Epitope mapping of recombinant Leishmania donovani virulence factor A2 (recLdVFA2) and canine leishmaniasis diagnosis using a derived synthetic bi-epitope
Bibliographic record
Abstract
BACKGROUND: Leishmaniasis is one of the most important zoonotic diseases spread in Latin America. Since many species are involved in dog infection with different clinical manifestations, the development of specific diagnostic tests is mandatory for more accurate disease control and vaccine strategies. METHODOLOGY/PRINCIPAL FINDINGS: Seventy-five 15-mer peptides covering the sequence of recombinant Leishmania donovani virulence factor A2 (recLdVFA2) protein were prepared by Spot synthesis. Membrane-bound peptides immunoreactivity with sera from dogs immunized with recLdVFA2 and with a specific anti-recLdVFA2 monoclonal antibody allowed mapping of continuous B-cell epitopes. Five epitopes corresponding to the N-terminal region of recLdVFA2 (MKIRSVRPLVVLLVC, RSVRPLVVLLVCVAA, RPLVVLLVCVAAVLA, VVLLVCVAAVLALSA and LVCVAAVLALSASAE, region 1-28) and one located within the repetitive units (PLSVGPQAVGLSVG, regions 67-81 and 122-135) were identified. A 34-mer recLdVFA2-derived bi-epitope containing the sequence MKIRSVRPLVVLLVC linked to PLSVGPQAVGLSVG by a Gly-Gly spacer was chemically synthesized in its soluble form. The synthetic bi-epitope was used as antigen to coat ELISA plates and assayed with dog sera for in vitro diagnosis of canine visceral leishmaniasis (CVL). The assay proved to be highly sensitive (98%) and specific (99%). CONCLUSIONS/SIGNIFICANCE: Our work suggests that synthetic peptide-based ELISA strategy may be useful for the development of a sensitive and highly specific serodiagnosis for CVL or other parasitic diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".