Utilizing previously identified <i>in vitro</i> correlates of protection to predict the efficacy of a novel vaccine candidate against the intracellular bacterium <i>Francisella tularensis</i>
Bibliographic record
Abstract
Abstract Francisella tularensis (Ft) is an intracellular bacterium that causes tularemia, a disease with a low incidence in US. The only available vaccine, the Live Vaccine Strain (LVS), is investigational and is derived from Type B Ft, not the more virulent Type A Ft. Previous work produced potential correlates to predict successful vaccination. These were determined by in vitro stimulation of murine Ft LVS-immune cells and analyses of their gene expression. We used this approach to investigate correlates of protection for the novel ΔclpB vaccine, derived from Type A Ft SchuS4. Mice were vaccinated with ΔclpB as well as LVS-derived vaccines and subsequently challenged with a lethal dose of LVS, after which all mice vaccinated with ΔclpB and LVS survived. The in vivo survival data was compared with in vitro data obtained from PBLs and splenocytes from vaccinated mice. In general, the in vitro functions of leukocytes from ΔclpB-vaccinated mice were comparable or exceeded those of leukocytes from LVS-vaccinated mice, including control of LVS intramacrophage replication, IFN-gamma secretion, and NO production. Correlates up-regulated in cells from mice vaccinated with ΔclpB included IFN-gamma, IL-21, Nos2, LTA, T-bet, IL-12rbeta2, CCL5 and GzmB; in some cases up-regulation was higher than that from LVS-derived PBLs. Other genes were up-regulated in ΔclpB-derived but not LVS-derived leukocytes, suggesting that improved protection stimulated by the ΔclpB vaccine may be related to the change in strain and/or to stronger immune responses. Hence, this panel of correlates could contribute to the screening of new vaccine candidates, bridging from animal models to humans, and augmenting clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".