Mobile learning between opportunities and risks. A critical thinking

Muchas veces se concibe la introducción de tecnologías digitales en las instituciones educativas de forma acrítica, como una panacea en grado de mejorar por sí misma las prácticas y potenciar el rendimiento. Tales expectativas se fundan en una visión que considera las tecnologías como un factor autónomo y aislado, independientemente de las actividades para las cuales se emplean, de su significado y de las expectativas que el usuario les atribuye, además de las relaciones sociales y los contextos culturales y organizativos en las cuales son introducidas y utilizadas. El riesgo que se presenta para las tecnologías de aprendizaje electrónico móvil (m-Learning) es que sean objeto de la misma parcialidad interpretativa. La finalidad de nuestro artículo es presentar las aplicaciones de las mencionadas tecnologías, así como sus perspectivas de desarrollo.Often the introduction of the digital technologies in higher education is considered in itself as a panacea capable of improving its performance. These expectations are based on a theoretical framework that considers the technologies as isolated and autonomous factors, regardless of the activities for which they are employed and independently of the meaning and expectations that its users will perceive as well as the socio-cultural and organizational contexts where they are introduced and used. We are facing the risk that also mobile learning technology could be object of this interpretative bias. Our article aims to present its applications and development prospects

Open innovation ed ecosistemi dell’innovazione: Nuovi trend a servizio del settore sanitario

La pandemia da COVID-19 ha generato cambiamenti dirompenti nel settore sanitario, trasformando drasticamente il funzionamento degli assetti organizzativi. L’obiettivo di questo contributo è analizzare le strategie di open innovation e gli ecosistemi dell’innovazione adottati all’interno del settore sanitario in risposta alla crisi pandemica

Whole-body radioiodine effective half-life in patients with differentiated thyroid cancer

Background: Radioactive 131I (RAI) therapy is used in patients with differentiated thyroid cancer (DTC) after total thyroidectomy for remnant ablation, adjuvant treatment or treatment of persistent disease. 131I retention data, which are used to indicate the time at which a 131I treated DTC patient can be released from the hospital, may bring some insights regarding clinical factors that prolong the length of hospitalization. The aim of this study was to investigate the 131I whole-body retention in DTC patients during 131I therapy. Methods: We monitored 166 DTC patients to follow the 131I whole-body retention during 131I therapy with a radioactivity detector fixed on the ceiling of each protected room. A linear regression fit permitted us to estimate the whole-body 131I effective half-life in each patient, and a relationship was sought between patients’ clinical characteristics and whole-body effective 131I half-life. Results: The effective 131I half-life ranged from 4.08 to 56.4 h. At multivariable analysis, longer effective 131I half-life was related to older age and extensive extra-thyroid disease. Conclusions: 131I effective half-life during 131I treatment in DTC patients is highly variable among patients and is significantly longer in older and in patients with RAI uptake in large thyroid remnants or in extrathyroidal disease that significantly prolongs the whole-body retention of 131I

Initial Testing of an Approximated, Fast Calculation Procedure for Personalized Dosimetry in Radionuclide Therapy Based on Planar Whole-Body Scan and Monte-Carlo Specific Dose Rates from the OpenDose Project

Individualized dosimetry in nuclear medicine is currently at least advisable in order to obtain the best risk–benefit balance in terms of the maximal dose to lesions and under-threshold doses to radiosensitive organs. This article aims to propose a procedure for fast dosimetric calculations based on planar whole-body scintigraphy (WBS) images and developed to be employed in everyday clinical practice. Methods: For simplicity and legacy reasons, the method is based on planar imaging dosimetry, complemented with some assumptions on the radiopharmaceutical kinetics empirically derived from single-photon emission tomography/computed tomography (SPECT/CT) image analysis. The idea is to exploit a rough estimate of the time-integrated activity as has been suggested for SPECT/CT dosimetry but using planar images. The resulting further reduction in dose estimation accuracy is moderated by the use of a high-precision Monte-Carlo S-factor, such as those available within the OpenDose project. Results: We moved the problem of individualized dosimetry to a transformed space where comparing doses was imparted to the ICRP Average Male/Female computational phantom, resulting from an activity distribution related to patient’s pharmaceutical uptake. This is a fast method for the personalized dosimetric evaluation of radionuclide therapy, bearing in mind that the resulting doses are meaningful in comparison with thresholds calculated in the same framework. Conclusion: The simplified scheme proposed here can help the community, or even the single physician, establish a quantitative guide-for-the-eye approach to individualized dosimetry

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

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19