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      Fluvoxamine and long COVID-19; a new role for sigma-1 receptor (S1R) agonists

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      1 , 1 , 2 ,
      Molecular Psychiatry
      Nature Publishing Group UK
      Diseases, Depression

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          Abstract

          To the Editor: We read with interest Hashimoto et al. study about mechanisms of action of fluvoxamine in COVID-19 [1]. As they mentioned, fluvoxamine offers some key mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It inhibits acid sphingomyelinase (ASM) activity, the formation of ceramide-enriched membrane domain, and attenuates SARS-CoV-2 cell entry. Interestingly, fluvoxamine acts as a potent sigma-1 receptor (S1R) agonist that may decrease SARS-CoV-2 replication and subsequent endoplasmic reticulum (ER) stress and inflammation. Based on the evidence, S1R agonists prevent inositol requiring enzyme 1α (IRE1) from splicing of mRNA that encodes X-box binding protein-1 (XBP-1). Hence S1R-mediated reduction in XBP1 activation modulates the ER stress response pathway and reduces cytokine storm [2]. XBP1 plays a major role in the reactivation of the Epstein-Barr virus (EBV). It has been indicated that ER stress and unfolded-protein response induce the expression of lytic EBV gene in EBV-infected cells, suggesting a pathway in virus-associated complications [3, 4]. Gold et al. reported that ~70% of patients with long COVID-19 versus 10% of the control group were positive for EBV reactivation according to the early antigen-diffuse immunoglobulin G or EBV viral capsid antigen immunoglobulin M [5]. It has been suggested that more than 50% of patients who recovered from COVID-19 experienced long-term symptoms such as headache, fatigue, anxiety, depression, and cognitive features within 6 months [6]. These findings suggest that most of the long COVID-19 symptoms following the recovery from the acute disease might not be directly affected by SARS-CoV-2 but probably result from COVID-19-associated inflammation and EBV reactivation. Recently, infection with EBV was suggested as the possible leading cause for multiple sclerosis (MS), in which inflammation plays a key role [7]. Given the link between EBV replication and XBP1 activation and modulatory effects of S1R agonists in XBP1 and ER stress response, we proposed that fluvoxamine might have beneficial effects in reducing long-term symptoms of COVID-19. However, further clinical studies are required to confirm this hypothesis.

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          Most cited references6

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          Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis

          Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system of unknown etiology. We tested the hypothesis that MS is caused by Epstein-Barr virus (EBV) in a cohort comprising more than 10 million young adults on active duty in the US military, 955 of whom were diagnosed with MS during their period of service. Risk of MS increased 32-fold after infection with EBV but was not increased after infection with other viruses, including the similarly transmitted cytomegalovirus. Serum levels of neurofilament light chain, a biomarker of neuroaxonal degeneration, increased only after EBV seroconversion. These findings cannot be explained by any known risk factor for MS and suggest EBV as the leading cause of MS.
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            Is Open Access

            Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19

            Background Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues. Methods and findings We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score–matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan–Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control. Among COVID-19 survivors (mean [SD] age: 46.3 [19.8], 55.6% female), 57.00% had one or more long-COVID feature recorded during the whole 6-month period (i.e., including the acute phase), and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing (18.71% in the 1- to 180-day period; 7.94% in the 90- to180-day period), fatigue/malaise (12.82%; 5.87%), chest/throat pain (12.60%; 5.71%), headache (8.67%; 4.63%), other pain (11.60%; 7.19%), abdominal symptoms (15.58%; 8.29%), myalgia (3.24%; 1.54%), cognitive symptoms (7.88%; 3.95%), and anxiety/depression (22.82%; 15.49%). All 9 features were more frequently reported after COVID-19 than after influenza (with an overall excess incidence of 16.60% and hazard ratios between 1.44 and 2.04, all p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms. Conclusions Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity. Maxime Taquet and colleagues investigate the incidence, co-occurrence and evolution of long-COVID features in more than a quarter of a million people. Why was this study done? Long-COVID has been described in recent studies. But we do not know the risk of developing features of this condition and how it is affected by factors such as age, sex, or severity of infection. We do not know if the risk of having features of long-COVID is more likely after Coronavirus Disease 2019 (COVID-19) than after influenza. We do not know about the extent to which different features of long-COVID co-occur. What did the researchers do and find? This research used data from electronic health records of 273,618 patients diagnosed with COVID-19 and estimated the risk of having long-COVID features in the 6 months after a diagnosis of COVID-19. It compared the risk of long-COVID features in different groups within the population and also compared the risk to that after influenza. The research found that over 1 in 3 patients had one or more features of long-COVID recorded between 3 and 6 months after a diagnosis of COVID-19. This was significantly higher than after influenza. For 2 in 5 of the patients who had long-COVID features in the 3- to 6-month period, they had no record of any such feature in the previous 3 months. The risk of long-COVID features was higher in patients who had more severe COVID-19 illness, and slightly higher among females and young adults. White and non-white patients were equally affected. What do these findings mean? Knowing the risk of long-COVID features helps in planning the relevant healthcare service provision. The fact that the risk is higher after COVID-19 than after influenza suggests that their origin might, in part, directly involve infection with SARS-CoV-2 and is not just a general consequence of viral infection. This might help in developing effective treatments against long-COVID. The findings in the subgroups, and the fact that the majority of patients who have features of long-COVID in the 3- to 6-month period already had symptoms in the first 3 months, may help in identifying those at greatest risk.
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              Investigation of Long COVID Prevalence and Its Relationship to Epstein-Barr Virus Reactivation

              Coronavirus disease 2019 (COVID-19) patients sometimes experience long-term symptoms following resolution of acute disease, including fatigue, brain fog, and rashes. Collectively these have become known as long COVID. Our aim was to first determine long COVID prevalence in 185 randomly surveyed COVID-19 patients and, subsequently, to determine if there was an association between occurrence of long COVID symptoms and reactivation of Epstein–Barr virus (EBV) in 68 COVID-19 patients recruited from those surveyed. We found the prevalence of long COVID symptoms to be 30.3% (56/185), which included 4 initially asymptomatic COVID-19 patients who later developed long COVID symptoms. Next, we found that 66.7% (20/30) of long COVID subjects versus 10% (2/20) of control subjects in our primary study group were positive for EBV reactivation based on positive titers for EBV early antigen-diffuse (EA-D) IgG or EBV viral capsid antigen (VCA) IgM. The difference was significant ( p < 0.001, Fisher’s exact test). A similar ratio was observed in a secondary group of 18 subjects 21–90 days after testing positive for COVID-19, indicating reactivation may occur soon after or concurrently with COVID-19 infection. These findings suggest that many long COVID symptoms may not be a direct result of the SARS-CoV-2 virus but may be the result of COVID-19 inflammation-induced EBV reactivation.
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                Author and article information

                Contributors
                tentezari@gmail.com
                Journal
                Mol Psychiatry
                Mol Psychiatry
                Molecular Psychiatry
                Nature Publishing Group UK (London )
                1359-4184
                1476-5578
                6 April 2022
                : 1
                Affiliations
                [1 ]GRID grid.412888.f, ISNI 0000 0001 2174 8913, Department of Clinical Pharmacy, Faculty of Pharmacy, , Tabriz University of Medical Sciences, ; Tabriz, Iran
                [2 ]GRID grid.412888.f, ISNI 0000 0001 2174 8913, Cardiovascular Research Center, , Tabriz University of Medical Sciences, ; Tabriz, Iran
                Author information
                http://orcid.org/0000-0001-6877-3366
                http://orcid.org/0000-0003-3653-3653
                Article
                1545
                10.1038/s41380-022-01545-3
                8985056
                35388182
                07d44e75-3d7f-4ea5-9b6d-b941e326bee9
                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 24 January 2022
                : 10 March 2022
                : 22 March 2022
                Categories
                Correspondence

                Molecular medicine
                diseases,depression
                Molecular medicine
                diseases, depression

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