Evaluating the immunogenicity of an intranasal vaccine against nicotine in mice using the Adjuvant Finlay Proteoliposome (AFPL1)
Bibliographic record
Abstract
Tobacco smoking is recognized as a global pandemic resulting in 6 million deaths per year. Despite a variety of anti-smoking products available to aid with tobacco cessation, the majority of people who attempt to quit smoking relapse within 6 months due to the addictive nature of nicotine. An immunotherapy approach could offer a promising treatment option by inducing a potent selective antibody response against nicotine in order to block its distribution to the brain and its addictive effects in the central nervous system. Our nicotine vaccine candidate was administered intranasally using the Neisseria meningitidis serogroup B Adjuvant Finlay Proteoliposome 1 (AFPL1) as a part of the delivery system. This system was designed to generate a robust immune response by stimulating IL-1β production through Toll-like receptor 4 (TLR4), a potent mechanism for mucosal immunity. The vaccine induced high antibody titers in mice sera in addition to inducing mucosal antibodies. The efficacy of our vaccine was demonstrated using in vivo challenge experiments with radioactive [(3)H]-nicotine, followed by an analysis of nicotine distribution in the lung, liver, blood and brain. Our results were encouraging as the nicotine concentration in the brain tissue of mice vaccinated with our candidate vaccine was four times lower than in non-vaccinated controls; suggesting that the anti-nicotine antibodies were able to block nicotine from crossing the blood brain barrier. In summary, we have developed a novel nicotine vaccine for the treatment of tobacco addiction by intranasal administration and also demonstrated that the AFPL1 can be used as a potential adjuvant for this vaccine design.
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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 | 0.001 |
| 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".