Dendritic Cells/Macrophages-Targeting Feature of Ebola Glycoprotein and its Potential as Immunological Facilitator for Antiviral Vaccine Approach
Bibliographic record
Abstract
In the prevention of epidemic and pandemic viral infection, the use of the antiviral vaccine has been the most successful biotechnological and biomedical approach. In recent times, vaccine development studies have focused on recruiting and targeting immunogens to dendritic cells (DCs) and macrophages to induce innate and adaptive immune responses. Interestingly, Ebola virus (EBOV) glycoprotein (GP) has a strong binding affinity with DCs and macrophages. Shreds of evidence have also shown that the interaction between EBOV GP with DCs and macrophages leads to massive recruitment of DCs and macrophages capable of regulating innate and adaptive immune responses. Therefore, studies for the development of vaccine can utilize the affinity between EBOV GP and DCs/macrophages as a novel immunological approach to induce both innate and acquired immune responses. In this review, we will discuss the unique features of EBOV GP to target the DC, and its potential to elicit strong immune responses while targeting DCs/macrophages. This review hopes to suggest and stimulate thoughts of developing a stronger and effective DC-targeting vaccine for diverse virus infection using EBOV GP.
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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) | 0.001 | 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) | 0.002 | 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".