Machine learning to investigate superficial white matter integrity in early multiple sclerosis.

BACKGROUND AND PURPOSE: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC). METHODS: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank-sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1-SWM, 2-DWM, 3-SWM and DWM, 4-cortical thickness, or 5-RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5-folds cross-validation. Area under the receiver operating characteristic curve (AUCroc ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUCroc values. RESULTS: Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre- and post-central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUCroc = 0.75). The SWM model provided higher AUCroc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all-features (0.68) models (p < .001 for all). CONCLUSION: Our results reveal a non-random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.

Adaptive learning objects in the context of eco-connectivist communities using learning analytics.

Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach.

Tentativa de suicídio com milnaciprano: relato de caso / Attempted suicide with milnacipran: a case report

O milnaciprano é um novo antidepressivo que alega ter menos efeitos colaterais do que os antidepressivos clássicos. Relatamos um caso no qual a paciente tentou suicídio por overdose. Durante a primeira hora após ingestão desse medicamento, a paciente apresentou náusea, vômitos proeminentes, diaforese e perda de consciência. A paciente recuperou-se satisfatoriamente da overdose de milnaciprano e clonazepam. Demonstramos que o milnaciprano é um medicamento seguro em casos de overdose. Atenção especial e pesquisas futuras sobre esse fenômeno são altamente indicadas em função do uso comum desse medicamento.

Milnacipran is a new antidepressant, which is claimed to have fewer side effects than classic antidepressives. We report one case where patient attempted suicide by overdose. During the first hour following the ingestion of this drug, the patient presented nausea, notable emesis, diaphoresis and loss of consciousness. The patient's recovery from the overdose of milnacipran and clonazepam was satisfactory. We can show that milnacipran is a safe medication in overdose cases. Special awareness and future research of this phenomenon is highly indicated because of the common use of this drug.