Background: Inflammation and autoimmune responses contribute to the pathophysiology of Long COVID, and its affective and chronic fatigue syndrome (CFS) symptoms, labeled “the physio-affective phenome.” Objectives: To investigate whether Long COVID and its physio-affective phenome are linked to autoimmunity to the tight junction proteins, zonulin and occludin (ZOOC), and immune reactivity to lipopolysaccharides (LPS), and whether the latter are associated with signs of human herpes virus-6 reactivation (HHV-6), autoimmunity directed against oligodendrocyte and neuronal proteins, including myelin basic protein (MBP). Methods: IgA /IgM/IgG responses to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), HHV-6, ZOOC, and neuronal proteins, C-reactive protein (CRP) and advanced oxidation protein products (AOPP), were measured in 90 Long COVID patients and 90 healthy controls. The physio-affective phenome was conceptualized as a factor extracted from physical and affective symptom domains. Results: Neural network identified IgA directed to LPS (IgA-LPS), IgG-ZOOC, IgG-LPS, and IgA-ZOOC as the most important variables associated with Long COVID diagnosis with an area under the ROC curve of 0.755. Partial Least Squares analysis showed that 40.9% of the variance in the physio-affective phenome was explained by CRP, IgA-MPB and IgG-MBP. A large part of the variances in both autoimmune responses to MBP (36.3-39.7%) was explained by autoimmunity (IgA and IgG) directed to ZOOC. The latter was strongly associated with indicants of HHV-6 reactivation, which in turn was associated with increased IgM-SARS-CoV-2. Conclusions: Autoimmunity against components of the tight junctions and increased bacterial translocation may be involved in the pathophysiology of Long COVID’s physio-affective phenome.

Schizophrenia comprises various symptom domains the two most important being positive and negative symptoms. Nevertheless, using (un)supervised machine learning techniques it was shown that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility, excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the above-mentioned symptom domains. In MNP, there were significant associations between negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially unidimensional, underlies these key domains of schizophrenia and additionally shows excellent internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad Analysis indicates that this latent vector fits a reflective model. Soft Independent Modeling of Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better modeled with PHEM symptoms, FTD and PMR than with negative symptoms. In conclusion, in MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD and PMR belong to one underlying latent vector reflecting general psychopathology and, therefore, may be used as an overall severity of schizophrenia (OSOS) index. The bi-dimensional concept of positive and negative symptoms and type I and II schizophrenia is revised.