SARS-CoV-2 and the immune response; the establishment of a SARS-CoV-2 neutralization \nassay and subsequent immune utilization
Bibliographic record
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has become increasingly \nimportant since the onset of the coronavirus disease 2019 (COVID-19) pandemic. In less than a \nyear, there were numerous vaccination guidelines. After the vaccination program, the scientific \nfield raced to answer questions regarding the immune response at the cellular and molecular \nlevels. We aimed to bridge innate and adaptive immune responses to uncover the \npathophysiological factors that mediate immune pathogenicity. The introductory part of the \nproject was focused on the adaptive immune response to measure how antibodies and sera \naffect spike-mediated viral infection. To monitor disease outcomes and determine vaccine \nefficacy, we evaluated the neutralizing antibody potency using an HIV-1 pseudovirus in a cohort \nof SARS-CoV-2 infection and/or vaccinated individuals. We hypothesized that high \nneutralization titer would positively correlate to immune protection and decreased disease \nseverity. The SARS-CoV-2 pseudovirus system and neutralization assay are established and \nfuture work could gain neutralization data. These results highlight a valuable complement to \nELISA-based methods and the importance of studying spike-mediated viral infection on the \nimmune response. The accompanying part of this research concentrated on the innate immune \nresponses to SARS-CoV-2 ORF3a viral infection. In this regard, it was apparent early on that \nCOVID-19 involved extensive inflammation and immune dysregulation. Therefore, we decided \nto evaluate the key regulatory factors and mediators involved in inflammasome \nformation/activation and programmed cell death by an inflammasome array. We hypothesized \nthat the SARS-CoV-2 ORF3a protein would induce pyroptosis through the upregulation of the \nNLRP3 inflammasome and its components. We found that the CCL5, MEFV, NLRC5, NLRP3, \nMOK, and TNF genes were all statistically significantly upregulated. These genes have \nimplications in both apoptosis and pyroptosis, and both cell death pathways could be activated \nsimultaneously in response to the SARS-CoV-2 ORF3a protein. These results highlight a future \napplication for therapeutic development for SARS-CoV-2 and other inflammatory diseases.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".