COVID-19 AND POST COVID-19 AND THE EMERGENCE OF SLE AND EXACERBATIONS
Bibliographic record
Abstract
O046a / #565 Topic:AS01 - Adaptive Immunity ABSTRACT CONCURRENT SESSION 08: RECENT ADVANCES IN LUPUS BIOMARKERS 23-05-2025 1:40 PM - 2:40 PM Background/Purpose Covid-19 virus is an autoimmune virus and more notorious than EBV. It induces autoimmune diseases by hyperstimulation combined with induction of autoimmune disease by molecular mimicry. There are many autoimmune conditions which have emerged following the COVID 19 infection. One of them is SLE. We will summarize the various publications discussing de novo eruption of SLE as well as induction of exacerbation. Methods 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 dysautonomia. Results We will discuss all the autoimmune ramifications of the virus, the CFS / fibromyalgia / post Covid syndrome.[1-7] and their association with SLE. Conclusions The detection of novel autoantibodies to the autonomic nervous system receptors will explain the pathogenic mechanism of many of the new complaints.References: [1.] Shoenfeld Y. Clin Immunol 2020;214:108384. [2.] Cabral-Marques O. Nature Commun 2022;13:1220. [3.] Dotan A. Int J Infect Dis 2022;114:233-5. [4.] Jara LJ. Clin Rheumatol 2022;41:1603-9. [5.] Ehrenfeld M. Autoimmun Rev 2020;19:102597. [6.] Perricone C. Immunol Res 2020;68:213-24. [7.] Halpert G. Autoimmun Rev 2020;19:102695.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".