Assessment of Postural Control in Children with Movement Disorders by Means of a New Technological Tool: A Pilot Study.

Considering the variability and heterogeneity of motor impairment in children with Movement Disorders (MDs), the assessment of postural control becomes essential. For its assessment, only a few tools objectively quantify and recognize the difference among children with MDs. In this study, we use the Virtual Reality Rehabilitation System (VRRS) for assessing the postural control in children with MD. Furthermore, 16 children (mean age 10.68 ± 3.62 years, range 4.29-18.22 years) were tested with VRRS by using a stabilometric balance platform. Postural parameters, related to the movements of the Centre of Pressure (COP), were collected and analyzed. Three different MD groups were identified according to the prevalent MD: dystonia, chorea and chorea-dystonia. Statistical analyses tested the differences among MD groups in the VRRS-derived COP variables. The mean distance, root mean square, excursion, velocity and frequency values of the dystonia group showed significant differences (p < 0.05) between the chorea group and the chorea-dystonia group. Technology provides quantitative data to support clinical assessment: in this case, the VRRS detected differences among the MD patterns, identifying specific group features. This tool could be useful also for monitoring the longitudinal trajectories and detecting post-treatment changes.

Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training.

BACKGROUND: Nowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants' motor activity. MATERIALS AND METHODS: Starting from data acquired by accelerometers placed on infants' wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models. CONCLUSIONS: Despite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models. TRIAL REGISTRATION NUMBER: ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.

Melanoma Prevention: Comparison of Different Screening Methods for the Selection of a High Risk Population.

BACKGROUND: Guidelines recommend limiting melanoma screening in a population with known risk factors, but none indicates methods for efficient recruitment. The purpose of this study is to compare three different methods of recruiting subjects to be screened for melanoma to detect which, if any, is the most efficient. METHODS: From 2010 to 2019, subjects were recruited as follows: (1) regular skin examinations (RS), mainly conducted through the Associazione Contro il Melanoma network; (2) occasional melanoma screening (OS), during annual public campaigns; (3) and selective screening (SS), where people were invited to undergo a skin check after filling in a risk evaluation questionnaire, in cases where the assigned outcome was intermediate/high risk. Melanoma risk factors were compared across different screening methods. Generalized Linear Mixed Models were used for multivariable analysis. RESULTS: A total of 2238 subjects (62.7% women) were recruited, median age 44 years (2-85), and 1094 (48.9 %) records were collected through RS, 826 (36.9 %) through OS, and 318 (14.2 %) through SS. A total of 131 suspicious non-melanoma skin cancers were clinically diagnosed, 20 pathologically confirmed, and 2 melanomas detected. SS performed significantly better at selecting subjects with a family history of melanoma and I-II phototypes compared to OS. CONCLUSIONS: Prior evaluation of melanoma known risk factors allowed for effective selection of a population to screen at higher risk of developing a melanoma.

Expression Analysis of Muscle-Specific miRNAs in Plasma-Derived Extracellular Vesicles from Patients with Chronic Obstructive Pulmonary Disease.

MicroRNAs (miRNAs) are a class of short non-coding RNAs involved in the regulation of gene expression and the control of several cellular processes at physiological and pathological levels. Furthermore, extracellular vesicles (EV), which are small membrane-bound vesicles secreted by cells in the extracellular environment, contain functional miRNAs. The remarkable deregulation of many miRNAs has been demonstrated in respiratory diseases. Among them, miR-206, miR-133a-5p, and miR-133a-3p are striated muscle-specific miRNAs (myo-miRNA), related to skeletal muscle dysfunction, one of the commonest systemic manifestations in patients with chronic obstructive pulmonary disease (COPD). Nevertheless, their circulating expression in COPD patients is not demonstrated. For these reasons, we performed a pilot study to analyze the expression profiles of myo-miRNAs in plasma-derived EV from patients with COPD. We analyzed the expression profiles of selected myo-miRNAs in plasma-derived EV from COPD. Receiver operating characteristic analyses were carried out to evaluate whether selected plasma miRNAs were able to discriminate between different groups of COPD patients. We found EV-embedded myo-miRNAs in the bloodstream of COPD patients. Specifically, miR-206, miR-133a-5p and miR-133a-3p were significantly upregulated in group B patients. Receiver operating characteristic analyses of the combination of these selected miRNAs showed their high capacity to discriminate group B from other COPD patients. Our data provide evidence that myo-miRNA are present in EV in the plasma of COPD patients and their expression (miR-206, miR-133a-5p, and miR-133a-3p) can discriminate group B from group C patients. The future analysis of a larger number of patients should allow us to obtain more refined correlations.

Identification of plasma microRNAs as new potential biomarkers with high diagnostic power in human cutaneous melanoma.

Melanoma is a devastating disease with few therapeutic options in the advanced stage and with the urgent need of reliable biomarkers for early detection. In this context, circulating microRNAs are raising great interest as diagnostic biomarkers. We analyzed the expression profiles of 21 selected microRNAs in plasma samples from melanoma patients and healthy donors to identify potential diagnostic biomarkers. Data analysis was performed using global mean normalization and NormFinder algorithm. Linear regression followed by receiver operating characteristic analyses was carried out to evaluate whether selected plasma miRNAs were able to discriminate between cases and controls. We found five microRNAs that were differently expressed among cases and controls after Bonferroni correction for multiple testing. Specifically, miR-15b-5p, miR-149-3p, and miR-150-5p were up-regulated in plasma of melanoma patients compared with healthy controls, while miR-193a-3p and miR-524-5p were down-regulated. Receiver operating characteristic analyses of these selected microRNAs provided area under the receiver operating characteristic curve values ranging from 0.80 to 0.95. Diagnostic value of microRNAs is improved when considering the combination of miR-149-3p, miR-150-5p, and miR-193a-3p. The triple classifier had a high capacity to discriminate between melanoma patients and healthy controls, making it suitable to be used in early melanoma diagnosis.