Rapid and reliable detection of Leishmania antibodies in canine serum with double-antigen sandwich homogeneous chemical luminescence
Bibliographic record
Abstract
BACKGROUND: Leishmaniasis, caused by Leishmania spp. parasites, is an important zoonotic disease globally, posing severe threats to humans and animals. In the absence of effective vaccines, reliable serological diagnostic methods are critical for disease control. However, the enzyme-linked immunosorbent assay (ELISA) and immunochromatographic assay have limitations due to complexity, time required and/or sensitivity. Therefore, our objective was to develop an accurate, rapid and user-friendly detection method of canine leishmania antibody based on double-antigen sandwich homogeneous chemical luminescence. METHODS: Homogeneous chemiluminescent technology was employed, and expressed recombinant fusion proteins containing full-length K9, K39 and K26 repeat sequences were used as diagnostic antigens. To establish a dual-antigen sandwich serological assay capable of detecting various antibody types, a factorial design was used to optimize concentrations of diagnostic antigen-receptor microspheres and of biotinylated diagnostic antigens, as well as of reaction solution composition and reaction duration. To evaluate and validate this newly developed method, we collected 41 Leishmania-positive serum samples, 30 Leishmania-negative control serum samples and 78 clinical serum samples for which no diagnostic information was available. Comparative analyses were performed using parasitological testing and an indirect ELISA as reference methods, focusing on diagnostic sensitivity and specificity. RESULTS: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis confirmed the purification of the diagnostic antigens, which exhibited clear bands without impurities. Based on results from the 41 Leishmania-positive samples and 30 Leishmania-negative samples, there was sufficient sensitivity to detect samples diluted up to 256-fold, with analytical specificity of 100%. Overall diagnostic sensitivity was 100% and diagnostic specificity was 93.3%. Diagnostic performance was highly consistent between the newly developed method and the indirect ELISA (Kappa = 0.82, P < 0.01). Testing could be completed within 35 min with the new method CONCLUSIONS: We have developed a novel double-antigen sandwich homogeneous chemical luminescence method to detect canine Leishmania antibodies, with high sensitively and specificity, a short incubation interval and a simple protocol. This streamlined approach not only offers a sensitive and efficient method for clinical diagnosis but also has great potential for use in automated testing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".