at Monte Baldo (Veneto, Verona province), two specimens of Epuraea deubeli Reitter, 1898 (Coleoptera, Nitidulidae) were recently collected. It is the first known sure record of this species in Italy (previously known from Northern, Eastern, and Central Europe, southwards to Austria, and from Western Siberia)