Functioning issues in inpatients affected by COVID-19-related moderate pulmonary impairment: a real-practice observational study

Objective To investigate the correlations between clinical, functional, and radiological outcomes in inpatients with coronavirus disease 2019 (COVID-19). Methods In this observational study, we recruited inpatients affected by moderate COVID-19 disease. The clinical evaluation comprised the Cumulative Illness Rating Scale (CIRS), numerical rating scale (NRS), modified Rankin scale (mRS), and the modified Borg dyspnea scale (mBDS). Respiratory involvement was assessed with computed tomography (CT) and graded with a CT-severity score (CT-SS). We retrospectively assessed functioning using the International Classification of Functioning, Disability and Health (ICF) codes of the Clinical Functioning Information Tool (ClinFIT) COVID-19 in the acute phase. Correlation analysis was performed 1) between clinical, instrumental, and functional parameters and 2) between ICF categories. Results The data showed statistically significant moderate correlations between CT-SS and the following categories: b152 “emotional functions” and b440 “respiratory functions”. Conclusion This is the first study to use the ICF framework in people with a moderate form of COVID-19 in the acute phase. Considering the correlations between some ICF categories and radiological findings, our results support the use of the ClinFIT COVID-19 for a comprehensive assessment of COVID-19 patients

ENPP1 Affects Insulin Action and Secretion: Evidences from In Vitro Studies

The aim of this study was to deeper investigate the mechanisms through which ENPP1, a negative modulator of insulin receptor (IR) activation, plays a role on insulin signaling, insulin secretion and eventually glucose metabolism. ENPP1 cDNA (carrying either K121 or Q121 variant) was transfected in HepG2 liver-, L6 skeletal muscle- and INS1E beta-cells. Insulin-induced IR-autophosphorylation (HepG2, L6, INS1E), Akt-Ser473, ERK1/2-Thr202/Tyr204 and GSK3-beta Ser9 phosphorylation (HepG2, L6), PEPCK mRNA levels (HepG2) and 2-deoxy-D-glucose uptake (L6) was studied. GLUT 4 mRNA (L6), insulin secretion and caspase-3 activation (INS1E) were also investigated. Insulin-induced IR-autophosphorylation was decreased in HepG2-K, L6-K, INS1E-K (20%, 52% and 11% reduction vs. untransfected cells) and twice as much in HepG2-Q, L6-Q, INS1E-Q (44%, 92% and 30%). Similar data were obtained with Akt-Ser473, ERK1/2-Thr202/Tyr204 and GSK3-beta Ser9 in HepG2 and L6. Insulin-induced reduction of PEPCK mRNA was progressively lower in untransfected, HepG2-K and HepG2-Q cells (65%, 54%, 23%). Insulin-induced glucose uptake in untransfected L6 (60% increase over basal), was totally abolished in L6-K and L6-Q cells. GLUT 4 mRNA was slightly reduced in L6-K and twice as much in L6-Q (13% and 25% reduction vs. untransfected cells). Glucose-induced insulin secretion was 60% reduced in INS1E-K and almost abolished in INS1E-Q. Serum deficiency activated caspase-3 by two, three and four folds in untransfected INS1E, INS1E-K and INS1E-Q. Glyburide-induced insulin secretion was reduced by 50% in isolated human islets from homozygous QQ donors as compared to those from KK and KQ individuals. Our data clearly indicate that ENPP1, especially when the Q121 variant is operating, affects insulin signaling and glucose metabolism in skeletal muscle- and liver-cells and both function and survival of insulin secreting beta-cells, thus representing a strong pathogenic factor predisposing to insulin resistance, defective insulin secretion and glucose metabolism abnormalities

On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection

Coronavirus covid-19 detection by means of explainable deep learning

Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95

THE BAROQUE MONUMENTS OF MODICA (EASTERN SICILY): ASSESSMENT OF CAUSES OF CHROMATIC ALTERATION OF STONE BUILDING MATERIALS

Published on line 08 april 201

2-Hydroxypropyl-β-cyclodextrin strongly improves water solubility and anti-proliferative activity of pyrazolo[3,4-d]pyrimidines Src-Abl dual inhibitors.

The main aim of this study was to enhance the solubility of pyrazolo[3,4-d]pyrimidines 1-8 able to strongly inhibit Src and Abl tyrosine kinase phosphorylation in cell-free assays and to significantly reduce leukemic and osteosarcoma cell lines growth, but characterized by very low solubility in aqueous media. Their water solubility was improved between 100 and 1000 folds by solubilization with 2-hydroxypropyl-β-cyclodextrin (HPβCD) and ratio of inclusion complex were determined by phase solubility method. Finally, some complexed compounds were tested on different leukemic (K-652, KU-812 and HL-60) and osteosarcoma (SaOS-2) cell lines showing a good enhancement of biological response in comparison with the not complexed compounds