Instructional planning and new technologies in teacher education: the initial phase of a research project

The purpose of this work is to present the initial phase of a research project focused on the integration of technologies in the education of kindergarten and primary school student teachers through instructional planning. Firstly, we illustrate a tool designed for planning integrated learning units and describe the training path in which it was used. Secondly, we report the results of a preliminary study conducted with 96 students attending a university course that investigates two personal traits considered as prerequisites for using the tool: perceived proficiency in technology use, and opinions on the importance of each constituent element of the tool. With regard to both traits, some statistically significant variations emerged. The results obtained are an encouragement to continue the research project to verify whether the tool could be suitable to help student teachers develop an integrated planning procedure

BIOTEX-biosensing textiles for personalised healthcare management.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting

Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment

Objective: Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. Methods: The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). Results: In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. Conclusions: The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma's MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences

Acute kidney injury and chronic kidney disease after liver transplant: A retrospective observational study.

BACKGROUND AND RATIONALE Chronic kidney disease remains an important risk factor for morbidity and mortality among LT recipients, but its exact incidence and risk factors are still unclear. MATERIAL AND METHODS We carried out a retrospective cohort study of consecutive adults who underwent liver transplant (January 2009-December 2018) and were followed (at least 6 months) at our institution. CKD was defined following the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guidelines. Long-term kidney function was classified into 4 groups: no CKD (eGFR, ≥60mL/min/1.73m2), mild CKD (eGFR, 30-59mL/min/1.73m2), severe CKD (eGFR, 15-29mL/min/1.73m2), and end-stage renal disease (ESRD). RESULTS We enrolled 410 patients followed for 53.2±32.6 months. 39 had CKD at baseline, and 95 developed de novo CKD over the observation period. There were 184 (44.9%) anti-HCV positive, 47 (11.5%) HBsAg positive, and 33 (8.1%) HBV/HDV positive recipients. Recipient risk factors for baseline CKD were advanced age (P=0.044), raised levels of serum uric acid (P<0.0001), and insulin dependent DM (P=0.0034). Early post-transplant AKI was common (n=95); logistic regression analysis found that baseline serum creatinine was an independent predictor of early post-LT AKI (P=0.0154). According to our Cox proportional hazards model, recipient risk factors for de novo CKD included aging (P<0.0001), early post-transplant AKI (P=0.007), and baseline serum creatinine (P=0.0002). At the end of follow-up, there were 116 LT recipients with CKD - 109 (93.9%) and 7 (6.1%) had stage 3 and advanced CKD, respectively. Only two of them are undergoing long-term dialysis. CONCLUSION The incidence of CKD was high in our cohort of LT recipients, but only a slight decline in kidney function over time was recorded. Prevention of post-transplant AKI will improve kidney function in the long run. We need more studies to analyze the function of kidneys among LT recipients over extended follow-ups and their impact on mortality

Electrocardiographic and other noninvasive hemodynamic markers in decompensated CHF patients

cutely decompensated chronic heart failure (adCHF) is among the most important causes of in-hospital mortality. R-wave peak time (RpT) or delayed intrinsicoid deflection was proposed as a risk marker of sudden cardiac death and heart failure decompensation. Authors want to verify if QR interval or RpT, obtained from 12-lead standard ECG and during 5-min ECG recordings (II lead), could be useful to identify adCHF. At hospital admission, patients underwent 5-min ECG recordings, obtaining mean and standard deviation (SD) of the following ECG intervals: QR, QRS, QT, JT, and T peak–T end (Te). The RpT from a standard ECG was calculated. Patients were grouped by the age-stratified Januzzi NT-proBNP cut-off. A total of 140 patients with suspected adCHF were enrolled: 87 (mean age 83 ± 10, M/F 38/49) with and 53 (mean age: 83 ± 9, M/F: 23/30) without adCHF. V5-, V6- (p &lt; 0.05) RpT, and QRSD, QRSSD, QTSD, JTSD, and TeSD p &lt; 0.001 were significantly higher in the adCHF group. Multivariable logistic regression analysis demonstrated that the mean of QT (p &lt; 0.05) and Te (p &lt; 0.05) were the most reliable markers of in-hospital mortality. V6 RpT was directly related to NT-proBNP (r: 0.26, p &lt; 0.001) and inversely related to a left ventricular ejection fraction (r: 0.38, p &lt; 0.001). The intrinsicoid deflection time (obtained from V5-6 and QRSD) could be used as a possible marker of adCHF

Age-related clinical characteristics of children and adolescents with ADHD

IntroductionAttention deficit hyperactivity disorder (ADHD) has been associated with difficulties in regulating aversion states, high functional impairment, and a high risk of psychopathology across the lifespan. ADHD is clinically heterogeneous, with a wide spectrum of severity and associated symptoms. Clinical characteristics need to be carefully defined in different periods of life as ADHD course, symptoms, and comorbidities may fluctuate and change over time. Adolescence usually represents the transition from primary to secondary education, with a qualitative and quantitative change in environmental and functional demands, thus driving symptoms’ change.MethodsIn order to characterize age-related clinical features of children (&lt;11 years) and adolescents (≥11 years) with ADHD, we conducted a naturalistic study on 750 children and adolescents assessed for ADHD at our Neuropsychiatry Unit over the course of 3 years (2018–2020).ResultsWe found that ADHD symptoms were significantly higher in children than adolescents. More importantly, we found worse global functioning, lower adaptive skills, higher levels of anxiety and depressive symptoms, somatic complaints, emotional dysregulation, social problems, and aggression in adolescents, despite a lower severity of ADHD-specific symptoms.ConclusionThese results should be confirmed in longitudinal observational studies of adequate sample size in order to reliably describe a potential course characterized by worsening of functioning, reduction in ADHD-specific symptoms and increase in general psychopathology during the transition from childhood to adolescence