Development of an antigen detection assay for early point-of-care diagnosis of Zaire ebolavirus
Bibliographic record
Abstract
The 2013-2016 Ebola virus (EBOV) outbreak in West Africa and the ongoing cases in the Democratic Republic of the Congo have spurred development of a number of medical countermeasures, including rapid Ebola diagnostic tests. The likelihood of transmission increases as the disease progresses due to increasing viral load and potential for contact with others. Early diagnosis of EBOV is essential for halting spread of the disease. Polymerase chain reaction assays are the gold standard for diagnosing Ebola virus disease (EVD), however, they rely on infrastructure and trained personnel that are not available in most resource-limited settings. Rapid diagnostic tests that are capable of detecting virus with reliable sensitivity need to be made available for use in austere environments where laboratory testing is not feasible. The goal of this study was to produce candidate lateral flow immunoassay (LFI) prototypes specific to the EBOV glycoprotein and viral matrix protein, both targets known to be present during EVD. The LFI platform utilizes antibody-based technology to capture and detect targets and is well suited to the needs of EVD diagnosis as it can be performed at the point-of-care, requires no cold chain, provides results in less than twenty minutes and is low cost. Monoclonal antibodies were isolated, characterized and evaluated in the LFI platform. Top performing LFI prototypes were selected, further optimized and confirmed for sensitivity with cultured live EBOV and clinical samples from infected non-human primates. Comparison with a commercially available EBOV rapid diagnostic test that received emergency use approval demonstrates that the glycoprotein-specific LFI developed as a part of this study has improved sensitivity. The outcome of this work presents a diagnostic prototype with the potential to enable earlier diagnosis of EVD in clinical settings and provide healthcare workers with a vital tool for reducing the spread of disease during an outbreak.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".