Impact of SARS-CoV2 infection on the neuropathogenic potential of myelin-primed Th17 cells in an animal model of MS 2436
Bibliographic record
Abstract
Abstract Description Multiple sclerosis (MS) is a chronic disease of the central nervous system (CNS). Comparison of COVID-19 and MS brain autopsies showed similar neuropathology and preliminary work in our lab revealed the presence of immune cell aggregates in the meningeal layers surrounding the brain of Syrian Hamsters infected with SARS-CoV2. As such, it is critical to learn whether SARS-CoV2-induced brain pathology augments MS disease severity or accelerates progression. More specifically, we wish to determine whether SARS-CoV2 infection augments the neuropathogenic potential of myelin-primed Th17 cells in animal models of MS. I have established a working model in humanized ACE2 knock-in (hACE2-KI) mice that combines experimental autoimmune encephalomyelitis (EAE) with SARS-CoV-2 infection. Following resolution of infection with a non-lethal dose of the delta strain of SARS-CoV2, I induce passive EAE and assess: (1) clinical presentation of disease and (2) CNS pathology. Thus far, we have seen that mice with prior SARS-CoV2 infection exhibited reduced incidence of EAE by approximately 50% and less severe EAE clinical symptoms during the chronic phase of EAE. Immunophenotyping of the spinal cord revealed a reduction in CD4+ T cell numbers in these mice. These findings suggest that SARS-CoV2 infection prior to EAE reduces the impact of encephalogenic T cells and/or delays their progression to the CNS. We wish to further investigate the mechanism behind this observed phenotype. Funding Sources Supported by the Coronavirus Variants Rapid Response Network (CoVaRR-Net), the MS Society of Canada, and the Emerging and Pandemic Infections Consortium Topic Categories Neuroimmunology (NEUR)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".