Covalently Oriented AuNP–Antibody Bioconjugates Enable Early and Sensitive Detection of <i>Opisthorchis viverrini</i> Urinary Antigen
Bibliographic record
Abstract
(OV) infection remains a major public health concern in Southeast Asia due to its strong association with cholangiocarcinoma. Early and accurate detection of OV infection is crucial for timely intervention and reduction of cancer risk. While enzyme-linked immunosorbent assay (ELISA)-based urine antigen detection methods have shown high sensitivity, their dependence on laboratory infrastructure limits their field utility. This study aims to develop and compare two gold nanoparticle (AuNP)-based lateral flow immunoassay (LFIA) formats utilizing distinct antibody conjugation strategies for rapid, noninvasive detection of OV excretory-secretory (OV-ES) antigens in urine. Two types of AuNP-antibody bioconjugates were prepared: (i) physical adsorption of monoclonal anti-OV antibodies on citrate-capped AuNPs (AuNP-Citr-mAb-OV) and (ii) covalent conjugation with orientation control on amino-terminated AuNPs (AuNP-TEG-NH-mAb-OV). These were incorporated as signal reporters in LFIAs, which were evaluated for sensitivity, specificity, detection limit, cross-reactivity, and diagnostic performance against urinary ELISA results by using clinical urine samples. LFIA based on covalently oriented AuNP-TEG-NH-mAb-OV (LFIA-TEG-NH-mAb-OV) demonstrated significantly lower limits of detection (1.54 vs 5.94 ng/mL), higher sensitivity (98.81 vs 96.83%), and improved specificity (81.94 vs 62.37%) compared to the LFIA based on passive adsorption format (LFIA-AuNP-Citr-mAb-OV). Moreover, LFIA-TEG-NH-mAb-OV showed better agreement with the ELISA reference (κ = 0.805) and lower cross-reactivity with other helminth infections. This is the first study to report the use of covalently oriented antibody-AuNP conjugates for early urinary detection of OV, offering a field-deployable, accurate, and scalable solution for the diagnosis and control of opisthorchiasis in endemic regions.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".