A Nutritional Supplement as Adjuvant of Gabapentinoids for Adults with Neuropathic Pain following Spinal Cord Injury and Stroke: Preliminary Results.

Gabapentinoids are first choice drugs for central neuropathic pain (CNP) despite limited evidence of efficacy and side effects affecting therapy outcomes. Nutraceuticals could improve their efficacy and tolerability. Our aim is to investigate the effect of NACVAN®, in addition to gabapentinoids, on pain symptomatology in CNP patients. The effect of 6 weeks of treatment of NACVAN® was preliminary observed among 29 adult inpatients with spinal cord injury (SCI) or stroke-related CNP recruited to the experimental group. Pain intensity, neuropathic pain, and quality-of-life were measured at baseline (T0) and after 3 (T1) and 6 weeks (T2). Change in each outcome over time was assessed through a repeated measures analysis of variance or Wilcoxon matched-pairs test. Preliminary results show a significant reduction in pain intensity (T0 → T1, p = 0.021; T0 → T2, p = 0.011; T1 → T2, p = 0.46), neuropathic symptoms (T0 → T1, p = 0.024; T0 → T2, p = 0.003), and evoked pain (T0 → T2, p = 0.048). There were no significant reductions in other neuropathic pain dimensions and in quality-of-life components. No side-effects were detected. NACVAN® could have a beneficial adjuvant effect when used as an add-on to gabapentinoids in patients suffering from CNP due to SCI or stroke, with no adverse effect. Future analysis on a larger sample, compared with a placebo condition, could confirm these preliminary results.

Robust fitting of mixtures of GLMs by weighted likelihood.

Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.

Misspecified modeling of subsequent waves during COVID-19 outbreak: A change-point growth model.

In the analysis of cumulative counts of SARS-CoV-2 infections, such as deaths or cases, common parametric models based on log-logistic growth curves adapt well to describe a single wave at a time. Unfortunately, in Italy, as well as all over the globe, from February 2020 to March 2021 more than one wave has been observed. In this paper, we propose a method to fit more than one wave in the same model. In particular, we discuss an approach based on a change-point model in a pseudo-likelihood framework that takes into account some model misspecification issues, such as those concerning the assumption of Poisson marginals and those relating to overdispersion and autocorrelation. An application to data collected in Italy is discussed.

Robust inference for non-linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy.

We discuss an approach of robust fitting on non-linear regression models, in both frequentist and Bayesian approaches, which can be employed to model and predict the contagion dynamics of the coronavirus disease 2019 (COVID-19) in Italy. The focus is on the analysis of epidemic data using robust dose-response curves, but the functionality is applicable to arbitrary non-linear regression models.

Trends in IoT based solutions for health care: Moving AI to the edge.

In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

Empirical Likelihood-Based ANOVA for Trimmed Means.

In this paper, we introduce an alternative to Yuen's test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen's test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch's heteroscedastic F test. The analysis of a real data example illustrates the use of Yuen's test and the new EL ANOVA test for trimmed means for different trimming levels. Based on the results of our study, we recommend the use of Yuen's test for situations involving the comparison of population trimmed means between groups of interest.