Post-COVID syndrome (PCS) is a medical condition characterized by the persistence of a wide range of symptoms after acute infection by SARS-CoV-2. The work capacity consequences of this disorder have scarcely been studied. We aimed to analyze the factors associated with occupational status in patients with PCS. This cross-sectional study involved 77 patients with PCS on active work before SARS-CoV-2 infection. Patients were evaluated 20.71 ± 6.50 months after clinical onset. We conducted a survey on occupational activity and cognitive and clinical symptoms. The association between occupational activity and fatigue, depression, anxiety, sleep quality, and cognitive testing was analyzed. Thirty-eight (49.4%) patients were working, and thirty-nine (50.6%) patients were not. Of those not working at the moment of the assessment, 36 (92.3%) patients were on sick leave. In 63 patients (81.8% of the sample), sick leave was needed at some point due to PCS. The mean duration of sick leave was 12.07 ± 8.07 months. According to the patient’s perspective, the most disabling symptoms were cognitive complaints (46.8%) and fatigue (31.2%). Not working at the moment of the assessment was associated with higher levels of fatigue and lower cognitive performance in the Stroop test. No association was found between occupational status with depression and anxiety questionnaires. Our study found an influence of PCS on work capacity. Fatigue and cognitive issues were the most frequent symptoms associated with loss of work capacity.

Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.

Fatigue is one of the most disabling symptoms in several neurological disorders and has an important cognitive component. However, the relationship between self-reported cognitive fatigue and objective cognitive assessment results remains elusive. Patients with post-COVID syndrome often report fatigue and cognitive issues several months after the acute infection. We aimed to develop predictive models of fatigue using neuropsychological assessments to evaluate the relationship between cognitive fatigue and objective neuropsychological assessment results. We conducted a cross-sectional study of 113 patients with post-COVID syndrome, assessing them with the Modified Fatigue Impact Scale (MFIS) and a comprehensive neuropsychological battery including standardized and computerized cognitive tests. Several machine learning algorithms were developed to predict MFIS scores (total score and cognitive fatigue score) based on neuropsychological test scores. MFIS showed moderate correlations only with the Stroop Color–Word Interference Test. Classification models obtained modest F1-scores for classification between fatigue and non-fatigued or between 3 or 4 degrees of fatigue severity. Regression models to estimate the MFIS score did not achieve adequate R² metrics. Our study did not find reliable neuropsychological predictors of cognitive fatigue in the post-COVID syndrome. This has important implications for the interpretation of fatigue and cognitive assessment. Specifically, MFIS cognitive domain could not properly capture actual cognitive fatigue. In addition, our findings suggest different pathophysiological mechanisms of fatigue and cognitive dysfunction in post-COVID syndrome.

Background: We aimed to evaluate personality traits in patients with post-COVID syndrome, as well as the association with neuropsychiatric symptoms present in this disorder. Methods: The Big Five Structure Inventory was administered to 93 consecutive patients with a diagnosis of post-COVID syndrome as defined by the WHO and to demographically matched controls. We also performed a comprehensive evaluation of depression, anxiety, fatigue, sleep quality, cognitive function, and olfactory function. Results: Patients with post-COVID syndrome scored lower for emotional stability, equanimity, positive mood, and self-control. Extraversion, emotional stability, and openness correlated negatively with anxiety and depression levels. Conscientiousness correlated negatively with anxiety. No statistically significant correlations were observed between personality traits and cognitive function, sleep quality, olfactory function, or fatigue. Personality scores explained 36.3% and 41% of the variance in scores on the anxiety and depression scales, respectively. Two personality profiles with lower levels of emotional stability were associated with depression and anxiety. Conclusions: Our study shows higher levels of neuroticism in patients with post-COVID syndrome. Personality traits were predictive of the presence of depression and anxiety, but not cognitive function, sleep quality, or fatigue, in the context of post-COVID syndrome. These findings may have implications for the detection of patients at risk of depression and anxiety in post-COVID syndrome, and for the development of preventive and therapeutic interventions.

Objectives: We aimed to evaluate and compare the diagnostic capacity of five cognitive screening tests for the diagnosis of mild cognitive impairment (MCI) in patients consulting by memory loss. Methods: A cross-sectional study involving 140 participants with a mean age of 74.42 ± 7.60 years, 87 (62.14%) women. Patients were classified as MCI or cognitively unimpaired according to a comprehensive neuropsychological battery. The diagnostic properties of the following screening tests were compared: Mini-Mental State Examination (MMSE), Addenbrooke’s Cognitive Examination III (ACE-III) and Mini-Addenbrooke (M-ACE), Memory Impairment Screen (MIS), Montreal Cognitive Assessment (MoCA), and Rowland Universal Dementia Assessment Scale (RUDAS). Results: The area under the curve (AUC) was 0.861 for the ACE-III, 0.867 for M-ACE, 0.791 for MoCA, 0.795 for MMSE, 0.731 for RUDAS, and 0.672 for MIS. For the memory components, the AUC was 0.869 for ACE-III, 0.717 for MMSE, 0.755 for MoCA, and 0.720 for RUDAS. Cronbach’s alpha was 0.827 for ACE-III, 0.505 for MMSE, 0.896 for MoCA, and 0.721 for RUDAS. Correlations with Free and Cued Selective Reminding Test were moderate with M-ACE, ACE-III, and MoCA, and moderate for the other tests. The M-ACE showed the best balance between diagnostic capacity and time of administration. Conclusions: ACE-III and its brief version M-ACE showed better diagnostic properties for the diagnosis of MCI than the other screening tests. MoCA and MMSE showed adequate properties, while the diagnostic capacity of MIS and RUDAS was limited.

Background/Objectives: Fatigue is a prevalent and debilitating symptom in Post-COVID Condition (PCC), fibromyalgia, and multiple sclerosis (MS). Although these conditions share clinical similarities, the underlying mechanisms of fatigue across these conditions may differ and remain poorly understood. This study aimed to compare the intensity and characteristics of fatigue in these three conditions to identify shared and distinct features. Methods: We conducted a cross-sectional study involving 429 participants: 219 with PCC, 112 with fibromyalgia, and 98 with MS. Participants completed a questionnaire specifically developed for the study via the Google Forms platform. This questionnaire was developed by a group of professionals in the hospital specializing in fatigue related to these three conditions, in collaboration with expert patients. The questionnaire was reported following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) recommendations. Results: Fatigue intensity was significantly higher in PCC and fibromyalgia compared to MS. Some differences in fatigue characteristics were observed: MS patients reported more fatigue in response to heat and a greater impact of mood on fatigue. Furthermore, delayed fatigue and reduced benefits from rest were more pronounced in both PCC and fibromyalgia. No significant differences were found regarding cognitive fatigue or difficulties in predicting the ability to perform activities. Conclusions: These results underscore some clinical characteristics in the intensity and quality of fatigue across PCC, fibromyalgia, and MS. These findings could suggest different mechanisms in the pathophysiology of the fatigue. Our study underscores the need for tailored diagnostic tools and interventions in managing fatigue in these three conditions.