Cytokine profiles and neurological manifestations in post-COVID syndrome
Bibliographic record
Abstract
Aim: The objectives of our study were to evaluate a range of circulating biomarkers in COVID-19-related long-term neurological dysfunction. Methods: The study involved 30 patients with post-COVID syndrome (PCS) and 28 patients after COVID-19 without PCS. The third cohort consisted of 29 patients with acute COVID-19 of varying severity. The severity of COVID-19 was classified as mild and moderate to severe. The Montreal Cognitive Assessment (MOCA) and the SAGE test were used to study cognitive functions. The Hospital Anxiety and Depression Scale (HADS), the Sheehan Anxiety Scale, and the Beck Depression Inventory were used to study affective functions. The levels of serum cytokines and IgM, IgG, IgA to the SARS-CoV-2 coronavirus were determined using the Vector-Best test systems (Novosibirsk, Russia). We also studied the IgG subclasses to the spike protein of the SARS-CoV-2. Results: А mild to moderate COVID-19 infection primarily increases the risk of affective disorders and asthenia and, to a lesser extent, the development of cognitive impairment. The levels of IFN-α, IL-6, as well as serum antibodies to the SARS-CoV-2 among patients with PCS were significantly higher compared to convalescents without PCS. IgM to the SARS-CoV-2 was detected in the blood of patients with PCS during 2–7 months after the disease. After moderate and severe COVID-19, IgG2 and IgG4 were predominant in the blood of patients with PCS and neurological symptoms. The levels of IL-1, IL-4, IL-6, IL-8 in the blood serum of patients with PCS were higher after moderate and severe COVID-19 compared to patients who had mild COVID-19. Conclusions: The obtained data on an elevated level of cytokines and IFN-α in the blood of PCS patients can suggest the hypothesis about the participation of chronic inflammation in neurological disorders. The main limitation of the study is the relatively small sample size, which limits the statistical analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".