No abstract

Neurologic impairment persisting months after acute severe SARS-CoV-2 infection has been described because of several pathogenic mechanisms, including persistent systemic inflammation. The objective of this study is to analyze the selective involvement of the different cognitive domains and the existence of related biomarkers. Cross-sectional multicentric study of patients who survived severe infection with SARS-CoV-2 consecutively recruited between 90 and 120 days after hospital discharge. All patients underwent an exhaustive study of cognitive functions as well as plasma determination of pro-inflammatory, neurotrophic factors and light-chain neurofilaments. A principal component analysis extracted the main independent characteristics of the syndrome. 152 patients were recruited. The results of our study preferential involvement of episodic and working memory, executive functions, and attention and relatively less affectation of other cortical functions. In addition, anxiety and depression pictures are constant in our cohort. Several plasma chemokines concentrations were elevated compared with both, a non-SARS-Cov2 infected cohort of neurological outpatients or a control healthy general population. Severe Covid-19 patients can develop an amnesic and dysexecutive syndrome with neuropsychiatric manifestations. We do not know if the deficits detected can persist in the long term and if this can trigger or accelerate the onset of neurodegenerative diseases.

No abstract

Introduction: In the absence of a gold standard for in vivo Alzheimer disease (AD) diagnosis, AD biomarkers such as cerebrospinal fluid biomarkers (CSF-B) and PET-Amyloid are considered diagnostically useful in clinical practice guidelines and have consensual appropriate use criteria (AUC). However, little evidence has been published on their utilization in the clinical setting or on approaches to mismatched results. The objective of this work was to evaluate the use of AD biomarkers in clinical practice, focusing on the implementation of PET-Amyloid in cases of inconclusive CSF-B. Methods: This naturalistic, ambispective case series included patients fulfilling AUC for CSF-B and PET-Amyloid whose CSF-B results were non-diagnostic (target population), analyzing the diagnostic certainty, the treatment approach, and the relationship between CSF-B and PET-Amyloid results. Results: Out of 2373 eligible patients, AD biomarkers were studied in 417 (17.6%), most frequently due to cognitive impairment in under 65-year-olds, using CSF-B in 311 patients and PET-Amyloid in 150. CSF-B results were non-diagnostic for 44 patients (52.3% male; aged 60.9±6.6 years), who then underwent PET-Amyloid study, which was positive in 31. A ‘k’ coefficient of 0.108 was obtained between CSF-B and PET-amyloid (54.5% concordance). In multivariate regression analysis, Aβ42 was the only significant predictor (p= 0.018) of a positive PET-Amyloid result. In the target population, PET-Amyloid increased diagnostic confidence by 53.7% (p <0.001) and modified the therapeutic approach in 36.4% of cases. Conclusion: These findings support the duplication of AD biomarkers and demonstrate that the implementation of PETAmyloid provides an early and certain diagnosis to guide appropriate treatment.

Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.

Background: Neurologic impairment persisting months after acute severe SARS-CoV-2 infection has been described because of several pathogenic mechanisms, including persistent systemic inflammation. The objective of this study is to analyze the selective involvement of the different cognitive domains and the existence of related biomarkers. Methods: Cross-sectional multicentric study of patients who survived severe infection with SARS-CoV-2 consecutively recruited between 90 and 120 days after hospital discharge. All patients underwent an exhaustive study of cognitive functions as well as plasma determination of pro-inflammatory, neurotrophic factors and light-chain neurofilaments. A Principal Component Analysis extracted the main independent characteristics of the syndrome. Results: 152 patients were recruited. The results of our study preferential involvement of episodic and working memory, executive functions, and attention and relatively less affectation of other cortical functions. In addition, anxiety and depression pictures are constant in our cohort. Several plasma chemokines concentrations were elevated compared with both, a non-SARS-Cov2 infected cohort of neurological outpatients or a control healthy general population. Conclusion: Severe Covid-19 patients can develop an amnesic and dysexecutive syndrome with neuropsychiatric manifestations. We do not know if the deficits detected can persist in the long term and if this can trigger or accelerate the onset of neurodegenerative diseases.

We provide normative data for total and domain Fototest results for each of the groups defined according to sex, age, and level of education. We also provide a percentile distribution of scores. We hope that these normative data will translate into increases in efficiency in Fototest administration in the clinical setting.

No abstract