Development of a sandwich ELISA to detect Leishmania 40S ribosomal protein S12 antigen from blood samples of visceral leishmaniasis patients
Bibliographic record
Abstract
BACKGROUND: Visceral leishmaniasis (VL), caused by Leishmania donovani complex parasites, is a neglected parasitic disease that is generally fatal if untreated. Despite decades of research to develop a sensitive VL diagnostic test, definitive diagnosis of VL still mainly relies on the visualization of the parasite in aspirates from the spleen, liver or bone marrow, an invasive and dangerous process with variable sensitivity. A sensitive assay that can detect Leishmania antigen from blood samples will help confirm cause, cure or recurrence of VL. METHODS: In this study, rabbit polyclonal antibodies were raised against eight recombinant Leishmania proteins that are highly abundant in Leishmania. The antibodies were purified and labeled with biotin for developing a prototype sandwich enzyme-linked immunosorbent assay (ELISA). RESULTS: The ELISA for the Leishmania 40S ribosomal protein S12 detected target antigen with the highest sensitivity and specificity and could detect 1 pg of purified protein or as few as 60 L. donovani parasites. The 40S ribosomal protein S12 sandwich ELISA could detect the target antigen from Peripheral Blood Mononuclear Cell (PBMC) samples in 68% of VL patients and post-kala-azar dermal leishmaniasis (PKDL) patients, providing an estimation of parasitemia ranging from 15 to 80 amastigotes per ml of blood. CONCLUSION: These results indicate that the 40S ribosomal protein S12 sandwich ELISA warrants further tests with more clinical samples of VL patients and other parasitic diseases. It is hopeful that this ELISA could become a useful tool for confirming VL diagnosis, monitoring treatment progress, disease recurrence and possibly detecting asymptomatic Leishmania infections with a high parasite load.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".