Diagnostic potential of GLP recombinant antigens in whole blood assays for Leishmania infantum infection
Bibliographic record
Abstract
BACKGROUND: The whole blood stimulation assay (WBA) is a valuable tool for detecting asymptomatic Leishmania infection and monitoring the treatment of visceral leishmaniasis (VL). This study sought to identify specific recombinant proteins to replace the nonspecific soluble Leishmania antigen in this assay, which could be useful for developing a standardized diagnostic test that complies with good manufacturing practice. METHODS: Employing a cell lymphoproliferative assay, we here assessed the behaviour of 11 recombinant antigens in 61 subjects who had either been successfully treated for or had spontaneously recovered from Leishmania infantum infection. We then selected those antigens showing significant differences in immune cell stimulation indices and cytokine secretion between a responder and non-responder group, respectively, showing a cellular response to L. infantum or not. The three best candidate antigens, ΔCpB, NSC and ENSC, were then used in a WBA conducted on peripheral blood from 53 subjects stratified according to leishmaniasis status [cured VL, cured cutaneous leishmaniasis (CL), asymptomatic leishmaniasis (AS) and healthy controls]. RESULTS: ENSC was found to be the most effective antigen to detect cured VL by measuring specific IP-10 production (90% recognition) and TNF induced by ΔCpB to detect cured CL (71.4% recognition). Although the cytokines IL-2 and IP-10 elicited by NSC and ENSC were able to detect AS, this capacity was only moderate (60%). CONCLUSIONS: We propose that, once validated in larger studies, these GLP Leishmania antigens might help improve the accuracy of treatment monitoring and diagnosing cure.
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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) | 0.000 | 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 | 0.000 |
| 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".