High-throughput malaria serosurveillance using a one-step multiplex bead assay
Bibliographic record
Abstract
BACKGROUND: Serological data indicating the presence and level of antibodies against infectious disease antigens provides indicators of exposure and transmission patterns in a population. Laboratory testing for large-scale serosurveys is often hindered by time-consuming immunoassays that employ multiple tandem steps. Some nations have recently begun using malaria serosurveillance data to make inferences about the malaria exposure in their populations, and serosurveys have grown increasingly larger as more accurate estimates are desired. Presented here is a novel approach of antibody detection using bead-based immunoassay that involves incubating all assay reagents concurrently overnight. RESULTS: multiplex bead-based assay (MBA) was used to detect total IgG against 8 malaria antigens: PfMSP1, PvMSP1, PmMSP1, PfCSP, PfAMA1, PfLSA1, PfGLURP-R0, PfHRP2. All sera and DBS samples were assayed by MBA using a standard immunoassay protocol with multiple steps, as well a protocol where sample and all reagents were incubated together overnight-termed here the OneStep assay. When compared to a standard multi-step assay, this OneStep assay amplified the assay signal for IgG detection for all 8 malaria antigens. The greatest increases in assay signal were seen at the low- and mid-range IgG titers and were indicative of an enhancement in the analyte detection, not simply an increase in the background signal of the assay. Seroprevalence estimates were generally similar for this sample Haitian population for all antigens regardless of serum or DBS sample type or assay protocol used. CONCLUSIONS: When using the MBA for IgG detection, overnight incubation for the test sample and all assay reagents greatly minimized hands-on time for laboratory staff. Enhanced IgG signal was observed with the OneStep assay for all 8 malaria antigens employed in this study, and seroprevalence estimates for this sample population were similar regardless of assay protocol used. This overnight incubation protocol has the potential to be deployed for large-scale malaria serosurveys for the high-throughput and timely collection of antibody data, particularly for malaria seroprevalence estimates.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".