Evaluation of different ways of presenting LipL32 to the immune system with the aim of developing a recombinant vaccine against leptospirosis
Bibliographic record
Abstract
Leptospirosis, caused by bacteria of the genus Leptospira, is a direct zoonosis with wide geographical distribution. The implications in terms of public health and the economical losses caused by leptospirosis justify the use of a vaccine against Leptospira in human or animal populations at risk. In this study, we used the external membrane protein LipL32 as a model antigen, as it is highly immunogenic. The LipL32 coding sequence was cloned into several expression vectors: (i) pTarget, to create a DNA vaccine; (ii) pUS973, pUS974, and pUS977 for expression in BCG (rBCG); and (iii) pAE, to express the recombinant protein in Escherichia coli, for a subunit vaccine. Mice were immunized with the various constructs, and the immune response was evaluated. The highest humoral immune response was elicited by the subunit vaccine (rLipL32). However, with rBCG, the titer was still rising at the end of the experiment. The serum of vaccinated animals was able to recognize LipL32 on the membrane of the Leptospira, detected by indirect immunofluorescence. A monoclonal antibody anti-LipL32 was shown to inhibit the growth of Leptospira in vitro, indicating potential protection induced by the LipL32 antigen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".