Post COVID-19 Syndrome in Patients with Asymptomatic/Mild Form.
Anna MalkovaIgor KudryavtsevAnna StarshinovaDmitry Anatolievich KudlayYulia ZinchenkoAnzhela GlushkovaPiotr YablonskiyYehuda ShoenfeldPublished in: Pathogens (Basel, Switzerland) (2021)
Post COVID-19 Syndrome (PCS) is a complex of various symptoms developing a month or more after the acute phase of the disease. The cases of PCS development among patients with asymptomatic/mild forms are frequently reported; however, the pathogenesis of PCS in this group of patients is still not completely clear. The publications about COVID-19 which were published in online databases from December 2019 to September 2021 are analyzed in this review. According to the analysis, PCS develops on average in 30-60% of patients, mainly among women. Fatigue, shortness of breath, cough, and anosmia were reported as the most common symptoms. The possible association between the described PCS symptoms and brain damage was revealed. We assume the possibility of an alternative course of COVID-19, which develops in genetically predisposed individuals with a stronger immune response, in which it predominantly affects the cells of the nervous system, possibly with the presence of an autoimmune component, which might have similarity with chronic fatigue syndrome or autoimmune disautonomia. Thus, the gender (female) and the presence of anosmia during an asymptomatic or mild course of the disease can be predictive factors for the development of PCS, which can be caused by autoimmune damage to neurons, glia, and cerebral vessels.
Keyphrases
- coronavirus disease
- sars cov
- end stage renal disease
- immune response
- newly diagnosed
- chronic kidney disease
- sleep quality
- ejection fraction
- multiple sclerosis
- prognostic factors
- randomized controlled trial
- spinal cord
- healthcare
- social media
- peritoneal dialysis
- depressive symptoms
- polycystic ovary syndrome
- cell death
- metabolic syndrome
- white matter
- resting state
- patient reported
- cell proliferation
- machine learning
- functional connectivity
- deep learning
- inflammatory response
- cervical cancer screening
- pi k akt