A recombinant non-pathogenic Leishmania vaccine expressing human immunodeficiency virus 1 (HIV-1) Gag elicits cell-mediated immunity in mice and decreases HIV-1 replication in human tonsillar tissue following exposure to HIV-1 infection
Bibliographic record
Abstract
Live-vector human immunodeficiency virus (HIV) vaccines are an integral part of a number of HIV vaccine regimens currently under evaluation that have yielded promising results in pre-clinical testing. In this report, a non-pathogenic protozoan parasitic vector, Leishmania tarentolae, which shares common target cells with HIV-1, was used to express full-length HIV-1 Gag protein. Immunization of BALB/c mice with recombinant L. tarentolae led to the expansion of HIV-1 Gag-specific T cells and stimulated CD8(+) T cells to produce gamma interferon in response to specific viral Gag epitopes. A booster immunization with recombinant L. tarentolae elicited effector memory HIV-1 Gag-specific CD4(+) T lymphocytes and increased antibody titres against HIV-1 Gag. Most importantly, immunization of human tonsillar tissue cultured ex vivo with Gag-expressing L. tarentolae vaccine vector elicited a 75% decrease in virus replication following exposure of the immunized tonsils to HIV-1 infection. These results demonstrated that recombinant L. tarentolae is capable of eliciting effective immune responses in mice and human systems, respectively, and suggest that this novel non-pathogenic recombinant vaccine vector shows excellent promise as a vaccination strategy against HIV-1.
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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".