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 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 | 0.000 |
| 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 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".