Mucosal immunization with lipopeptides derived from conserved regions of SARS-CoV-2 antigens induce robust cellular and cross-variant humoral immune responses in mice
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, has infected >600 million people in the ongoing global pandemic. Several variants of the SARS-CoV-2 have emerged in the last >2 years, challenging the continued efficacy of current COVID vaccines. Therefore, there is a crucial need to investigate a highly cross-protective vaccine effective against variants of SARS-CoV-2. In this study, we examined seven lipopeptides derived from highly conserved, immunodominant epitopes from the S, N, and M proteins of SARS-CoV-2, that are predicted to contain epitopes for clinically protective B cells, helper T cells (TH) and cytotoxic T cells (CTL). Intranasal immunization of mice with most of the lipopeptides led to significantly higher splenocyte proliferation and cytokine production, mucosal and systemic antibody responses, and induction of effector B and T lymphocytes in both lungs and spleen, compared to immunizations with the corresponding peptides without lipid. Immunizations with Spike-derived lipopeptides led to cross-reactive IgG, IgM and IgA responses against Alpha, Beta, Delta, and Omicron Spike proteins as well as neutralizing antibodies. These studies support their potential for development as components of a cross-protective SARS-CoV-2 vaccine.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".