Polysaccharide Nanoparticles Can Efficiently Modulate the Immune Response against an HIV Peptide Antigen
Bibliographic record
Abstract
The development of an effective HIV vaccine continues to be a major health challenge since, so far, only the RV144 trial has demonstrated a modest clinical efficacy. Recently, the targeting of the 12 highly conserved protease cleavage sites (PCS1-12) has been presented as a strategy seeking to hamper the maturation and infectivity of HIV. To pursue this line of research, and because peptide antigens have low immunogenicity, we have included these peptides in engineered nanoparticles, aiming at overcoming this limitation. More specifically, we investigated whether the covalent attachment of a PCS peptide (PCS5) to polysaccharide-based nanoparticles, and their coadministration with polyinosinic:polycytidylic acid (poly(I:C)), improved the generated immune response. To this end, PCS5 was first conjugated to two different polysaccharides (chitosan and hyaluronic acid) through either a stable or a cleavable bond and then associated with an oppositely charged polymer (dextran sulfate and chitosan) and poly(I:C) to form the nanoparticles. Nanoparticles associating PCS5 by ionic interactions were used in this study as the control formulation. In vivo, all nanosystems elicited high anti-PCS5 antibodies. Nanoparticles containing PCS5 conjugated and poly(I:C) seemed to induce the strongest activation of antigen-presenting cells. Interestingly, T cell activation presented different kinetics depending on the prototype. These findings show that both the nanoparticle composition and the conjugation of the HIV peptide antigen may play an important role in the generation of humoral and cellular responses.
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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) | 0.000 | 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".