Purpose of review Varicella zoster virus (VZV) causes varicella, establishes latency, then reactivates to produce herpes zoster. VZV reactivation can also cause central nervous system (CNS) disease with or without rash. Herein, we review these CNS diseases, pathogenesis, diagnosis, and treatment. Recent findings The most common CNS manifestation of VZV infection is vasculopathy that presents as headache, cognitive decline, and/or focal neurological deficits. VZV vasculopathy has also been associated with cerebral amyloid angiopathy and moyamoya syndrome. Rarely, VZV will produce a meningitis, encephalitis, cerebellitis, and myelopathy. Pathogenic mechanisms include direct VZV infection of affected tissue, persistent inflammation, and/or virus-induced hypercoagulability. Diagnosis is confirmed by the temporal association of rash to disease onset, intrathecal synthesis of anti-VZV antibodies, and/or the presence of VZV DNA in CSF. Most cases respond to intravenous acyclovir with corticosteroids. Summary VZV produces a wide spectrum of CNS disorders that may be missed as some cases do not have an associated rash or a CSF pleocytosis. Clinicians must be vigilant in including VZV in their differential diagnosis of CNS infections as VZV is a ubiquitous pathogen; importantly, VZV CNS infections are treatable with intravenous acyclovir therapy and corticosteroids.

Central nervous system infections produced by varicella zoster virus.

BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

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Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

Grey relational analysis method based on cumulative prospect theory for intuitionistic fuzzy multi-attribute group decision making

Despite their proven efficacy and huge contribution to the health of humankind, vaccines continue to be a source of concern for some individuals around the world. Vaccinations against COVID-19 increased the number of distressed people and intensified their distrust, particularly as the pandemic was still emerging and the populations were encouraged to be vaccinated under various slogans like “back to normal life” and “stop coronavirus”, goals which are still to be achieved. As fear of vaccination-related adverse events following immunization (AEFIs) is the main reason for vaccine hesitancy, we reviewed immune and autoimmune AEFIs in particular, though very rare, as the most worrisome aspect of the vaccines. Among others, autoimmune AEFIs of the most commonly administered COVID-19 vaccines include neurological ones such as Guillain-Barre syndrome, transverse myelitis, and Bell’s palsy, as well as myocarditis. In addition, the newly introduced notion related to COVID-19 vaccines, “vaccine-induced immune thrombotic thrombocytopenia/vaccine-induced prothrombotic immune thrombotic thrombocytopenia” (VITT/VIPITT)”, is of importance as well. Overviewing recent medical literature while focusing on the major immune and autoimmune AEFIs, demonstrating their rate of occurrence, presenting the cases reported, and their link to the specific type of COVID-19 vaccines represented the main aim of our work. In this narrative review, we illustrate the different vaccine types in current use, their associated immune and autoimmune AEFIs, with a focus on the 3 main COVID-19 vaccines (BNT162b2, mRNA-1273, and ChAdOx1). While the rate of AEFIs is extremely low, addressing the issue in this manner, in our opinion, is the best strategy for coping with vaccine hesitancy.

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COVID-19 Vaccination and the Rate of Immune and Autoimmune Adverse Events Following Immunization: Insights From a Narrative Literature Review

Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%-30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates. Recent progress in the BCIs based on the visually evoked potentials (VEPs) necessitates re-considering of this term, as very often all subjects are able to use VEP-based BCI systems. This study examines correlations among BCI performance, personal preferences, and further demographic factors for three different modern visually evoked BCI paradigms: (1) the conventional Steady-State Visual Evoked Potentials (SSVEPs) based on visual stimuli flickering at specific constant frequencies (fVEP), (2) Steady-State motion Visual Evoked Potentials (SSmVEP), and (3) code-modulated Visual Evoked Potentials (cVEP). Demographic parameters, as well as handedness, vision correction, BCI experience, etc., have no significant effect on the performance of VEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only 20 out of a total of 86 participants indicated a change in fatigue during the experiment. 83 subjects were able to successfully finish all spelling tasks with the fVEP speller, with a mean (SD) information transfer rate of 31.87 bit/min (9.83) and an accuracy of 95.28% (5.18), respectively. Compared to that, 80 subjects were able to successfully finish all spelling tasks using SSmVEP, with a mean information transfer rate of 26.44 bit/min (8.04) and an accuracy of 91.10% (6.01), respectively. Finally, all 86 subjects were able to successfully finish all spelling tasks with the cVEP speller, with a mean information transfer rate of 40.23 bit/min (7.63) and an accuracy of 97.83% (3.37).

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces.

Background Since the SARS-CoV-2 pandemic has started in December 2019, millions of people have been infected all over the world. Vaccination is the most efficient tool to end this pandemic, but vaccine surveillance is necessary to identify side effects. Some studies have shown that neurological complications after COVID-19 vaccination are rare and dominated by demyelinating disease. Case presentation We present a case of a 67-year-old man who presented 7 days following his first dose of Pfizer-BioNTech COVID-19 vaccine a rapidly progressive ascending muscle weakness. The diagnosis of Guillain-Barre syndrome (GBS) was confirmed according to the clinical features, the albumino-cytological dissociation in the cerebrospinal fluid, and the electroneuromyography findings. The workup for all known infections associated with immune-mediated GBS was negative. The patient received treatment with intravenous immunoglobulin. Neurological examination 1 month after discharge showed full recovery and he regained his baseline functional status. Conclusions As far as we know, this is the first reported case in Tunisia. Although extremely rare, neurologists should remain vigilant for acute inflammatory demyelinating polyradiculoneuropathy after COVID-19 vaccination.

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Guillain-Barré syndrome following the first dose of Pfizer-BioNTech COVID-19 vaccine: case report and review of reported cases.

Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis. Improving the BCI performances leads to faster and easier use and less fatigue. In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI. Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step. We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP. The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase. The piecewise linear regression allows a sampler comprehension of the phase signal. Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality. Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm. The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85 bits/min, respectively, in case of 2 s segment length. On the other hand, the spatial filtering methods of the literature weaken the system performances.

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SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features

The presence of artifacts in the EEG signals can cause a misunderstanding of the sought neurophysiological phenomena. In particular, the eye blink artifacts frequently contaminate the EEG and deteriorate its quality. Unfortunately, removing this artifact can lose useful information. The most popular approach in this field uses the independent component analysis to decompose the signal into different independent components and then eliminate those that are related to the artifact. Despite its reputation, this approach is computationally intensive, requires an acquisition from large number of channels and can alter the original EEG signal. In this paper, we propose a new method for blink artifact reduction. Several reference signals representing the eye blink are created then used in an orthogonal projection in order to cancel the artifact. The results of experiments, using 54 datasets from 27 subjects, show that the proposed method significantly outperformed the standard ADJUST, MARA and SASICA methods in removing the artifacts while preserving the pure EEG signals.

Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection

Resume Les objectifs de cette etude etaient d’evaluer la prevalence des dysfonctions sexuelles chez les patients atteints de la sclerose en plaques (SEP) et de determiner les facteurs qui pourraient favoriser la survenue de ces troubles. Il s’agit d’une etude transversale et analytique, realisee aupres de 42 patients atteints de SEP. Nous avons utilise l’Expanded disability status scale (EDSS) pour l’etude de la gravite du handicap lie a la SEP et l’echelle de Rosenberg (RSE) pour evaluer l’estime de soi. La fonction sexuelle masculine a ete evaluee par l’International index of erectile function (IIEF15) et celle feminine par le Female sexual function index (FSFI). La prevalence des dysfonctions sexuelles chez les patients atteints de SEP etait de 66,7 %. Une faible estime de soi a ete notee chez 59,1 % des femmes et 55 % des hommes. Un mauvais fonctionnement sexuel etait statistiquement correle a un score plus eleve a l’EDSS, a une duree plus prolongee de la maladie, a la presence de troubles vesico-sphincteriens, ainsi qu’a une faible estime de soi.Nos resultats mettent en exergue l’origine multifactorielle des troubles sexuels chez les malades atteints de SEP. L’adoption d’une approche multimodale s’avere ainsi indispensable.

http://www.jle.com/fr/revues/ipe/e-docs/dysfonctions_sexuelles_et_estime_de_soi_dans_la_sclerose_en_plaques_etude_tunisienne_314520/article.phtml?tab=download&pj_key=doc_attach_42369

Dysfonctions sexuelles et estime de soi dans la sclérose en plaques : étude tunisienne

After primary infection, varicella zoster virus (VZV) becomes latent in ganglionic neurons. If immunity declines, VZV is reactivated and can spread to the dermatome depending from this ganglion and...

Sawsan Daoud

Papers

Guillain-Barré syndrome following the first dose of Pfizer-BioNTech COVID-19 vaccine: case report and review of reported cases.

SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features

Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection

Dysfonctions sexuelles et estime de soi dans la sclérose en plaques : étude tunisienne

Myelopathy after zoster virus infection in immunocompetent patients: A case series.