An integrated EEG and eye-tracking approach for the study of responding and initiating joint attention in Autism Spectrum Disorders.

Autism Spectrum Disorders (ASD) are characterised by impairment in joint attention (JA), which has two components: the response to JA and the initiation of JA. Literature suggests a correlation between JA and neural circuitries, although this link is still largely unexplored in ASD. In this pilot study, we aimed at investigating the neural correlates of responding and initiating JA in high-functioning children with ASD and evaluating the changes in brain function and visual pattern after six months of rehabilitative treatment using an integrated EEG/eye-tracking system. Our results showed that initiating and responding JA subtend both overlapping (i.e. frontal and temporal) and specialized (i.e. parietal for responding JA and occipital for initiating JA) neural circuitries. In addition, in a subgroup of subjects, we observed trends of changes in both brain activity and connectivity after rehabilitative treatment in both the two tasks, which were correlated with modifications in gaze measures. These preliminary results, if confirmed in a larger sample, suggest the feasibility of using the proposed multimodal approach to characterise JA-related brain circuitries and visual pattern in ASD individuals and to monitor longitudinal changes in response to rehabilitative intervention.

Efficacy of a remote web-based lung ultrasound training for nephrologists and cardiologists: a LUST trial sub-project.

Within the framework of the LUST trial (LUng water by Ultra-Sound guided Treatment to prevent death and cardiovascular events in high-risk end-stage renal disease patients), the European Renal and Cardiovascular Medicine (EURECA-m) working group of the European Renal Association-European Dialysis Transplant Association established a central core lab aimed at training and certifying nephrologists and cardiologists participating in this trial. All participants were trained by an expert trainer with an entirely web-based programme. Thirty nephrologists and 14 cardiologists successfully completed the training. At the end of training, a set of 47 lung ultrasound (US) videos was provided to trainees who were asked to estimate the number of B-lines in each video. The intraclass correlation coefficient (ICC) for the whole series of 47 videos between each trainee and the expert trainer was high (average 0.81 ± 0.21) and >0.70 in all but five cases. After further training, the five underperforming trainees achieved satisfactory agreement with the expert trainer (average post-retraining ICC 0.74 ± 0.14). The Bland-Altman plot showed virtually no bias (difference between the mean 0.03) and strict 95% limits of agreement lines (-1.52 and 1.45 US B-lines). Only four cases overlapped but did not exceed the same limits. Likewise, the Spearman correlation coefficient applied to the same data series was very high (r = 0.979, P < 0.0001). Nephrologists and cardiologists can be effectively trained to measure lung congestion by an entirely web-based programme. This web-based training programme ensures high-quality standardization of US B-line measurements and represents a simple, costless and effective preparatory step for clinical trials targeting lung congestion.

Simple and accurate prediction of the clinical probability of pulmonary embolism.

RATIONALE: Clinical probability assessment is a fundamental step in the diagnosis of pulmonary embolism. OBJECTIVES: To develop a predictive model for pulmonary embolism based on clinical symptoms, signs, and the interpretation of the electrocardiogram. METHODS: The model was developed from a database of 1,100 patients with suspected pulmonary embolism, of whom 440 had the disease confirmed by angiography or autopsy findings. It was validated in an independent sample of 400 patients with suspected pulmonary embolism (71% were inpatients). Easy-to-use software was developed for computing the clinical probability on palm computers and mobile phones. MEASUREMENTS AND MAIN RESULTS: The model comprises 16 variables of which 10 (older age, male sex, prolonged immobilization, history of deep vein thrombosis, sudden-onset dyspnea, chest pain, syncope, hemoptysis, unilateral leg swelling, electrocardiographic signs of acute cor pulmonale) are positively associated, and 6 (prior cardiovascular or pulmonary disease, orthopnea, high fever, wheezes, or crackles on chest auscultation) are negatively associated with pulmonary embolism. In the validation sample, 165 (41%) of 400 patients had pulmonary embolism confirmed by angiography. The prevalence of pulmonary embolism was 2% when the predicted clinical probability was slight (0 to 10%), 28% when moderate (11 to 50%), 67% when substantial (51 to 80%), and 94% when high (81 to 100%). There was no significant difference between inpatients and outpatients with respect to the prevalence of pulmonary embolism in the four probability categories. CONCLUSIONS: The proposed model is simple and accurate, and it may aid physicians when assessing the clinical probability of pulmonary embolism.