Structural and biochemical characteristics of mRNA nanoparticles determine anti–SARS-CoV-2 humoral and cellular immune responses
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic underlines the urgent need for effective mRNA vaccines. However, current understanding of the immunological outcomes of mRNA vaccines formulated under different nanoplatforms is insufficient. Here, severe acute respiratory syndrome coronavirus 2 receptor binding domain mRNA delivered via lipid nanoparticle (LNP), cationic nanoemulsion (CNE), and cationic liposome (Lipo) was constructed. Results demonstrated that the structural and biochemical characteristics of nanoparticles shaped their tissue dissemination, cellular uptake, and intracellular trafficking, which eventually determined the activation of antiviral humoral and cellular immunity. Specifically, LNP was mainly internalized by myocyte and subsequently circumvented lysosome degradation, giving rise to humoral-biased immune responses. Meanwhile, CNE and Lipo induced cellular-preferred immunity, which was respectively attributed to the better lysosomal escape in dendritic cells and the superior biodistribution in secondary lymphoid organs. Overall, this study may guide the design and clinical use of mRNA vaccines against COVID-19.
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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) | 0.000 | 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) | 0.001 | 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".